BEHAVIORAL MECHANISMS OF Δ9-THC IN NONHUMAN PRIMATES

BY

WILLIAM JOHN

A Dissertation Submitted to the Graduate Faculty of

WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES

In Partial Fulfillment of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY

Integrative Physiology and Pharmacology

December, 2016

Winston-Salem, North Carolina

Approved By:

Michael A. Nader, Ph.D., Advisor

Thomas J. Martin, Ph.D., Chair

Paul W. Czoty, Ph.D.

Linda J. Porrino, Ph.D.

Jenny L. Wiley, Ph.D.

ii

ACKNOWLEDGEMENTS

First and foremost, I would like to extend my gratitude to my graduate advisor,

Dr. Nader. I could not have asked for a better graduate research experience within your

laboratory thanks to your constant support and guidance. You have taught me to

approach science from a perspective that will influence the rest of my career and has

changed the way I see the world. I am also grateful for my dissertation committee

members, Drs. Martin, Czoty, Porrino, and Wiley for all their time, support, and

constructive criticism. Thank you to Sue Nader and all other current and past members

of the Nader lab, each one of you has been instrumental in completing my graduate

research. I wish to thank my parents for their unconditioned love and support and for

always sacrificing so much of your time and energy to make sure I always had the best

opportunity to succeed. I would like to express my deepest love and appreciation to my

wife, Joanna, for her support during my graduate education. And finally, to my son,

Oliver- you are gift of happiness, love, and hope. Thank you for enriching our lives so

much during your six months on this earth and know that you are deeply cherished.

This research was supported by an Individual Predoctoral National Research

Service Award (F31 DA041825) and grants R37 DA10584, P50 DA06634 from the

National Institute on Drug Abuse and a Predoctoral Training Fellowship (T32 AA-

007565) from the National Institute on Alcohol Abuse and Alcoholism.

iii

TABLE OF CONTENTS

PAGE

LIST OF ABBREVIATIONS ................................................................................................ iv LIST OF TABLES................................................................................................................ vi LIST OF FIGURES ............................................................................................................ vii ABSTRACT ......................................................................................................................... ix CHAPTER I INTRODUCTION..........................................................................................1

II DETERMINANTS OF CANNABINOID SELF-ADMINISTRATION IN NONHUMAN PRIMATES ..........................................................................61

Submitted for publication to Neuropsychopharmacology, August, 2016

III CHRONIC Δ9-THC IN RHESUS MONKEYS: EFFECTS ON COGNITIVE

PERFORMANCE AND DOPAMINE D2/D3 AVAILABILITY.......................93 Submitted for publication to Biological Psychiatry, August 2016

IV DISCUSSION ...........................................................................................132 SCHOLASTIC VITAE.......................................................................................................202

iv

LIST OF ABBREVIATIONS

µg microgram ANOVA analysis of variance CANTAB Cambridge Neuropsychological Test Automated Battery CBR cannabinoid receptor Cd caudate CD compound discrimination CDC Center for Disease Control CDR reversal of compound discrimination cm centimeter CPP conditioned place preference CS conditioned stimulus d days D2 dopamine D2-like receptor superfamily D2 dopamine D2 receptor subtype D3 dopamine D3 receptor subtype DA dopamine DEA Drug Enforcement Agency DMS delayed-match-to-sample DSM Diagnostic and Statistical Manual of Mental Disorders DVR distribution volume ratio ED extradimensional shift ED50 interpolated effective dose that engenders 50% response FI fixed-interval FR fixed-ratio FT fixed-time g gram h hours HCl hydrochloride icv intracerebroventricular inf infusion ID intradimensional shift IRP Intramural Research Program i.v. intravenous k.g. kilogram LSD least significant difference m meters mm millimeter methA methamphetamine mg milligram min minute(s) ml milliliter MR magnetic resonance NIDA National Institute on Drug Abuse NHP nonhuman primate PCP phencycidine PET positron emission topography PFC prefrontal cortex

v

Pt putamen PT pretreatment R response ROI region of interest s second(s) S+ stimulus signaling reinforcement S- stimulus signaling trial termination, lack of reinforcement SC consequent stimulus SD discriminative stimulus SA self-administration SAMHSA Substance Abuse and Mental Health Services Administration s.c. subcutaneous SC consequence stimulus SD standard deviation SR reinforcer SEM standard error of the mean THC Δ9-tetrahydrocannabinol TO timeout US United States WCST Wisconsin Card Sort Test WM Working memory

vi

LIST OF TABLES

PAGE

CHAPTER I Table I. Cannabinoid self-administration studies in experimental animals ............13 CHAPTER II Table I. Ages, weights, and self-administration histories for individual rhesus

monkeys .....................................................................................................66 Table 2. Relationship between CP55,940 potency to decrease food-maintained

responding and CP55,940 self-administration ..........................................73 CHAPTER III Table I. Individual parameters used throughout study that produced delay-

dependent effects on DMS performance.................................................104 Table II. Baseline percent accuracy (± SD) at each delay for individual monkeys

responding under the DMS task ..............................................................111 Table III. Individual and mean (±SEM) [11C]-raclopride DVRs in control and THC

treated monkeys.......................................................................................116

vii

LIST OF FIGURES

PAGE

CHAPTER I Figure 1. Dose comparisons for preclinical THC self-administration studies in

nonhuman primates ...................................................................................19 CHAPTER II Figure 1. Effects of CP 55,940 and THC on food maintained responding and body

temperature in individual rhesus monkeys ................................................72 Figure 2. Individual self-administration dose-response curves for CP 55,940, THC,

and THC before after the development of tolerance to rate-decreasing effects on food-maintained responding under an FR 10 schedule of reinforcement .............................................................................................74

Figure 3. Acquisition of CP 55,940 self-administration.............................................75 Figure 4. Transient reinforcing effects of THC in individual rhesus monkeys ..........76 Figure 5. THC functioned as a reinforcer in three monkeys after daily THC

treatment ....................................................................................................78 Figure 6. THC self-administration under a [FI 10-min (FR 30:S)] second-order

schedule of reinforcement .........................................................................80 CHAPTER III Figure 1. Experimental timeline...............................................................................102 Figure 2. Effects of acute THC on food-maintained responding and body

temperature before and during chronic THC (1.0 mg/kg, s.c.) treatment (A) and mean (±SD) food-reinforced response rates for sessions before (BL, baseline) during, and after chronic THC treatment for individual monkeys (B) .............................................................................................109

Figure 3. Effects of acute THC on cognitive performance before and during chronic

THC 1.0 mg/kg, s.c.) treatment ...............................................................113 Figure 4. Residual effects (i.e., 22 hrs after administration) of chronic THC

treatment on delayed match-to-sample performance (A) and discrimination and reversal learning and attentional set-shifting performance (B) .......................................................................................115

viii

Figure 5. Effects of chronic THC treatment on dopamine D2/D3 receptor availability as measured with [11C]-raclopride ...........................................................117

CHAPTER IV Figure 1. Hypothetical dose-response curve under a concurrent THC-food schedule

of reinforcement .......................................................................................145 Figure 2. Effects of THC (A) and CP 55,940 (B) pretreatment on cocaine-drug

choice in rhesus monkeys .......................................................................161 Figure 3. Rate dependency of THC’s potency to decrease rates of food-maintained

responding ...............................................................................................178

ix

ABSTRACT

John, William S.

BEHAVIORAL MECHANISMS OF Δ9-THC IN NONHUMAN PRIMATES

Dissertation under the direction of Michael A. Nader, Ph.D., Professor of Physiology and

Pharmacology

A comprehensive understanding of the reinforcing effects and cognitive

consequences of Δ9-tetrahydrocannabinol (THC), the primary psychoactive constituent

in marijuana, is critical for developing treatments in cannabis dependent patients.

Despite the fact that marijuana is the most commonly abused illicit substance in the

U.S., self-administration (SA) of THC has only been established in one laboratory using

New World primates (squirrel monkeys). In Chapter 2, THC and the synthetic

cannabinoid agonist CP55,940 were made available for SA in Old World monkeys, a

species more closely related to humans, by using similar parameters deemed successful

in squirrel monkeys. CP55,940 functioned as a reinforcer in a subset (3 of 8) of rhesus

monkeys, with an inverse relationship between magnitude of SA and sensitivity to rate-

decreasing effects. On the other hand, THC did not function as a reinforcer in any

monkey. To test the hypothesis that attenuating the rate-decreasing effects of THC

would increase the likelihood THC would function as a reinforcer, monkeys were

administered THC daily until tolerance developed. When THC SA was reexamined, it

functioned as a reinforcer in a subset (3 of 8) of subjects. Further, the reinforcing effects

of THC were noted in two of four cynomolgus monkeys responding under a second-

order schedule of reinforcement in which behavior was maintained by conditioned

stimuli. These studies indicate that sensitivity to rate-decreasing effects and schedule of

reinforcement are important determinants for cannabinoid-maintained behavior.

x

In Chapter 3, monkeys were chronically treated with THC and the residual effects

(22 hrs after THC administration) were assessed on cognitive performance. Impairments

were found in working memory (WM) but not in other aspects of executive function.

These cognitive deficits were independent of changes in dopamine D2/3 receptor

availability as measured with PET and [11C]-raclopride. THC-induced impairments to WM

recovered during abstinence. Moreover, chronic THC treatment resulted in tolerance to

the acute impairments in compound discrimination learning but only to impairments in

working memory when the cognitive demand was low.

Overall, these studies provide insight on the behavioral mechanisms underlying

the reinforcing, unconditioned, and cognitive effects of THC, which is important for

understanding the abuse-related properties of cannabis. Such knowledge should aid in

the development of treatments for cannabis use disorder.

1

CHAPTER I

INTRODUCTION

Drug abuse is a major public health problem worldwide, with an estimated 21.6

million individuals that meet DSM IV criteria for substance dependence or abuse in the

United States alone (SAMSHA, 2013). Drug abuse is a chronic relapsing brain disease

characterized by compulsion to seek and take a drug, loss of control in limiting intake,

and the emergence of negative affect (e.g., dysphoria, anxiety) when access to the drug

is withheld. In addition to the deleterious effects on the individual, drug abuse poses an

enormous economic cost to society including costs from crime, loss in productivity,

health care, incarceration, and drug enforcement operation costs. Estimates indicate that

overall, these costs exceed $600 billion annually in the United States (National Drug

Intelligence Center, 2011; Rehm et al., 2009; CDC, 2014).

Marijuana (Cannabis sativa) is the most commonly abused illicit substance in the

United States with more than 4 million Americans that meet DSM IV criteria for

marijuana dependence (SAMSHA, 2013). In fact, this number is greater than

dependence on painkillers, cocaine, heroin, and stimulants combined (SAMSHA, 2013).

Furthermore, the second most substance abuse treatment admissions in the U.S. were

for cannabis-related disorders, with alcohol-related disorders being the first (SAMSHA,

2013). The number of marijuana users has been steadily increasing among 12th graders,

which is associated with a decrease in the perceived risks of regular use (Johnston et

al., 2014). Moreover, marijuana is now legal in 4 states, suggesting that use will

increase. The behavioral therapies that have been evaluated as treatments for

marijuana dependence are only modestly effective while there are currently no effective

pharmacotherapies (Nordstrom and Levin, 2007). As a result, there is a compelling need

for more research on the impact of marijuana on neurobiology and behavior in order to

develop more targeted therapeutic strategies for cannabis dependence.

2

Cannabis contains more than 400 different chemicals including

phytocannabinoids, terpenoids and essential oils (ElSohly and Slade, 2005); however,

the behavioral effects of cannabis (i.e. euphoria, relaxation, anxiety, confusion, memory

loss, and paranoia) are primarily the result of the psychoactive ingredient, Δ9-

tetrahydrocannabinol (THC) (Adams and Martin, 1996; Wachtel et al., 2002; Hall and

Degenhardt, 2009). The behavioral effects of THC are largely mediated by its action as a

partial agonist at the G-protein coupled cannabinoid-type 1 (CB1) receptor, which is the

most-abundantly expressed G-protein-coupled receptor in the brain. In particular, CB1

receptors have dense expression in brain regions involved in reward, addiction, and

cognitive function, including the amygdala, cingulate cortex, prefrontal cortex, ventral

pallidum, caudate-putamen, nucleus accumbens, ventral tegmental area, and lateral

hypothalamus (Glass et al., 1997; Wang et al., 2003). The endocannabinoid system also

contains cannabinoid type 2 (CB2) receptors, at which THC is a partial agonist as well.

CB2 receptors are known to mainly be expressed by immune cells; however, recent

evidence suggest CB2 receptors are also found centrally, albeit at much lower levels

than CB1 receptors, and may mediate some of THC’s actions as well (Atwood and

Mackie, 2010). CB1 and CB2 receptors are coupled to similar transduction systems,

primarily through Gi or Go proteins, which directly inhibit the release of GABA, glutamate,

and acetylcholine. Moreover, THC has been demonstrated to stimulate neuronal firing of

dopamine (DA) through the mesolimbic DA system in animals (Tanda et al., 1997) and

humans (Bossong et al., 2009; Voruganti et al., 2001; Bossong et al., 2015; Van de

Giessen et al., 2016), which has been proposed as a contributing factor to its reinforcing

effects.

Moreover, a fundamental principle governing the field of behavioral

pharmacology is the notion that the behavioral action of a drug cannot be confined to the

inherent pharmacological properties of the drug itself. Rather, the behavior-environment

3

contingencies must be considered in determining the effects of a drug in addition to its

pharmacological properties. These conditions include, but are not limited to, the

schedule of reinforcement, the stimulus context, the type and level of the establishing

operation, the type of consequent event, and the behavioral history of the subject. For

instance, the same dose range of nicotine has been shown to maintain or punish

responding that produces its injection or to maintain responding that prevents its

injection (Goldberg et al., 1983). Similarly, cocaine has been shown to simultaneously

maintain responding by scheduled injections of the drug and by termination of the

schedule of cocaine injection (Spealman, 1979). These pioneering behavioral

pharmacological studies demonstrate that drugs affect behavior in many different ways

depending on the prevailing environmental conditions and that these effects cannot be

predicted simply on the basis of their pharmacological nature.

The overarching goal of this dissertation was to apply this concept of behavioral

mechanisms and extend it to an examination of the factors that influence the reinforcing

effects of THC and the specificity and temporal nature of the cognitive consequences of

THC by using a within-subject study design in nonhuman primates. A better

understanding of these behavioral mechanisms of THC will be important for 1)

identifying variables that may render certain individuals more vulnerable to cannabis

abuse, 2) discovering parametric manipulations that can be used to advance the

cannabinoid self-administration methodology in preclinical animal models and 3) for

identifying specific attributes of cannabis dependent patients that may be targeted by

pharmacological and behavioral interventions to improve treatment outcome.

In the context of these aims, the following chapter will present an overview of

previous studies that have examined the effects of THC in 1) preclinical animal models

of abuse liability, 2) human laboratory models of reinforcement, and 3) studies assessing

the neurocognitive effects of cannabis/THC. This overview will serve to identify the

4

recent advances made in these areas of research as well as the current gaps in

knowledge and how that information has shaped the studies within this dissertation.

Drug discrimination

Much of the information we know regarding the abuse-related effects of

cannabinoids comes from preclinical studies using the drug discrimination procedure

(Balster and Prescott, 1992; Wiley, 1999). The drug discrimination procedure is based

on the ability of a drug to induce a specific set of interoceptive stimulus conditions

perceived by animals that might be predictive of the subjective reports of

perceptions/feelings induced by the same drug in humans. As a result, the drug

discrimination procedure does not measure reinforcing effects of drugs; however, there

is usually a good correlation between those drugs belonging to a particular

pharmacological class that serve as reinforcers and those that produce

subjective/discriminative effects characteristic of that pharmacological class (Schuster

and Johanson, 1988). Therefore, studies of the subjective effects of new drugs in both

humans and animals have been relatively good predictors of whether or not a drug will

be abused.

Animal drug discrimination procedures consist of an initial period of training that

consists of alternating the administration of either a specific drug dose or the drug's

vehicle before each session. Typically, sessions take place within an experimental

chamber, which has two different response manipulanda. Animals learn to make a

response on one manipulandum when sessions are preceded by injection of the training

drug and make a response on the other manipulandum during sessions preceded by a

vehicle injection. In order to maintain this behavior, responding is intermittently

reinforced by food pellets or by postponement or escape from electric shocks. Once

subjects reach a criterion level of correct performance (e.g. 80% drug appropriate

5

responding), test sessions are occasionally scheduled, during which they receive either

the training drug at a different dose or a different drug, to test whether or not such a

drug/dose will produce a discriminative stimulus effect similar to the training drug.

Generally, if the training drug is an agonist, only agonist drugs belonging to the same

pharmacological class will produce full substitution to the training stimulus and only

antagonists belonging to the same pharmacological class will selectively block the

training stimulus.

Cannabinoid drugs, indeed, show a pharmacological specificity in this behavioral

procedure. For example, in animals trained to discriminate injections of THC from

injections of its vehicle, only drugs that selectively activate CB1 receptors fully substitute

for the THC training stimulus (Wiley et al. 1993a, 1993b, 1995c; Barret et al. 1995).

Furthermore, CB1 receptor agonists have been shown to substitute for the THC training

stimulus with a potency that is consistent with their in vitro affinity for the CB1 receptors

(Balster and Prescott 1992; Gold et al. 1992; Wiley et al. 1995b).

In addition to THC, other cannabinoid CB1 receptor agonists have also been

used as the training stimulus (Wiley et al. 1995a, 1995b; Perio et al. 1996; Järbe et al.

2001). For example, Wiley et al. (1995b) trained rats to discriminate the synthetic CB1

receptor agonist CP 55,940 from saline and found that THC, the synthetic CB1 receptor

agonist WIN 55,212-2, and cannabinol all substituted for the CP 55,940 training stimulus

and did so at potencies similar to those that displace CP 55,940 in binding studies.

Similarly, other investigators have trained rats to discriminate WIN 55,212-2 from vehicle

(Perio et al. 1996), and found that WIN 55,212-2 completely generalized to THC and CP

55,940. Moreover, the discriminative stimulus effects of THC and other synthetic CB1

agonists have been shown to be antagonized by the selective CB1 receptor antagonist

SR 141716A, further demonstrating that the cannabinoid discrimination is mediated by

CB1 receptors (Mansbach et al. 1996; Perio et al. 1996). It has been demonstrated that

6

these discriminative stimulus effects are centrally mediated behaviors through studies

showing that a selective CB1 receptor antagonist that does not cross the blood-brain

barrier, SR 140098, does not antagonize the discriminative stimulus effects of

cannabinoids (Perio et al. 1996).

Conditioned place preference/aversion

Another animal model of abuse liability that has provided much information on

the behavioral effects of cannabinoids is the conditioned place preference (CPP)

procedure. This procedure is based on the principles of classical conditioning and

provides an indication of drug-related reward/aversion effects in animals. The

rewarding/aversive stimulus properties of a drug assessed under these procedures refer

to the appetitive nature of the stimulus as opposed to the ability of a drug to increase the

probability of a given behavior (i.e. reinforcing effects). Although methodological details

differ among laboratories, CPP procedures typically begin by allowing animals to freely

explore two distinct environmental contexts within a chamber, which differ in color, size,

or texture, and are connected by an open door. The time spent in each context is then

recorded. During conditioning sessions, a drug injection (unconditioned stimulus) is

repeatedly paired with one of the two environmental contexts while access to the other

context is prohibited. On alternate sessions, vehicle injections are paired with the other

environmental context. On the final test day, no injections are given and the relative time

spent in each environmental context is measured and the difference is taken in order to

provide a measure of preference. Using this dependent variable, the CPP model has

been shown to have high predictive validity in that most drugs abused by humans are

able to increase the time spent in the drug-paired context (i.e., produce a place

preference); however, many interpretations of results are limited due to the frequency of

false positives and the difficulties encountered in obtaining systematic dose-response

7

relationships over a limited range of doses (Bardo and Bevins 2000; Tzschentke 1998).

CPP studies with cannabinoids have been largely ineffective at producing

positive place preferences and the studies that have shown cannabinoid place

preference have done so at various doses and magnitudes depending on the training

procedure used across studies. Although very specific dosing parameters are required to

produce place preferences for many drugs in the CPP paradigm (Bardo and Bevins

2000; Tzschentke 1998, the ability of cannabinoids, such as THC and the synthetic CB1

receptor high-efficacy agonist CP 55,940, to produce a place preference in rats and mice

are particularly sensitive to the timing of injections as well as on the range of doses used

(Lepore et al. 1995; Valjent and 2000; Braida et al. 2001; Ghozland et al. 2002). For

instance, Lepore et al. (1995) demonstrated that when a standard schedule of daily

training injections was used (i.e., vehicle, drug, vehicle, drug, etc.), a relatively low dose

of THC, 1.0 mg/kg, produced a conditioned place aversion, however, higher doses of 2.0

and 4.0 mg/kg THC produced positive place preferences. When the schedule of daily

injections was changed and a day where no drug or vehicle injection was administered

was added in between test sessions (i.e., vehicle, day off, drug, day off, vehicle, day off,

drug, etc.), the results were opposite such that THC produced a conditioned place

preference at 1.0 mg/kg but place aversions were found at the 2.0 and 4.0 mg/kg doses

of THC. These findings indicated that the rewarding effects of THC as assessed under

the CPP paradigm might be qualitatively changed by increasing the interval between

drug injections due to what the authors referred to as a “postdrug dysphoric rebound.”

As a result, the pharmacological properties of THC that may be contributing to this effect

may help to explain why THC and synthetic cannabinoids often produce conditioned

place aversions, rather than place preferences, in rats and mice using similar dose

ranges and standard place-preference conditioning procedures (Lepore et al. 1995;

McGregor et al. 1996; Sanudo-Pena et al. 1997; Mallet and Beninger 1998; Chaperon et

8

al. 1998; Cheer et al. 2000; Zimmer et al. 2001).

A study by Valjent and Maldonado (2000) also found positive place preferences

with THC by using another variation in the procedure. These investigators found that

when using a standard place-conditioning protocol in mice, a relatively low dose of THC

(i.e., 1.0 mg/kg) produced neither place aversion nor preference while a high dose (i.e.,

5.0 mg/kg) produced place aversion. On the other hand, when mice received an

additional exposure to THC before the conditioning test such that a 1.0 mg/kg injection

was administered in their home cage 24 h before the start of the experiment and were

exposed to the drug-paired conditioning chamber for a longer period of time (45 min

instead of 15–30 min), the lower dose of 1.0 mg/kg THC produced a place preference

while the higher dose of 5.0 mg/kg produced neither place preference nor aversion. This

finding has also been replicated by a separate group of investigators (Ghozland et al.

2002). Overall, these results suggested that prior exposure to the drug may facilitate the

development of positive place preferences, which may be otherwise overridden by

certain aversive effects of THC that are apparent upon initial exposure. These findings

may be particularly informative for cannabinoid self-administration experiments because

they imply that certain parametric manipulations may need to be designed into studies in

order to account for the effects of THC may inhibit the expression of its reinforcing

effects.

Drug self-administration

The drug self-administration procedure is the primary methodology for

demonstrating the reinforcing effects of drugs that are abused by humans. The

procedure is built on principles of operant conditioning such that laboratory animals are

trained to respond on a manipulandum to receive an injection of a drug. Like any other

operant behavior, the drug self-administration procedure can be broken down into

9

specific components using Skinner’s 3-term contingency (Skinner, 1938), which is

described as follows:

SD R SC,

where SD designates the discriminative stimulus, R designates an operant response, and

SC designates a consequent stimulus. The arrows specify the contingency (i.e.

performance of the response during the SD) that will result in delivery of the consequent

stimulus SC. Thus, a typical drug self-administration experiment would involve the

illumination of a stimulus light inside an operant chamber (the SD), signaling to an animal

that depression of a response lever (R) will result in delivery of a drug injection via an

indwelling intravenous catheter (SC). Consequent injections of a drug that increase the

probability of responding leading to their delivery are operationally defined as

reinforcers, while the discriminative stimulus, responses, and consequent stimuli are

defined by the schedule of reinforcement.

The first demonstrations of drug self-administration came in the 1960s where rats

and monkeys previously made physically dependent on morphine were trained to press

a lever when those responses produced intravenous injections of morphine (Weeks,

1962; Thompson and Schuster 1964). Shortly following, the reinforcing effects of opioids

in subjects that had not previously been made dependent prior to the start of the self-

administration studies were demonstrated (Yanagita et al., 1965a, b; Deneau et al.,

1969). Since then, the reinforcing effects of practically every drug abused by humans

have been demonstrated in experimental animals by the self-administration procedure

under a variety of routes, schedules of reinforcement, and species. These studies have

been paramount in advancing our understanding of drug abuse on both a conceptual

and scientific level. For example, prior to the demonstrations that laboratory animals will

self-administer a variety of drugs, drug abuse was widely considered to be a result of an

“addictive personality” or weakness in self-control. Moreover, self-administration studies

10

have shed light on many of the pharmacological, environmental, and biological

determinants of drug-taking behavior as a model of addiction. However, the one drug

class that has remained a “false negative” in the self-administration procedure has been

the cannabinoids including THC. Successful efforts have been limited to one laboratory

using squirrel monkeys and a unique set of parameters that have not been previously

employed (Tanda et al., 2000; Justinova et al., 2003). Every other THC self-

administration attempt to date using Old World nonhuman primate species (rhesus

monkeys) or rodents has been unsuccessful (Kaymakcalan 1972, 1973; Pickens et al.,

1973; Harris et al., 1974; Leite and Carlini 1974; Carney et al., 1977; Van Ree et al.,

1978; Mansbach et al., 1994; Li et al., 2012; Lefever et al., 2014). Therefore, the lack of

a widespread, suitable method to establish robust levels of THC SA in animals remains

an impediment for directly assessing the abuse-liability of novel cannabinoid compounds

and for developing treatments for human marijuana abuse. Thus, the first aim of this

dissertation was to establish the experimental conditions necessary for THC SA to be

maintained by Old World monkeys, a species more closely related to humans than the

New World squirrel monkeys. Previous studies on the reinforcing effects of cannabinoids

in human subjects and preclinical animals will be presented. This will be followed by a

discussion on potential factors that may be limiting the expression of cannabinoid

reinforcement in animal models and how the studies within this dissertation sought to

address those factors in order to demonstrate the reinforcing effects of THC.

THC self-administration in humans

Several studies in humans have provided experimental data supporting THC's

integral role in the reinforcing effects of smoked marijuana. For instance, when subjects

are given a choice between THC-containing cigarettes and a non-drug reinforcer (e.g.,

snack food) or between marijuana cigarettes with different THC contents, the THC

11

cigarettes are consistently chosen over non-drug reinforcers and cigarettes with a

greater THC content have been shown to be consistently preferred to cigarettes with a

lower THC content, even when the choice between the two was not mutually exclusive

(Haney et al., 1997, Kelly et al., 1997 and Ward et al., 1997). Other studies in humans

have also attempted to better define THC's role as the primary psychoactive ingredient

in marijuana by investigating whether the its reinforcing effects are dose-dependent. In

order to do this, the THC content in a cigarette is systematically manipulated during self-

administration sessions and the behavioral endpoints associated with reinforcement are

compared to placebo self-administration or self-administration of a cigarette containing a

low amount of THC. For example, Herning et al. (1986) investigated whether marijuana

smoking behavior was dose-dependent in experienced users by examining whether the

subjects would titrate their intake according to differing THC concentrations in order to

achieve a defined subjective state. It was observed that when cigarettes containing a

high concentration of THC (3.9% THC) was available, the subjects took significantly

more puffs with longer intervals between puffs compared to when cigarettes containing

low amounts of THC (1.2% THC) were available. It was also found that the subjects

inhaled substantially larger (46%) volumes of air when self-administering the high THC-

content cigarettes. Thus, it was concluded that perhaps the subjects were demonstrating

different smoking-related behavior as a mechanism to titrate the THC dose, thus

indicating a dose-related effect. Another study by Nemeth-Coslett et al. (1986) also

demonstrated compensatory changes in marijuana smoking as a function of THC dose

where expired air carbon monoxide (CO) levels following smoking were inversely related

to THC content of the marijuana (1.29%, 2.84% or 4.0% THC). Thus, it was suggested

that subjects reduced their smoke intake as THC content increased thereby providing

evidence for dose-dependency in THC’s reinforcing effects.

12

Taken together these studies demonstrate that, aside from epidemiological

evidence, THC produces clear and mostly dose-related reinforcing effects in controlled

experimental studies using human subjects. What is not clear, however, is that despite

these demonstrations in humans, every attempt in experimental animals with the

exception of one laboratory has failed to demonstrate the ability of THC to function as a

reinforcer. Going forward, it will be important to “reverse translate” these findings from

humans to animals in order to optimize the cannabinoid self-administration model. For

instance, given the limited dose range that smoking titration occurs in humans, careful

consideration of the unit dose made available for self-administration as well as the

pharmacokinetic factors of cannabinoids will be vital for optimizing the cannabinoid self-

administration model in experimental animals.

Cannabinoid self-administration in experimental animals

Numerous attempts have been made over the past 3 decades to demonstrate

persistent, dose-related reinforcing effects of THC in experimental animals; however,

successful efforts have been limited to one laboratory using squirrel monkeys and a set

of unique experimental parameters (see Table 1). Therefore, the majority THC self-

administration studies in animal models are incongruent to the numerous laboratory

demonstrations of the reinforcing effects of THC in human subjects. In early preclinical

studies, many procedural factors were taken into consideration including route of

administration (e.g., inhalation, oral, intravenous), previous drug history, and schedule of

reinforcement, however, none of these studies showed that THC could maintain rates of

responding above vehicle levels. For example, in one early study, Harris et al. (1974)

failed to demonstrate reinforcing effects of THC (0.025-0.3 mg/kg) under an FR1

schedule (12 h access per day, 20 days per dose) in drug-naïve rhesus monkeys or

monkeys that had histories of cocaine, phenobarbital, PCP, ethanol, or amphetamine

13

self-administration. THC also failed to function as a reinforcer in this study following 30

days of programmed infusions of THC (2.0 mg/kg/day) or in monkeys that had been

trained to self-administer a cocaine-THC mixture. In another early study using rhesus

monkeys trained to self-administer PCP, THC was substituted and maintained low rates

of behavior but did not persist above vehicle levels over repeated sessions (Pickens,

1973). Kaymakcalan (1973) provided the most positive demonstration of THC

reinforcement in rhesus monkeys where two of six monkeys acquired THC self-

administration under an FR1 schedule of reinforcement, but only during withdrawal

following the development of physical dependence via 36 days of forced automatic THC

Table 1. Cannabinoid self-administration studies in experimental animals.

Reference Dose and vehicle of drug

Species Procedure/schedule Main outcome

THC

Deneau and Kaymakcalan

(1971), Harris et al., (1974)

100–400 μg/kg Tween 20/saline

or 25–300 μg/kg PVP/saline

Rhesus monkeys

IVSA, FR1, drug naïve

Negative

Pickens et al. (1973)

0.025-0.1 mg/kg PVP/saline

Rhesus monkeys

IVSA, FR1, substitution for

phencyclidine

Negative

Pickens et al. (1973)

0.025-0.1 mg/kg and 25 mg burned hashish

Rhesus monkeys

Inhalation and food presentation/FR3 and 4 s mouth contact to

smoke tube, 24 h per day, 4 days each week

Negative

Carney et al.

(1977), Harris et al. (1974), Kaymakcalan

(1973)

3–300 μg/kg

EL620; 25–200 μg/kg PVP/saline; 100–

400 μg/kg Tween 20/saline

IVSA, substitution for

cocaine

Negative

Kaymakcalan (1973)

100–400 μg/kg Tween 20/saline

Rhesus monkeys

IVSA, monkeys made dependent on THC

33% positive

Mansbach et al.

(1994)

17–100 μg/kg in

EL620/EtOH/saline

Rhesus

monkeys

IVSA, FI 10 min (2 hr

TO), substitution for phencyclidine

Negative

Li et al. (2012) 3.2–32 μg/kg EL620/EtOH/sali

ne

Rhesus monkeys

IVSA, history of heroin SA, or naive

Negative

Corcoran and Amit 0.25 mg Rats Forced drinking Negative

14

(1974) (hashish)/ml

Van Ree et al. (1978)

7.5–300 μg/kg Tween 20/saline

Rats IVSA, naïve 40 % positive

Takahashi and Singer (1979, 1980)

6.25–50 μg/kg Tween 80/saline

Rats IVSA, naïve, food deprived, with

automatic food pellet delivery

Positive only with

deprivation conditions

Braida et al. (2004) 0.01–1 μg/inf cerebrospinal

fluid, EtOH, cremophor

Rats ICVSA, trained with water

Positive, max responding at

0.02 μg/inf

Lefever et al., (2014)

3-10 μg/kg in polysorbate

80/saline

Long0Evans Rats

IVSA, FR 3, substitution for WIN

55,212-2

Negative

Tanda et al. (2000), Justinova et al. (2008), Justinova et

al. (2003)

1–16 μg/kg EtOH/Tween 80/saline

Squirrel monkeys

IVSA, naïve, or cocaine trained with saline extinction/FR

or second order

Positive, max responding at 4 μg/kg

2-AG

De Luca et al. (2014)

12.5–50 μg/kg Tween 80/saline/eth

Sprague-Dawley rats

IVSA, naïve 90 % positive, max responding at

25 μg/kg

Justinova et al. (2011)

0.1–100 μg/kg EtOH/Tween 80/saline

Squirrel monkeys

IVSA, nicotine substitution or history IVSA of anandamide,

anandamide reuptake inhibitors or nicotine

100 % Positive, max responding at

3 μg/kg

CP 55,940

Mansbach et al. (1994)

0.3–3 μg/kg emulphor/EtOH/s

aline

Rhesus monkey

IVSA, FI 10 min (2 hr TO), PCP substitution

Negative

Braida et al. (2001a, b)

0.1–1.6 μg cerebrospinal fluid, EtOH,

cremophor

Wistar rats IVSA Positive, max responding at 0.4 μg/kg

WIN 55,212-2

Martellotta et al. (1998)

10–500 μg/kg CD1 mice IVSA, naïve Positive, max responding at 100 μg/kg

Fattore et al. (2001) 6.25–50 μg/kg

Tween 80/heparin/saline

Wistar rats IVSA, naïve 87 %

positive, max responding at 12.5 μg/kg

Solinas et al.

(2007a, b)

12.5–25 μg/kg

Tween 80/saline

Long-Evans

rats

Positive, max

responding at 12.5 μg/kg

Justinova et al. (2013)

12.5 μg/kg Tween

80/heparin/saline

Long-Evans rats/Sprague-

Dawley

IVSA, naïve Positive

Lecca et al. (2006) 12.5 μg/kg Tween 80/saline

Sprague-Dawley rats

IVSA, naïve Positive

Lefever et al., (2014)

5.6-30 μg/kg in polysorbate

80/saline

Rats IVSA, FR 3, naïve Positive

15

JWH 018

De Luca et al. (2015)

10–20 μg/kg EtOH/Tween 80/saline

Sprague-Dawley rats

IVSA, naïve 90 % positive, max responding at

20 μg/kg

De Luca et al. (2015)

15–30 μg/kg EtOH/Tween 80/saline

C57BL/6 mice

IVSA, naïve 90 % positive, at 30 μg/kg

Injections (0.4 mg/kg/injection, i.v.). Surprisingly, only two other studies in rhesus

monkeys involving cannabinoid self-administration have been published since the

1970s. One of these by Mansbach et al. (1994) used a schedule of reinforcement

designed to compensate for the biodispositional factors of THC (i.e. slow-onset of

behavioral effects and extended elimination phase) that may limit the expression of

reinforcing effects. Here, individual drug injections were spaced by 2-hour intervals and

paired with a distinct conditioned stimulus; however, THC still did not maintain rates of

responding above vehicle levels although this factor of temporal contiguity was taken

into consideration. The other study, Li et al., 2012, substituted THC for heroin SA under

an FR 50, 180-s timeout schedule of reinforcement in male rhesus monkeys. Although

some evidence indicates a shared mechanism between cannabinoid and opioid receptor

compounds, these conditions were also unsuccessful in demonstrating the reinforcing

effects of THC.

Studies in rodents have also been largely unsuccessful in demonstrating the

reinforcing effects of THC by the self-administration paradigm. One exception was a

study by Takahashi and Singer (1979) where 6.25 and 12.5 μg/kg/injection THC

maintained responding above vehicle in food-restricted rats at 80% of normal body

weight and by using a concurrent fixed-time (FT) 1-min schedule of food-delivery and

FR1 schedule THC self-administration. THC self-administration above vehicle levels was

not obtained in rats that were only food-restricted (without the FT 1-min schedule of food

16

delivery) or in rats with the FT1-min schedule at free-feeding body weight. Therefore,

these results indicated a role of adjunctive behavior, that is the environmental

contingencies introduced by a combination of the FT 1-min schedule and food

deprivation were the critical variables in demonstrating THC reinforcing effects in this

study. On the other hand, several studies in rodents have demonstrated reinforcing

effects of synthetic CB receptor agonists by intravenous self-administration. The first

report by Martellotta et al. (1998) showed reinforcing effects of the synthetic CB1 receptor

agonist WIN 55,212-2 in drug-naïve mice. In that study, self-administration was only

studied during one experimental session in which WIN 55,212-2 was made available

under an FR 1 schedule for one mouse while another served as a yoked control. The

ratio between the number of responses by the active-lever mice and yoked controls

indicated that WIN 55,212-2 reinforcing effects were obtained in a dose-related manner.

Despite the fact that “reinforcing effects” were determined by using a grouped

experimental design, these findings were replicated by another laboratory using the

same procedure (Ledent et al., 1999).

The early studies of synthetic cannabinoid self-administration in rodents by

Martellotta et al. (1998) and Ledent et al. (1999) were important advancements in

cannabinoid self-administration methodology; however, these studies also contained

several caveats that limited the translational nature of the procedure, such as studying

each dose during a single self-administration session and the use of a grouped design to

measure reinforcing effects. Since then, other studies have addressed these issues by

showing that synthetic cannabinoids can function as reinforcers within-subject and over

consecutive experimental sessions. For example, Fattore et al. (2001) showed that

stable and robust rates of i.v. WIN 55,212-2 self-administration was maintained in rats

responding under an FR 1 schedule (10-s timeout) in 3-h sessions. Stable responding

was acquired in approximately 16 sessions at a dose of 12.5 μg/kg/injection where

17

maximal rates of responding occurred at approximately 25 injections per session. It was

also shown that WIN 55,212-2 self-administration was subject to saline extinction and

antagonism by the CB1 antagonist/inverse agonist SR 141716A. It is also important to

note that rats were maintained at 80% of free-feeding weights in these studies.

Furthermore, reinforcing effects of the bicyclic cannabinoid analog, CP 55,940 have

been demonstrated in rodents via intracerebroventricular (ICV) self-administration

(Braida et al., 2001). This study also used a form of diet restriction as an antecedent

variable in which the animals only had access to water during the experimental session.

During each session, two levers were made available and water delivery was contingent

on responding on either lever. In addition to response contingent water delivery,

responding on one lever also delivered a paired ICV injection of CP 55,940 while

responding on the other delivered an ICV injection of vehicle paired with water delivery.

The lever producing water delivery paired with ICV CP 55,940 dose-dependently

maintained greater rates of responding than the lever paired with vehicle. One limitation

to these studies, however, is that diet restriction was required for the acquisition and

maintenance of self-administration. Food restriction is a well-established procedural

factor in drug self-administration studies that has been shown to enhance both the

acquisition (Carroll et al., 1979; de la Garza and Johanson, 1987; Cabeza de Vaca and

Carr, 1998) and maintenance (Carroll and Meisch, 1984) of drug-maintained behavior.

More recently, Lefever et al. (2014) also demonstrated the reinforcing effects of WIN

55,212-2 in rodents responding under an FR 3 schedule of reinforcement and

maintained at 90% free-feeding weight, as opposed to 80% in the initial successful

demonstration of WIN 55,212-2 self-administration by Fattore et al. (2001). However,

self-administration was not consistent among all subjects in this study with considerable

within-group variability in the number of infusions earned per session.

18

The initial demonstration of robust THC-maintained responding in experimental

animals by Tanda et al. (2000) was achieved by using a set of experimental parameters

not previously employed. First, squirrel monkeys (Saimiri sciureus), a species of New-

World primates, served as subjects. In contrast, previous unsuccessful attempts of THC

self-administration in nonhuman primates exclusively used Old-World primates (i.e.

rhesus monkeys). Furthermore, the squirrel monkeys used were not food restricted in

contrast to studies in rodents that demonstrated reinforcing effects of synthetic

cannabinoids. The initial demonstration of THC self-administration in squirrel monkeys

was performed in monkeys that had a prior history of cocaine self-administration and at

the time, was considered by others to be an antecedent variable that contributed to its

effectiveness as a reinforcer in these studies (Maldonado, 2002). However, subsequent

work showed that drug-naïve squirrel monkeys readily acquired and maintained robust

rates of THC self-administration (Justinova et al., 2003) thereby demonstrating that a

prior cocaine self-administration history was not a necessary condition among the other

procedural variations.

The successful demonstrations of THC reinforcement in squirrel monkeys

employed an FR 10 schedule of drug injection with a 60-s timeout after each injection. In

the initial study, saline was substituted for cocaine until behavior was reliably

extinguished. Subsequently, THC was made available for self-administration by

substituting the drug for saline with periods of vehicle substitution between different

doses of THC. This substitution procedure is in contrast to earlier studies in rhesus

monkeys where THC was substituted directly for cocaine or other drugs like PCP or

heroin (Mansbach et al., 1994, Pickens et al., 1973; Carney et al., 1977; Harris et al.,

1974; Kaymakcalan, 1973; Li et al., 2012). In fact, this substitution procedure for

studying self-administration is in contrast to what has been used historically to assess

the reinforcing effects of a stimulus where a test drug is substituted for a stimulus (e.g.

19

food pellets, another drug) that previously maintained responding. The significance of

substituting THC for saline vs. a stimulus with reinforcing effects in order facilitate the

acquisition of THC-maintained behavior remains to be determined. This procedural

variation could suggest that THC is more likely to maintain high levels of behavior only

when it is substituted for a stimulus that is either not reinforcing or is of lower reinforcing

efficacy than THC.

Perhaps the most critical procedural factor other than species differences for

initially demonstrating the reinforcing effects of THC in squirrel monkeys was the dose

range used. Specifically, the dose range of THC (2.0 – 8.0 μg/kg/injection) that

maintained behavior above vehicle levels in the squirrel monkeys used by Tanda et al.

(2000) was relatively lower than what was used in previously unsuccessful attempts in

rhesus monkeys (Fig. 1).

Fig. 1. Dose comparisons for preclinical monkey

THC self-administration studies. Note: there is no ordinate.

10-15puffs=2.9-4.3μg/kgTHC

0 100 200 300 400

THC (µg/kg/injection)

Tanda et al. (2000)

Justinova et al. (2003)

Li et al. (2012)

Mansbach et al. (1994)

Harris et al. (1974)

Carney et al. (1977)

Kaymakcalan (1973)

Pickens et al. (1973)

20

Peak rates of responding and maximal numbers of injections were maintained by

2.0 – 4.0 μg/kg/injection doses of THC in squirrel monkeys whereas many of the early

unsuccessful studies with rhesus monkeys did not test doses lower than 100

μg/kg/injection. It is interesting to note that squirrel monkeys have been reported to show

no visible signs of intoxication after reinforcing doses of THC are made available for self-

administration. In contrast, studies in rhesus monkeys have reported that monkeys did

appear intoxicated after the session when relatively higher doses of THC were used,

although few injections were received. These observations suggest that the dose range

necessary for engendering THC-maintained behavior might be significantly lower what

appears to be behaviorally active by visual inspection. Furthermore, the dose range of

THC that maintains peak levels of behavior in squirrel monkeys has been reported to be

analogous to the dose range self-administered by humans smoking a single marijuana

cigarette. For instance, the average marijuana cigarette contains approximately 15 mg of

THC but the actual THC intake is only 3 mg considering the bioavailability of THC from

smoking is about 20% (Agurell et al., 1986). Distributed over 10 to 15 puffs per cigarette,

this equates to approximately 2.9 – 4.3 μg/kg of THC delivered per puff, which

corresponds to the unit doses used to maintain responding in squirrel monkeys. The

total intake of THC obtained in squirrel monkeys when 2 - 4 μg/kg/injection of THC was

available for self-administration is 50 and 120 μg/kg, respectively, over a 1-hour session.

This is also analogous to a report in humans that showed an i.v. injection of 5 mg of THC

(approximately 70 μg/kg) produced the same degree of euphoria compared to smoking a

marijuana cigarette (Ohlsson et al., 1980).

Another methodological difference between successful THC self-administration

studies in squirrel monkeys compared to others involves the vehicle used to dissolve

THC in solution for intravenous injection. Many early studies that assessed i.v. THC self-

administration were conducted with solutions of THC in suspension due to its lipophilic

21

nature, low solubility in water, and high doses used. In contrast, THC was dissolved in a

Tween 80/ethanol/saline vehicle to produce a clear solution for the studies in squirrel

monkeys. This vehicle has been shown to provide rapid distribution of THC to the brain

compared to when the drug is in suspension (Mantilla-Plata and Harbison, 1975) and

significantly elevates dopamine levels in the nucleus accumbens shell of rodents (Tanda

et al., 1997). The bioavailability of a drug is certainly a factor that affects its behavioral

effects. Therefore, vehicle preparation as a means for increasing the bioavailability of

THC likely represents an important methodological factor that should be taken into

consideration for enhancing the likelihood of THC to function as a reinforcer in

experimental animals.

Potential factors limiting the reinforcing effects of cannabinoids in experimental

animals

It is currently unknown whether previous unsuccessful attempts at establishing

the reinforcing effects of cannabinoids in rhesus monkeys were due to methodology or a

direct species difference between squirrel (New World) and rhesus (Old World)

monkeys. To address this, in Chapter II, THC was made available to rhesus monkeys

under similar SA methodological conditions as used in the successful demonstrations in

squirrel monkeys, including schedule of reinforcement, dose range, and vehicle

preparation. It is important that the THC SA procedure be extended to include rhesus

monkeys because this species is phylogenetically closer to humans than any other

currently available preclinical laboratory species including squirrel monkeys, which would

enhance the ability to accurately generalize results to human drug abuse. The

reinforcing effects of the synthetic bicyclic cannabinoid analog, CP 55,940, a potent

CB1/CB2 agonist, were also evaluated in Chapter II using the same monkeys, which

allowed for a better understanding of the role of pharmacological efficacy at the CB

22

receptor in SA, such as differences in potency and magnitude of response. It was

hypothesized that by using conditions that promoted THC self-administration in squirrel

monkeys, successful self-administration in rhesus monkeys would be obtained.

Besides methodological factors, one hypothesis for the failure of THC to function

as a reinforcer in most studies using experimental animals is that the initial aversive

(e.g., rate-decreasing) effects of THC counteract the reinforcing effects. Multiple stimulus

effects of drugs of abuse have been increasingly recognized, not all of which are

rewarding. Thus, drug-taking behavior has been conceptualized as a balance between a

drug’s reinforcing effects and the rate-suppressing effects (e.g., aversion) that limit

responding leading to a drug’s presentation (Verendeev and Riley, 2013). The impact of

these two opposing systems is demonstrated through the inverted U-shaped nature of a

drug self-administration dose-response curve. For example, on the ascending limb, the

reinforcing effect of a drug increases in proportion to the size of the drug’s unit dose and

thus leads to an increase in rate of responding. However, on the descending limb,

response rate and reinforcer magnitude are inversely proportional due to the direct rate-

suppressing effects of high drug doses (e.g., sedation, induction of stereotypies,

aversion, etc.) that serve to counteract the drug’s reinforcing (i.e., rate-increasing

effects). This concept has been experimentally validated by cocaine self-administration

studies showing that the development of tolerance to the rate-decreasing effects leads to

a vertical shift of the dose-response curve, which primarily occurs on the descending

limb (Ahmed and Koob, 1998; Deroch et al., 1999; for review see Zernig et al., 2004).

The notion of THC-maintained behavior being limited by the drug’s initial rate-

decreasing effects is an empirical question and one not previously tested in animal

models of self-administration. Supporting evidence for this hypothesis, however, has

been demonstrated in parallel studies involving conditioned place preference in rodents

as well as epidemiological evidence in humans. For instance, as described previously,

23

cannabinoids have been shown to generally induce conditioned place aversion rather

than place preference in rodents (e.g. Lepore et al., 1995; McGregor et al., 1996;

Sanudo-Pena et al., 1997; Mallet and Beninger, 1998; Chaperon et al., 1998; Cheer et

al., 2000; Zimmer et al., 2001) suggesting that there may be an initial

dysphoric/anxiogenic effect of THC upon first exposure which may correspond to

negative results in self-administration studies. Moreover, retrospective studies in

humans show that individuals who are more sensitive to the aversive effects of cannabis

were less likely to be current users (Haertzen et al., 1983; Thomas, 1996).

In order to test the hypothesis that sensitivity to the rate-decreasing effects would

influence measures of cannabinoid reinforcement, the potency of both THC and CP

55,940 to decrease food-maintained responding was correlated with rates of SA in each

subject. It was hypothesized that individual subjects less sensitive to the rate-decreasing

effects of CP 55,940 and THC will have greater rates of SA of both drugs. Furthermore,

the rate-decreasing effects of cannabinoids that may be counteracting initial reinforcing

effects may also represent a variable that could be modified to facilitate acquisition of

SA. Preclinical data show that tolerance to the effects of repeated THC administration

can differentially develop depending on the behaviors studied (Tanda and Goldberg,

2003). For instance, tolerance to repeated THC administration has been demonstrated

for discriminative stimulus effects, decreases in operant responding, hypothermia,

locomotor activity, nociception, and ingestion (McMillan et al., 1970; Miczek, 1979; Wiley

et al., 1993a; Fan et al., 1994; MicKinney et al., 2008; McMahon, 2011). However, it has

been reported that tolerance does not develop to the subjective “high” of marijuana after

chronic consumption in humans (Lindgren et al., 1981; Perez-Reyes et al., 1991; Kirk

and de Wit, 1999). These effects are consistent with the lack of tolerance development

to THC-induced increases in ventral tegmental area dopamine neuron firing rates both in

vivo (Wu and French, 2000) and in vitro (Cheer et al., 2000). Overall, these data suggest

24

that tolerance may preferentially develop to the rate-decreasing effects and not the

reinforcing effects of THC. Experiments within Chapter II extended the assessment of

tolerance to the behavioral effects of CB agonists to include THC-induced decreases in

operant responding that may impact measures of THC reinforcement using a drug SA

paradigm. We hypothesized that tolerance would develop to the rate-decreasing effects

of THC and this would result in increases in rates of THC SA compared to the initial

assessment.

The pharmacokinetic profiles of drugs are crucial for their reinforcing effects.

Thus, another reason for the relativeness ineffectiveness of cannabinoids to function as

reinforcers in preclinical animal models is because the appropriate conditions have not

been implemented to control for the pharmacokinetic and metabolic factors that may limit

drug maintained responding. Cannabinoids have a relatively delayed onset of their

behavioral effects and an extended elimination phase (Ohlson et al., 1980; Hollister et

al., 1981). For instance, experiments in humans have shown that maximal intoxication

produced by THC can occur 30 min or more after intravenous injection. In drug

discrimination studies in rodents, maximal THC-appropriate responding was not

observed until 60 min after i.m. injection and substantial THC-appropriate responding

was observed for as long as 6 h after the initial administration (Prescott et al., 1992). In

rhesus monkeys, it has been reported that the discriminative stimulus effects of THC can

be detected within 20 min but remain present for up to 4 h after intravenous

administration (McMahon, 2006). Therefore, under schedules of self-administration

where response rates and reinforcement is directly related (i.e., fixed ratio schedules),

these pharmacokinetic variables likely limit subsequent self-administration either by

reducing the temporal contiguity between the drug effect and the response that produces

it or through rate-decreasing effects (e.g., satiation, decreased discriminability of

additional drug injections) resulting from slow offset of drug effects. In Chapter II, these

25

possibilities were examined by studying THC self-administration under a second-order

schedule where behavior was maintained across long intervals between drug injections

by conditioned stimuli. Mansbach et al. (1994) attempted to also take these factors into

consideration in order to demonstrate the reinforcing effects of THC in rhesus monkeys.

That study used a procedure where individual THC injections were spaced by 2 h

intervals and paired with a distinct visual stimulus; however, THC-maintained responding

above vehicle levels was not obtained. Notably, the dose range tested in this study was

0.017 – 0.1 mg/kg/injection, which was considerably higher than the dose range that was

eventually found to maintain the highest amount of behavior leading to THC injections in

squirrel monkeys (0.001 – 0.004 mg/kg/injection). Therefore, we hypothesized that by

using a lower range of doses coupled with second-order contingencies that would help

to mitigate the biodispositional factors of THC that may inhibit the expression of its

reinforcing effects, THC-maintained behavior would be obtained.

The role of cognitive function in substance use disorders

Despite advances in pharmacotherapies over the last few decades, behavioral

treatments are often the most efficacious option for the treatment of various substance

use disorders. Among those with the strongest level of empirical support from

randomized clinical trials are 1) contingency management, where abstinence or other

targeted outcomes are reinforced with incentives (Higgins et al., 1991; Petry, 2006), 2)

cognitive behavioral therapy, which teaches specific strategies and skills to reduce

substance use (Carroll et al., 1994), and 3) motivational interviewing, where a specific,

nonjudgmental interviewing style is used to enhance motivation and harness the

patient’s capacity for change (Hettema et al., 2005; Miller, 1985). However, despite

empirical support, the established efficacy of many of these approaches remain at low

levels. For instance, a meta-analysis of randomized controlled trials for cognitive-

26

behavioral therapy approaches to treat adult alcohol and illicit drug users reported only a

small, but statistically significant treatment effect (g = 0.154), with effect sizes depending

on the substance of abuse (Magill and Ray, 2009). Moreover, the successful

implementation and broad dissemination of behavioral treatments are hindered by

considerable costs and resources needed. Thus, many areas for improvement are

present and more efficient ways to implement behavioral treatments are needed. In an

effort to do so, it is important to better understand how and when the treatments work.

This information can then be used to maximize the effects and develop a more targeted

approach.

One strategy is through understanding the cognitive mechanisms underlying the

effectiveness of behavioral treatments, which can ultimately be used to develop

cognitive enhancement-based interventions to supplement standard behavioral

therapies. Indeed, the idea of cognitive control is central to many contemporary models

of addiction (Jentch and Pennington, 2014) and is a factor that can affect treatment

outcome (Sofuoglu et al., 2010). For example, cognitive impairments to executive control

in drug-dependent populations have been proposed as one mechanism to explain the

maintenance of drug use and the difficulty many individuals have in resisting habitual

drug use once established (Everitt et al., 2007; Goldstein and Volkow, 2011; Li and

Sinha, 2008; Porrino et al., 2007). In particular, aspects of executive function such as

decision-making, response inhibition (failure to abstain from substance use), error

processing (failure to adapt/learn with respect to prior harmful behavior), planning,

working memory, set-shifting, and attention appear to be associated with substance use

and substance use disorders when studied in adults (Sofuoglu et al., 2013). Further,

cognitive impairments in substance abusers at treatment initiation are generally

associated with poorer treatment retention and treatment outcomes. For instance,

research with polydrug users in a residential treatment program found that participants

27

who scored low on a neuropsychological test battery had had shorter lengths of stay in

treatment and were viewed less favorably by the treatment staff (Fals-Stewart, 1993).

Therefore, evidence indicates that attenuating cognitive impairments may serve as

potential treatment targets for substance use disorders.

The effects of THC and marijuana use on cognitive function have been studied

extensively in humans and animal models given the increasing prevalence of cannabis

use and the number of treatment-seeking patients with cannabis use disorder. Although

much progress has been made in understanding the acute (i.e., in the presence of

intoxication) cognitive consequences of cannabis use (Crean et al., 2011; Crane et al.,

2013), the residual effects of cannabis on cognitive function (i.e., after the intoxicating

effects have subsided) and cannabis-induced impairments to cognitive function during

abstinence are much more equivocal. It is particularly important to better characterize

these cognitive effects of THC because growing evidence suggests that cannabis-

induced cognitive deficits are not only apparent after smoking, but persist for a period of

time after the drug has been used (Bolla et al., 2005), which may hinder an individual’s

ability to make the best use of behavioral therapies. This issue, however, lies in better

characterizing the time course of effects and determining which specific cognitive

domains affected are most strongly related to treatment outcome. The remainder of this

chapter will present an overview of the effects of cannabis on neurocognitive function

and how these studies will inform the research within this dissertation focused on

elucidating the exact nature, and persistence of THC-induced cognitive impairment that

can ultimately be used to develop cognitive enhancement approaches to treat cannabis

use disorder.

Residual effects of cannabis on cognition in humans

For the following studies reviewed in this section, the residual effects of cannabis

28

on cognition refer to a period of time after which the direct intoxicating effects of

cannabis have subsided. Pharmacokinetic studies in humans showed that the peak level

of subjective “high” of smoked cannabis was observed after 20-30 minutes and

decreased to baseline levels by 4 hours after smoking. Thus, the current operational

definition of residual effects only includes assessments occurring after this time frame

but within 24 hours of last use. Furthermore, the cognitive domains discussed only

pertain to the domains studied in the present dissertation. These cognitive domains have

been selected from a therapeutic standpoint because they have been shown to have

prognostic significance or relevance in mediating the skills necessary for successful

behavioral modification for cannabis or other substance use disorders.

One of the domains of cognitive function most extensively studied as it relates to

the residual effects of cannabis in humans is inhibition. Inhibition is a significant feature

of many drug dependent individuals, which is defined by an inability to override a

prepotent response (i.e. drug taking) in response to drug cues. As a result, improving

behavioral inhibition by shifting focus away from drug use and toward non-drug

reinforcement (e.g., improved health, stronger interpersonal relationships, stable

employment, etc.) represents a viable cognitive domain to be targeted by cognitive

rehabilitation strategies. One of the first groups (Pope et al., 1996) to study inhibition in

cannabis users examined heavy and light users after a minimum of 19 hours of

abstinence. Heavy cannabis users demonstrated significantly more errors of inhibition

and perseveration compared with light users. Solowij et al. (2002) replicated these

findings in cannabis users after at least 12 hours of abstinence. The severity of these

deficits was correlated with years of use. Several other researchers have found a similar

pattern of impairment within inhibitory functioning (Aharonovich et al., 2008; Cunha et al,

2010). In contrast, a number of researchers found no residual effects of cannabis use on

inhibition (Whitlow et al., 2004; Gruber et al., 2005; Hermann et al., 2007); however,

29

these studies had small sample sizes (e.g. N=10) and the length of abstinence was

unspecified or was highly variable, ranging from 12 hours to 18 years. Thus, the residual

effects of cannabis use within this cognitive domain are equivocal, although clear

indication exists of impairment after acute cannabis intoxication (McDonald et al., 2003;

Ramaekers et al., 2006). As a result, studies are needed that more strictly control for

amount of time of THC intake.

Another cognitive domain with strong implications in substance use disorders

and treatment response is attentional control. Measures of attention have been closely

linked with inhibitory control and are also largely mediated by the prefrontal cortex.

Several studies have examined the residual effects chronic cannabis use on measures

of attention and as with inhibition, the findings have been inconsistent. In one study for

example, Pope et al. (2002) tested current, heavy cannabis users (i.e., users that had

smoked cannabis at least 5000 times and were smoking daily at the time of study entry),

former heavy cannabis users (i.e., users that had also smoked at least 5000 times but

had smoked fewer than 12 times in the past 3 months), and control subjects (who had

only tried cannabis < 50 times during their lifetime) on various measures of selective and

divided attention during experimentally controlled days of abstinence (i.e. 0, 1, 7, and 28

days since last drug use). At all four time points, no significant differences were found on

attentional abilities. Similarly, Jager et al., (2006) also found no differences in attentional

abilities as a result of the residual effects of long-term cannabis use. However, in

contrast, Solowij et al. (1995, 2002) found significantly impaired attentional abilities in

short- and long-term cannabis users compared to control subjects when assessed 24

hours since last use. It was also found that cannabis users demonstrated longer reaction

times to complete the tasks and impairments involved in information processing abilities

associated with the task. Furthermore, Wadsworth et al. (2006) examined attentional

capacities under a choice reaction time task within the context of a “real world” setting,

30

such that performance was assessed before and immediately after work, at both the

beginning and end of the work week. They found that, cannabis use was associated with

significantly impaired performance on the attention task both at the beginning of the work

week and at the end, which was significantly correlated with duration of cannabis use.

However, cannabis use was not found to be associated with an increase in workplace

errors.

Working memory, the ability to hold and manipulate information and remember it

after a short delay, is a major component of executive function and is a cognitive domain

associated with well documents in chronic drug abusers. Working memory has

consistently been shown to be impaired by cannabis shortly after use (e.g. Hart et al.,

2001, 2010); however, the residual effects of cannabis use on measures of working are

a lot more inconsistent. For example, some studies show residual impairments in heavy

cannabis using young adults on immediate recall (Fried et al., 2005), verbal reasoning

(Wadsworth et al., 2006), and verbal n-back tasks (Herzig et al., 2014). In contrast,

several studies have found no residual impairments in working memory abilities between

heavy and light cannabis users compared with control subjects (Pope et al., 1996;

Kanayama et al., 2004; Jager et al., 2006; Solowij et al., 2002; Whitlow et al., 2004; Fisk

and Montgomery., 2008); however, it is unclear whether these results are due to the

duration and quantity of cannabis use or a lack of temporal specificity to detect deficits or

small sample size.

Another cognitive domain associated with substance abuse and treatment

outcome is associative learning. For instance, impairments in associative learning have

been hypothesized to play a role in drug craving and relapse through a process of

“overlearning” drug-stimulus reinforcement-outcome pairings at the expense of nondrug

reinforcers (Gould, 2010). However, intact associative learning systems are crucial for

acquiring new skills and coping strategies during behavioral therapy sessions and

31

applying those skills outside of the treatment setting to facilitate abstinence (Rezapour et

al., 2016). In humans, associative learning is typically measured by word-list learning

tasks and much like the previously discussed cognitive domains, findings for the residual

effects of chronic cannabis use are equivocal. For instance, residual impairments (i.e. >

12 hours since last use) have been reported in adolescents (Harvey et al., 2007; Solowij

et al., 2011; Dougherty et al., 2013) and young adults (Cuttler et al., 2012; Gonzalez et

al., 2012; Becker et al., 2014) as well as in occasional users (Hanson et al., 2010).

Several of these studies report significant associations between impairment and

frequency, quantity, and duration of use and age of onset (e.g., Solowij et al., 2011;

Becker et al., 2014). On the other hand, several studies report no difference in

associative learning performance in recently abstinent cannabis users compared to

nonusers (Fisk and Montgomery, 2008; Gruber et al., 2012).

Long-term effects of cannabis on cognition in humans

While the residual effects of cannabis use on cognition have been

operationalized to refer to the period after the intoxicating effects have subsided but

within 24 hours of last use, evidence indicates that certain cognitive impairments may

persist for several weeks into abstinence. As a result, the human literature has

designated the term “long-term effects” to indicate a period of 3 weeks or longer since

last use (Crean et al., 2011). This cutoff ensures that the acute and residual effects of

cannabis in the brain were eliminated by the time of assessment and thus provides a

reflection of any long-lasting impairments in basal functioning. The long-term effects of

cannabis use on cognition have received considerable research attention; however,

much like the literature on the residual effects, the findings for most cognitive domains

are equivocal. The one exception is episodic memory, which has been shown by a

number of investigators to be impaired for up to 28 days following abstinence (Gonzalez

32

2007; Grant et al. 2003; Solowij and Battisti, 2008).

For the long-term effects of cannabis on behavioral inhibition, studies using the

Stroop Test have consistently found no significant differences between chronic cannabis

users and control groups at 28 days of abstinence (Pope et al., 2001; 2002; 2003) or up

to a year of abstinence (Lyons et al., 2004; Verdejo-Garcia et al., 2005). In contrast,

studies using the Wisconsin Card Sort Test have all found significant differences in

performance up to 28 days of abstinence (Bolla et al., 2002, Pope et al., 2001; 2002;

2003), with the exception of Lyons et al. (2004), who compared cannabis use in heavy

users (>1 time per week for a minimum of 1 year) vs. non using (< 5 times in their life

time) male monozygotic twins. These findings may indicate that certain aspects of

inhibition as assessed by different tasks are more vulnerable to persistent impairment

during abstinence. Other measures of inhibition in abstinent cannabis users derived from

tasks such as the go/no-go or stop-signal reaction time tasks have not been assessed.

Much like the residual effects, the long-term effects of cannabis on attention are

also mixed. In adolescent cannabis users, measures of attentional abilities have been

found to be impaired after at least 21 days of abstinence (Hanson et al. 2010; Jacobsen

et al. 2004; Medina et al. 2007; Tapert et al. 2007), with evidence of a dose-dependent

relationship with amount of lifetime use (Medina et al. 2007). Conversely, others studies

in adolescent cannabis users show that performance on measures of selective and

divided attention were not different compared to controls after 45 days of abstinence

(Jacobsen et al. 2004). In samples of heavy, chronic adult cannabis users, attentional

deficits have been reported after 28 days of abstinence (Bolla et al., 2002) as well as

after more variable periods of abstinence (i.e., 6 weeks to 2 years) (Solowij, 1995).

Moreover, several studies have shown no attentional deficits in heavy cannabis users

after periods of abstinence ranging from 28 days to one year (Lyons et al., 2004; Pope et

al., 2001; 2002; 2003; Verdejo-Garcia et al., 2005), with some indication that disparate

33

findings are the result of task complexity.

In studies examining working performance during long-term abstinence from

chronic cannabis use, there appears to be a relationship between the age of the user

and the cognitive load. For instance, adolescent regular cannabis users that met DSM

criteria for cannabis abuse or dependence demonstrated poorer working memory

compared to controls after 3 and 13 days of abstinence during the Letter-Number

Sequencing task, which required the active manipulation of multidimensional items

(Hanson et al. 2010). However, working memory performance in adolescent users with

similar use characteristics was not different from controls after 8 days of abstinence

during the simpler Sternberg’s item recognition matching task (Jager et al. 2010). After

longer periods of abstinence, the evidence is mixed regarding the persistence of working

memory deficits among adolescents. For instance, some studies reported deficits after

28 days of monitored abstinence when performance was assessed with the n-back task

(Jacobsen et al. 2004; Jacobsen et al. 2007), whereas others reported no deficits

compared to control subjects after similar lengths of abstinence and on similar tasks

(Hanson et al. 2010; Padula et al. 2007; Schweinsburg et al. 2010). In contrast to studies

with adolescent samples, most studies using adult subjects have reported no differences

in working memory performance compared to controls after long-term abstinence

(Becker et al. 2010; Chang et al. 2006; Fisk and Montgomery 2008; J. Grant et al. 2011;

Jager et al. 2006) indicating a potential interaction between cannabis use and

neurodevelopmental processes that mediate working memory. On the other hand,

working memory deficits may also be related to amount of cannabis use, as Harvey and

colleagues (2007) found that the amount of cannabis used was one of the strongest

predictors of poorer spatial working memory performance in regular adolescent cannabis

compared to occasional users.

The long-term effects of chronic cannabis use on associative learning in humans

34

have also been mixed with some studies showing improvement during prolonged

periods of abstinence while others do not. For instance, in one study where cognitive

performance was studied during closely monitored abstinence periods, chronic

marijuana users showed impaired performance on a verbal list learning task at 3 days

since last use compared to non-using controls, although performance improved to

control levels by two weeks of abstinence (Hanson et al., 2010). In a study with lower

temporal resolution, it was also found that performance on a verbal list learning task in

former heavy cannabis users (> 12 months since last use) had improved to similar

performance levels of non-users (Tait et al., 2011). In contrast, Medina et al. (2007)

found impaired associative learning performance in adolescent marijuana users

compared to controls after > 23 days of monitored abstinence suggesting persistent

impairment in some populations of users.

Overall, the non-acute studies of cannabis on cognition in humans seem to raise

more questions than answers with regard to the specific cognitive domain that is

affected. In particular, some of the inconsistencies among these studies arise from the

heterogeneity of the participants. For instance, factors such as length of abstinence, age

at which cannabis users discontinue their use, amount and duration of cannabis use,

concurrent use of other substances, and whether prospective study designs were used

all complicate the interpretation of many of these studies. In addition, another factor that

seems to be overlooked when considering the effects of cannabis on cognition is the

cognitive load for each task. For example, it is unknown for many studies whether a lack

of effect is merely due to the task not containing a high enough complexity in order to

detect more subtle brain abnormalities among cannabis users. Moreover, studies in

humans cannot distinguish whether marijuana abusers exhibit innate cognitive

differences prior to initiating drug taking. Therefore, it is difficult to clearly establish a

causal role of long-term THC exposure in producing cognitive deficits.

35

Cognitive effects of THC in preclinical animal models

The utilization of preclinical animal models to study the long-term effects of THC

on cognition is crucial because many of the variables that contribute to the equivocal

findings in studies with human subjects can be controlled for in a systematic manner.

The acute effects of THC and synthetic cannabinoids on learning and memory

processes have been extensively studied in rodents and nonhuman primates and have

been shown to produce impairments in a variety of tasks including the eight-arm radial

maze (Han and Robinson, 2001; Mallet and Beninger, 1998; Winsauer et al. 1999), two-

component instrumental discrimination task (Han & Robinson, 2001; Mallet and

Beninger, 1998; Nava et al., 2000; Winsauer et al., 1999); conditional discriminations

(Han & Robinson, 2001; Mallet & Beninger, 1998; Nava et al., 2000; Winsauer et al.,

1999; Zimmerberg et al., 1971), recognition memory tasks (Aigner, 1988; Schulze et al.,

1989; Kangas et al., 2016), spatial delayed response tasks (Rupniak et al., 1991; Taffe,

2012), and time estimation (Peiper, 1976; Schulze et al., 1988, 1989).

In addition to examining the acute effects of THC on cognition, it is critical to

employ longitudinal studies in preclinical animal models in order to disentangle the

temporal ordering of cannabis-related cognitive deficits involving the exact nature,

persistence, and reversibility of these deficits. The use of nonhuman primates is

especially well-suited for these types of studies due to their long lifespan, capability to

learn complex cognitive tasks, and similarity to humans in regards to neuroanatomy and

neurochemistry (Berger et al., 1991; Joel and Weiner, 2000). However, to date, only one

preclinical study in nonhuman primates has examined the residual effects of chronic

THC exposure (Verrico et al., 2014). In that study, adolescent rhesus monkeys were

treated with THC (120 or 240 µg/kg, i.v.), 5 days per week for approximately 6 months

during which working memory performance was assessed 23 hours after drug

36

administration throughout the study. It was found that chronic THC exposure impaired

spatial-, but not object-working memory; tolerance or sensitization did not develop to

these effects.

Studies in Chapter 3 expanded on these findings by examining both the residual

effects of THC administration (i.e., 22 hours after administration) and the effects of

several weeks of abstinence (i.e., 4 -6 weeks since discontinuation of chronic THC

administration) on four cognitive domains that assessed 1) associative learning through

simple and multidimensional discrimination learning, 2) attention and working memory

through delayed-match to sample performance, 3) inhibition though reversal learning

requiring the ability to inhibit a previously established response based on an acquired

stimulus-reinforcement association, and 4) behavioral flexibility through set-shifting

performance requiring the formation of an attentional set based on relevant stimuli for

reinforcement and the disregard to irrelevant stimuli, all by using a within-subjects design

in monkeys. The development of tolerance to the acute effects of THC during chronic

exposure was also examined for each of these cognitive domains. These particular

domains of cognition were assessed because of their relevance in a cognitive

rehabilitation context, in that they underlie the skills necessary to assimilate and

integrate new information into a plan for behavior change that can lead to sobriety and

the ability to maintain focus on long-term goals (Rezapour et al., 2016). Moreover, as

described earlier, the residual effects of THC within these domains are equivocal in

studies with human subjects, most likely due to the numerous variables that are difficult

to control for in human research including differences in time since last use, amount and

duration of cannabis use, previous drug histories, social variables, and concurrent use of

other substances. In addition, studies in humans cannot distinguish whether marijuana

abusers exhibit innate cognitive differences prior to initiating drug taking so it is difficult

to clearly establish a causal role of long-term THC exposure in producing cognitive

37

deficits. By controlling for these factors in a systematic manner, the results of the studies

within this dissertation can be used to improve the efficacy of behavioral treatments for

cannabis use disorder by identifying the specific domains of cognitive functioning and a

temporal window that can be targeted for successful treatment outcome.

38

REFERENCES

Adams IB, Martin BR (1996) Cannabis: Pharmacology and toxicology in animals and

humans. Addiction 91: 1585– 1614.

Agurell S, Halldin M, Lindgren JE, Ohlsson A, Widman M, Gillespie H, et al. (1986)

Pharmacokinetics and metabolism of delta 1-tetrahydrocannabinol and other

cannabinoids with emphasis on man. Pharmacol Rev. 38: 21–43.

Aharonovich E, Brooks AC, Nunes EV, Hasin DS (2008) Cognitive deficits in marijuana

users: Effects on motivational enhancement therapy plus cognitive behavioral

therapy treatment outcome. Drug Alcohol Depend 95: 279-283.

Ahmed SH, Koob GF (1998) Transition from moderate to excessive drug intake: change

in hedonic set point. Science 282: 298–300.

Aigner TG (1988) Delta-9-tetrahydrocannabinol impairs visual recognition memory but

not discrimination learning in rhesus monkeys. Psychopharmacology (Berl). 95:

507-511.

Atwood, B. K. and Mackie, K (2010) CB2: a cannabinoid receptor with an identity crisis.

Br. J. Pharmacol. 160: 467–479.

Balster RL, Prescott WR (1992) Delta 9-tetrahydrocannabinol discrimination in rats as a

model for cannabis intoxication. Neurosci Biobehav Rev. 16: 55–62.

Balster RL, Schuster CR (1973) Fixed-interval schedule of cocaine reinforcement: effect

of dose and infusion duration. J Exp Anal Behav. 20: 119–29.

Bardo MT, Bevins RA (2000) Conditioned place preference: what does it add to our

preclinical understanding of drug reward? Psychopharmacology. 153: 31–43.

Barrett RL, Wiley JL, Balster RL, Martin BR (1995) Pharmacological specificity of Δ9-

tetrahydrocannabinol discrimination in rats. Psychopharmacology. 118: 419–

424.

39

Becker B, Wagner D, Gouzoulis-Mayfrank E, Spuentrup E, and Daumann J (2010) The

impact of early-onset cannabis use on functional brain correlates of working

memory. Progress in Neuro-Psychopharmacology & Biological Psychiatr. 34:

837–845.

Becker MP, Collins PF, Luciana M (2014) Neurocognition in college-aged daily

marijuana users. J Clin Exp Neuropsychol. 36:379-398.

Berger B, Gaspar P, Verney C (1991) Dopaminergic innervation of the cerebral cortex:

unexpected differences between rodents and primates. Trends Neurosci. 14: 21–

27.

Bolla KI, Brown K, Eldreth D, Tate K, Cadet JL (2002) Dose-related neurocognitive

effects of marijuana use. Neurology. 59: 1337-1343.

Bolla KI, Eldreth DA, Matochik JA, Cadet JL (2005) Neural substrates of faulty decision-

making in abstinent marijuana users. Neuroimage 26: 480–492.

Bossong MG, van Berckel BN, Boellaard R, Zuurman L, Schuit RC, Windhorst AD et al.

(2009) Delta 9-tetrahydrocannabinol induces dopamine release in the human

striatum. Neuropsychopharmacology. 34: 759–766.

Bossong MG, Mehta MA, van Berckel BN, Howes OD, Kahn RS, Stokes PR (2015)

Further human evidence for striatal dopamine release induced by administration

of 9-tetrahydrocannabinol (THC): selectivity to limbic striatum.

Psychopharmacology. 232: 2723–2729.

Braida D, Pozzi M, Cavallini R, Sala M (2001a) Conditioned place preference induced by

the cannabinoid agonist CP 55,940: interaction with the opioid system.

Neuroscience 104:923–926.

Braida D, Pozzi M, Parolaro D, Sala M (2001b) Intracerebral self-administration of the

cannabinoid receptor agonist CP 55,940 in the rat: interaction with the opioid

system. Eur J Pharmacol 413:227–234.

40

Braida D, Iosue S, Pegorini S, Sala M (2004) Delta9- tetrahydrocannabinol-induced

conditioned place preference and in- tracerebroventricular self-administration in

rats. Eur J Pharmacol 506: 63–69.

Cabeza de Vaca S, Carr KD (1998) Food restriction enhances the central rewarding

effect of abused drugs. J Neurosci. 18: 7502–7510.

Cappell H, Kuchar E, Webster CD (1973) Some correlates of marihuana self-

administration in man: a study of titration of intake as a function of drug potency.

Psychopharmacologia. 29: 177–184.

Carney JM, Uwaydah IM, Balster RL (1977) Evaluation of a suspension system for

intravenous self-administration of water insoluble substances in the rhesus

monkey. Pharmacol Biochem Behav. 7: 357–364.

Carroll ME, Meisch RA. Increased drug-reinforced behavior due to food deprivation. In:

Thompson T, Dews PB, Barrett JE, editors. Advances in Behavioral

Pharmacology, vol. 4. New York’ Academic Press; 1984. 47– 88.

Carroll ME, France CP, Meisch RA (1979) Food deprivation increases oral and

intravenous drug intake in rats. Science. 205: 319– 321.

Carroll KM, Rounsaville BJ, Gordon LT, Nich C, Jatlow P, Bisighini RM, Gawin FH

(1994) Psychotherapy and pharmacotherapy for ambulatory cocaine abusers.

Arch. Gen. Psychiatry 51: 177-187.

Centers for Disease Control and Prevention. Best Practices for Comprehensive Tobacco

Control Programs — 2014. Atlanta: U.S. Department of Health and Human

Services, Centers for Disease Control and Prevention, National Center for

Chronic Disease Prevention and Health Promotion, Office on Smoking and

Health, 2014.

Chait LD (1989) Delta-9-tetrahydrocannabinol content and human marijuana self-

administration. Psychopharmacology (Berl). 98: 51–55.

41

Chang L, Yakupov R, Cloak C, Ernst T (2006) Marijuana use is associated with a

reorganized visual-attention network and cerebellar hypoactivation. Brain 129:

1096–1112.

Chaperon F, Soubrie P, Puech AJ, Thiebot MH (1998) Involvement of central

cannabinoid (CB1) receptors in the establishment of place conditioning in rats.

Psychopharmacology. 135: 324–332

Cheer JF, Marsden CA, Kendall DA, Mason R (2000) Lack of response suppression

follows repeated ventral tegmental CB administration: an in vitro

electrophysiological study. Neuroscience. 99: 661–667.

Corcoran ME, Amit Z (1974) Reluctance of rats to drink hashish suspensions: free-

choice and forced consumption, and the effects of hypo- thalamic stimulation.

Psychopharmacologia 35: 129–147

Crane NA, Schuster RM, Fusar-Poli P, et al. (2013) Effects of cannabis on

neurocognitive functioning: recent advances, neurodevelopmental influences,

and sex differences. Neuropsychol Rev. 23: 117–37.

Crean, RD., Crane, NA, and Mason, BJ (2011) An evidence based review of acute and

long-term effects of cannabis Use on executive cognitive functions. Journal of

Addiction Medicine. 5: 1–8.

Cunha, P. J., Nicastri, S., de Andrade, A. G., & Bolla, K. I. (2010) The frontal

assessment battery (FAB) reveals neurocognitive dysfunc- tion in substance-

dependent individuals in distinct executive domains: abstract reasoning, motor

programming, and cognitive flexibility. Addictive Behaviors. 35: 875–881.

Cuttler C, McLaughlin RJ, Graf P (2012) Mechanisms underlying the link between

cannabis use and prospective memory. PLoS One. 7:e36820.

de la Garza R, Johanson CE (1987) The effects of food deprivation on the self-

administration of psychoactive drugs. Drug Alcohol Depend. 19: 17– 27.

42

De Luca MA, Valentini V, Bimpisidis Z, Cacciapaglia F, Caboni P, Di Chiara G (2014)

Endocannabinoid 2-arachidonoylglycerol self-administration by Sprague-Dawley

rats and stimulation of in vivo dopamine transmission in the nucleus accumbens

shell. Front Psychiatr. 5: 140.

De Luca MA, Bimpisidis Z, Melis M, Marti M, Caboni P, Valentini V, Margiani G, Pintori

N, Polis I, Marsicano G, Parsons LH, Di Chiara G (2015) Stimulation of in vivo

dopamine transmission and intravenous self-administration in rats and mice by

JWH-018, a Spice cannabinoid. Neuropharmacology. 99: 705-14.

Deneau G, Yanagita T, Seevers MH (1969) Self-administration of psychoactive

substances by the monkey-a measure of psychological dependence.

Psychopharmacologia. 16: 30–48.

Deneau GA, Kaymakcalan S (1971) Physiological and psychological dependence to

synthetic delta-9-tetrahydrocannabinol (THC) in rhesus monkeys.

Pharmacologist. 246.

Deroche V, Le Moal M, Piazza PV (1999) Cocaine self-administration increases the

incentive motivational properties of the drug in rats. Eur J Neurosci 11: 2731–

2736.

Elsohly MA, Slade D (2005) Chemical constituents of marijuana: the complex mixture of

natural cannabinoids. Life Sci. 78: 539-548.

Everitt BJ, Hutcheson DM, Ersche KD, Pelloux Y, Dalley JW, and Robbins TW (2007)

The orbital prefrontal cortex and drug addiction in laboratory animals and

humans. Annals of the New York Academy of Sciences. 112: 576–597.

Fals-Stewart W (1993) Neurocognitive defects and their impact on substance abuse

treatment. Journal of Addictions & Offender Counseling. 13: 46–57.

Fan F, Compton DR, Ward S, Melvin L, Martin BR (1994) Development of cross-

43

tolerance between delta 9-tetrahydrocannabinol, CP 55,940 and WIN 55,212. J

Pharmacol Exp Ther. 271: 1383–1390.

Fattore L, Cossu G, Martellotta CM, Fratta W (2001) Intravenous self- administration of

the cannabinoid CB1 receptor agonist WIN 55, 212-2 in rats. Psychopharmacol

(Berl). 156: 410–416 .

Ferraro DP and Grilly DM (1974) Effects of chronic exposure to delta-9-

tetrahydrocannabinol on delayed matching-to-sample in chimpanzees.

Psychopharmacologia. 37: 127–138.

Fisk JE and Montgomery C (2008) Real-world memory and executive processes in

cannabis users and non-users. Journal of Psychopharmacology 22: 727-736.

Fried PA, Watkinson B, Gray R (2005) Neurocognitive consequences of marihuana-a

comparison with pre-drug performance. Neurotoxicol Teratol. 27:231-239.

Ghozland S, Mathews H, Simonin F, Filliol D, Kieffer BL, Maldonado R (2002)

Motivational effects of cannabinoids are mediated by µ- and κ-opioid receptors. J

Neurosci 22: 1146–1154.

Glass M, Dragunow M, and Faull RL (1997) Cannabinoid receptors in the human brain:

a detailed anatomical and quantitative autoradiographic study in the fetal,

neonatal and adult human brain. Neuroscience. 77: 299–318.

Gold LH, Balster RL, Barrett RL, Britt DT, Martin BR (1992) A comparison of the

discriminative stimulus properties of delta 9-tetrahydrocannabinol and CP 55,940

in rats and rhesus monkeys. J Pharmacol Exp Ther. 262: 479-86.

Goldberg SR, Spealman RD, Risner ME, Henningfield JE (1983) Control of behavior by

intravenous nicotine injections in laboratory animals. Pharmacol Biochem

Behav.. 19: 1011-20.

44

Goldstein RZ, Volkow ND (2011) Dysfunction of the prefrontal cortex in addiction:

neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12: 652-669.

Gonzalez, R (2007) Acute and non-acute effects of cannabis on brain functioning and

neuropsychological performance. Neuropsychology Review. 17: 347–361.

Gonzalez R, Schuster RM, Mermelstein RJ, Vassileva J, Martin EM, Diviak KR (2012)

Performance of young adult cannabis users on neurocognitive measures of

impulsive behavior and their relationship to symptoms of cannabis use disorders.

J Clin Exp Neuropsychol. 34:962-76.

Gould TJ (2010) Addiction and cognition. Addict. Sci. Clin. Pract. 5: 4–14.

Grant I, Gonzalez R, Carey CL, et al. (2003) Non-acute (residual) neurocognitive effects

of cannabis use: A meta-analytic study. J Int Neuropsychol Soc. 9:679–689.

Grant JE, Chamberlain SR, Schreiber L, and Odlaug BL (2011). Neuropsychological

deficits associated with cannabis use in young adults. Drug and Alcohol

Dependence. 121: 159–162.

Griffiths RR, Roache JD, Ator NA, Lamb RJ, Lukas SE (1985) Similarities in reinforcing

and discriminative stimulus effects of diazepam, triazolam, and pentobarbital in

animals and humans. In: Seiden LS, Balster RL (eds) Behavioral pharmacology:

the current status. Liss, New York. 419–432.

Gruber SA and Yurgelun-Todd DA (2005) Neuroimaging of marijuana smokers during

inhibitory processing: a pilot investigation. Brain Res Cogn Brain Res. 23: 107–

118.

Gruber SA, Sagar KA, Dahlgren MK, Racine M, Lukas SE (2012) Age of onset of

marijuana use and executive function. Psychol Addict Behav. 26:496-506.

Haertzen CA, Kocher TR, Miyasato K (1983) Reinforcements from the first drug

experience can predict later drug habits and/or addiction: results with coffee,

cigarettes, alcohol, barbiturates, minor and major tranquilizers, stimulants,

45

marijuana, hallucinogens, heroin, opiates and cocaine. Drug Alcohol Depend 11:

147–165.

Hall W, Degenhardt L (2009) Adverse health effects of non- medical cannabis use.

Lancet 374: 1383–1391.

Han CJ, and Robinson JK (2001) Cannabinoid modulation of time estimation in the rat.

Behavioral Neuroscience. 115: 243– 246.

Haney M, Comer SD, Ward AS, Foltin RW, Fischman MW (1997) Factors influencing

marijuana self-administration by humans. Behav Pharmacol 8: 101–112.

Haney M, Spealman R (2008) Controversies in translational research: drug self-

administration. Psychopharmacology. 199: 403-419.

Hanson KL, Winward JL, Schweinsburg AD, Medina KL, Brown SA, Tapert SF (2010)

Longitudinal study of cognition among adolescent marijuana users over three

weeks of abstinence. Addict Behav. 35:970-976.

Harris RT, Waters W, McLendon D (1974) Evaluation of reinforcing capability of D9-

tetrahydrocannabinol in monkeys. Psychopharmacologia. 37: 23–29.

Hart CL, van Gorp W, Haney M, et al. (2001) Effects of acute smoked marijuana on

complex cognitive performance. Neuropsychopharmacology. 25: 757–765.

Hart CL, Ilan AB, Gevins A, Gunderson EW, Role K, Colley J, et al. (2010)

Neurophysiological and cognitive effects of smoked marijuana in frequent users.

Pharmacology, Biochemistry, and Behavior. 96: 333–341.

Harvey MA, Sellman JD, Porter RJ, Frampton CM (2007) The relationship between non-

acute adolescent cannabis use and cognition. Drug Alcohol Rev. 26:309-319.

Heishman SJ, Stitzer ML, Yingling JE (1989) Effects of tetrahydrocannabinol content on

marijuana smoking behavior, subjective reports, and performance. Pharmacol

Biochem Behav. 34:1 73–179.

46

Herkenham M, Lynn AB, Little MD, Johnson MR, Melvin LS, de Costa BR et al. (1990)

Cannabinoid receptor localization in brain. Proc Natl Acad Sci USA. 87: 1932–

1936.

Hermann D, Sartorius A, Welzel H, Walter S, Skopp G, Ende G, et al. (2007)

Dorsolateral prefrontal cortex N acetylaspartate/total creatine (NAA/tCr) loss in

male recreational cannabis users. Biol Psychiatry 61: 1281-1290.

Herning RI, Hooker WD, Jones RT (1986) Tetrahydrocannabinol content and differences

in marijuana smoking behavior. Psychopharmacology (Berl). 90: 160–162.

Herzig DA, Nutt DJ, Mohr C (2014) Alcohol and relatively pure cannabis use, but not

schizotypy, are associated with cognitive attenuations. Front Psychiatry. 5: 133.

Hettema J, Steele J, Miller WR (2005) Motivational interviewing. Annu. Rev. Clin.

Psychol. 1. 91-111.

Higgins ST, Delaney DD, Budney AJ, Bickel WK, Hughes JR, Foerg F, Fenwick JW

(1991) A behavioral approach to achieving initial cocaine abstinence. Am. J.

Psychiatry. 148. 1218-1224.

Hollister LE, Gillespie HK, Ohlsson A, Lindgren JE, Wahlen A, Agurell S (1981) Do

plasma concentrations of delta 9-tetrahydrocannabinol reflect the degree of

intoxication? J Clin Pharmacol. 21: 171S-177S.

Jacobsen, LK, Mencl, WE, Westerveld, M, and Pugh, KR (2004) Impact of cannabis use

on brain function in adolescents. Annals of the New York Academy of Sciences.

1021: 384–390.

Jacobsen LK, Pugh KR, Constable RT, Westerveld M, and Mencl WE (2007) Functional

correlates of verbal memory deficits emerging during nicotine withdrawal in

abstinent adolescent cannabis users. Biological Psychiatry. 61: 31–40.

47

Jager G, Kahn RS, Van Den Brink W, Van Ree JM, and Ramsey NF (2006) Long-term

effects of frequent cannabis use on working memory and attention: an fMRI

study. Psychopharmacology. 185: 358–368.

Jager G, Block RI, Luijten M, and Ramsey NF (2010) Cannabis use and memory brain

function in adolescent boys: a cross- sectional multicenter functional magnetic

resonance imaging study. Journal of the American Academy of Child and

Adolescent Psychiatry. 49: 561–572.

Järbe TU, Lamb RJ, Lin S, Makriyannis A (2001) (R)-methanan- damide and Delta 9-

THC as discriminative stimuli in rats: tests with the cannabinoid antagonist SR-

141716 and the endoge- nous ligand anandamide. Psychopharmacology 156:

369–380.

Jentsch JD, Pennington ZT (2014) Reward, interrupted: Inhibitory control and its

relevance to addictions. Neuropharmacology 76: 479-486.

Joel D, Weiner I (2000) The connections of dopaminergic system with the striatum in

rats and primates: an analysis with respect to the functional and compartmental

organization of the striatum. Neuroscience. 96: 451–474.

Johnston LD, et al. (2014) Monitoring the Future: National Survey Results on Drug Use,

1975 2014-Overview, Key Findings on Adolescent Drug Use. (Institute for Social

Research, University of Michigan, Ann Arbor) Available at

http://monitoringthefuture.org//pubs/monographs/mtf-vol1_2014.pdf.

Justinova Z, Tanda G, Redhi GH, Goldberg SR (2003) Self-administration of delta(9)

tetrahydrocannabinol (THC) by drug naive squirrel monkeys.

Psychopharmacology (Berl). 169: 135–140.

Justinova Z, Munzar P, Panlilio LV, Yasar S, Redhi GH, Tanda G, Goldberg SR (2008)

Blockade of THC-seeking behavior and relapse in monkeys by the cannabinoid

48

CB(1)-receptor antagonist rimonabant. Neuropsychopharmacology. 33: 2870–

2877.

Justinova Z, Yasar S, Redhi GH, Goldberg SR (2011) The endogenous cannabinoid 2-

arachidonoylglycerol is intravenously self- administered by squirrel monkeys. J

Neurosci: Off J Soc Neurosci. 31: 7043–7048.

Justinova Z, Mascia P, Wu HQ, Secci ME, Redhi GH, Panlilio LV, Scherma M, Barnes

C, Parashos A, Zara T, Fratta W, Solinas M, Pistis M, Bergman J, Kangas BD,

Ferre S, Tanda G, Schwarcz R, Goldberg SR (2013) Reducing cannabinoid

abuse and preventing relapse by enhancing endogenous brain levels of

kynurenic acid. Nat Neurosci. 16:1652–1661.

Kanayama G, Rogowska J, Pope HG, Gruber SA, Yurgelun-Todd DA (2004) Spatial

working memory in heavy cannabis users: a functional magnetic resonance

imaging study. Psychopharmacology. 176: 239-247.

Kangas BD, Leonard MZ, Shukla VG, Alapafuja SO, Nikas SP, Makriyannis A, Bergman

J (2016) Comparisons of Δ9-Tetrahydrocannabinol and Anandamide on a Battery

of Cognition-Related Behavior in Nonhuman Primates. J Pharmacol Exp Ther.

357: 125-133.

Kaymakcalan S (1972) Physiology and psychological dependence on THC in rhesus

monkeys. In: Paton WDM, Crown J (eds) Cannabis and its derivatives. Oxford

University Press, London. 142–149.

Kaymakcalan S (1973) Tolerance to and dependence on cannabis. Bull Narc 25:39–47.

Kelly TH, Foltin RW, Emurian CS, Fischman MW (1994) Effects of delta 9-THC on

marijuana smoking, dose choice, and verbal report of drug liking. J Exp Anal

Behav.. 61: 203–11.

49

Kelly TH, Foltin RW, Emurian CS, Fischman MW (1997) Are choice and self-

administration of marijuana related to delta 9-THC content? Exp Clin

Psychopharmacol. 5: 74–82.

Kirk JM, De Wit H (1999) Responses to oral delta9-tetrahydro- cannabinol in frequent

and infrequent marijuana users. Phar- macol Biochem Behav. 63: 137–142.

Lecca D, Cacciapaglia F, Valentini V, Di Chiara G (2006) Monitoring extracellular

dopamine in the rat nucleus accumbens shell and core during acquisition and

maintenance of intravenous WIN 55,212-2 self-administration. Psychopharmacol

(Berl). 188:63–74.

Ledent C, Valverde O, Cossu G, Petitet F, Aubert JF, Beslot F, Bohme GA, Imperato A,

Pedrazzini T, Roques BP, Vassart G, Fratta W, Parmentier M (1999)

Unresponsiveness to cannabinoids and re- duced addictive effects of opiates in

CB1 receptor knockout mice. Science. 283: 401–404.

Lefever, TW, Marusich, JA, Antonazzo, KR, and Wiley, JL (2014). Evaluation of WIN

55,212-2 self-administration in rats as a potential cannabinoid abuse liability

model. Pharmacol. Biochem. Behav.. 118: 30–35.

Leite JR, Carlini EA (1974) Failure to obtain “cannabis directed behavior” and abstinence

syndrome in rats chronically treated with cannabis sativa extracts.

Psychopharmacologia. 36: 133–145.

Lepore M, Vorel SR, Lowinson J, Gardner EL (1995) Conditioned place preference

induced by Δ9-tetrahydrocannabinol: comparison with cocaine, morphine, and

food reward. Life Sci. 56: 2073–2080.

Li CS, Sinha R, (2008) Inhibitory control and emotional stress regulation: neuro- imaging

evidence for frontal-limbic dysfunction in psycho-stimulant addiction. Neurosci.

Biobehav. Rev. 32: 581-597.

50

Li J-X, Koek W, France CP (2012) Interactions between Δ(9)-tetrahydrocannabinol and

heroin: self-administration in rhesus monkeys. Behav Pharmacol. 23: 754–761.

Lindgren JE, Ohlsson A, Agurell S, Hollister L, Gillespie H (1981) Clinical effects and

plasma levels of delta 9-tetrahydrocannabinol (delta 9-THC) in heavy and light

users of cannabis. Psychopharmacology. 74: 208–212.

Lupica CR, Riegel AC, Hoffman AF (2004) Marijuana and cannabinoid regulation of

brain reward circuits. Br J Pharmacol. 143: 227–234.

Lyons MJ, Bar JL, Panizzon MS, et al. (2004) Neuropsychological consequences of

regular marijuana use: A twin study. Psychol Med. 34: 1239 –1250.

Magill M, Ray LA (2009) Cognitive-behavioral treatment with adult alcohol and illicit drug

users: a meta-analysis of randomized controlled trials. J Stud Alcohol Drugs.

70:516-527.

Maldonado R (2002) Study of cannabinoid dependence in animals. Pharmacol Ther. 95:

153–64.

Mallet PE, Beninger RJ (1998) Delta 9-tetrahydrocannabinol, but not the endogenous

cannabinoid receptor ligand anandamide, produces conditioned place avoidance.

Life Sci. 62: 2431–2439.

Mansbach RS, Nicholson KL, Martin BR, Balster RL (1994) Failure of Δ9-

tetrahydrocannabinol and CP 55,940 to maintain intravenous self administration

under a fixed-interval schedule in rhesus monkeys. Behav Pharmacol. 5: 219–

225.

Mansbach RS, Rovetti CC, Winston EN, Lowe JA III (1996) Effects of the cannabinoid

CB1 receptor antagonist SR141716A on the behavior of pigeons and rats.

Psychopharmacology. 124: 315–322.

Mantilla-Plata B, Harbison RD (1975) Distribution studies of (14C) delta-9-

51

tetrahydrocannabinol in mice: effect of vehicle, route of administration, and

duration of treatment. Toxicol Appl Pharmacol. 34: 292–300.

Martellotta MC, Cossu G, Fattore L, Gessa GL, Fratta W (1998) Self- administration of

the cannabinoid receptor agonist WIN 55,212-2 in drug-naive mice.

Neuroscience. 85: 327–330.

McDonald, J, Schleifer, L, Richards, JB, and de Wit, H (2003) Effects of THC on

behavioral measures of impulsivity in humans. Neuropsychopharmacology 28:

1356-1365.

McMahon LR (2006) Characterization of cannabinoid agonists and apparent pA2

analysis of cannabinoid antagonists in rhesus monkeys discriminating delta9-

tetrahydrocannabinol. J Pharmacol Exp Ther. 319: 1211-1218.

McMahon LR (2011) Chronic Δ⁹-tetrahydrocannabinol treatment in rhesus monkeys:

differential tolerance and cross-tolerance among cannabinoids. Br J Pharmacol..

162: 1060-1073.

McKinney DL, Cassidy MP, Collier LM, Martin BR, Wiley JL, Selley DE et al. (2008)

Dose-related differences in the regional pattern of cannabinoid receptor

adaptation and in vivo tolerance development to D9-tetrahydrocannabinol. J

Pharmacol Exp Ther. 324: 664–673.

McGregor IS, Issakidis CN, Prior G (1996) Aversive effects of the synthetic cannabinoid

CP 55,940 in rats. Pharmacol Biochem Behav. 53: 657–664.

McMillan DE, Harris LS, Frankenheim JM, Kennedy JS (1970). l-D9-trans-

tetrahydrocannabinol in pigeons: tolerance to the behavioral effects. Science.

169: 501–503.

Medina KL, Hanson KL, Schweinsburg AD, Cohen-Zion M, Nagel BJ, Tapert SF (2007)

Neuropsychological functioning in adolescent marijuana users: subtle deficits

52

detectable after a month of abstinence. J Int Neuropsychol Soc. 13: 807-820.

Miczek KA (1979) Chronic D9-tetrahydrocannabinol in rats: effect on social interactions,

mouse killing, motor activity, consummatory behavior, and body temperature.

Psychopharmacology. 60: 137–146.

Miller WR (1985) Motivation for treatment: a review with special emphasis on alcoholism.

Psychol. Bull. 98. 84-107.

National Drug Intelligence Center (2011) The Economic Impact of Illicit Drug Use on

American Society. Washington, DC: United States Department of Justice.

Nordstrom BR, Levin FR (2007) Treatment of cannabis use disorders: A review of the

literature. Am J Addict. 16: 331–342.

Ohlsson A, Lindgren JE, Wahlen A, Agurell S, Hollister LE, Gillespie HK (1980) Plasma

delta-9 tetrahydrocannabinol concentrations and clinical effects after oral and

intravenous administration and smoking. Clin Pharmacol Ther. 28: 409–416.

Padula CB, Schweinsburg AD, and Tapert SF (2007). Spatial working memory

performance and fMRI activation interaction in abstinent adolescent marijuana

users. Psychology of Addictive Behaviors. 21: 478–487.

Perez-Reyes M, Di Guiseppi S, Davis KH, Schindler VH, Cook CE (1982) Comparison of

effects of marihuana cigarettes to three different potencies. Clin Pharmacol Ther.

31: 617–624.

Perez-Reyes M, White WR, McDonald SA, Hicks RE, Jeffcoat AR, Cook CE (1991) The

pharmacologic effects of daily marijuana smoking in humans. Pharmacol

Biochem Behav. 40: 691–694.

Perio A, Rinaldi-Carmona M, Maruani J, Barth F, Le Fur G, Soubrie P (1996) Central

mediation of the cannabinoid cue: activity of a selective CB1 antagonist, SR

141716A. Behav Pharmacol. 7: 65–71.

53

Petry NM (2006) Contingency management treatments. Br. J. Psychiatry 189. 97-98.

Pickens R, Thompson T, Muchow DC (1973) Cannabis and phencyclidine self-

administered by animals. In: Goldfarb L, Hoffmeister F (eds) Psychic

dependence [Bayer-Symposium IV]. Springer, Berlin Heidelberg New York. 78–

86.

Pieper WA (1976) Great apes and rhesus monkeys as subjects for psycho-

pharmacological studies of stimulants and depressants. Fed Proc. 35: 2254–

2257.

Pope HG, Jr, Yurgelun-Todd D (1996) The residual cognitive effects of heavy marijuana

use in college students. JAMA. 275: 521-527.

Pope HG Jr, Gruber AJ, Hudson JI, et al. (2001) Neuropsychological performance in

long-term cannabis users. Arch Gen Psychiatry. 58: 909–915.

Pope HG Jr, Gruber AJ, Hudson JI, et al. (2002) Cognitive measures in long-term

cannabis users. J Clin Pharmacol. 42: 41S–47S.

Pope HG, Gruber AJ, Hudson JI, et al. (2003) Early-onset cannabis use and cognitive

deficits: What is the nature of the association? Drug Alcohol Depend. 69: 303–

310.

Porrino LJ, Smith HR, Nader MA, Beveridge TJ, (2007) The effects of cocaine: a shifting

target over the course of addiction. Prog. Neuropsychopharmacol. Biol.

Psychiatry. 31: 1593-1600.

Prescott WR, Gold LH, Martin BR (1992) Evidence for separate neuronal mechanisms

for the discriminative stimulus and catalepsy induced by delta 9-THC in the rat.

Psychopharmacology (Berl). 107: 117-124.

Ramaekers JG, Kauert G, van Ruitenbeek P, Theunissen EL, Schneider E, Moeller MR

(2006) High-potency marijuana impairs executive function and inhibitory motor

54

control. Neuropsychopharmacology. 31: 2296-2303.

Rehm J, Mathers C, Popova S, Thavorncharoensap M, Teerawattananon Y, Patra J

(2009) Global burden of disease and injury and economic cost attributable to

alcohol use and alcohol-use disorders. Lancet. 373: 2223-2233.

Rupniak NM, Samson NA, Steventon MJ, et al. (1991) Induction of cogni- tive

impairment by scopolamine and noncholinergic agents in rhesus monkeys. Life

Sci 48: 893–899.

SAMSHA (20103) Results from the 2013 National Survey on Drug Use and Health:

National Findings. Rockville, MD: Office of Applied studies, DHHS.

Sanudo-Pena MC, Tsou K, Delay ER, Hohman AG, Force M, Walker JM (1997)

Endogenous cannabinoids as an aversive or counter-rewarding system in the rat.

Neurosci Lett. 223:125–128.

Schulze GE, McMillan DE, Bailey JR, et al. (1988) Acute effects of delta-9-

tetrahydrocannabinol in rhesus monkeys as measured by performance in a

battery of complex operant tests. J Pharmacol Exp Ther. 245: 178–186.

Schulze GE, McMillan DE, Bailey JR, et al. (1989) Acute effects of marijuana smoke on

complex operant behavior in rhesus monkeys. Life Sci 45: 465–475.

Schuster CR, Johanson CE (1988) Relationship between the discriminative stimulus

properties and subjective effects of drugs. Psychopharmacol Ser. 4: 161-175.

Schweinsburg AD, Schweinsburg BC, Medina KL, McQueeny T, Brown SA, and Tapert

SF (2010) The influence of recency of use on fMRI response during spatial

working memory in adolescent marijuana users. Journal of Psychoactive Drugs.

42: 401–412.

Skinner BF (1938) The Behavior of Organisms. New York, NY, USA: Appleton-Century-

Crofts.

Sofuoglu, M, DeVito, EE, Waters, AJ, and Carroll, KM (2013): Cognitive Enhancement

55

as a Treatment for Drug Addictions. Neuropharmacology. 64: 452-463.

Solinas M, Scherma M, Fattore L, Stroik J, Wertheim C, Tanda G, Fratta W, Goldberg

SR (2007a) Nicotinic alpha 7 receptors as a new target for treatment of cannabis

abuse. J Neurosci. 27: 5615–5620.

Solinas M, Tanda G, Justinova Z, Wertheim CE, Yasar S, Piomelli D, Vadivel SK,

Makriyannis A, Goldberg SR (2007b) The endogenous cannabinoid anandamide

produces delta-9-tetrahydrocannabinol- like discriminative and neurochemical

effects that are enhanced by inhibition of fatty acid amide hydrolase but not by

inhibition of anandamide transport. J Pharmacol Exp Ther. 321: 370–380

Solowij N (1995) Do cognitive impairments recover following cessation of cannabis use?

Life Sciences. 56: 2119-2126.

Solowij N, Michie PT, Fox AM (1995) Differential impairments of selective attention due

to frequency and duration of cannabis use. Biol Psychiatry 37:731-739.

Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, et al. (2002)

Cognitive functioning of long-term heavy cannabis users seeking treatment.

JAMA 287:1123-31. Erratum in: JAMA. 287:1651.

Solowij N, and Battisti R (2008) The chronic effects of cannabis on memory in humans: a

review. Current Drug Abuse Reviews. 1: 81–98.

Solowij N, Jones KA, Rozman ME, Davis SM, Ciarrochi J, Heaven PC, Lubman DI,

Yücel M (2011) Verbal learning and memory in adolescent cannabis users,

alcohol users and non-users. Psychopharmacology (Berl). 216:131-44.

Spealman RD (1979) Behavior maintained by termination of a schedule of self-

administered cocaine. Science. 204: 1231-1233.

Taffe MA (2012) Δ9-Tetrahydrocannabinol attenuates MDMA-induced hyperthermia in

rhesus monkeys. Neuroscience. 201: 125-133.

Tait RJ, Mackinnon A, Christensen H. Cannabis use and cognitive function: 8-year

56

trajectory in a young adult cohort (2011) Addiction. 106: 2195-2203.

Tanda G, Pontieri FE, Di Chiara G (1997) Cannabinoid and heroin activation of

mesolimbic dopamine transmission by a common mu1 opioid receptor

mechanism. Science. 276: 2048–2050.

Takahashi RN, Singer G (1979) Self-administration of delta 9-tetrahydrocannabinol by

rats. Pharmacol Biochem Behav.. 11: 737-740.

Takahashi RN, Singer G (1980) Effects of body weight levels on cannabis self-injection.

Pharmacol Biochem Behav. 13: 877–881.

Tanda G, Pontieri FE, Di Chiara G (1997) Cannabinoid and heroin activation of

mesolimbic dopamine transmission by a common mu opioid receptor

mechanism. Science. 276: 2048–2050.

Tanda G, Munzar P, Goldberg SR (2000) Self-administration behavior is maintained by

the psychoactive ingredient of marijuana in squirrel monkeys. Nat Neurosci. 3:

1073–107 4.

Tanda G, Goldberg SR (2003) Cannabinoids: reward, dependence, and underlying

neurochemical mechanisms--a review of recent preclinical data.

Psychopharmacology (Berl). 169: 115-134.

Tapert SF, Schweinsburg AD, Drummond SP, Paulus MP, Brown SA, et al. (2007)

Functional MRI of inhibitory processing in abstinent adolescent marijuana users.

Psychopharmacology (Berl). 194: 173–183.

Thomas H (1996) A community survey of adverse effects of cannabis use. Drug Alcohol

Depend. 42: 201–207.

Thompson T, Schuster CR (1964) Morphine self-administration, food-reinforced, and

avoidance behaviors in rhesus monkeys. Psychopharmacologia. 5: 87-94.

Tzschentke TM (1998) Measuring reward with the conditioned place preference

paradigm: a comprehensive review of drug effects, recent progress and new

57

issues. Prog Neurobiol. 56: 613–672.

Valjent E, Maldonado R (2000) A behavioural model to reveal place preference to Δ9-

tetrahydrocannabinol in mice. Psychopharmacology. 147:436–438.

van de Giessen E, Weinstein JJ, Cassidy CM, Haney M, Dong Z, Ghazzaoui R, et al.

(2016) Deficits in striatal dopamine release in cannabis dependence. Mol

Psychiatry. In press.

Van Ree JM, Slangen JL, de Wied D (1978) Intravenous self- administration of drugs in

rats. J Pharmacol Exp Ther. 204: 547– 557.

Verdejo-Garcia AJ, Lo ́pez-Torrecillas F, Aguilar de Arcos F, et al (2005) Differential

effects of MDMA, cocaine, and cannabis use severity on distinctive components

of the executive functions in polysubstance users: A multiple regression analysis.

Addict Behav. 30: 89–101.

Verendeev A, Riley AL (2013) The role of the aversive effects of drugs in self-

administration: assessing the balance of reward and aversion in drug-taking

behavior. Behav Pharmacol. 24: 363-374.

Verrico CD, Gu H, Peterson ML, Sampson AR, Lewis DA (2014) Repeated Δ9-

tetrahydrocannabinol exposure in adolescent monkeys: persistent effects

selective for spatial working memory. Am J Psychiatry. 171: 416-425.

Voruganti LN, Slomka P, Zabel P, Mattar A, Awad AG (2001) Cannabis induced

dopamine release: an in-vivo SPECT study. Psychiatry Res. 107: 173–177.

Wadsworth EJ, Moss SC, Simpson SA, and Smith AP (2006) Cannabis use, cognitive

performance and mood in a sample of workers. Journal of Psychopharmacology.

20: 14–23.

Wachtel SR, ElSohly MA, Ross SA, Ambre J, de WH (2002) Comparison of the

subjective effects of delta 9-tetrahydrocannabinol and marijuana in humans.

Psychopharmacol- ogy (Berl). 161: 331–339.

58

Wang X, Dow-Edwards D, Keller E and Hurd YL (2003) Preferential limbic expression of

the cannabinoid receptor mRNA in the human fetal brain. Neuroscience. 118:

681–694.

Ward AS, Comer SD, Haney M, Foltin RW, Fischman MW (1997) The effects of a

monetary alternative on marijuana self-administration. Behav Pharmacol. 8 :275–

286.

Weeks JR (1962) Experimental morphine addiction: method for automatic intravenous

injections in unrestrained rats. Science. 138: 143–144.

Whitlow CT, Liguori A, Livengood LB, Hart SL, Mussat-Whitlow BJ, Lamborn CM, et al.

(2004) Long-term heavy marijuana users make costly decisions on a gambling

task. Drug Alcohol Depend. 76: 107-11.

Wiley JL. Cannabis (1999) discrimination of "internal bliss"? Pharmacol Biochem Behav.

64: 257-60.

Wiley JL, Barrett RL, Balster RL, Martin BR (1993a) Tolerance to the discriminative

stimulus effects of delta(9)-tetrahydrocannabinol. Behav Pharmacol. 4: 581–585.

Wiley JL, Barrett RL, Britt DT, Balster RL, Martin BR (1993b) Discriminative stimulus

effects of delta 9-tetrahydrocannabinol and delta 9–11-tetrahydrocannabinol in

rats and rhesus monkeys. Neuropharmacology. 32: 359–365.

Wiley JL, Balster RL, Martin BR (1995a) Discriminative stimulus effects of anandamide

in rats. Eur J Pharmacol. 276: 49–54.

Wiley JL, Barrett RL, Lowe J, Balster RL, Martin BR (1995b) Discriminative stimulus

effects of CP 55,940 and structurally dissimilar cannabinoids in rats.

Neuropharmacology. 34: 669–676.

Wiley JL, Huffman JW, Balster RL, Martin BR (1995c) Pharmacological specificity of the

discriminative stimulus effects of Δ9-tetrahydrocannabinol in rhesus monkeys.

59

Drug Alcohol Depend. 40: 81–86.

Wiley JL, Golden KM, Ryan WJ, Balster RL, Razdan RK, Martin BR (1997) Evaluation of

cannabimimetic discriminative stimulus effects of anandamide and methylated

fluoroanandamide in rhesus monkeys. Pharmacol Biochem Behav. 58: 1139–

1143.

Wiley JL, Ryan WJ, Razdan RK, Martin BR (1998) Evaluation of cannabimimetic effects

of structural analogs of anandamide in rats. Eur J Pharmacol. 355: 113–118.

Wiley JL, Martin BR (1999) Effects of SR141716A on diazepam substitution for delta9-

tetrahydrocannabinol in rat drug discrimination. Pharmacol Biochem Behav. 64:

519–522.

Winsauer PJ, Lambert P, and Moerschbaecher JM (1999) Cannabinoid ligands and their

effects on learning and performance in rhesus monkeys. Behav Pharmacol. 10:

497–511.

Wu TC, Tashkin DP, Rose JE, Djahed B (1988) Influence of marijuana potency and

amount of cigarette consumed on marijuana smoking pattern. J Psychoactive

Drugs. 20: 43–46.

Wu X, French ED (2000) Effects of chronic delta9-tetrahydrocannabinol on rat midbrain

dopamine neurons: an electrophysiological assessment. Neuropharmacology.

39: 391–398.

Yanagita T, Deneau GA, Seevers MH (1965a) Evaluation of pharmacologic agents in the

monkey by long term intravenous self- or programmed administration. Excerpta

Medica Int Congr Ser. 87: 453–457.

Yanagita T, Deneau GA, Seevers MH (1965b) A technique for chronic intravenous

programmed or self-administration of drugs to monkeys. Proc Int Union of

Physiol Sci, Tokyo. 453–457.

Zernig G, Wakonigg G, Madlung E, Haring C, Saria A (2004) Do vertical shifts in dose-

60

response rate-relationships in operant conditioning procedures indicate

"sensitization" to "drug wanting"? Psychopharmacology (Berl). 171: 349-351.

Zimmer A, Valjent E, Konig M, Zimmer AM, Robledo P, Hahn H, Valverde O, Maldonado

R (2001) Absence of Δ-9-tetrahydrocannabinol dysphoric effects in dynorphin-

deficient mice. J Neurosci. 21: 9499–9505.

61

Chapter II

BEHAVIORAL DETERMINANTS OF CANNABINOID SELF-ADMINISTRATION IN

OLD-WORLD MONKEYS

William S. John1, Thomas J. Martin2, and Michael A. Nader1

1Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC 2Department of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, NC

The following manuscript was submitted to Neuropsychopharmacology on August 3,

2016. Stylistic variations are due to the requirements of the journal. William S. John

performed experiments, analyzed the data, and prepared the manuscript. Thomas J.

Martin and Michael A. Nader acted in an advisory and editorial capacity.

62

Abstract

Reinforcing effects of Δ9-tetrahydrocannabinol (THC), the primary active ingredient in

marijuana, as assessed with self-administration (SA), has only been established in New

World primates (squirrel monkeys). The objective of this study was to investigate some

experimental factors that may enhance intravenous SA of THC and the cannabinoid

receptor (CBR) agonist CP55,940 in Old World monkeys (rhesus and cynomolgus), a

species more closely related to humans. In one experiment, male rhesus monkeys (N=9)

were trained to respond under a fixed-ratio 10 schedule of food presentation. The effects

of CP55,940 and THC on food-maintained responding and body temperature were

determined in these subjects prior to giving them access to SA each drug. Both drugs

dose-dependently decreased food-maintained responding. CP55,940 (0.001-3.0 μg/kg)

functioned as a reinforcer in three monkeys, while THC (0.01-10 μg/kg) did not have

reinforcing effects in any subject. CP55,940 was least potent to decrease food-

maintained responding in the monkeys in whom CP55,940 functioned as a reinforcer.

Next, monkeys were administered THC daily until tolerance developed to rate-

decreasing effects. When THC SA was reexamined, it functioned as a reinforcer in three

monkeys. In a group of cocaine-experienced male cynomolgus monkeys (N=4), THC SA

was examined under a second-order schedule of reinforcement in which reinforcement

frequency is relatively independent of response rates; THC functioned as reinforcer in

two monkeys. These data suggest that sensitivity to rate-decreasing effects may not be

a critical determinant for the lack of CBR agonist SA by Old World monkeys.

Understanding individual differences in vulnerability to THC SA may lead to novel

treatment strategies for marijuana abuse.

Key Words: THC; CP55,940; Marijuana abuse; Self-administration; Rhesus monkey;

cynomolgus monkey

63

INTRODUCTION

Marijuana (Cannabis sativa) is the most commonly abused illicit drug in the

United States, with an estimated 4.2 million people that meet DSM criteria for

dependence (SAMHSA, 2013). Moreover, marijuana use has been steadily increasing

among 12th graders and the current watershed in cannabis legalization suggests that

rates of use will increase further (Johnston et al, 2014; Cerdá et al, 2012). As a result,

there is a need for more preclinical research to better understand the pharmacological,

environmental, and biological determinants of cannabis use in order to develop more

effective treatment strategies for cannabis use disorder. A major obstacle preventing

such progress has been the difficulty in demonstrating the reinforcing effects of Δ9-

tetrahydrocannabinol (THC), the main psychoactive component of marijuana, using self-

administration (SA) procedures in animals. Successful efforts have been limited to one

laboratory using squirrel monkeys (Tanda et al, 2000; Justinova et al, 2003). Every other

THC SA attempt using other nonhuman primate (NHP) species (i.e. rhesus monkeys) or

rodents has been unsuccessful (Kaymakcalan 1972, 1973; Pickens et al, 1973; Harris et

al, 1974; Leite and Carlini 1974; Carney et al, 1977; Van Ree et al, 1978; Mansbach et

al, 1994; Li et al, 2012; Lefever et al, 2014).

It is currently unknown whether previous unsuccessful attempts in rhesus

monkeys were due to methodology or a direct species difference between squirrel (New

World) and rhesus (Old World) monkeys. Thus, one goal of the present study was to

make THC available to rhesus monkeys under similar SA conditions as used in squirrel

monkeys, schedule of reinforcement and vehicle preparation. With THC being a low

efficacy, partial agonist at the CB1/CB2 receptors, the present study also evaluated the

reinforcing effects of the synthetic bicyclic cannabinoid receptor (CBR) analog,

CP55,940, a high efficacy, full CB1/CB2 agonist, in the same monkeys to allow for a

better understanding of the role of pharmacological efficacy at the CBR in SA.

64

Furthermore, the characterization of individual qualitative and quantitative differences in

CBR agonist SA was examined. One hypothesis to explain the failure of THC to function

as a reinforcer among experimental animals involves the initial rate-decreasing effects of

THC, which may mask the reinforcing effects, such that subjects less sensitive to the

rate-decreasing effects of CP55,940 and THC would have greater rates of SA of both

drugs. To test this hypothesis, the potency of both drugs to decrease food-maintained

responding was correlated with rates of SA.

The rate-decreasing effects of cannabinoids represent a variable that could be

modified to facilitate acquisition of SA. For instance, data suggest that through repeated

drug exposure, tolerance may develop to the aversive/rate-decreasing effects but not the

reinforcing effects of THC (Lepore et al, 1995; Valjent and Maldonado, 2000; Ghozland

et al, 2002; Lindgren et al, 1981; Perez-Reyes et al, 1991; Kirk and de Wit, 1999;

Sherma et al, 2016). Whether tolerance to THC-induced rate-decreasing effects will

enhance THC SA is an empirical question that has not been documented in animal

models; this was examined in the present study. We hypothesized that tolerance would

develop to the rate-decreasing effects of THC and this would result in increases in rates

of THC SA compared to the initial assessment.

Another behavioral variable that can influence drug-self-administration is the

schedule of reinforcement, especially for drugs that are not considered highly efficacious

reinforcers (Nader et al, 2015). As it relates to THC self-administration, schedules of

reinforcement where the frequency of reinforcement is relatively rate independent may

be better suited to uncover the reinforcing effects. Thus, the present study also

examined THC SA in another group of monkeys in whom responding was maintained

under a second-order schedule of reinforcement. It was hypothesized that responding

under a second-order schedule, in which behavior was maintained across long intervals

between drug injections by conditioned stimuli, would lead to THC reinforcement.

65

MATERIALS AND METHODS

Subjects. Nine individually housed adult male rhesus (Macaca mulatta) and four pair

housed adult male cynomolgus (M. fascicularis) monkeys served as subjects. Six rhesus

monkeys had histories of methamphetamine or cocaine SA (John et al, 2015a,b) but had

been abstinent for approximately four months before these experiments began. Three

rhesus monkeys did not have a SA history prior to this study (Table 1). All four

cynomolgus monkeys had extensive cocaine histories (~ 8 years) at the initiation of this

study. Each monkey was fitted with an aluminum collar (Model B008, Primate Products,

Redwood City, CA) and trained to sit in a primate restraint chair (Primate Products).

Monkeys were fed sufficient standard laboratory chow (Purina LabDiet 5045, St Louis,

MO) to maintain healthy body weights (determined by veterinary staff). Animal housing

and all experimental procedures were performed in accordance with the 2011 National

Research Council Guidelines for the Care and Use of Mammals in Neuroscience and

Behavioral Research and were approved by the Animal Care and Use Committee of

Wake Forest University.

66

Table 1. Ages, weights, and self-administration histories for rhesus monkeys

Subject Age (years) Weight (kg) Drug SA history (mg/kg)

R-1567 20 10.0 134.9 (methA)

R-1690 12 12.2 373.9 (methA)

R-1691 10 8.7 366.8 (methA)

R-1693 11 11.7 283.7 (methA)

R-1710 9 9.8 68.7 (cocaine)

R-1711 9 11.2 10.3 (cocaine)

R-1682 15 13.8 0

R-1713 9 14.4 0

R-1756 19 13.3 0

Surgery. Subjects were prepared with chronic indwelling venous catheters under

aseptic conditions as previously described (John et al, 2015a,b). All monkeys were

implanted subcutaneously with a transponder (Model IPTT-300; Bio Medic Data

Systems, Inc., Seaford, DE) to non-invasively measure body temperature.

Apparatus. Behavioral sessions were carried out in ventilated, sound-attenuating

chambers (1.5x0.74x0.76m; Med Associates, East Fairfield, VT) designed to

accommodate a primate chair. Two photo-optic switches (5 cm wide; Model 117-1007;

Stewart Ergonomics, Inc., Furlong, PA) were located on one side of the chamber with a

horizontal row of three stimulus lights positioned 14 cm above each switch. A food

receptacle was located between the switches and connected with a Tygon tube to a

pellet dispenser (Med Associates) located on the top of the chamber. A peristaltic

infusion pump (7531-10, Cole-Parmer Co., Chicago, IL) for delivering drug injections at a

67

rate of approximately 1.5 ml/10 sec was also located on the top of the chamber. White

noise was continuously present to mask extraneous noise.

Experiment 1: Self-administration of THC and CP55,940 and potential behavioral

phenotypes related to reinforcement in rhesus monkeys.

Behavioral phenotype assessments: Rhesus monkeys were trained to respond

under a fixed-ratio (FR) 10 schedule of food presentation, followed by a 60-sec time-out

(TO). Sessions began with illumination of a white stimulus light above one of two photo-

optic switches in the chamber, counterbalanced between monkeys. Ten consecutive

responses emitted on the appropriate switch delivered a 1.0-g banana-flavored food

pellet (Bio-Serv, Frenchtown, NJ) accompanied by extinction of the white light and

illumination of a red light above the food receptacle for 3 sec. Responses emitted on the

alternate switch had no programmed consequence. Sessions lasted 60 min or until 30

reinforcers were obtained, whichever occurred first. Once responding was stable (3

consecutive sessions with response rates within ±20% of the mean rate for those

sessions, with no trends), non-contingent injections of CP55,940 (1.0-10 μg/kg, i.v.) and

THC (3.0-300 μg/kg, i.v.) were administered one minute prior to operant sessions. All

doses were tested 2-3 times for each monkey; CP55,940 was tested first in all monkeys.

Body temperature was taken non-invasively using the implanted telemetry device (Bio

Medic Data Systems, Inc.) immediately prior to CP55,940 or THC administration and

then again 60-min following the start of the session.

Substitution studies: After completion of the non-contingent dose-response

curves and when stable food-maintained responding was re-established, i.v. injections of

vehicle (0.4-1.0% Tween 80 and 0.4-1.0% ethanol in sterile water) and different doses of

CP55,940 (0.001-3.0 μg/kg) and THC (0.01-10 μg/kg) were substituted for food pellets

for at least 5 sessions and until responding was stable. CP55,940 SA dose-response

68

curves were determined first in all monkeys followed by THC SA dose-response curves

with the exception of one monkey that only completed the CP55,940 dose-response

curve and another monkey that only completed the THC dose-response curve.

Completion of each FR delivered an i.v. infusion (~ 0.5 ml in 3-s), followed by a 60-s TO;

sessions ended after a maximum of 30 injections or 60 min elapsed, whichever occurred

first. There was a return to food-maintained responding for at least 3 sessions between

substitutions of different drug doses. Doses were tested in quasi-random order for each

monkey, such that the highest dose was not tested first in any monkey.

Experiment 2. Effects of chronic THC treatment on THC SA

Following determination of SA dose-response curves for both drugs, responding

was maintained by food presentation for at least 5 sessions and until stable. Next, the

ED50 for THC to decrease food-maintained responding, individually determined for each

monkey, was administered non-contingently (i.v.) one min prior to each session for at

least 3 sessions and until tolerance developed to the rate-decreasing effects. Tolerance

was said to occur when average rates of responding were within 20% of baseline rates

for three consecutive sessions. At that point, the dose for non-contingent administration

was increased between an eighth and one-half log-units, depending on the monkey, and

administered for at least 5 sessions and until tolerance developed to those rate-

decreasing effects on food-maintained responding. Once responding was stable, THC

SA dose-response curves were redetermined. During THC SA, non-contingent THC

injections (10-100 μg/kg, i.v.) were administered after SA sessions in order to avoid a

direct interference with SA but to maintain consistent daily levels of THC. Between THC

SA dose substitutions, there was a return to food-maintained responding for at least 3

sessions with THC administered non-contingently, using a dose that reduced food-

maintained responding to below 20% of baseline rates, prior to those sessions.

69

Experiment 3. THC self-administration under a second-order schedule of reinforcement

Four cynomolgus monkeys had been trained to self-administer cocaine under a

second-order fixed-interval (FI) 600-s [FR 30:S] schedule of i.v. cocaine injection. At the

beginning of the session, a white light was illuminated over one of the photo optic

switches (counterbalanced between monkeys), which served as a discriminative

stimulus. Following the completion of every 30th response (FR 30) during the 600-s FI,

the white light was extinguished and a red light was illuminated for 2-s. Once the interval

elapsed, the first FR 30 completed produced an i.v. injection of cocaine delivered over

10 s paired with the red light. Thus, the red light served as the conditioned stimulus

(CS). A 60-s TO followed each injection, during which all lights were extinguished and

responses had no scheduled consequences. Daily sessions ended after the completion

of five cycles of the schedule or 90 min elapsed, whichever occurred first.

Following the determination of a cocaine dose-response curve (3.0 – 560

μg/kg/injection), saline was substituted for the training dose of cocaine (100

μg/kg/injection) and the CS was turned off until responding was reduced to 20% of

baseline rates for 3 consecutive sessions. Next, the CS was reintroduced for at least 3

consecutive sessions during which saline was still available for SA. When responding

was stable, 3.0 μg/kg THC was substituted for at least 7 consecutive sessions and until

the overall rate of responding was stable (3 consecutive sessions with response rates

within ±20% of the mean rate for those sessions, with no trends). A dose response curve

was determined by substituting vehicle, which included presentation of the CS, and a

range of doses (1.0 – 30 μg/kg/injection) in random order separated by five consecutive

sessions of substitution of either 3.0 μg/kg THC or the dose of THC that maintained the

highest rate of responding.

70

Data analysis. For the effects of CP55,940 and THC on food-maintained responding,

the primary dependent variable was response rate (responses/second). Drug effects

were expressed as a percentage of baseline responding, which was designated as 3

consecutive sessions of stable responding prior to the start of the study. Change in body

temperature (°C) following administration of either CP55,940 or THC was determined by

subtracting the temperature recorded prior to the start of the session from the

temperature recorded at the end of the 60-min session. The potency of CP55,940 and

THC to decrease food-maintained responding was estimated by calculating the ED50

using the linear portion of the curve that crossed 50% reduction in baseline response

rate. To calculate the relative potency of both drugs to decrease body temperature, the

dose decreasing temperature by 0.5 °C was estimated by interpolation of the data.

For SA, the primary dependent variables were the number of reinforcers earned

per session and mean rate of responding. Data were expressed as the mean (± S.D.)

over the last three sessions for each drug dose. Dose-response curves for THC and

CP55,940 were analyzed using one-way repeated-measures ANOVA; significant main

effect was followed by Dunnett’s multiple comparisons post-hoc tests. The criterion for

significance was set a priori at p<0.05. For Experiment 1, Pearson’s correlation analysis

was used to examine the relationship between reinforcing effects and sensitivity to rate-

decreasing and hypothermic effects. For this analysis, reinforcing effects were quantified

by calculating the area under the curve; sensitivity to rate-decreasing effects and

hypothermic effects were quantified by the CP55940 and THC ED50 values to decrease

food-maintained responding and the slope of the regression lines of temperature change

relative to vehicle, respectively.

Drugs. Δ9-tetrahydrocannabinol (THC) and CP55,940, both obtained from the National

Institute on Drug Abuse (NIDA), Bethesda, MD, were dissolved in a vehicle containing

71

0.001–4% Tween 80 and 0.001–4.0 % ethanol in sterile water. (−)-Cocaine HCl (NIDA)

was dissolved in sterile 0.9% saline. Different SA doses were studied by varying the

concentration of drug available per injection. Drug doses are expressed in terms of the

salt form of cocaine.

RESULTS

Experiment 1. The mean (± S.E.M.) rate of FR food-maintained responding was

1.16±0.2 responses/second in which the maximum number of reinforcers (30) was

always earned. Although two monkeys showed increases at low THC doses, mean

response rates were dose-dependently decreased by non-contingent administration of

CP55,940 [F(5,36)=11.5; p<0.0001; Fig. 1, closed circles] and THC [F(5,33)=3.37;

p<0.05; Fig. 1, closed triangles]. The mean ED50 values (95% confidence limits) for

CP55,940 and THC to decrease food-maintained responding were 3.8 (2.1–6.8) and

59.8 (28.6–125.1) μg/kg, respectively. CP55,940 was ~16-fold more potent at

decreasing food-maintained responding than THC. Non-contingent administration of

CP55,940 and THC also produced dose-dependent reductions in body temperature,

which corresponded to the rate-decreasing effects on food-maintained responding,

although individual differences were noted (Fig. 1, open symbols). The dose of CP

55,940 and THC that produced a 0.5 °C decrease in body temperature (ED-0.5 °C value)

was estimated to be 17 and 180 μg/kg, respectively; CP55,940 was ~11-fold more

potent than THC.

72

Figure 1. Effects of CP55,940 (circles) and THC (triangles) on food-maintained

responding (filled symbols) and body temperature (open symbols) in individual rhesus monkeys (n = 9). CP55,940 and THC were administered non-contingently 1 min prior to the start of the session and body temperature was recorded prior to each injection and then again 60 min following the start of the session. Tolerance to the rate-decreasing effects of THC on food-maintained responding was developed by repeated administration across consecutive sessions (filled squares). Abscissae: Dose of CP55,940 and THC in μg/kg. Left ordinate: Mean (± S.D.) response rate

expressed as a percentage of baseline. Right ordinate: Mean (± S.D.) change in body temperature expressed in °C.

73

When CP55,940 was substituted for food pellets, reinforcing effects were

achieved in 3 of 8 rhesus monkeys (Fig. 2, closed circles, Fig. 3) as determined by

repeated-measures one-way ANOVA (R-1567: F(5,10)=21.76, p<0.0001; R-1690:

F(5,10)=13.48, p<0.001; R-1691: F(5,10)=9.07, p<0.01). The peak injections of

CP55,940 occurred at 0.04, 0.02 and 0.00125 μg/kg/injection for R-1567, R-1690 and R-

1691, respectively. CP55,940 was least potent at decreasing food-maintained

responding in the 3 monkeys in whom the drug functioned as a reinforcer (Table 2). No

relationship was found between CP55,940 reinforcing effects and CP55,940-induced

changes in body temperature.

In contrast to CP55,940, THC over a broad range of doses (0.03-10 μg/kg) did

not maintain responding above vehicle levels, even in the 3 monkeys in which CP55,940

functioned as a reinforcer (Fig. 2, closed triangles). In 4 of 8 monkeys, however, THC

produced transient reinforcing effects in which the number of injections earned was

greater than vehicle levels for 2 or more consecutive sessions (Fig. 4).

Table 2. Relationship between CP55,940 potency to decrease food-maintained

responding and CP55,940 self-administration Subject ED50 (95% confidence interval) Max injections (SD) AUC

R-1567 0.007 (0.006-0.008) 20.67 (1.16) 68.67

R-1691 0.006 (0.004-0.009) 11.67 (1.16) 39.33

R-1690 0.006 (0.004-0.009) 6.67 (0.58) 13.83

R-1693 0.0009 (NA) 1 (0) 4.5

R-1682 0.003 (0.001-0.01) 2.0 (1.73) 4.83

R-1711 0.001 (0.0006-0.002) 1.67 (0.58) 6.17

R-1713 0.003 (0.002-0.006) 1.33 (0.58) 4.33

R-1756 0.003 (0.001-0.008) 2.33 (1.53) 7.33

74

0

5

10

15

20

25

30THC

********

THC post tolerance

CP 55,940

R-1567

****

0

5

10

15

20

25

30

R-1693

V 0.001 0.01 0.1 1 10 100

0

5

10

15

20

25

30R-1711

***

*

****

R-1691

R-1756

V 0.001 0.01 0.1 1 10 100

R-1713

***

R-1690

R-1682

V 0.001 0.01 0.1 1 10 100

R-1710

***

Unit Dose (µg/kg/injection)

Nu

mb

er

of

Inje

cti

on

s

Figure 2. Self-administration dose-response curves for CP55,940 (filled circles),

THC (closed triangles) and THC after the development of tolerance to rate-decreasing effects on food-maintained responding (open triangles). Abscissae: Unit

dose (μg/kg/injection) available for self-administration. Ordinate: Number of injections earned in the 60-min session. Data represent mean ± S.D. of last three sessions. *p < 0.05, **p < 0.01 compared to vehicle.

75

Figure 3. Acquisition of CP 55,940 self-administration in individual rhesus monkeys.

Data are expressed as number of reinforcers earned across consecutive sessions. Open squares represent number of food reinforcers earned prior to substituting CP 55,940 (filled symbols) or vehicle (open circles) for self-administration.

1 2 3 4 5 6-3 -2 -1

0

5

10

15

20

25

300.0025 µg/kg CP 55,940

R-1567

0.02 µg/kg CP 55,940

Food

Vehicle

0.04 µg/kg CP 55,940

1 2 3 4 5 6 7 8 9 10-3 -2 -1

R-1691

1 2 3 4 5 6 7 8-3 -2 -1

R-1690

Nu

mb

er

of R

ein

forc

ers

Session

76

Figure 4. Transient reinforcing effects of THC in individual rhesus monkeys. Data

are expressed as number of reinforcers earned across consecutive sessions. Squares represent number of food reinforcers earned prior to substituting THC

(filled symbols) or vehicle (open circles) for self-administration.

1 2 3 4 5 6 7-3 -2 -1

0

5

10

15

20

25

30

1.0 µg/kg THC

0.1 µg/kg THC

R-1710

0.3 µg/kg THC

1.7 µg/kg THC

10 µg/kg THC

Food

Vehicle

1 2 3 4 5 6 7 8 9-3 -2 -1

R-1691

1 2 3 4 5 6 7-3 -2 -1

R-1690

1 2 3 4 5 6 7 8 9 10-3 -2 -1

0

5

10

15

20

25

30

R-1711

Session

Nu

mb

er

of

Rein

forc

ers

77

Experiment 2. Daily administration of the THC ED50 resulted in tolerance to the

rate-decreasing effects within 5.25 (±0.75 S.E.M.) sessions. This dose was increased by

an eighth to one-half log units and tolerance developed to those effects within 6.17

(±1.14 S.E.M.) sessions (closed squares, Fig. 1). Following the development of

tolerance to the rate-decreasing effects of THC, SA was reexamined and THC

functioned as a reinforcer in 3 monkeys (Fig. 2 open triangles; Fig. 5), as determined by

repeated measures one-way ANOVA (R-1710: F(3,6)=65.69; p<0.001; R-1567:

F(3,6)=95.43; p<0.0001; R-1711: F(4,8)=19.47; p<0.001).

78

Figure 5. THC functioned as a reinforcer in three monkeys after the development of

tolerance to rate-decreasing effects on food-maintained responding. Open squares represent food-maintained responding following increasing doses of daily THC treatment. The vertical line designates the development of tolerance after which THC (open triangles) and vehicle (closed triangles) were substituted for self-administration. There was a return to food-maintained responding between doses of THC and vehicle. Left ordinate: Percentage of baseline rate of food-maintained responding. Right ordinate: Number of THC or vehicle injections earned per session. Abscissae: Consecutive sessions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

25

50

75

100

125

150

175

0

5

10

15

20

25

30

+ THC ED50

(7.0 µg/kg)

R-1710

+ 10 µg/kgTHC

Vehicle

Food

THC (3.0 µg/kg/injection)

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

0

25

50

75

100

125

150

175

200

225

0

5

10

15

20

25

30

+ THC ED50 (12 µg/kg)+ 30 µg/kg

THC

R-1711

THC (30 µg/kg/injection)

Vehicle

Food

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

0

25

50

75

100

125

150

0

5

10

15

20

25

30

+ THC ED50 (60 µg/kg) + 100 µg/kgTHC

R-1567

THC (100 µg/kg/injection)

Food

Vehicle

Resp

on

se R

ate

(%

of b

aselin

e)

Session

Nu

mb

er o

f Inje

ctio

ns

79

Experiment 3. Prior to the start of THC SA, monkeys responded under a second-

order FI 600-s (FR 30:S) schedule of cocaine reinforcement. Cocaine dose-response

curves, determined for each monkey, were represented as an inverted U-shaped

function of cocaine dose (Fig. 6, open squares). After saline substitution, with removal of

the CS, responding declined; response-contingent presentation of the CS did not

increase responding. Substituting different THC doses resulted in THC maintaining

responding at significantly higher rates than vehicle in two of four subjects (Fig. 6, closed

triangles) as determined by repeated measures one-way ANOVA (C-7427: F(4,8)=14.94,

p<0.001; C-6526: F(4,8)=41.72, p<0.001), with maximal rates maintained when 10 and

3.0 μg/kg/injection were available, respectively. All monkeys responded for the maximal

number of injections available for each dose of THC and vehicle except C-6526, who

responded for no vehicle injections and 0.67 (± 0.58 S.D.) and 2.67 (±1.53 S.D.) THC

injections at 1.0 and 30 μg/kg/injection, respectively.

80

Figure 6. THC self-administration under a second-order [FI 600-s (FR 30:S)]

schedule of reinforcement. Each dose was studied for a minimum of 7 sessions and until responding was stable. Data represent mean (±S.D.) response rates for THC (filled triangles) and cocaine (open squares) during last 3 sessions of substitution. Abscissae: Unit dose (μg/kg/injection) available for self-administration. Ordinate: Mean overall rate of responding (responses/sec).

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

D9-THC

Cocaine

C-7427

***

V 1 10 100 1000

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4C-7078

C-6526

*******

*

V 1 10 100 1000

C-7084

Unit Dose (µg/kg/injection)

Resp

on

se R

ate

(re

sp

/sec)

81

DISCUSSION

One goal of the present study was to investigate cannabinoid SA in rhesus

monkeys under similar parameters successfully used to demonstrate the reinforcing

effects of cannabinoids in squirrel monkeys. We also examined behavioral phenotypes

that may mediate individual differences in SA. The CBR agonist CP55,940 functioned as

a reinforcer in 3 of 8 monkeys; reinforcing effects were inversely related to CP55,940-

induced behavioral disruption. In those same monkeys, THC did not have reinforcing

effects in any animal. Daily THC treatment with a dose that initially produced significant

rate-decreasing effects resulted in THC functioning as a reinforcer in three monkeys. In

another group of monkeys, THC functioned as a reinforcer in two of four animals

responding under a second-order schedule. Across all conditions, of the 13 monkeys

studied, CBR agonists functioned as reinforcers in 7 animals. Understanding these

individual differences may lead to novel treatments for marijuana abuse.

Studies in humans show that individuals who are more sensitive to the aversive

effects of cannabis were less likely to be current users (Haertzen et al, 1983; Thomas,

1996). The prominent aversive effects of cannabinoids have been demonstrated in

conditioned place preference studies in rodents where several studies report that THC

and synthetic CBR agonists generally induce conditioned place avoidance rather than

place preference, except following a period of extended drug exposure (Lepore et al,

1995; Valjent and Maldonado, 2000; Ghozland et al, 2002). Thus, one hypothesis for the

lack of cannabinoid SA in experimental animals is that the aversive (and rate-

decreasing) effects of THC and other CBR agonists are inhibiting measures of

reinforcement in macaques (and probably rodents) to a greater degree than is apparent

in squirrel monkeys. Results with CP55,940 supported this hypothesis; CP55,940

functioned as a reinforcer in the three monkeys least sensitive to the drug’s rate-

disruptive effects. However, in those same three monkeys, THC was also the least

82

potent to decrease rates of food-maintained responding, but THC did not function as a

reinforcer in any monkey following the initial determination.

These results suggest that there may be a relationship between CB1 receptor

agonist efficacy and reinforcing effects considering the low efficacy CB1 partial agonist

THC did not function as reinforcer in the same monkeys that the high efficacy CB1 full

agonist CP55,940 did. However, due to the large number of CB1 receptors in the central

nervous system (Gifford et al, 1999), THC often produces the same maximum effect as

that obtained with high efficacy CB1 agonists for a range of behaviors including drug

discrimination, antinociception, and locomotor activity (Fan et al, 1994; McMahon, 2011);

however, exceptions have been noted for hypothermic effects (Paronis et al., 2012). The

other exception may also include reinforcing effects. Several CBR agonist SA studies in

rodents support this notion in which the high efficacy CBR agonist WIN55,212, but not

THC, maintained behavior above vehicle levels (Martellotta et al, 1998; Fattore et al,

2001; Lefever et al, 2014).

These data also suggest a direct inter-species difference between rhesus and

squirrel monkeys as it relates to the initial reinforcing effects of THC. Consistent with our

hypothesis, the ED50 for intravenous THC to decrease food-maintained responding in

squirrel monkeys (Justinova et al, 2013) is ~1.0 log-units higher (i.e., less potent)

compared to the rhesus monkeys used in the present study. These data indicate that the

reinforcing effects of THC may only be unmasked when there is a certain degree of

insensitivity to the rate-decreasing effects, similar to what was shown in the present

study involving CP55,940 SA.

The precise mechanism underlying the rate-decreasing effects of CBR agonists,

including individual-subject variability, is uncertain. Previous studies in rats and monkeys

indicate that CB1 receptors do not exclusively mediate the rate-decreasing effects of

THC as evidenced by the inability of the prototypic CB1 antagonist SR141716A to

83

attenuate these effects (McMahon et al, 2005; Järbe et al, 2003; DeVry and Jentzsch,

2004). On the other hand, SR141716A has been reported to attenuate many of the

physiological and unconditioned behavioral effects of THC such as the hypothermic,

antinociceptive, respiratory depressant, and cardiovascular effects (McMahon et al,

2005; Vivian et al, 1998). As a result, the factors mediating individual differences in the

ability of CP55,940 to function as a reinforcer may not be related to CB1 receptor

function considering no relationship was found between reinforcing effects and

CP55,940-induced changes in body temperature. Moreover, the doses of CP55,940 that

had reinforcing effects in these subjects were approximately 1.0-log units lower than

what was previously tested in an unsuccessful attempt to obtain CP55,940

reinforcement in rhesus monkeys (Mansbach et al, 1994). These results highlight the

importance of using low doses to demonstrate the reinforcing effects of CBR agonists,

as emphasized by Goldberg colleagues (Tanda et al, 2000).

To further evaluate the relationship between sensitivity to the rate-decreasing

effects of THC and SA, we conducted two additional experiments. In one study, THC

was administered daily in order for tolerance to develop to the rate-decreasing effects of

THC. After reassessing SA, THC only functioned as a reinforcer in three animals.

Interestingly, one of these subjects (R-1567) did not completely develop tolerance to the

rate-decreasing effects prior to reassessing SA. This suggests that a period of pre-

exposure to THC, rather than the development of tolerance to rate-decreasing effects,

could be the more crucial factor for acquiring THC SA. Future studies could determine if

other experimental manipulations (e.g., lengthen TO or change the schedule to

progressive-ratio) could yield more robust THC SA. Furthermore, no observational signs

of physical withdrawal were noted during daily THC administration, which is consistent

with previous studies showing that much higher doses (e.g., 1.0 mg/kg/12 hr or 0.05-

0.17 mg/kg/hr) were required to produce physical withdrawal symptoms in rhesus

84

monkeys (Beardsley et al., 1986; Stewart and McMahon, 2010) than what was used for

the present study. It remains to be determined if physical dependence would enhance

the likelihood of THC SA.

A second method to address the role of sensitivity to rate-disruptive effects and

SA was to use a schedule of reinforcement in which response rates and reinforcement

frequency were not directly related. Two of four monkeys self-administering THC under a

second-order FI 600-s (FR30:S) schedule showed reinforcing effects at a doses similar

to those that resulted in reinforcing effects in squirrel monkeys responding under a

second order schedule (Justinova et al., 2008); there was no apparent relationship

between rates of responding maintained by cocaine and the ability for THC to have

reinforcing effects. Mansbach et al. (1994) also used an FI 600-s schedule but did not

report any evidence that THC maintained responding higher than vehicle, suggesting

that perhaps the use of conditioned stimuli may enhance the likelihood that future

investigators can obtain robust THC self-administration under similar schedule

conditions.

The failure of THC to function as a reinforcer in the majority of subjects following

the development of tolerance to rate-decreasing effects suggests other factors may be

involved. One possible explanation is that THC reinforcement was related to individual

differences in THC pharmacokinetics and/or metabolism following tolerance. However,

studies in animals and humans have shown that chronic THC exposure does not

significantly change THC metabolism or disposition (Dewey et al, 1973; McMillan et al,

1973; Siemens and Kalant, 1974; Martin et al, 1976; Hunt and Jones, 1980; Ginsburg et

al, 2014). Even so, this possibility cannot be rejected and should be investigated with

particular attention to individual differences in correspondence with other behavioral

endpoints related to reinforcement.

85

It is important to note that there was a relationship between drug history and

CBR agonist SA in that three of the four monkeys that had an extensive

methamphetamine SA history were the subjects in whom CP55,940 functioned as a

reinforcer. Although previous studies in squirrel monkeys have demonstrated that a drug

SA history is not necessary for acquiring robust levels of THC SA (Justinova et al, 2003),

the present results suggest a potential relationship as it relates to the acquisition of

CP55,940 SA. A better understanding of the importance of drug history, schedule of

reinforcement, behavioral, and physiological phenotypes related to pharmacological

sensitivity to cannabinoid SA are needed to establish a reliable preclinical model of CBR

abuse.

86

ACKNOWLEDGEMENTS. This work was supported by the National Institutes of Health

grants R37 DA10584 (MAN), P50 DA06634 (MAN), F31 DA041825 (WSJ) and T32 AA-

007565 (WSJ).

CONFLICT OF INTEREST. The authors declare no conflicts of interest.

CONTRIBUTIONS. WSJ, TJM, and MAN designed the studies. WSJ conducted the

experiments and analyzed the data. WSJ, TJM, and MAN wrote the manuscript.

87

REFERENCES

Beardsley PM, Balster RL, and Harris LS (1986). Dependence on tetrahydrocannabinol

in rhesus monkeys. J Pharmacol Exp Ther 239:311-319.

Carney JM, Uwaydah IM, Balster, RL (1977). Evaluation of a suspension system for

intravenous self-administration of water insoluble substances in the rhesus

monkey. Pharmacol Biochem Behav 7:357–364.

Cerdá M, Wall M, Keyes KM, Galea S, Hasin D (2012). Medical marijuana laws in 50

states: investigating the relationship between state legalization of medical

marijuana and marijuana use, abuse and dependence. Drug Alcohol Depend

120: 22–27.

Dewey WL, McMillan DE, Harris LS, Turk RF (1973). Distribution of radioactivity in brain

of tolerant and nontolerant pigeons treated with 3H-Δ9-tetrahydrocannabinol.

Biochem Pharmacol 22: 399–405.

De Vry J, Jentzsch KR (2004). Partial agonist-like profile of the cannabinoid receptor

antagonist SR141716A in a food-reinforced operant paradigm. Behav Pharmacol

15: 13 20.

Fan, F, Compton, DR, Ward, S, Melvin, L, Martin, BR, (1994). Development of cross-

tolerance between delta 9-tetrahydrocannabinol, CP 55,940 and WIN 55,212. J.

Pharmacol. Exp. Ther. 271: 1383–1390.

Fattore L, Cossu G, Martellotta CM, Fratta W (2001). Intravenous self-administration of

the cannabinoid CB1 receptor agonist WIN 55,212-2 in rats.

Psychopharmacology 156: 410–416.

Ghozland S, Mathews H, Simonin F, Filliol D, Kieffer BL, Maldonado R (2002).

Motivational effects of cannabinoids are mediated by - and k-opioid receptors. J

Neurosci 22: 1146– 1154.

Gifford AN, Bruneus M, Lin S, Goutopoulos A, Makriyannis A, Volkow ND, Gatley SJ

88

(1999). Potentiation of the action of anandamide on hippocampal slices by the

fatty acid amide hydrolase inhibitor, palmitylsulphonyl fluoride (AM 374). Eur J

Pharmacol 383: 9–14.

Ginsburg BC, Hruba L, Zaki A, Javors MA, McMahon LR (2014). Blood levels do not

predict behavioral or physiological effects of Δ9-tetrahydrocannabinol in rhesus

monkeys with different patterns of exposure. Drug Alcohol Depend 139: 1-8.

Haertzen CA, Kocher TR, Miyasato K (1983). Reinforcements from the first drug

experience can predict later drug habits and/or addiction: results with coffee,

cigarettes, alcohol, barbiturates, minor and major tranquilizers, stimulants,

marijuana, hallucinogens, heroin, opiates and cocaine. Drug Alcohol Depend 11:

147–165.

Harris RT, Waters W, McLendon D (1974). Evaluation of reinforcing capability of 9-

tetrahydrocannabinol in monkeys. Psychopharmacologia 37:23–29

Hunt CA, Jones RT (1980). Tolerance and disposition of tetrahydrocannabinol in man. J

Pharmacol Exp Ther 215: 35–44.

Järbe TU, Lamb RJ, Liu Q, Makriyannis A (2003). (R)-Methanandamide and delta9-

tetrahydrocannabinol-induced operant rate decreases in rats are not readily

antagonized by SR-141716A. Eur J Pharmacol 466: 121–127.

John WS, Banala AK, Newman AH, Nader MA (2015a). Effects of buspirone and the

dopamine D3 receptor compound PG619 on cocaine and methamphetamine self-

administration in rhesus monkeys using a food-drug choice

paradigm.Psychopharmacology (Berl). 232: 1279-89.

John WS, Newman AH, Nader MA (2015b). Differential effects of the dopamine D3

receptor antagonist PG01037 on cocaine and methamphetamine self-

administration in rhesus monkeys. Neuropharmacology. 92: 34-43.

Johnston LD, et al (2014). Monitoring the Future: National Survey Results on Drug Use,

89

1975 2014-Overview, Key Findings on Adolescent Drug Use. (Institute for Social

Research, University of Michigan, Ann Arbor) Available at

http://monitoringthefuture.org//pubs/monographs/mtf-vol1_2014.pdf.

Justinova Z, Tanda G, Redhi GH, Goldberg SR (2003). Self-administration of delta(9)-

tetrahydrocannabinol (THC) by drug naive squirrel monkeys.

Psychopharmacology 169: 135-140.

Justinova Z, Munzar P, Panlilio LV, Yasar S, Redhi GH, Tanda G, Goldberg SR (2008).

Blockade of THC-seeking behavior and relapse in monkeys by the cannabinoid

CB(1)-receptor antagonist rimonabant. Neuropsychopharmacology 33: 2870-

2877.

Justinova Z, Mascia P, Wu H-Q, Secci ME, Redhi GH, Panlilio L et al (2013). Reducing

cannabinoid abuse and preventing relapse by enhancing endogenous brain

levels of kynurenic acid. Nat Neurosci 16: 1652–1661.

Kaymakcalan S. (1972). Physiology and psychological dependence on THC in rhesus

monkeys. In: Paton WDM, Crown J (eds) Cannabis and its derivatives. Oxford

University Press, London 142–149.

Kirk JM, De Wit H (1999). Responses to oral delta9-tetrahydrocannabinol in frequent

and infrequent marijuana users. Pharmacol Biochem Behav 63: 137–142.

Lefever, TW, Marusich, JA, Antonazzo, KR, and Wiley, JL (2014). Evaluation of WIN

55,212-2 self-administration in rats as a potential cannabinoid abuse liability

model. Pharmacol. Biochem. Behav. 118: 30–35.

Leite JR, Carlini EA (1974). Failure to obtain “cannabis directed behavior” and

abstinence syndrome in rats chronically treated with cannabis sativa extracts.

Psychopharmacologia 36: 133– 145.

Lepore M, Vorel SR, Lowinson J, Gardner EL (1995). Conditioned place preference

90

induced by D9-tetrahydrocannabinol: comparison with cocaine, morphine, and

food reward. Life Sci 56: 2073–2080.

Li J-X, Koek W, France CP (2012). Interactions between Δ(9)-tetrahydrocannabinol and

heroin: self-administration in rhesus monkeys. Behav Pharmacol 23: 754–761.

Lindgren JE, Ohlsson A, Agurell S, Hollister L, Gillespie H (1981). Clinical effects and

plasma levels of delta 9-tetrahydrocannabinol (delta 9-THC) in heavy and light

users of cannabis. Psychopharmacology 74: 208–212.

Mansbach RS, Nicholson KL, Martin BR, Balster RL (1994). Failure of Δ9-

tetrahydrocannabinol and CP 55,940 to maintain intravenous self administration

under a fixed-interval schedule in rhesus monkeys. Behav Pharmacol 5: 219–

225.

Martellotta MC, Cossu G, Fattore L, Gessa GL, Fratta W (1998). Self-administration of

the cannabinoid receptor agonist WIN 55,212–2 in drug-naive mice.

Neuroscience 85: 327–330.

Martin BR, Dewey WL, Harris LS, Beckner JS (1976). 3H-Δ(9)-tetrahydrocannabinol

tissue and subcellular distribution in the central nervous system and tissue

distribution in peripheral organs of tolerant and nontolerant dogs. J Pharmacol

Exp Ther 196: 128–144.

McMahon LR, Amin MR, and France CP (2005). SR 141716A differentially attenuates

the behavioral effects of delta9-THC in rhesus monkeys. Behav. Pharmacol. 16:

363–372.

McMahon LR (2011). Chronic delta9-tetrahydrocannabinol treatment in rhesus monkeys:

differential tolerance and cross-tolerance among cannabinoids. Br. J. Pharmacol.

162: 1060–1073.

McMillan DE, Dewey WL, Turk RF, Harris LS, McNeil JH (1973). Blood levels of 3H-D9-

tetrahydrocannabinol and its metabolites in tolerant and nontolerant pigeons.

91

Biochem Pharmacol 22: 383–397.

Nader MA, Lile JA, John WS, Czoty PW (2015). Nonhuman primate models of abuse

liability. In GF Weinbauer, F Vogel (Eds) “Primate Biologics Research at a

Crossroads”. Waxmann Publishing Co: Muenster, Germany, pp. 37-59.

Paronis CA, Nikas SP, Shukla VG, Makriyannis A (2012). Δ(9)-Tetrahydrocannabinol

acts as a partial agonist/antagonist in mice. Behav Pharmacol 23:802-805.

Perez-Reyes M, White WR, McDonald SA, Hicks RE, Jeffcoat AR, Cook CE (1991). The

pharmacologic effects of daily marijuana smoking in humans. Pharmacol

Biochem Behav 40: 691–694.

Pickens R, Thompson T, Muchow DC. Cannabis and phencyclidine self-administered by

animals (1973). Goldfarb L, Hoffmeister F (eds) Psychic dependence [Bayer-

Symposium IV]. Springer, Berlin Heidelberg New York, pp. 78–86.

Scherma M, Dessì C, Muntoni AL, Lecca S, Satta V, Luchicchi A, Pistis M, Panlilio LV,

Fattore L, Goldberg SR, Fratta W, Fadda P (2016). Adolescent Δ(9)-

tetrahydrocannabinol exposure alters WIN55,212-2 self-administration in adult

rats. Neuropsychopharmacology 41: 1416-1426.

Siemens AJ, Kalant H (1974). Metabolism of delta1-tetrahydrocannabinol by rats tolerant

to cannabis. Can J Physiol Pharmacol 52: 1154–11566.

Stewart JL and McMahon LR (2010). Rimonabant-induced Δ9-THC withdrawal in rhesus

monkeys: discriminative stimulus effects and other withdrawal signs. J

Pharmacol Exp Ther. 334: 347-356.

Tanda G, Munzar P, Goldberg SR (2000). Self-administration behavior is maintained by

the psychoactive ingredient of marijuana in squirrel monkeys. Nat Neurosci 3:

1073-1074.

Thomas H (1996). A community survey of adverse effects of cannabis use. Drug Alcohol

Depend 42: 201–207.

92

Valjent E, Maldonado R (2000). A behavioural model to reveal place preference to D9-

tetrahydrocannabinol in mice. Psychopharmacology 147: 436–438.

Van Ree JM, Slangen JL, de Wied D (1978). Intravenous self- administration of drugs in

rats. J Pharmacol Exp Ther 204: 547– 557.

Vivian JA, Kishioka S, Butelman ER, Broadbear J, Lee KO, Woods JH (1998).

Analgesic, respiratory and heart rate effects of cannabinoid and opioid agonists

in rhesus monkeys: antagonist effects of SR 141716A. J Pharmacol Exp Ther

286: 697–703.

93

CHAPTER III

CHRONIC Δ9-THC IN RHESUS MONKEYS: EFFECTS ON COGNITIVE

PERFORMANCE AND DOPAMINE D2/D3 RECEPTOR AVAILABILITY

William S. John1, Thomas J. Martin2, Kiran Kumar Solingapuram Sai3, Susan H. Nader1,

H. Donald Gage3, Akiva Mintz3, and Michael A. Nader1,3

1Department of Physiology and Pharmacology, Wake Forest School of Medicine,

Winston-Salem, NC

2Department of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, NC

3Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC

The following manuscript was submitted to Biological Psychiatry August, 2016. Stylistic

variations are due to the requirements of the journal. William S. John performed

experiments, analyzed the data, and prepared the manuscript. Kiran K. S. Sai, Donald

Gage, and Akiva Mintz conducted positron emission tomography studies. Susan H.

Nader assisted in the analysis of positron emission tomography data. Thomas J. Martin

and Michael A. Nader acted in an advisory and editorial capacity.

94

ABSTRACT

Background: Cannabis-related impairments to cognitive function may represent novel

therapeutic targets for cannabis-use disorder; however, the specificity, persistence, and

reversibility of those deficits remain unclear.

Methods: Each day, adult male rhesus monkeys (N=6) performed cognitive tasks and

responded under a fixed-ratio (FR)10 schedule of food presentation afterwards. After the

acute effects of Δ9-tetrahydrocannabinol (THC; 0.01-0.56 mg/kg, i.v.) on cognitive

performance and food-maintained responding were assessed, 1.0–2.0 mg/kg THC (s.c.)

was administered after FR10 sessions for 12 weeks during which the effects on

cognition, 22 hours after administration, were assessed daily. Prior to discontinuing

treatment, the acute THC dose-response curve was redetermined. Dopamine D2/D3

receptor availability was assessed after 4 weeks of chronic THC exposure and

compared to drug-naïve controls (N=4/group) using positron emission tomography and

[11C]-raclopride.

Results: Acute administration of THC dose-dependently deceased cognitive

performance and food-maintained responding. During chronic treatment, THC produced

persistent impairment 22 hrs after administration to working memory but not to

discrimination and reversal learning nor attentional set-shifting performance. Chronic

THC administration resulted in tolerance to acute rate-decreasing effects on food-

maintained responding and the acute impairments on cognitive performance. During

abstinence, there was recovery of impairments within 2 weeks. D2/D3 receptor

availability was not different in chronic THC-treated monkeys compared to control

subjects.

Conclusions: Chronic THC-induced disruptions in cognitive performance depended on

the cognitive domain, cognitive load, and time of assessment following THC

administration. These cognitive-domain specific effects of THC were not accompanied

95

by changes in dopamine D2/D3 receptor availability, which suggest that long-term

marijuana use may not produce comparable neuropharmacological adaptations as other

drugs of abuse.

Key Words: THC; CANTAB; Marijuana; Rhesus monkey; delayed-match-to sample,

PET imaging, set-shifting

96

INTRODUCTION

The disruption in cognitive function is a major consequence of drug abuse

(Jentsch and Pennington, 2014) and a factor that has been shown to predict relapse and

is associated with poorer treatment retention and outcome (Aharonovich et al., 2006,

2008; Carroll et al., 2011). As a result, cognitive enhancement may hold promise as an

adjunct treatment strategy for substance use disorders (Sofuoglu et al., 2013; Rezapour

et al., 2016).

Marijuana is the most widely abused illicit substance in the United States

(SAMHSA, 2013) and the neurocognitive effects of Δ9-tetrahydrocannabinol (THC), the

major psychoactive constituent of marijuana, have been extensively studied (Batalla et

al., 2013). Although studies in humans have shown that the acute effects of cannabis

use consistently impair executive cognitive functions such as attention (Hart et al., 2001;

D’Souza et al., 2008; Anderson et al., 2010), working memory (Heishman et al., 1997;

Hart et al., 2001, 2010;), and inhibition/impulsivity (McDonald et al., 2003; Ramaekers et

al., 2006), the residual effects (i.e. after the intoxicating effects have subsided) and

effects during abstinence, which are relevant for treatments that target cognitive

functioning, are equivocal for most cognitive domains (Crean et al., 2011; Crane et al.,

2013). Some of these disparate findings may be the result of experimental factors that

are difficult to control in studies with human subjects such as differences in time since

last use, amount and duration of cannabis use, previous drug histories, social variables,

as well as concurrent use of other substances. In addition, studies in humans cannot

distinguish whether marijuana abusers exhibit innate cognitive differences prior to

initiating drug taking so it is difficult to clearly establish a causal role of long-term THC

exposure in producing cognitive deficits. Therefore, it is critical to employ longitudinal,

within-subject studies in preclinical animal models that can control for these variables in

a systematic manner.

97

The present study used a within-subjects study design in monkeys to examine

the acute effects of THC 30-min prior to cognitive sessions and during chronic treatment,

and the effects of THC 22 hours after administration in an effort to model residual drug

effects, on multiple cognitive domains. Long-term cannabis use has also been shown to

produce differential tolerance to the acute effects of THC on cognitive function compared

to infrequent users (Kirk and de Wit, 1999; D’Souza et al., 2008; Ramaekers et al.,

2009). In the present study, the cognitive domains were 1) attention and working

memory using the delayed match to sample task, 2) associative learning using the

simple/compound discrimination task, 3) behavioral inhibition using the reversal learning

task, and 4) behavioral flexibility using dimensional set-shifting tasks.

Intact functioning within these particular cognitive domains is important for

positive treatment response as they underlie the skills necessary for behavioral change

and for reducing the likelihood of relapse during treatment (Rezapour et al., 2016);

however, studies in chronic cannabis users show differential effects within these

cognitive domains regarding the time course of impairments (Crane et al., 2013; Crean

et al., 2011). For instance, some studies report deficits in attentional abilities, learning

and memory, inhibition, and cognitive flexibility in recently abstinent (12 hr–3 d) cannabis

users (Solowij et al., 1995, 2002, 2011; Pope and Yurgelun-Todd, 1996; Harvey et al.,

2007; Hanson et al., 2010) while others do not (Whitlow et al., 2004; Kanayama et al.,

2004; Fisk and Montgomery, 2008; Grant et al., 2011). Moreover, other studies show

cognitive impairments after 28 days of abstinence or longer (Solowij et al., 1995; Bolla et

al., 2002; Jacobson et al., 2004, 2007; Medina et al., 2007). As a baseline for

comparison, the effects of chronic THC treatment was also studied on food-maintained

responding, which has been shown to result in tolerance to THC-induced decreases in

operant responding (McMahon, 2011). It was hypothesized that THC-induced

impairments to cognitive function would be independent of nonselective disruption to

98

operant responding and be most sensitive to working memory performance based on

previous research in nonhuman primates (Taffe, 2012; Wright Jr. et al., 2013; Kangas et

al., 2016).

As it relates to neurobiological substrates that mediate cognition and executive

function, dopamine (DA) D2-like receptors, consisting of the DA D2 and D3 receptor

subtypes, are particularly relevant (Tomasi and Volkow, 2013). For instance, D2/D3

receptors in the dorsal striatum have been shown to be associated with activation of

prefrontal brain regions implicated in executive function (Volkow et al., 1993, 1998) and

with frontostriatal circuitry implicated in inhibitory control (Ghahremani et al., 2012).

Moreover, chronic drug abuse in humans has been associated with a reduction in D2/D3

receptor availability compared to healthy control subjects for a variety of substances

(Volkow et al., 2012). Taken together, cognitive impairment resulting from chronic drug

use may be related to low striatal D2/D3 availability. Indeed, cognitive impairment in long-

term marijuana users has been associated with reduced activity in frontal cortical regions

(Block et al., 2002; Eldreth et al., 2004; Gruber et al. 2005; Bolla et al., 2005); however,

the relationship between chronic THC exposure, cognitive performance, and D2/D3

receptor availability has not been investigated. In the present study we assessed D2/D3

receptor availability using PET and [11C]-raclopride in relation to cognitive performance

during chronic THC treatment.

METHODS

Subjects

Eight individually housed adult male rhesus monkeys (Macaca mulatta) served

as subjects. Four monkeys were drug-naïve at the start of these studies and baseline

[11C]-raclopride scans were obtained (see below). Two of these monkeys, along with four

animals with extensive drug histories (see John et al., 2015a,b) were involved in the

99

THC-cognition studies. Each monkey was fitted with an aluminum collar (Model B008,

Primate Products, Redwood City, CA) and trained to sit in a primate restraint chair

(Primate Products). Monkeys were housed in stainless-steel cages with visual and

auditory contact with each other, ad libitum access to water in their home cage and were

fed sufficient standard laboratory chow (Purina LabDiet 5045, St Louis, MO) to maintain

healthy body weights as determined by veterinary staff. Environmental enrichment was

provided as outlined in the Guide for the Care and Use of Laboratory Animals (National

Research Council, 2011) and was approved by the Animal Care and Use Committee of

Wake Forest University.

Apparatus

The apparatus for food-maintained responding was a ventilated, sound-

attenuating chamber (1.5 x 0.74 x 0.76 m; Med Associates, East Fairfield, VT) designed

to accommodate a primate chair. Two photo-optic switches (5 cm wide) were located on

one side of the chamber with a horizontal row of three stimulus lights 14 cm above each

switch and a food receptacle between the switches. The food receptacle was connected

with tygon tubing to a pellet dispenser (Gerbrands Corp., Arlington, MA) located on the

top of the chamber for delivery of 1.0-g banana-flavored food pellets (Bio-Serv,

Frenchtown, NJ). An infusion pump (Cole-Palmer, Inc., Chicago, IL) was located on the

top of the chamber.

The apparatus for cognitive testing was a separate sound-attenuating, ventilated

chamber (0.8 x 0.8 x 1.32 m) consisting of a CANTAB panel (0.38 x 0.56 x 0.31 m) that

included a touch-sensitive screen (0.3 x 0.23 m), a stimulus light, a non-retractable

response lever, and a pellet receptacle located on the right side. For each task,

responding was maintained by 190-mg sucrose pellets.

100

Surgery

Monkeys included in behavioral experiments were surgically prepared with a

chronic indwelling venous catheter (femoral, internal or external jugular) and

subcutaneous vascular port (Access Technologies, Skokie, IL) using aseptic surgical

procedures, as described in detail elsewhere (John et al., 2015a). These monkeys were

also implanted subcutaneously with a transponder (Model IPTT-300; Bio Medic Data

Systems, Inc., Seaford, DE) to non-invasively measure body temperature.

Experimental Timeline

Six monkeys underwent two behavioral sessions each day (Figure 1). First,

cognitive performance was assessed using the CANTAB apparatus (Lafayette

Instruments, Lafayette, IN). Immediately following, monkeys were transported to a

different room and operant sessions were conducted in separate experimental chambers

where monkeys responded under a fixed-ratio (FR) 10 schedule of food (1.0 g banana-

flavored pellets) reinforcement with a 60 second timeout (TO) following each food

presentation. Monkeys were returned to their cages following the completion of the

second behavioral session.

Acute THC Treatments: The acute effects of THC were determined separately on

food-maintained responding and cognitive performance. For FR 10 food-maintained

responding, when stable (± 20% of the mean rate of responding for 3 consecutive

sessions, with no trends), non-contingent injections of THC (vehicle, 0.003-0.3 mg/kg,

i.v.) were administered one minute prior to operant sessions; all doses were tested 2-3

times for each monkey. Body temperature was taken immediately prior to THC

administration and then again 60-min following the start of the session. Between each

test session, at least two sessions were conducted where no drug or vehicle was

administered. Following one week of chronic THC (1.0 mg/kg, s.c.) administration, the

101

effects of acutely administered THC on FR responding was redetermined during which

monkeys were still treated daily with 1.0 mg/kg THC after the session. For cognitive

performance, following stable performance, the acute effects of vehicle and THC (0.003

– 0.3 mg/kg, i.v.) were determined on each task prior to chronic THC treatment. Each

dose was administered 30-min prior to the start of the session; all doses were tested 2-3

times for each monkey and at least two intervening sessions were conducted where no

drug or vehicle was administered. Following 8 weeks of chronic THC administration, the

acute effects of THC on cognitive performance were redetermined over the course of

two weeks while monkeys still received 1.0 mg/kg THC at the end of FR sessions.

Chronic THC Treatments: After completion of all acute THC dose-response

curves, THC was administered chronically for 12 weeks with daily injections (1.0 mg/kg

for 10 weeks and 2.0 mg/kg, s.c., for 2 weeks) occurring immediately following the

second behavioral session (i.e. FR food-maintained responding) or at similar times when

sessions did not occur (i.e. weekends). Thus, during this phase of the study, cognitive

performance and FR responding were measured approximately 22 and 23 hours after

daily THC administration, respectively. These doses were selected based on pilot

studies on FR responding. Moreover, doses of THC within the same range administered

to rhesus monkeys via the same route (s.c.) have been shown to produce similar blood

levels of THC as those of humans smoking cannabis in controlled laboratory studies

(Ginsberg et al., 2014) The daily dose was not raised to 2.0 mg/kg in R-1710 due to

marked behavioral disruption during 1.0 mg/kg THC, to which tolerance did not develop.

Following completion of the chronic dosing phase, THC administration was discontinued

and performance during 4-6 weeks of abstinence was examined.

102

Experiment 1: Effects of acute and chronic THC on food-maintained responding

Sessions began with illumination of a white stimulus light above one of two

photo-optic switches in the chamber, counterbalanced between monkeys. Ten

consecutive responses emitted on the appropriate switch produced the delivery of a

banana-flavored food pellet accompanied by extinction of the white light and illumination

of a red stimulus light (conditioned stimulus) above the food receptacle for 3 sec.

Responses emitted on the alternate photo-optic switch had no programmed

consequence. Sessions lasted 60 min or until 30 reinforcers were obtained, whichever

occurred first. The effects of acute (1 min pretreatment) and chronic (23 hrs after daily

administration) THC were assessed on FR 10 responding, as described above.

Figure 1. Experimental timeline. Monkeys performed two operant behavioral

sessions each day, which included cognitive testing immediately followed by responding for food pellets under an FR 10 schedule. Following stable performance, the acute effects of THC (0.003–0.3 mg/kg, i.v.) were determined on FR responding (Experiment 1) and cognition (Experiment 2). THC (1.0–2.0 mg/kg, s.c.) was then administered chronically for 12 weeks during which the effects 22 hrs after administration were assessed daily on cognitive performance and FR responding (Experiment 2). During chronic treatment, dopamine D2 receptor availability was assessed using PET and [11C]-raclopride at four weeks (Experiment 3) and the acute effects of THC were redetermined on FR responding (Experiment 1) and cognition (Experiment 2) at one week and 8 weeks, respectively (Experiment 1). Behavioral performance continued to be assessed for 4 weeks following the discontinuation of THC treatment. Exp., Experiment; + THC #2, redetermination of acute effects of THC.

Cognitive assessment FR 10 (60 s TO)

+ 1.0 mg/kg THC (s.c.)

Baseline

+ 2.0 mg/kg THC (s.c.)

DMS/IDED

+ THC

(30 min PT, i.v.)

FR 10

+ THC

(1 min PT, i.v.)

PET

Week 1 Week 2

FR 10

+ THC #2

(1 min PT, i.v.)

DMS/IDED

+ THC #2

(30 min PT, i.v.)

Week 3 Week 4 Week 6Week 5 Week 7 Week 8 Week 9 Week 12Week 11Week 10 Weeks 13-16

Exp. 1 Exp. 1

Exp. 2

Exp. 2Exp. 3

Exp. 2

103

Experiment 2: Effects of acute and chronic THC on cognitive performance

Each week, DMS performance was assessed Monday-Wednesday and stimulus

discrimination, reversal learning, and set-shifting performance were assessed on

Thursday and Friday.

Delayed Match-to-Sample (DMS): The DMS task is a measure of attention and

working memory. Each trial began with the appearance of a “target” stimulus in the

center of the screen (sample phase). A response on this stimulus was followed by a

delay and then the presentation of a stimulus matching the previous image and at least

two distractor stimuli that do not match. Responding on the match resulted in delivery of

a sucrose pellet followed by a 10-s TO, whereas responding on the non-match resulted

in trial termination and the 10-s TO. Three delays were randomly distributed throughout

a total of 60 trials per session (20 trials/delay). Delays and number of distractor stimuli

were individually determined (Table 1) in order to generate delay-effect curves that met

the following criteria: short delay (80-100% accuracy); middle delay (60-79% accuracy);

long delay (<60% accuracy). The stimuli used were unique abstract patterns

preprogrammed in CANTAB software, which consisted of four rectangular quadrants of

different colors and shapes.

104

Table 1. Individual parameters used throughout study that produced delay-dependent

effects on DMS performance; dist., distractor stimuli.

Delays (sec)

# dist. Subject Short Mid Long

R-1567 1 30 60 2

R-1682 0 30 90 4

R-1710 0 10 30 2

R-1711 0 30 100 4

R-1713 0 30 60 3

R-1756 1 30 60 3

Stimulus discrimination, reversal learning and intradimensionl/extradimensional

set-shifting (ID/ED): This task involved 5 distinct stages that tested attention, rule

learning and reversal, and executive function. Stages advanced within-session following

acquisition of performance criteria, defined as 16 of 20 consecutive trials correct. Stage

1, simple discrimination (SD): Two stimuli (e.g. shapes) were presented, one of which

was reinforced upon selecting (S+) while the other was not reinforced (S-) and resulted

in trial termination. Stage 2, compound discrimination (CD): The same stimuli as in the

previous stage remained present, however, additional stimuli (e.g. lines) were

superimposed to construct compound stimuli that consisted of shape and line

dimensions. The same stimulus dimension that was associated with reinforcement in the

first stage (e.g. shapes) remained the relevant stimuli in this stage, while the other

dimension (e.g., lines) was irrelevant. Stage 3, compound reversal (CR): Stage 2 rules

were reversed such that the previously non-reinforced stimulus within the dimension was

reinforced. Stage 4, intra-dimensional shift (ID): A new pair of compound stimuli

consisting of shape and line dimensions was presented; however, despite new stimuli,

105

the same dimension of the stimuli as in stages 1 - 3 (e.g. shapes) remained relevant for

reinforcement. Stage 5, extra-dimensional shift (ED): another novel set of compound

stimuli was introduced; however, in this stage the previous irrelevant stimulus dimension

(e.g. lines) became relevant for reinforcement. Each session lasted for 100 min or after

criterion was met for each stage, whichever occurred first.

Experiment 3: Effects of chronic THC on D2/D3 receptors

Two groups of monkeys underwent [11C]-raclopride PET scans. One group (N=4)

was studied after 4 weeks of daily THC administration while another group (N=4) was

drug-naïve and served as control subjects. Two of the control subjects became subjects

in Experiments 1 and 2 and were also included in the group of monkeys scanned after

THC administration. Magnetic resonance imaging (MRI) scans were acquired for each

monkey. For those images, subjects were anesthetized with ketamine (10 mg/kg, i.m.)

and transported to the MRI facility, where anesthesia was maintained during the

scanning procedure with supplemental ketamine (up to 15 mg/kg, i.m.). T1-weighted

images of the brain were acquired with a 3.0-T Siemans SKYRA scanner. Images were

used to anatomically define regions of interest (ROI), including the caudate nucleus,

putamen, ventral striatum, and cerebellum, for later registration with PET images.

For the [11C]-raclopride studies, approximately 30 min prior to the PET scan, the

monkey was anesthetized with ketamine (10 mg/kg, i.m.) and transported to the PET

Center. Anesthesia was maintained during the scan by inhaled isoflurane (1.5 %). The

monkey was placed in the scanner (GE 64 slice PET/CT Discover VCT scanner, GE

Medical Systems, Milwaukee, WI) and a catheter was inserted into an external vein for

tracer injection and fluid replacement throughout the study. Body temperature was

maintained at 40°C and vital signs (heart rate, blood pressure, respiration rate, and

temperature) were monitored throughout the scanning procedure. A 5-min transmission

106

scan was acquired in 2D mode. Next, the monkey received a bolus dose of [11C]-

raclopride (6.5–8.0 mCi) and a 90-min dynamic acquisition scan was acquired consisting

of 18 frames over 90 min (18 × 5 min) in 3D mode (i.e., septa retracted). Image

reconstruction of 3D data was done using the 3D-reprojection method (Kinahan and

Rogers, 1989) with full quantitative corrections. Once scanning was complete, the

transmission scan data were smoothed transaxially using a 4-mm Gaussian filter and

segmented (Bettinardi, 1999). Emission data were corrected for attenuation and

reconstructed into 128 × 128 matrices using a Hanning filter with a 4-mm cutoff

transaxially and a ramp filter with an 8.5-mm cutoff axially.

Data Analysis

Experiment 1: The primary dependent variable for food-maintained responding

was response rate (responses/second). Drug effects were expressed as a percentage of

baseline responding, which was designated as 3 consecutive sessions of stable

responding prior to the start of the study. Change in body temperature (°C) following

administration of either vehicle or THC was determined by subtracting the temperature

recorded prior to the start of the session from the temperature recorded at the end of the

60-min session. The potency of THC to decrease food-maintained responding was

estimated by calculating the ED50 using the linear portion of the curve that crossed 50%

reduction in baseline response rate.

Experiment 2: The primary dependent variable for DMS performance was

percent accuracy, which was determined by dividing the number of correct responses by

the total number of trials completed for each delay (short, mid, long) multiplied by 100.

Omitted trials during either the sample or match phase were not counted towards the

total trials. During each condition (THC treatment and abstinence), changes in percent

accuracy from baseline, defined as 7 consecutive sessions with percent accuracy for

107

each delay ±25% of the mean rate for those sessions with no trends, were examined by

repeated-measures two-way repeated measures ANOVA using delay (short, middle,

long) and treatment condition (baseline, chronic, abstinence) as factors. Additional

dependent variables for DMS performance measured were number of omissions for

sample and match phases, response latencies for sample and match phases, and pellet

retrieval latencies. One-way repeated measures ANOVA was used to compare test

conditions with baseline conditions. Sessions during the chronic THC treatment phase in

which the total number of omissions exceeded 50% of the total trials were excluded from

analyses. The primary dependent variable for the ID/ED task was percent accuracy

(number of correct trials divided by number of total trials X 100) from baseline (four

consecutive sessions over two weeks with percent accuracy for each stage ±25% of the

mean rate for those sessions, with no trends). Repeated measures two-way ANOVA

was conducted using stage (SD, CD, CDR, ID, ED) and treatment condition (baseline,

chronic, abstinence) as factors. Omitted trials were excluded from all analyses.

Significant main effects were followed by post-hoc Fisher LSD tests. For all analyses, p

< 0.05 was considered statistically significant.

Experiment 3: PET data were co-registered to individual T1-weighted MRIs,

which were used to anatomically define regions of interest (ROI) including the caudate

nucleus, putamen, ventral striatum, and cerebellum. PMOD Biomedical Image

Quantification Software (version 3.1; PMOD Technologies, Zurich, Switzerland) was

used for image registration and was used to calculate distribution volume ratios (DVRs)

for the caudate nucleus, putamen, and ventral striatum by implementing the “Logan

method” of analysis (Logan et al. 1990) using the cerebellum as the reference region.

DVRs for each region were not different between left and right sides therefore data from

each monkey was expressed as a mean of both sides. To compare DVRs between

108

control and THC-treated subjects, a two-way ANOVA was conducted using region and

group as factors.

RESULTS

Experiment 1: Effects of acute and chronic THC on food-maintained responding

Mean (±SEM) rate of responding under the FR 10 (TO 60-s) schedule of food

presentation, before THC administration was 1.18 (0.19) responses/sec and mean body

temperature was 35.97 (0.54) °C. Acute THC administration resulted in dose-dependent

decreases in food-maintained responding (Figure 2A, open circles) and biphasic effects

on body temperature (Figure 2A, open triangles). The ED50 (± 95% confidence limits) for

THC on reducing response rates was 0.034 (0.012-0.099) mg/kg and for hypothermia

was 0.068 (0.024-0.19) mg/kg. In general, daily post-session administration with 1.0

mg/kg THC for 10 weeks and 2.0 mg/kg THC for 2 weeks, did not affect food-maintained

responding (Figure 2B). However, after one week of daily THC treatment (1.0 mg/kg,

s.c.), the ED50 for acutely administered THC to decrease food-maintained responding

was 0.34 (0.16-0.51) mg/kg - a 9.7–fold (p < 0.05) reduction in potency (Figure 2A,

closed circles). The ED50 for THC to decrease body temperature after treatment was

0.46 (0.23-0.9) mg/kg, which was increased by 6.8-fold (Figure 2A, closed triangles).

109

Experiment 2: Effects of THC on cognitive performance

DMS performance

For each monkey, three delays were then chosen (short, mid, long) that resulted

in similar delay-effect curves between monkeys (Table 2). For the acute effects of THC

on DMS performance prior to chronic treatment, there was a main effect of dose [F(4,

63) = 13.55, p < 0.0001]; post-hoc analyses revealed that 0.03 mg/kg THC significantly

reduced percent accuracy from baseline at the short delay and 0.03 and 0.056 mg/kg

THC dose-dependently decreased percent accuracy from baseline at the middle and

long delays (Fig. 3A, open symbols). These effects occurred at doses that did not

produce omissions in more than 30% of total trials (Fig. 3B, open symbols). When the

Figure 2. Effects of acute THC on food-maintained responding and body

temperature before and during chronic THC (1.0 mg/kg, s.c.) treatment (A) and mean (±SD) food-reinforced response rates for sessions before (BL, baseline), during (23 hours after daily administration), and after chronic THC treatment for individual monkeys (B). Panel A, Abscissae: Dose of THC in mg/kg. Left ordinate: Mean (± SEM) response rate expressed as a percentage of baseline (n = 6). Right ordinate: Mean (± SEM) change in body temperature expressed in °C. Panel B,

Abscissae: Consecutive weeks before, during, and after chronic THC treatment. Ordinate: Response rate (responses/sec). Filled symbols indicate weeks during chronic THC treatment. Open symbols indicate weeks where THC was not administered. All monkeys were treated with 1.0 mg/kg THC during weeks 1-10 and 2.0 mg/kg THC during weeks 11-12 with the exception of one monkey, R-1710, who was only treated with 1.0 mg/kg THC during weeks 1-8.

V 0.01 0.1 1

0

25

50

75

100

125

150

-1.0

-0.5

0.0

0.5

D9-THC (mg/kg)

Re

sp

on

se

Rate

(%

of

bas

elin

e)

Before (food)

During (food)

Ch

an

ge

In B

od

y T

em

p. (°C

)

During (Δ temp)

Before (Δ temp)

BL 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

0.0

0.4

0.8

1.2

1.6

2.0

2.4

Week

Resp

on

se R

ate

(resp

/sec)

R-1756

R-1567

R-1682

R-1710

R-1711

R-1713

A B

110

THC dose-response curve was redetermined during chronic THC treatment, there was a

main effect of delay [F(2, 54) = 37.36, p < 0.0001], dose [F(5, 54) = 5.38, p < 0.0001],

and a significant interaction [F(10, 54) = 4.52; p = 0.0001). Post-hoc analyses indicated

that tolerance developed to the performance-disrupting effects of THC such that 0.03

and 0.056 mg/kg THC did not alter percent accuracy from baseline at the short and

middle delays. However, a larger dose (0.3 mg/kg) of THC produced comparable

decreases to other doses in the initial assessment at the middle but not short delay.

The effects of THC (1.0-2.0 mg/kg, s.c.) 22 hours after treatment on DMS

performance over the course of 12 weeks of treatment and during 4 weeks of abstinence

(Figure 4A) showed a significant main effect of condition (THC treatment regimen) on

percent change in accuracy from baseline [F(14, 42) = 2.29; p < 0.05). Significant

impairments were detected at the middle delay during week four of treatment and at the

long delay during the first four weeks of treatment and again during weeks 6 and 7. By

week 8, tolerance developed to THC-induced impairments such that decreases in

percent accuracy at the long delay were not different from baseline. At week 11, the

dose of daily administered THC was increased to 2.0 mg/kg (s.c.) and percent accuracy

from baseline at the longest delay was significantly decreased again at week 12, which

persisted until 2 weeks of abstinence (Figure 4A).

111

Table 2. Baseline percent accuracy (± SD) at each delay for individual monkeys

responding under the DMS task.

Delays

Subject Short Mid Long

R-1567 78.8 (7.2) 46.5 (17.2) 38.3 (9.9)

R-1682 81.7 (5.9) 70.9 (9.8) 58.7 (9.8)

R-1710 75.8 (4.0) 52.9 (11.4) 41.7 (13.6)

R-1711 82.2 (10.2) 72.0 (15.5) 58.1 (8.7)

R-1713 87.1 (5.7) 56.5 (10.2) 49.3 (14.0)

R-1756 72.7 (11.2) 52.0 (11.0) 48.5 (5.1)

Stimulus discrimination, reversal learning, and set-shifting performance

For the acute effects of THC on set-shifting performance prior to chronic THC

treatment, two-way ANOVA demonstrated a main effect of stage [F(16,98) = 10.69, p <

0.001] and dose [F(4, 98) = 3.49, p < 0.05]. Post-hoc analysis revealed a significant

decrease in percent accuracy for CD at 0.1 mg/kg THC (p < 0.05) and for ED at 0.056

and 0.1 mg/kg THC (p < 0.05) (Figure 3C). During chronic treatment, the acute effects of

only 0.1 mg/kg THC were reexamined because it was the highest dose that produced

impairments without producing a significant number of omissions in the initial

assessment prior to treatment. Two-way ANOVA demonstrated a main effect of stage

[F(4, 68) = 4.35, p < 0.05] and condition [F(3, 68) = 3.55, p < 0.05); post-hoc analysis

revealed that percent accuracy was significantly lower for vehicle administration during

chronic treatment compared to vehicle prior to treatment. Percent accuracy following 0.1

mg/kg THC was not significantly different compared to vehicle administration prior to

chronic treatment (Fig. 3D).

112

The effects of THC 22 hours after administration on SD, CD, CDR, ID and ED

set-shifting performance (Figure 4B) during chronic treatment and abstinence showed a

significant main effect of stage [F(4, 296) = 12.53; p < 0.0001] although post-hoc

analysis revealed no significant decreases in percent accuracy from baseline at any

stage during the course of treatment and throughout abstinence.

113

V 0.01 0.1 1

25

50

75

100

125

Perc

en

t C

orr

ec

t(%

of

ba

selin

e)

Short Delay

Before

During

**

V 0.01 0.1 1

Mid Delay

*

** **

V 0.01 0.1 1

Long Delay

**

****

*

** *

**

****

Δ9-THC (mg/kg)

V 0.01 0.1 10

10

20

30

40

50

60

Nu

mb

er

of

Om

issio

ns

Phase 1

Before

During

*

***

V 0.01 0.1 1

Phase 2

A

B

V 0.01 0.1 1

40

50

60

70

80

90

100

Δ9-THC (mg/kg)

Perc

en

t C

orr

ect

SD

EDIDCDR CD

DC

SD CD CDR ID ED50

60

70

80

90

100

Perc

en

t C

orr

ect

*

#

*

+ Vehicle

+ 0.1 mg/kg THC

+ Vehicle (During)

+ 0.1 mg/kg THC (During)

Δ9-THC (mg/kg)

114

Figure 3. Effects of acute THC on cognitive performance before and during chronic THC (1.0 mg/kg, s.c.) treatment. Doses of THC were administered 30 min before experimental sessions. Panel A: Delayed-match-to-sample performance at short (left panel), mid (middle panel), and long (right panel) delays. Abscissae: dose of THC in mg/kg that was administered 30 min prior to assessment before and during chronic THC treatment. Ordinate: Percent accuracy as a percent of baseline performance. Panel B: Number of omissions during sample (left panel) and target phase (right panel) of delayed-match-to-sample task. Panel C: Acute effects of THC on SD, CD, CDR, ID, and ED set-shifting performance prior to chronic THC treatment. Panel D: Effects of 0.1 mg/kg THC and vehicle on SD, CD, CDR, ID, and ED set-shifting performance before and during chronic THC treatment. Asterisks indicate significant difference from baseline. *p < 0.05, **p < 0.01, ****p < 0.0001. #, significant difference from vehicle administration during chronic THC treatment. Data represent mean (SEM) of n = 6. SD, stimulus discrimination, CD, compound discrimination, CDR, compound reversal, ID, intradimensional set-shifting, and ED, extradimensional set-shifting.

115

Figure 4. Effects of THC 22 hrs after administration during chronic treatment and effects of abstinence on delayed match-to-sample performance (panel A) and discrimination and reversal learning and attentional set-shifting performance (panel B). Data are expressed as percent of baseline performance. Filled symbols indicate a significant difference from baseline (p < 0.05). Break in x-axis indicates two-week period where acute effects of THC on cognitive performance were reassessed during THC treatment. Vertical line indicates discontinuation of chronic THC treatment after which effects during abstinence were assessed. SD, stimulus discrimination, CD, compound discrimination, CDR, compound reversal, ID, intradimensional set-shifting, and ED, extradimensional set-shifting.

1 2 3 4 5 6 7 8 11 12 13 14 15 16

50

75

100

125

150

DMS

+ 1.0 mg/kg THC(22 hr PT)

+ 2.0 mg/kgTHC

(22 hr PT)

Short

Mid

Long

1 2 3 4 5 6 7 8 11 12 13 14 15 16

50

75

100

125

150ID/ED

SD

CD

CDR

ID

ED + 1.0 mg/kg THC(22 hr PT)

+ 2.0 mg/kgTHC

(22 hr PT)

Week

Accu

racy (

% b

aselin

e)

A.

B.

116

Experiment 3: Effects of chronic THC on D2/D3 receptors

In control monkeys, the distribution volume ratio (DVR) for [11C]-raclopride was

highest in the putamen, followed by the caudate nucleus and ventral striatum (Table 3).

When D2/D3 receptor availability was compared between control monkeys and monkeys

receiving chronic THC administration, there were no significant differences in the

caudate nucleus, putamen and ventral striatum (Table 2, Fig. 5; p<0.05). For the two

monkeys that participated in scans both before and during THC treatment, DVRs were

decreased in the caudate nucleus (R-1710: -19.6%; R-1711: -1.51%) and increased for

both monkeys in the ventral striatum (R-1710: +10.2%; R-1711: +3.9%). However, DVRs

in the putamen were decreased by 14.2 percent during THC treatment in one monkey

(R-1710) and increased by 15.3 percent in the other (R-1711).

Table 3. Individual and mean (±SEM) [11C]-raclopride DVRs in control and THC treated

monkeys.

Caudate Putamen

Ventral Striatum

Control

R-1710 2.36 2.55 1.51 R-1712 2.14 2.15 1.89 R-1711 2.39 2.15 1.82 R-1681 1.68 2.53 1.99

Mean 2.14 2.35 1.80 ±SEM 0.16 0.11 0.10

Δ9-THC

R-1710 1.90 2.18 1.67 R-1711 2.35 2.48 1.89 R-1713 2.07 2.62 2.57 R-1682 2.04 2.32 1.54 Mean 2.09 2.40 1.92 ±SEM 0.09 0.10 0.23

Difference -2.3% +2.1% +6.7%

117

Control THC Control THC Control THC1.0

1.5

2.0

2.5

3.0

DV

R

Caudate Putamen Ventral Striatum

R-1710

R-1682

R-1711

R-1710

R-1712

R-1681

R-1711

R-1713

Figure 5. Effects of chronic THC treatment on DA D2/D3 receptor availability as

measured with [11C]-raclopride. PET scans occurred after four weeks of chronic THC treatment. Different shaped symbols represent individual subjects while open symbols represent control subjects and filled symbols represent THC-treated subjects. Two monkeys (R-1710, R-1711) served as both control and THC-treated subjects.

118

DISCUSSION

The main goal of the present study was to examine the effects of THC on

multiple cognitive domains in nonhuman primates. Two sets of studies were conducted:

examination of the acute effects of THC before and during chronic THC treatment and

examination of the effects of THC 22 hrs after daily, 12 week, chronic treatment as a

model of residual drug effects, which reflect the period after direct intoxicating effects

have subsided. Indeed, studies in humans and nonhuman primates have confirmed the

direct effects of THC are absent at the time of behavioral assessment used presently

(Hollister et al., 1981; Ginsberg et al., 2014). Acutely, THC dose-dependently decreased

working memory (DMS performance) and attentional set-shifting (ID/ED performance).

During chronic treatment, differential tolerance developed to impairments in

discrimination-based learning but not working memory when the cognitive demand was

high. When cognitive performance was assessed 22 hours after THC administration

during daily treatment, impairments were specific to working memory. Tolerance

developed to the deficits in working memory, but an increase in THC dose resulted the

reemergence of working memory impairments. Working memory deficits recovered

within 2 weeks of abstinence. In an effort to examine potential mechanisms of action

mediating the differential effects of THC on cognition, dopamine D2/D3 receptor

availability was examined and found not to be different compared to control monkeys.

Tolerance to the acute effects of cannabinoids following a period of chronic

cannabinoid exposure can differ depending on the behavioral outcome measured

(Lichtman and Martin, 2005). As such, studies in humans suggest that chronic THC

exposure can produce differential tolerance to the acute effects of THC on cognition by

comparing groups of frequent vs. infrequent cannabis users. For instance, compared to

infrequent users, studies showed that frequent users were less impaired to the acute

effects of THC on measures of critical thinking, divided attention, reaction time, verbal

119

learning, and memory (D’Souza et al., 2008; Ramaekers et al, 2009). However, these

studies depended on the use of a group design, therefore, the extent to which the drug

history was actually a factor is unclear without knowing the effects of acute THC on

cognitive performance prior to the initiation of frequent marijuana use. By using a within-

subjects design, the present study systematically showed that tolerance developed to

THC-induced cognitive impairments in working memory and discrimination learning.

These data may be particularly relevant for determining levels of THC that reflect

impairment for driving and other tasks and suggest that cannabis use history is a

corresponding factor that should be considered.

With regard to the residual effects of chronic THC exposure on cognition, results

from studies in humans that assessed cognitive performance within a similar time frame

are inconsistent with the present results. For instance, several studies in recently

abstinent (6 -36 hr) adult cannabis users showed no differences in working memory

performance compared to infrequent or non-users (Pope and Yurgelun-Todd, 1996;

Solowij et al., 2002; Kanayama et al., 2004; Whitlow et al., 2004; Fisk and Montgomery,

2008). Moreover, studies assessing the residual effects of cannabis use on inhibition

and cognitive flexibility are more mixed with some studies showing impairments (Pope

and Yurgelun-Todd, 1996; Solowig et al., 2002) and others that do not (Whitlow et al.,

2004; Gruber and Yurgelun-Todd, 2005; Herman et al., 2007); associative learning in

recently abstinent adolescent and adult users has been shown to be unaffected (Harvey

et al., 2007; Fisk and Montgomery, 2008; Jager et al., 2010), which is consistent to the

present findings.

One possibility for discrepant findings may be due to the daily amount of THC

consumed across studies. Although participants in the previously mentioned studies

used marijuana on a near-daily basis per month, the number of marijuana cigarettes

smoked per day is often not reported, which makes it difficult to discern THC-induced

120

effects across studies. It is also possible that tolerance or certain deficits emerge after

longer periods of chronic THC exposure than what was presently studied. For instance,

Solowij (2002) found that the severity of cognitive deficits after at least 12 hrs of

abstinence in frequent cannabis users was correlated with years of use. Furthermore,

inconsistent results between studies could be a reflection of differential cognitive

demand across the tasks employed. This notion is supported by several studies in

humans that have examined the effects of chronic cannabis use on working memory.

For example, in one study, adolescent regular cannabis users demonstrated worse

working memory after 3 and 13 days of abstinence compared to control subjects during

a challenging task that required active manipulation of items (Hanson et al., 2010), but

not after 8 days during a simpler matching task (Jager et al., 2010). Similarly, Jacobsen

et al. (2007) reported worse verbal working memory in the n-back test in abstinent

adolescent cannabis users relative to control subjects but only during high memory load.

Thus, it may be reasoned that the DMS task had the highest cognitive demand

compared to the other tasks used in the present study, thus making it the most sensitive

to impairment during chronic THC treatment. Future studies examining the effects of

THC on cognition should incorporate varying degrees of complexity into tasks that

assess different cognitive domains, which would eliminate potential ceiling effects and

make assessments more task-specific and not just related to general behavioral

complexity.

Another explanation for the specificity of chronic THC to impair working memory

in the present study may be the result of regional differences in brain CB1 receptor

function related to this domain of executive function. Electrophysiological and lesion

studies demonstrate that working memory is preferentially activated by the ventrolateral

prefrontal cortex (Jones and Miskin, 1978; Miskin and Manning, 1978; Wilson et al.,

1993), while discrimination reversal learning and set-shifting are preferentially

121

associated with activation of the orbitofrontal cortex and dorsolateral prefrontal cortex,

respectively (Dias et al., 1996a, b). Thus, it is likely that working memory impairments

during chronic THC treatment were a reflection of greater dysregulation of CB1 receptor

dynamics (e.g. downregulation/densensitization) or signaling in regions that mediate

working memory such as the ventrolateral prefrontal cortex.

Also related to the residual effects of chronic THC treatment on cognition in the

present study, previous studies in rhesus monkeys showed that indications of withdrawal

(e.g., yawning, anorexia, irritability, headshaking, onset of cannabinoid receptor

antagonist discriminative stimulus effects) can be observed within 24-48 hr of

discontinuation of chronic THC treatment (Deneau and Kaymakcalan, 1971; Beardsley

et al., 1986; Stewart and McMahon et al., 2011), thus raising the question of whether

cognitive deficits were related to withdrawal as opposed to pharmacological-induced

changes to brain function that mediate such behavior. Although cognitive assessment in

the present study occurred at a similar time frame as withdrawal symptoms detected in

previous studies, no overt physical signs of withdrawal were noted throughout the study.

This was mostly likely due to the once-a-day administration of a lower daily dose of THC

compared to continuous infusion of higher daily doses used in previous studies (e.g. 1.0

mg/kg/12hr by Stewart and McMahon, 2010 or 0.05-0.17 mg/kg/hr by Beardsley et al.,

1986) shown to produce physical withdrawal symptoms. Nevertheless, it cannot be

completely discounted that cognitive impairment was the result of THC withdrawal

considering this may have been the only measure sensitive enough to detect such

effects under the present treatment regimen. Regardless of whether impairments were

the result of physical withdrawal or pharmacokinetics, rates of food-maintained

responding and numbers of omissions in five of six monkeys were not altered during the

course or after discontinuation of chronic treatment, which suggests that cognitive

impairment was not the result of a non-selective disruption in operant responding.

122

The present study did not find any differences in striatal D2/D3 receptor

availability measured by PET and [11C]-raclopride in monkeys chronically treated with

THC compared to control subjects, even though deficits in working memory performance

at the same time point were apparent. These findings are consistent with studies in

humans also showing no differences in baseline D2/D3 receptor availability between

marijuana abusers and healthy controls (Stokes et al., 2012; Urban et al., 2012; Albrect

et al., 2013; Volkow et al., 2014) and suggest that THC-induced cognitive impairment

was not directly related to striatal DA D2/D3 receptor function, despite the fact that the DA

D2/D3 system plays a prominent role in modulating prefrontal cortical function (Groman

and Jentsch, 2012).

One of the two monkeys (R-1710) scanned both before and during chronic THC

treatment showed a large reduction in D2/D3 receptor availability in the dorsal striatum

(Cd: -19.6%, Pt: -14.2%) during chronic THC treatment. Interestingly, this monkey was

also the most sensitive to the residual rate-decreasing effects of chronic THC treatment

on food-reinforced responding in which reduced rates of responding did not fully recover

to baseline levels until 6 weeks after discontinuation of daily THC treatment. These

findings suggest that there may be an inverse relationship between behavioral sensitivity

to THC and THC-induced changes to D2/D3 receptor availability. These findings could

also imply that higher daily doses of THC may be required to produce reductions in

D2/D3 receptor availability. Despite showing no changes in D2/D3 receptor availability in

the striatum, recent PET studies have shown reduced amphetamine-elicited striatal

dopamine release in cannabis abusers (Volkow et al., 2014; Van Giessen et al., 2016).

These results suggest that striatal dysfunction is a component of cannabis abuse, albeit

to a lesser extent perhaps than other drugs of abuse, and may have implications in THC-

induced cognitive impairment.

123

In summary, the present study demonstrated that chronic THC exposure

produced persistent and selective impairment to aspects of working memory by using a

longitudinal, within-subject study design in nonhuman primates. These data suggest that

cognitive impairments in cannabis dependent patients, particularly those involving

working memory, may not be the result of innate differences in cognitive function, but

rather a vulnerable, pharmacological-induced target that can be harnessed for

therapeutic value.

124

REFERENCES

Aharonovich E, Brooks AC, Nunes EV, Hasin DS (2008): Cognitive deficits in marijuana

users: Effects on motivational enhancement therapy plus cognitive behavioral

therapy treatment outcome. Drug Alcohol Depend 95:279-283.

Aharonovich E, Hasin DS, Brooks AC, Liu X, Bisaga A, Nunes EV (2006): Cognitive

deficits predict low treatment retention in cocaine dependent patients. Drug

Alcohol Depend 81:313-322.

Albrecht DS, Skosnik PD, Vollmer JM, Brumbaugh MS, Perry KM, Mock BH, et al.

(2013): Striatal D(2)/D(3) receptor availability is inversely correlated with

cannabis consumption in chronic marijuana users. Drug Alcohol Depend 128:52-

57.

Anderson, B. M., Rizzo, M., Block, R. I., Pearlson, G. D., & O’Leary, D. S. (2010): Sex,

drugs, and cognition: effects of marijuana. Journal of Psychoactive Drugs 42:

413–424.

Batalla A, Bhattacharyya S, Yücel M, Fusar-Poli P, Crippa JA, Nogué S, Torrens M,

Pujol J, Farré M, Martin-Santos R (2013): Structural and functional imaging

studies in chronic cannabis users: a systematic review of adolescent and adult

findings. PLoS One 8: e55821.

Beardsley PM, Balster RL, and Harris LS (1986): Dependence on tetrahydrocannabinol

in rhesus monkeys. J Pharmacol Exp Ther 239:311-319.

Bettinardi V (1999): An automatic classification technique for attenuation correction in

positron emission tomography. Eur J Nucl Med 26:447–458.

Block RI, O’Leary DS, Hichwa RD, Augustinack JC, Boles Ponto LL, et al. (2002):

Effects of frequent marijuana use on memory-related regional cerebral blood

flow. Pharmacol Biochem Behav 72: 237–250.

Bolla KI, Brown K, Eldreth D, Tate K, Cadet JL (2002): Dose-related neurocognitive

125

effects of marijuana use. Neurology 59:1337-1343.

Bolla KI, Eldreth DA, Matochik JA, Cadet JL (2005): Neural substrates of faulty decision-

making in abstinent marijuana users. Neuroimage 26: 480–492.

Carroll KM, Kiluk BD, Nich C, Babuscio TA, Brewer JA, Potenza MN, et al. (2011):

Cognitive function and treatment response in a randomized clinical trial of

computer-based training in cognitive-behavioral therapy. Substance use and

misuse 46:23–34.

Crane NA, Schuster RM, Fusar-Poli P, et al. (2013): Effects of cannabis on

neurocognitive functioning: recent advances, neurodevelopmental influences,

and sex differences. Neuropsychol Rev. 23:117–37.

Crean, RD., Crane, NA, and Mason, BJ (2011): An evidence based review of acute and

long-term effects of cannabis Use on executive cognitive functions. Journal of

Addiction Medicine. 5(1), 1–8.

Deneau, GA and Kaymakcalan, S (1971): Physiological and psychological dependence

to synthetic delta-9-tetrahydocannabinol (THC) in rhesus monkeys.

Pharmacologist 13:246.

Dias R, Robbins TW, Roberts AC (1996a): Dissociation in prefrontal cortex of affective

and attentional shifts. Nature 380: 69-72.

Dias R, Robbins TW, Roberts AC (1996b): Primate analogue of the Wisconsin Card

Sorting Task: effects of excitotoxic lesions in the prefrontal cortex in the

marmoset. Behav Neurosci 110: 872-76.

D’Souza, D. C., Braley, G., Blaise, R., Vendetti, M., Oliver, S., Pittman, B., et al. (2008):

Effects of haloperidol on the behavioral, subjective, cognitive, motor, and

neuroendocrine effects of Delta-9-tetrahydrocannabinol in humans.

Psychopharmacology 198: 587–603.

Eldreth DA, Matochik JA, Cadet JL, Bolla KI (2004): Abnormal brain activity in prefrontal

126

brain regions in abstinent marijuana users. Neuroimage 23: 914– 920.

Fehr, C, Yakushev, I, Hohmann, N, Buchholz, HG, Landvogt, C, Deckers, H, et al.

(2008): Association of low striatal dopamine d2 receptor availability with nicotine

dependence similar to that seen with other drugs of abuse. Am. J. Psychiatry

165: 507–514.

Fisk JE and Montgomery C (2008): Real-world memory and executive processes in

cannabis users and non-users. Journal of Psychopharmacology 22:727-736.

Ghahremani DG, Lee B, Robertson CL, Tabibnia G, Morgan AT, De Shetler N, et al.

(2012): Striatal dopamine D₂/D₃ receptors mediate response inhibition and

related activity in frontostriatal neural circuitry in humans. J Neurosci 32:7316-

7324.

Ginsburg BC, Hruba L, Zaki A, Javors MA, McMahon LR (2014): Blood levels do not

predict behavioral or physiological effects of Δ⁹-tetrahydrocannabinol in rhesus

monkeys with different patterns of exposure. Drug Alcohol Depend 139:1-8.

Gould RW, Garg PK, Garg S, Nader M.A. Effects of nicotinic acetylcholine receptor

agonists on cognition in rhesus monkeys with a chronic cocaine self-

administration history. Neuropharmacology. 64: 479-488, 2013.

Grant, JE, Chamberlain, SR, Schreiber, L, and Odlaug, BL (2011): Neuropsychological

deficits associated with cannabis use in young adults. Drug and Alcohol

Dependence 121: 159-162.

Groman SM and Jentsch JD (2012): Cognitive control and the dopamine D₂-like

receptor: a dimensional understanding of addiction. Depress Anxiety 29:295-306.

Gruber SA and Yurgelun-Todd DA (2005): Neuroimaging of marijuana smokers during

inhibitory processing: a pilot investigation. Brain Res Cogn Brain Res 23:107–

118.

127

Hanson KL, Winward JL, Schweinsburg AD, Medina KL, Brown SA, Tapert SF (2010):

Longitudinal study of cognition among adolescent marijuana users over three

weeks of abstinence. Addict Behav. Nov;35(11):970-6.

Hart CL, van Gorp W, Haney M, et al. (2001): Effects of acute smoked marijuana on

complex cognitive performance. Neuropsychopharmacology 25:757–765.

Hart, C. L., Ilan, A. B., Gevins, A., Gunderson, E. W., Role, K., Colley, J., et al. (2010).

Neurophysiological and cognitive effects of smoked marijuana in frequent users.

Pharmacology, Biochemistry, and Behavior, 96(3), 333–341.

Harvey, MA, Sellman, JD, Porter, RJ, and Frampton, CM (2007): The relationship

between non-acute adolescent cannabis use and cognition. Drug and Alcohol

Review 26:309-319.

Heishman SJ, Arasteh K, Stitzer ML (1997): Comparative effects of alcohol and

marijuana on mood, memory, and performance. Pharmacol Biochem Behav

58:93-101.

Hermann D, Sartorius A, Welzel H, Walter S, Skopp G, Ende G, et al. (2007):

Dorsolateral prefrontal cortex N acetylaspartate/total creatine (NAA/tCr) loss in

male recreational cannabis users. Biol Psychiatry 61:1281-1290.

Hollister LE, Gillespie HK, Ohlsson A, Lindgren JE, Wahlen A, Agurell S (1981): Do

plasma concentrations of delta 9-tetrahydrocannabinol reflect the degree of

intoxication? J Clin Pharmacol 21 (8-9 Suppl.): 171S-177S.

Jacobsen, LK, Mencl, WE, Westerveld, M, and Pugh, KR (2004): Impact of cannabis use

on brain function in adolescents. Annals of the New York Academy of Sciences

1021:384–390.

Jacobsen LK, Pugh KR, Constable RT, Westerveld M, Mencl WE (2007): Functional

correlates of verbal memory deficits emerging during nicotine withdrawal in

abstinent adolescent cannabis users. Biol Psychiatry 61:31-40.

128

Jager, G, Block, RI, Luijten, M, and Ramsey, NF (2010). Cannabis use and memory

brain function in adolescent boys: a cross-sectional multicenter functional

magnetic resonance imaging study. Journal of the American Academy of Child

and Adolescent Psychiatry 49: 561–572.

Jentsch JD, Pennington ZT (2014): Reward, interrupted: Inhibitory control and its

relevance to addictions. Neuropharmacology 76: 479-486.

John WS, Newman AH, Nader MA (2015): Differential effects of the dopamine D3

receptor antagonist PG01037 on cocaine and methamphetamine self-

administration in rhesus monkeys. Neuropharmacology 92:34-43.

Jones B and Mishkin M (1972): Limbic lesions and the problem of stimulus

reinforcement associations. Exp Neurol 36:362–377.

Kanayama G, Rogowska J, Pope HG, Gruber SA, Yurgelun-Todd DA (2004): Spatial

working memory in heavy cannabis users: a functional magnetic resonance

imaging study. Psychopharmacology 176:239-247.

Kangas BD, Leonard MZ, Shukla VG, Alapafuja SO, Nikas SP, Makriyannis A, et al.

(2016): Comparisons of Δ9-Tetrahydrocannabinol and Anandamide on a Battery

of Cognition-Related Behavior in Nonhuman Primates. J Pharmacol Exp Ther

357:125-33.

Kinahan PE and Rogers JG (1989): Analytic 3-D image reconstruction using all detected

events. IEEE Trans Nucl Sci 36:964–968.

Kirk JM, de Wit H (1999): Responses to oral delta9-tetrahydrocannabinol in frequent and

infrequent marijuana users. Pharmacol Biochem Behav 63:137-142.

Lichtman AH and Martin BR (2005): Cannabinoid tolerance and dependence. Handb

Exp Pharmacol 168:691-717.

Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, et al. (1990):

Graphical analysis of reversible radioligand binding from time- activity

129

measurements applied to [N-11C-methyl]-(−)-cocaine PET studies in human

subjects. J Cereb Blood Flow Metab 10:740–747.

McDonald, J, Schleifer, L, Richards, JB, and de Wit, H (2003): Effects of THC on

behavioral measures of impulsivity in humans. Neuropsychopharmacology 28:

1356-1365.

McMahon LR (2011): Chronic Δ⁹-tetrahydrocannabinol treatment in rhesus monkeys:

differential tolerance and cross-tolerance among cannabinoids. Br J Pharmacol.

162:1060-73.

Medina KL, Hanson KL, Schweinsburg AD, Cohen-Zion M, Nagel BJ, Tapert SF (2007):

Neuropsychological functioning in adolescent marijuana users: subtle deficits

detectable after a month of abstinence. J Int Neuropsychol Soc 13:807-820.

Mishkin M and Manning FJ (1978): Non-spatial memory after selective prefrontal lesions

in monkeys. Brain Res 143:313–323.

Pope HG, Jr, Yurgelun-Todd D (1996): The residual cognitive effects of heavy marijuana

use in college students. JAMA 275:521-527.

Ramaekers JG, Kauert G, van Ruitenbeek P, Theunissen EL, Schneider E, Moeller MR

(2006): High-potency marijuana impairs executive function and inhibitory motor

control. Neuropsychopharmacology 31:2296-2303.

Ramaekers JG, Kauert G, Theunissen EL, Toennes SW, Moeller MR (2009):

Neurocognitive performance during acute THC intoxication in heavy and

occasional cannabis users. J Psychopharmacol 23:266–277.

Ramaekers JG, Theunissen EL, de Brouwer M, Toennes SW, Moeller MR, Kauert G

(2011): Tolerance and cross-tolerance to neurocognitive effects of THC and

alcohol in heavy cannabis users. Psychopharmacology (Berl) 214:391–401.

Rezapour T, DeVito EE, Sofuoglu M, Ekhtiari H (2016): Perspectives on neurocognitive

rehabilitation as an adjunct treatment for addictive disorders: From cognitive

130

improvement to relapse prevention. Prog Brain Res 224:345-69.

SAMSHA. Results from the 2013 National Survey on Drug Use and Health: National

Findings. Rockville, MD: Office of Applied studies, DHHS; 2013.

Sofuoglu, M, DeVito, EE, Waters, AJ, & Carroll, KM (2013): Cognitive Enhancement as a

Treatment for Drug Addictions. Neuropharmacology 64: 452-463.

Solowij N (1995): Do cognitive impairments recover following cessation of cannabis use?

Life Sciences 56:2119-2126.

Solowij N, Michie PT, Fox AM (1995): Differential impairments of selective attention due

to frequency and duration of cannabis use. Biol Psychiatry 37:731-739.

Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, et al. (2002):

Cognitive functioning of long-term heavy cannabis users seeking treatment.

JAMA 287:1123-31. Erratum in: JAMA2002 Apr 3;287(13):1651.

Solowij, N, Jones, KA, Rozman, ME, Davis, SM, Ciarrochi, J, Heaven, PC, et al. (2011):

Verbal learning and memory in adolescent cannabis users, alcohol users and

non-users. Psychopharmacology 216: 131–144.

Stewart JL and McMahon LR (2010): Rimonabant-induced Δ9-THC withdrawal in rhesus

monkeys: discriminative stimulus effects and other withdrawal signs. J

Pharmacol Exp Ther. 334: 347-356.

Stokes PR, Egerton A, Watson B, Reid A, Lappin J, Howes OD, et al. (2012): History of

cannabis use is not associated with alterations in striatal dopamine D2/D3

receptor availability. J Psychopharmacol 26:144-149.

Taffe MA (2012): Δ⁹Tetrahydrocannabinol impairs visuo-spatial associative learning and

spatial working memory in rhesus macaques. J Psychopharmacol 26:1299-306.

Tomasi D, Volkow ND (2013): Striatocortical pathway dysfunction in addiction and

obesity: differences and similarities. Crit Rev Biochem Mol Biol 48:1-19.

Urban NB, Slifstein M, Thompson JL, Xu X, Girgis RR, Raheja S, et al. (2012):

131

Dopamine release in chronic cannabis users: a [11c]raclopride positron emission

tomography study. Biol Psychiatry 71:677-683.

van de Giessen E, Weinstein JJ, Cassidy CM, Haney M, Dong Z, Ghazzaoui R, et al.

(2016): Deficits in striatal dopamine release in cannabis dependence. Mol

Psychiatry In press.

Volkow ND, Fowler JS, Wang GJ, Hitzemann R, Logan J, Schlyer DJ, et al (1993):

Decreased dopamine d2 receptor availability is associated with reduced frontal

metabolism in cocaine abusers. Synapse 14:169–177.

Volkow N, Gur R, Wang G, Fowler J, Moberg P, Ding Y, et al. (1998): Association

between decline in brain dopamine activity with age and cognitive and motor

impairment in healthy individuals. Am J Psychiatry 155:344–349.

Volkow ND, Wang GJ, Fowler JS, Tomasi D (2012): Addiction circuitry in the human

brain. Annu Rev Pharmacol Toxicol 52:321-336.

Volkow ND, Wang GJ, Telang F, Fowler JS, Alexoff D, Logan J, et al. (2014): Decreased

dopamine brain reactivity in marijuana abusers is associated with negative

emotionality and addiction severity. Proc Natl Acad Sci U S A 111:3149-3156.

Whitlow CT, Liguori A, Livengood LB, Hart SL, Mussat-Whitlow BJ, Lamborn CM, et al.

(2004): Long-term heavy marijuana users make costly decisions on a gambling

task. Drug Alcohol Depend 76:107-11.

Wilson FA, Scalaidhe SP, Goldman-Rakic PS (1993): Dissociation of object and spatial

processing domains in primate prefrontal cortex. Science 260:1955–1958.

Wright MJ Jr, Vandewater SA, Parsons LH, Taffe MA (2013): Δ(9)Tetrahydrocannabinol

impairs reversal learning but not extra-dimensional shifts in rhesus macaques.

Neuroscience 235:51-58.

132

CHAPTER IV

DISCUSSION

Recent epidemiological data report that nearly 3 of every 10 marijuana users,

approximately 6.8 million Americans, have a diagnosis of marijuana use disorder (Hasin

et al., 2015); however, there is currently no FDA-approved pharmacotherapy to treat

cannabis dependence and abuse and behavioral modification strategies are only

modestly effective (Nordstrom and Levin, 2007). Thus, more basic research is needed in

order to improve our understanding of the neurobiological and behavioral mechanisms

underlying the effects of THC, the main psychoactive component of marijuana. A better

understanding of these variables will aid in the development of new treatments for

maintaining abstinence in patients with cannabis use disorder. Preclinical studies with

other drugs of abuse demonstrate that a multitude of factors govern their contingent

delivery such as dose, behavioral history, drug history, and schedule of reinforcement.

These findings have been instrumental in shaping the current understanding of drug

abuse as a disease process and for guiding more effective treatment strategies. In this

regard, a major obstacle preventing such progress for cannabis abuse has been the

difficulty in demonstrating the reinforcing effects of THC in self-administration (SA)

animal models. Thus, the work described in this dissertation contributed to furthering our

understanding of the behavioral mechanisms involved in the reinforcing effects of THC

that has implications for 1) potential risk factors that may predict vulnerability to cannabis

abuse, 2) the variables that control cannabis use in humans that could be amenable to

therapeutic intervention and 3) parametric manipulations that can be used to advance

the cannabinoid self-administration methodology in preclinical animal models.

In addition to the reinforcing effects, a better understanding of the neurocognitive

effects of THC will also aid in the improvement of treatment strategies for cannabis

dependent patients. For instance, long-term marijuana use has been associated with

133

deficits in executive function across multiple domains integral in facilitating behavioral

modification including behavioral flexibility and memory (Bolla et al., 2002; Pope and

Yurgelun-Todd, 1995, 1996; Solowij e al., 1995, 2002). Moreover, cognitive function at

the time of treatment initiation has correlated with treatment success (Aharonovich et al.,

2003, 2006, 2008; Carroll et al., 2011). As a result, cognitive enhancement has been

proposed as a strategy to improve behavioral modification practices to treat substance

abuse, including cannabis use disorder (Sofuoglu et al., 2013). However, the exact

nature and time course of THC-induced deficits to executive function are unclear (Crean

et al., 2011; Crane et al., 2013). Through the use of a longitudinal, within-subjects study

design in nonhuman primates, the second major contribution of the work described in

this dissertation provided insight on the specificity and temporal nature of chronic THC

exposure on cognitive function. This information can be used to direct more targeted

therapies aimed at enhancing cognitive function in patients with cannabis use disorder.

These results, taken together, provide a better understanding of not only how

THC exerts its control over behavior, but also the resulting consequences of chronic

exposure, namely on cognitive functioning.

Cannabinoid self-administration in nonhuman primates

Reliable and persistent SA in experimental animals has been demonstrated for

virtually every drug that is abused by humans including psychostimulants, opiates,

alcohol, and nicotine. Thus, the SA procedure has strong face validity to the human

condition. However, the one drug class that has functioned as a false negative in the

drug self-administration procedure by a majority of laboratories is the cannabinoids. It is

important to establish the conditions for demonstrating the reinforcing effects of

cannabinoids through the SA procedure for multiple reasons. First, the SA procedure

provides a baseline of drug-reinforced behavior that can be used to better understand

134

the environmental, biological, and pharmacological determinants of the reinforcing

effects of drugs. This information can be useful for understanding both the factors that

render one more susceptible to the abuse-related effects of THC as well as those that

can be used to mitigate the reinforcing effects of THC in order to facilitate abstinence.

Secondly, development of cannabinoid SA will be important because of its utility to

screen novel cannabinoid compounds for abuse liability and their potential scheduling as

controlled substances by the DEA (see Ator and Griffiths, 2003; Carter and Griffiths,

2009). As such, there is an increasing interest for the use of cannabis and other

cannabinoids for medicinal purposes to relieve symptoms associated with glaucoma,

nausea in patients undergoing chemotherapy, AIDS-associated anorexia, chronic pain,

inflammation, multiple sclerosis, and epilepsy (Hill, 2015). However, the abuse liability of

THC and other cannabinoids raises particular concerns for long-term use by vulnerable

populations. In this regard, the preclinical SA model as an abuse liability assessment will

be an important component for their future development and ultimate utility of novel

cannabinoid compounds in the clinical context.

As described in Chapter 1, currently, successful efforts at demonstrating the

reinforcing effects of THC and other cannabinoids by the SA procedure is limited to one

laboratory in the world using squirrel monkeys and parameters not previously employed

in rhesus monkeys or rodents including schedule of reinforcement, a broader range of

doses, faster injection speed, and a clear drug solution rather than a drug in suspension

(Tanda et al., 2000). However, the extension of cannabinoid SA procedures to rhesus

monkeys is highly desirable because this species is phylogenetically closer to humans

than squirrel monkeys, and would aid in the most accurate translation of results. It is also

important to extend the characterization of cannabinoid SA to other species in addition to

squirrel monkeys and the use of rhesus monkeys will allow for the systematic

manipulation of environmental (e.g., schedule of reinforcement) and pharmacological

135

(e.g., chronic treatment) variables using a within-subjects, longitudinal design over

years. Such information will allow for a better understanding of the critical variables

necessary to establish cannabinoid SA that may extend to other species, including

rodent models.

However, it is currently unknown whether previous unsuccessful attempts in

rhesus monkeys were due to methodology or a direct species difference between

squirrel and rhesus monkeys. To address this, in Chapter 2, THC was made available to

rhesus monkeys under similar SA methodological conditions as used in the successful

demonstrations in squirrel monkeys, including dose range, schedule of reinforcement,

and vehicle preparation. Moreover, the reinforcing effects of THC were compared to

reinforcing effects of the synthetic bicyclic cannabinoid analog, CP 55,940, a high

efficacy CB1/CB2 agonist, which allowed for a better understanding of the role of

pharmacological efficacy at the CB receptor in SA, such as differences in potency and

magnitude of response. We hypothesized that the reinforcing effects of the synthetic

cannabinoid receptor agonist CP55,940 and THC will be established by utilizing

methods that have shown success for demonstrating the reinforcing effects of

cannabinoids in squirrel monkeys.

The results from these studies indicated that CP55,940, but not THC, functioned

as a reinforcer in a subset (3 of 8) of rhesus monkeys when studied under similar

experimental conditions that have been used to demonstrate cannabinoid reinforcement

in squirrel monkeys. Notably, the doses of CP55,940 that had reinforcing effects were

considerably lower than the doses tested in the only other study assessing the

reinforcing effects of this compound in rhesus monkeys, which was unsuccessful

(Mansbach et al., 1994). As described below, one goal from this initial assessment was

to determine differences in biomarkers related to the three monkeys that showed

CP55,940 reinforcement versus the five that did not.

136

The results from this part of the dissertation have several important implications.

First, these experiments demonstrated, for the first time, reinforcing effects of a

cannabinoid receptor agonist (i.e. CP55,940) without the use of induction procedures

such as food deprivation, forced automatic injections, or the development of physical

dependence. Although reinforcing effects were not achieved in all subjects, a surprising

discovery was the dose range that was required for the subjects that did demonstrate

reinforcing effects. These data can be especially useful for comparing the potency of

CP55,940 reinforcing effects to other behavioral effects of CP55,940 in nonhuman

primates. For example, the discriminative stimulus effects of i.v. CP55,940 in rhesus

monkeys occurred at a dose of 0.003 mg/kg (McMahon, 2006), which is 75 - 2,400-fold

higher than the unit doses of CP55,940 that had reinforcing effects in the 3 monkeys in

the present study. The enormous differences in potency between the reinforcing and

discriminative stimulus effects of CP55,940 remain an intriguing area for future

investigation.

Moreover, the SA data generated with CP55,940 provide an important foundation

that future studies can build on, especially with regard to the range of doses that may be

used as a starting point to demonstrate reinforcing effects of this compound. Although

CP55,940 SA may have limited utility as a screening procedure for the assessment of

cannabinoid abuse liability (since the reinforcing effects of THC, as a positive control,

were not also achieved in the same subjects that had reinforcing effects of CP55,940), it

would still provide valuable information on the reinforcing effects that may be extended

to other synthetic cannabinoids, which are becoming increasingly abused (Rosenbaum

et al., 2012; Johnson et al., 2013; Nelson et al., 2014).

Furthermore, these studies have important implications because they allow for a

direct comparison of THC SA between squirrel monkeys and rhesus monkeys. The fact

that THC reinforcement was not obtained in rhesus monkeys using the same schedule

137

of reinforcement, dose range, and vehicle used in THC SA studies with squirrel monkeys

suggest that inherent species differences may be the biggest determinant regarding the

expression of the reinforcing effects of THC under these conditions. Interestingly, other

atypical reinforcers such as nicotine and response-contingent shock presentation also

seem to be more effective reinforcers in squirrel monkeys compared to other species of

nonhuman primates (e.g., rhesus monkeys); however, the potency and efficacy of drugs

such as cocaine, methamphetamine, and heroin to maintain behavior are similar

between Old- (e.g., rhesus monkey) and New- (e.g., squirrel monkey) World primate

species. Therefore, to address the factors underlying the qualitative differences in the

reinforcing effects of cannabinoids across species, future studies should systematically

investigate the behavioral, physiological, and pharmacokinetic effects of THC between

species, using squirrel monkeys as a positive control. These data will provide valuable

information regarding the factors that may correlate with the reinforcing effects of

cannabinoids that can then be accounted for by future cannabinoid SA studies in order

to optimize the model for more widespread use.

Behavioral and Physiological phenotypes underlying the reinforcing effects of

cannabinoids

Another important contribution of the work presented in this dissertation was the

characterization of behavioral and physiological phenotypes underlying the individual

differences in cannabinoid SA. The characterization of such factors are important for

predicting drug effects and for understanding what renders certain individuals more

vulnerable to the abuse-related effects of marijuana. Ultimately, this information can be

used to generate more targeted treatment and prevention strategies. In addition, a better

understanding of the individual differences in cannabinoid SA will be helpful for

optimizing the SA model for more widespread use in laboratory animals. One such

138

variable that was investigated in Chapter 2 was the relationship between behavioral

sensitivity to CP55,940 and THC and the ability of those drugs to function as reinforcers.

It was hypothesized that individual subjects less sensitive to the rate-decreasing effects

of CP55,940 and THC will have greater rates of SA of both drugs. To test this

hypothesis, the potency of both drugs to decrease food-maintained responding was

correlated with rates of SA in each subject.

It was found that there was an inverse relationship between the reinforcing

effects of CP55,940 and its behavioral-disruptive effects (Chapter 2, figure 4). Such an

analysis was precluded for THC considering it did not function as a reinforcer in any

subject. When administered non-contingently, CP55,940 was ~ 15-times more potent

than THC; when studied in SA, doses of THC overlapped this potency relationship in

order to assure a broad range of doses that encompassed no behavioral effects to

doses that non-contingently decreased operant behavior. Nevertheless, these data

indicate a novel variable (i.e., sensitivity to rate-decreasing effects) involved in the

reinforcing effects of cannabinoids that has never been empirically validated. The

significance of this finding indicates that 1) this is a factor that may predispose certain

cannabis users to subsequent abuse and dependence and 2) this is a factor could be

accounted for in animal models in order to obtain cannabinoid SA at greater levels and

in more subjects.

Studies in humans have indicated other behavioral and biological phenotypes

that may predict vulnerability to cannabis abuse and dependence involving personality,

subjective effects, and genetic factors (Hurd et al., 2014). For instance, genes encoding

the cannabinoid type 1 receptor CB1R (CNR1) and fatty acid amide hydrolase (FAAH),

an enzyme responsible for the hydrolysis of the endogenous cannabinoid anandamide,

have been shown to modulate risk for developing cannabis dependence (Agrawal et al.,

2009; Hopfer et al., 2006; Tyndale et al., 2007). Other studies indicate that personality

139

traits such as neuroticism, anxiety, and depression are risk factors associated with

cannabis dependence. Moreover, in population based cohorts, increased risk of

cannabis dependence has been associated with self-reported subjective effects of

cannabis use such that positive effects (e.g., feeling energetic, creative, euphoric, and

sociable) were associated with cannabis abuse and dependence and negative effects

(e.g., feeling confused, paranoid, and anxious) were associated with shorter duration

and lower frequency of cannabis use (Lyons et al., 1997; Grant et al., 2005; Scherrer et

al., 2009). Although informative, the interpretation of these findings are complicated due

retrospective design, reliance on self-report, polysubstance use, and uncontrolled

exposure to THC. Thus, the present results represent the first empirical evidence in

animal models indicating a relationship between behavioral sensitivity and subsequent

SA of cannabinoids. These findings emphasize the role of individual differences involved

in the reinforcing effects of THC and suggest that the behavioral sensitivity to THC,

during the early stages of drug use, may form part of a liability for future cannabis use

and abuse.

Another phenotype that was found to predict the cannabinoid reinforcing effects,

specifically CP55,940, was the individual subject’s drug history. In particular, it was

found that three of the four monkeys that had an extensive methamphetamine SA history

were the subjects in whom CP55,940 functioned as a reinforcer. Previous studies in

squirrel monkeys have demonstrated that a drug SA history was not necessary for

acquiring robust levels of THC self-administration in that drug-naïve subjects with no

drug SA experience acquired and maintained THC SA at similar, if not higher, levels

than subjects with an extensive history of self-administering cocaine (Justinova et al.,

2003). The studies in Chapter 2 also found no relationship between previous SA history

and the acquisition of THC SA (only one subject with a history of methamphetamine SA

acquired THC SA but only after daily treatment), therefore, the relationship between

140

methamphetamine SA history and the acquisition of cannabinoid SA appears to be

specific to CP 55,940. The mechanisms underlying this relationship and why it is specific

to the synthetic full cannabinoid receptor agonist (i.e., CP55,940), however, remains to

be determined. One possibility may involve specific neurobiological adaptations induced

by a chronic methamphetamine SA history and the involvement of those changes in

enhancing the reinforcing effects of a full agonist at the cannabinoid receptor. For

instance, we have previously reported that monkeys with a methamphetamine SA

history demonstrated increased sensitivity to the unconditioned behavioral effects of the

dopamine D3 receptor preferring agonist quinpirole relative to drug-naïve monkeys or

those that had a cocaine SA history (Martelle et al., 2014). It has also been shown that

THC and synthetic CB1 agonists potentiate some behavioral effects (e.g.

hyperlocomotion) of quinpirole (Gorriti et al., 2005; Moreno et al., 2005). Taken together,

one might hypothesize that sensitivity at the D3 receptor is positively related to

cannabinoid SA. However, the present study found no relationship between the

unconditioned behavioral effects of quinpirole and CP55,940 SA, which suggests

another methamphetamine-induced neuroadaptation could be involved. A better

understanding of the relationship between previous drug history and qualitative and

quantitative differences in cannabinoid SA may provide novel insight into the

mechanisms involved in mediating the reinforcing effects of cannabinoids.

Effects of attenuating the rate-decreasing effects of THC on its reinforcing effects

In order to extend this observation to factors that may be inhibiting the

expression of THC’s reinforcing effects in experimental animals, monkeys were treated

daily with THC in order to develop tolerance to the rate-decreasing effects of non-

contingent THC on food-maintained responding. After daily treatment, THC SA was

reassessed and was found to function as a reinforcer in 3 subjects. Thus, despite daily

141

THC treatment in all eight monkeys, our hypothesis was confirmed in only three

monkeys. Interestingly, in one monkey in whom THC functioned as a reinforcer,

tolerance did not develop to the rate-decreasing effects prior to reassessment

suggesting that another factor resulting from the daily treatment was responsible for

uncovering the reinforcing effects of THC in these subjects. At present, there are no

substantial observational differences in those three monkeys compared to the other five,

but identification of potential biomarkers, including perhaps changes in CB1 receptor

density and function (as measured by PET) may provide further insight into vulnerable

phenotypes.

Furthermore, THC self-administration was also assessed in a separate group of

monkeys responding under a second-order schedule of reinforcement that used

response-contingent conditioned stimuli to maintain behavior by relatively few drug

injections spaced between long intervals of time. It was hypothesized that reducing the

frequency of drug injection would allow time for rate-decreasing effects to dissipate while

the presence of conditioned stimuli within those intervals would help to maintain high

rates of behavior leading to subsequent injections. Under these schedule contingencies,

THC functioned as a reinforcer in two of four monkeys.

The failure of THC to function as a reinforcer in the majority of subjects following

the development of tolerance to rate-decreasing effects and by accounting for those

effects through the use of a second-order schedule does not support our hypothesis and

suggest other factors may be involved, as described in Chapter 2. This is not to say,

however, that rate-decreasing effects have no role in influencing the reinforcing effects

of cannabinoids. The fact that reinforcing effects were achieved in a subset of subjects

suggest that attenuating the rate-decreasing effects is a feasible strategy but the

methods used in Chapter 2 may not have been the most appropriate. The following

142

sections will discuss other strategies to further investigate the reinforcing effects of

cannabinoids in the context of accounting for their simultaneous rate-decreasing effects.

Other schedules of reinforcement

Similar to animal models, the reinforcing effects of THC were also surprisingly

not reliably demonstrated in early laboratory studies in human subjects. For instance,

early studies showed that cigarettes containing THC were not self-administered more

than placebo under free-access conditions (Zacny and de Wit, 1991; Kelly et al., 1994a,

b). Early studies also could not demonstrate a systematic relationship between THC

dose and the amount self-administered (Chait, 1989), despite dose-related differences in

ratings of intoxication or “high.” However, one approach that was later implemented to

detect dose-related reinforcing effects of THC in human subjects was the use of a

discrete-trials choice procedure in which subjects were offered a choice between

obtaining a particular dose of THC and obtaining a non-drug alternative (Mendelson and

Mello, 1984; Chait and Zacny, 1992; Chait and Burke, 1994; Kelley et al., 1994; Haney

et al., 1997; Hart et al., 2005). Under these conditions, a subject first sampled a dose of

THC and its matching placebo, and on a subsequent occasion the subject chose which

of the two to self-administer. The frequency with which the active drug was chosen over

placebo provided an index of the reinforcing efficacy of the drug.

Chait and Zacny (1992) provided some of the first evidence of THC’s reinforcing

effects using a choice procedure. Here, a two-trial choice procedure was used in which

experienced marijuana smokers sampled active drug and placebo during separate

sessions and then on two subsequent sessions were required to choose between these

two options. They found that ten of ten participants chose to self-administer smoked

THC (2.3%) on both trials and ten of eleven participants chose to self-administer oral

THC (10-15 mg based their subjective responses during practice sessions) on both

143

trials, i.e., they chose active drug on ∼90% of choice opportunities. Further, Haney et al.

(1997) compared choice between marijuana cigarettes (0, 2.2, 3.9% THC w/w) and

snack food where multiple choices were available each day, i.e., each concentration of

THC was made available on 4 days with each day consisting of one sample trail and six

choice trials. It was found that the participants selected cigarettes containing THC on

more than 75% of choice opportunities compared to only about 40% when placebo

cigarettes were available. Other studies have also shown that marijuana cigarettes with

a greater THC content are consistently preferred to cigarettes containing lower amount

of THC, even when the choice between them was not mutually exclusive (Haney et al.,

1997; Kelly et al., 1997; Ward et al., 1997).

Despite the success of choice procedures to detect the reinforcing effects of THC

in human subjects, there are surprisingly no published reports attempting to use this

method to demonstrate the reinforcing effects of THC in laboratory animals. However,

many of the features inherent to choice procedures for demonstrating the reinforcing

effects of drugs may be particularly well suited for cannabinoids. For instance, one of the

major advantages of choice procedures is that this method permits the dissociation of

reinforcing effects from reinforcement-independent rate-decreasing effects because the

primary dependent variable is behavioral allocation (e.g., percent drug choice vs.

percent choice of a non-drug alternative) as opposed to rate of responding. As a result,

the dose-response curve under a choice schedule is represented as a monotonic

increase in frequency of drug choice with increases in dose because the high doses that

typically constitute the descending limb of dose-response function under single response

procedures are still chosen relative to a non-drug reinforcer (e.g. food pellets).

Therefore, this measure reinforcement (allocation of behavior away from a non-drug

144

reinforcer) may be more applicable for demonstrating the reinforcing effects of

cannabinoids because it would not be sensitive to the rate-decreasing effects of

cannabinoids that are hypothesized to inhibit rates of responding under single response

procedures.

Figure 1 shows a hypothetical dose-response curve for THC self-administration

under a concurrent THC-food choice procedure. Similar to what was demonstrated in

Chapter 2 when THC was initially made available for self-administration under a FR 10

schedule, it is likely that the number of injections of THC will not be very high relative to

vehicle across a similar dose range. However, at a certain dose of THC (e.g. 3.0 µg/kg),

there may be a reallocation of behavior where THC is now exclusively chosen over food

reinforcement, despite low rates of responding for those injections. If this were the case,

the reinforcing effects of THC would have been effectively demonstrated due to its

relative reinforcing efficacy compared to a non-drug alternative. Such an outcome would

suggest that perhaps we need to shift our perspective of viewing the reinforcing effects

of THC in terms of numbers of injections and rates of responding but instead to THC

choice in the presence of an alternative reinforcer.

145

Using other drugs that are “atypical reinforcers” in laboratory animals as models

for developing cannabinoid self-administration

Going forward, it will be of utmost importance to also study the methodology

used for demonstrating the reinforcing effects of other drugs that are relatively less

efficacious reinforcers in preclinical models in order to better understand potential

pharmacological and behavioral factors that may also apply to demonstrating the

reinforcing effects of cannabinoids. The reinforcing effects of nicotine in nonhuman

primates, for example, have been historically difficult to demonstrate and are largely

Figure 1. Hypothetical dose-response curve under a THC-food schedule of

reinforcement. Subjects chose between different doses of THC (0 – 10 µg/kg/injection) and food pellets under a concurrent FR 30:FR30 schedule of THC injections and food availability. Abscissa: unit dose of THC in micrograms per kilogram per injection. Left ordinate: percent THC choice relative to food pellets. Right ordinate: Number of injections earned per session.

0 0.3 1 3 10

0

25

50

75

100

0

2

4

6

8

10

Unit dose THC (µg/kg/injection)

% T

HC

ch

oic

e

Nu

mb

er o

f inje

ctio

ns

THC choice

Number of injections

146

dependent on several specific factors including the use intermittent schedules of

reinforcement, drug history, and repeated presentation of associated stimuli (Goldberg

and Henningfield, 1988). Similar to the hypothesis tested in Chapter 2 for THC, it has

been suggested that one of the reasons behind the difficulty in demonstrating the

reinforcing effects of nicotine in laboratory animals is because nicotine also has

prominent aversive effects that may be masking its reinforcing effects. For example,

studies in squirrel monkeys have shown that response-produced injections of the same

doses of nicotine (0.01 – 0.03 mg/kg/injection) that maintain behavior under a fixed-

interval (FI) or second-order schedules of reinforcement can also function effectively to

suppress food-maintained responding in squirrel monkeys under FR reinforcement

schedules (Goldberg and Spealman, 1982). Further, it has been shown that squirrel

monkeys will respond at high rates to postpone the scheduled delivery of the same

doses of nicotine (0.03 and 0.056 mg/kg/injection) that function as a positive reinforcer in

other studies with squirrel monkeys (Spealman and Goldberg, 1983). These examples

demonstrate that nicotine can function as both a reinforcer or punisher depending on the

conditions governing its delivery. Therefore, the experimental manipulations that have

been successful in producing the reinforcing effects of nicotine may have also been

successful in limiting its aversive effects to a degree that the reinforcing effects could be

unmasked. In this regard, studies of nicotine self-administration should serve as an

excellent resource for studying the reinforcing effects of cannabinoids as similar

behavioral mechanisms between these drug classes could be determining their ability to

maintain behavior. The following section will describe the conditions that are most

effective for demonstrating the reinforcing effects of nicotine and will also include a

discussion on how these principles could be applied to uncovering the reinforcing effects

of cannabinoids.

147

Early nicotine self-administration studies using FR 1 reinforcement schedules

resulted in very low rates of responding (0.008 – 0.005 responses/second) maintained

by nicotine injections (Deneau and Inoki, 1967; Hanson et al., 1979; Lang et al., 1977).

Although these rates of nicotine-maintained responding were higher than rates of

responding maintained by saline, they were relatively insensitive to changes in dose and

pretreatments to the nicotine antagonist mecamylamine. Thus, it could not be discounted

that nicotine-maintained responding in these studies were due to nonspecific rate-

increasing effects. In contrast, Goldberg and Spealman (1978) and Spealman and

Goldberg (1982) reported much higher rates of nicotine-maintained responding under an

FI 5-min schedule of reinforcement with a one-minute timeout. During acquisition, 0.03

mg/kg nicotine was made available for self-administration and the FI value was gradually

increased to 5 minutes. Another contingency during acquisition was also added in which

nicotine was automatically injected if a response did not occur within 2 minutes after the

interval elapsed. Once rates of responding were stable, the automatic injections were

discontinued and nicotine subsequently maintained stable rates of responding at

approximately 0.1 responses/second. Ator and Griffiths (1983) also studied nicotine self-

administration under an FI 5-minute, one-minute timeout schedule of reinforcement in

baboons. However, in contrast to the studies in squirrel monkeys by Goldberg and

Spealman, much lower rates (0.007 – 0.02 responses/second) of nicotine-maintained

responding were achieved, which were only marginally distinguishable from rates of

responding maintained by saline. The major methodological differences between these

two studies, despite species differences, was that Ator and Griffiths (1983) made

available a lower dose (0.01 mg/kg/injection) of nicotine during acquisition and the use of

non-contingent nicotine injections during the acquisition period were not employed.

Taken together, these studies demonstrated that interval-based schedules by

themselves were no more effective than low FR schedules of reinforcement for

148

demonstrating the reinforcing effects of nicotine in nonhuman primates. However, the

combination of a fixed-interval schedule with the implementation of forced, automatic

nicotine injections and a relatively higher training dose initially made available for self-

administration may have been the optimal conditions for demonstrating high levels of

nicotine-maintained responding. Moreover, several other sets of studies in squirrel

monkeys have demonstrated that nicotine can maintain even higher rates of responding

when studied under a second-order schedule of reinforcement (Goldberg et al., 1981;

Spealman and Goldberg, 1982). Under these conditions, completion of an FR 10 during

a 1-, 2-, or 5-minute interval produced a brief visual stimulus that had been paired with a

nicotine injection; the first ratio completed after the interval timed out then produced both

the visual stimulus and an i.v. injection of nicotine, which was followed by a 1- or 3-

minute timeout. Peak rates of responding maintained by nicotine ranged from 0.8 – 1.7

responses/second at a dose of 0.03 mg/kg/injection. Thus, the peak rates of nicotine-

maintained responding under the second-order contingencies were 8 - 17-fold higher

compared to what was produced under the fixed-interval schedule. Considering the

interval between consecutive nicotine injections was similar between the two schedules,

the higher overall rates produced by the second-order schedule were most likely

attributable to the presence of contingent conditioned stimuli.

Overall, these findings with nicotine SA have several important implications for

studying the reinforcing effects of cannabinoids in preclinical animal models. First of all,

a critical determinant of robust levels of nicotine-maintained responding was due to the

use of intermittent schedules of reinforcement where the frequency of reinforcement was

independent of the rate of responding. This is in contrast to ratio-based schedules of

reinforcement where the frequency of reinforcement is contingent on the rate of

responding. Importantly, nicotine SA under a second-order schedule, which employed

the use of conditioned stimuli, produced the most robust level of nicotine-maintained

149

behavior. These findings with nicotine parallel the findings in Chapter 2 (Figure 6) of this

dissertation in which the reinforcing effects of THC were demonstrated in some subjects

under a second-order schedule but in no subjects under an FR reinforcement schedule

(prior to chronic treatment). Considering both nicotine and THC have well documented

negative effects, the effectiveness of interval-based schedules of reinforcement,

particularly second-order schedules, may be due to their ability to limit these negative

effects that are probably more apparent from fast accumulation of drug intake. Moreover,

as the FI nicotine SA studies by Spealman and Goldberg (1982) and Ator and Griffiths

(1983) showed, the use of automatic, forced injections to facilitate acquisition of SA may

be a feature that can be added to future THC SA studies to engender even greater

levels of drug-maintained behavior. Although the principle behind this method is similar

to what was tested in Chapter 2 (i.e., developing tolerance to the rate-deceasing effects

in order to increase levels of SA), forced injections during the session could be a more

effective way to test this hypothesis.

The methods used to establish ethanol as an oral reinforcer in experimental

animals can also be used to recognize potential behavioral and environmental factors

that may determine the reinforcing effects of cannabinoids. Similar to cannabinoids,

ethanol does not readily initiate and maintain behavior in experimental animals despite

its clear reinforcing effects in humans. For instance, when ethanol is initially presented to

monkeys, they typically only drink low levels of ethanol and water is generally preferred

over even a small concentration (5%) of ethanol (Mello, 1973; Meisch, 1977). This

discrepancy has been attributed to a variety of factors including the taste of alcohol, the

delay between consumption and the onset of pharmacological effects, the volume

needed for a pharmacological effect, and negative effects of alcohol. As a result,

initiation procedures are commonly used to establish the reinforcing effects of ethanol in

laboratory animals. One of the most common ways to induce robust oral ethanol

150

consumption is through a schedule-induction procedure. Schedule-induced drinking

involves the intermittently scheduled presentation of one reinforcer that results in an

increase in the reinforcing effects of another stimulus concurrently available in the

environment. This procedure was first described by Falk (1961) in which he observed in

rats that the intermittent delivery of food pellets at fixed intervals of time was

accompanied by the consumption of large quantities of water as a form of adjunctive

behavior. Rats were maintained at 80 percent of their free-feeding weight and would

consume approximately one half of their total weight in water within a few hours while

food pellets were presented intermittently. Subsequent experiments showed that

explanations for this phenomenon were not attributable to thirst, as pre-session water

loading did not reduce polydipsic drinking (Falk, 1969), or by adventitious reinforcement

(Falk, 1964).

Since then, schedule-induced polydipsia has been widely used to address the

problem of demonstrating the reinforcing effects of ethanol in preclinical animal models.

The application of this procedure to induce high levels of ethanol drinking typically

involves food restricting an animal and then making a certain concentration of ethanol

available with small quantities of food delivered intermittently either contingently under

an FI or schedule or non-contingently under a fixed-time (FT) contingency. For instance,

in rodents, the optimal conditions typically involve an FT 2-min schedule of pellet

presentation to induce drinking of 5% v/v ethanol (Falk, 1966; Falk et al., 1972; Samson

and Falk, 1975). In cynomolgus monkeys, Shelton et al. (2001) determined that 4% (w/v)

ethanol and FT 5-min was the optimal schedule of food pellet delivery to induce

consumption of 1.0 g/kg ethanol. Other studies in monkeys have also demonstrated that

schedule-induced ethanol consumption can be produced without food restriction (Grant

and Johanson, 1988; Vivian et al., 2001). Furthermore, an important feature of this

method is that high concentrations of ethanol can maintain behavior following the

151

discontinuation of the scheduled delivery of food pellets (Vivian et al., 2001; Grant et al.,

2008).

Taken together, the effectiveness of schedule-induced procedures for

demonstrating the reinforcing effects of ethanol may represent an environmental factor

that is important for demonstrating the reinforcing effects of cannabinoids. Indeed,

schedule-induction has been used for intravenous SA of several drugs in rodents (Singer

et al., 1982) as well as for nicotine in nonhuman primates (Slifer, 1983; Slifer and

Balster, 1985). Only one study, however, has reported the use of a schedule-induction

procedure to study the reinforcing effects of THC (Takahasi and Singer, 1979). In this

study, THC (6.25-50 µg/kg) and vehicle were made available to rats under an FR 1

schedule of reinforcement during 1-hour sessions while food pellets were concurrently

delivered under an FT 1-min reinforcement schedule. This study included four groups of

rats, in which THC SA was assessed both in the presence or absence of the schedule-

induction procedure and at either 80% reduced body weight or at free-feeding weight. It

was found that reinforcing effects of THC were achieved only in the food-restricted

animals tested on an FT 1-min schedule. The peak number of infusions, 15.14 ± 4.31,

was maintained by 125 µg/kg, which was significantly different than the number of

injections earned when vehicle was available during the FT 1-min schedule (~ 4

injections).

Although the Takahasi and Singer (1979) study was successful in demonstrating

the initiation of THC’s reinforcing effects through the use of a schedule-induction

procedure, some limitations were also present. For instance, the reinforcing effects of

THC were only assessed when the inducing schedule was in effect. However, future

studies should also examine the maintenance of those reinforcing effects when

scheduled pellet delivery is discontinued and the necessary conditions may need to be

developed in order to do so. For instance, the schedule-induced ethanol self-

152

administration literature indicates a bitonic function that describes the relationship

between the FT interval of pellet delivery and the volume of ethanol consumed following

schedule removal with maximal intake occurring at FT values of 2-3 minutes for rats and

monkeys (see Ford, 2014). However, the optimal inter-pellet interval may be different for

inducing cannabinoid SA and subsequent maintenance of reinforcing effects. Thus,

future studies should pay particular attention to the relationship between the length of

the interval between pellet delivery and the amount of THC self-administered. The

optimization of such a procedural variable may be the difference between maintaining

cannabinoid SA after the removal of the inducing schedule.

Another aspect of ethanol SA procedures, particularly in rodent models, that

could apply to producing high levels of cannabinoid SA involves the frequency of drug

access. For instance, compared to ethanol SA protocols using a continuous-access-

drinking paradigm (i.e., 24 hrs/day, 7 days/week), intermittent access to ethanol SA (i.e.,

24 hrs/day, 3 days/week: typically Monday, Wednesday, and Friday) in rodents yields

considerably higher levels of ethanol intake (Wayner et al., 1972; Wise, 1973; Simms et

al., 2008). The effectiveness of this procedure may in part reflect negative reinforcement

to alleviate ethanol-induced symptoms of withdrawal. The potential effectiveness of

intermittent access to increase the reinforcing effects of THC may also involve negative

reinforcement, although probably unlikely considering the long half-life of THC

attenuates profound physical withdrawal (Hollister, 1986) and the doses required to

produce physical symptoms of withdrawal in nonhuman primates, for example (2.0

mg/kg/day; Stewart and McMahon, 2010), are unlikely to be self-administered in one

daily session. Instead, intermittent access to THC SA may allow time for other, more-

subtle drug effects to dissipate that may be inhibiting the expression of reinforcing

effects. Future studies should test this hypothesis by designing THC SA studies that

153

intersperse 24 hr-48 hour abstinence periods in between SA sessions, instead of making

the drug available for SA across consecutive daily sessions.

Using drug discrimination to complement THC self-administration

Until the cannabinoid SA procedure has been further refined to produce

consistent, reproducible reinforcing effects across multiple subjects and species, the

drug discrimination assay represents a valid alternative in terms of cannabinoid abuse

liability assessment. The discriminative stimulus effects of a drug have been suggested

to be analogous to human subjective effects (Schuster and Balster, 1977; Holtzman,

1985). Thus, although the drug discrimination procedure does not measure the

reinforcing effects of drugs per se, drugs that share discriminative stimulus effects

usually have similar reinforcing effects as a result of similar pharmacological

mechanisms of action (Schuster and Johanson, 1988).

As such, the discriminative stimulus effects of THC have been produced in

multiple experimental animal species and across several different laboratories (Tanda,

2016). Drug discrimination with cannabinoids is pharmacologically selective such that

naturally occurring psychoactive cannabinoids fully substitute for THC and drugs from

other classes do not. Thus, THC is a well-established training drug that can provide

rapid feedback on the underlying pharmacological mechanisms and abuse liability of

substituted novel cannabinoid compounds. For instance, there are a number of potential

therapeutic applications of cannabinoids involving their analgesic, antiemetic,

antiglaucoma, and anticonvulsive effects (Hill, 2015). However, a cannabinoid

compound considered for these uses would be void of any clinical utility if there also

exists abuse potential of that compound since THC is Schedule I. Therefore, the

evaluation of a candidate compound to produce THC-like discriminative stimulus effects

is an effective way to test for this, with the goal of developing one that either does not

154

substitute for THC or retains its therapeutic activity at doses below those that produce

THC-like discriminative stimulus effects. Moreover, cannabinoid drug discrimination

procedures can be useful for evaluating potential pharmacotherapies for cannabis

abuse. For instance, drugs that block or attenuate the discriminative stimulus effects of

THC might be considered for achieving abstinence or preventing relapse. On the other

hand, drugs that partially substitute for THC-like discriminative stimulus effects might be

considered as candidate agonist medications, similar to methadone for opioid

dependence, that can reduce cannabis SA or alleviate withdrawal symptoms associated

with relapse without too much concern for the abuse potential of the medication itself.

This is not to say, however, that drug discrimination should serve as a

replacement for cannabinoid SA. Although there is good correlation between a drug’s

subjective effects and its reinforcing effects, the SA procedure measures the reinforcing

effects of a drug directly, along with all the other complexities that determine its ability to

maintain behavior, which offers a more detailed perspective in terms of abuse liability

screening and pharmacotherapy assessment. For instance, drug-induced changes in

operant behavior can vary dramatically as a function of rates and patterns of behavior

independent of the reinforcer that maintains behavior (Kelleher and Morse, 1968).

Therefore, one can assume that even though a drug attenuates the discriminative

stimulus effects of THC, the effects of that drug to also attenuate the reinforcing effects

of THC may be more ambiguous depending on factors such as the contingencies for

THC reinforcement or other prevailing environmental conditions. Similarly, a drug that

substitutes for the THC stimulus may not necessarily produce reinforcing effects in the

self-administration procedure also depending on the schedule contingencies and other

trait/state variables. The point is that both drug discrimination and self-administration

should be used hand-in-hand to gain information about the abuse-related effects of

drugs of abuse from two different perspectives. For example, the optimal

155

pharmacotherapy may be one that reduces the reinforcing effects of THC, but not the

discriminative stimulus effects - this drug profile would be indicative of good therapeutic

efficacy without sacrificing medication compliance. Such an assessment could not be

gained from only using one procedure. However, until cannabinoid SA is more

developed, THC discrimination can provide useful information on cannabinoid

pharmacology, the abuse potential of novel cannabinoid therapeutic agents, and

potential pharmacotherapies to treat cannabis abuse.

Going forward, cannabinoid discrimination studies should also be utilized to

better understand the pharmacological variables that may influence cannabinoid SA. For

instance, a strength of the drug discrimination assay lies in its capacity to investigate not

only the receptor mechanisms of a drug class, but also other stimulus effects of a drug

such as time course of a drug (e.g. onset of behavioral effects, duration of action, etc.),

which have been shown to significantly affect the SA of other drugs of abuse (Balster

and Schuster, 1973; Stretch et al., 1976; Kato et al., 1987; Beardsley and Balster, 1994).

THC, in particular, has a relatively delayed onset of its behavioral effects and extended

elimination phase (Ohlson et al., 1980; Hollister et al., 1981). This time course of effects

is significantly different compared to other drugs that are readily self-administered (e.g.,

cocaine, methamphetamine, opiates, etc.) so it should not be assumed that similar self-

administration parameters will be effective for demonstrating the reinforcing effects of

cannabinoids. Instead, a more careful examination of the pharmacological profile of

cannabinoids needs to be taken into account and drug discrimination is one tool that can

elucidate many of these factors. For instance, in a study of the discriminative stimulus

effects of THC in rats, Prescot et al. (1992) showed that maximal THC-appropriate

responding did not occur until 60 min after intramuscular injection. Furthermore, this

study also showed that THC-appropriate responding persisted until 6 hours after initial

administration. These results suggest, inasmuch as discriminative stimulus effects

156

predict reinforcing effects, that THC’s delayed onset and long duration of action could be

preventing the expression of its reinforcing effects in studies that do not account for

these variables. Specifically, the delayed onset in action of THC could be functioning to

uncouple the behavioral effect of the drug from the response that produced it. On the

other hand, THC’s long duration of action could be preventing persistent SA within a

session either through satiation or decreased discriminability of additional drug

injections. In order to investigate these possibilities, future SA studies should implement

even longer intervals between drug injections than 10 minutes used during the second-

order schedule in Chapter 2.

Moreover, THC discrimination time course studies in the context of developing

the THC SA model should also be conducted with intravenous THC considering that is

the route of administration most likely to be used for contingent delivery. Future studies

should also attempt to train the discrimination of lower training doses of THC. For

instance, 0.1 mg/kg THC is typically used as the training dose in most drug

discrimination studies using nonhuman primates (McMahon 2006, 2011), which is

considerably higher than the unit dose of THC (0.003 mg/kg) that has reinforcing effects

in studies with squirrel monkeys and for the subjects in Chapter 2. Thus, the examination

of the discriminative stimulus effects of lower doses of THC would provide a better

understanding of the stimulus effects of doses of THC that are more likely to be self-

administered as opposed to studying the effects of a single, bolus dose that is probably

more of a reflection of the total intake during a self-administration session. However, the

relationship between doses of a drug that produce reinforcing effects compared to those

that produce discriminative stimulus effects should be interpreted cautiously. Previous

studies in nonhuman primates that were trained to discriminate and self-administer the

same drug demonstrated that, in general, the reinforcing effects of that drug were more

potent than its discriminative stimulus effects (Hoffmeister, 1988; Ator, 2002; Martelle

157

and Nader, 2009). For example, each of these studies showed that there were certain

doses of a drug that did not engender drug-appropriate responding during drug

discrimination but functioned as a reinforcer during self-administration. Some of these

discrepancies may be due to the training dose used for discrimination as this factor has

been shown to influence the potency of the dose-response curve (e.g. Terry et al., 1994;

Mumford and Holtzman, 1991; Young et al., 1992). Nevertheless, drug discrimination

can serve is an excellent platform to investigate many of the pharmacological factors

that may be related the reinforcing effects of cannabinoids.

Route of administration

One possibility for unsuccessful attempts at establishing cannabinoid SA in

preclinical animals may involve the route of administration, which is typically delivered

intravenously in preclinical animal models of SA, whereas in humans, cannabinoids are

most commonly abused via pulmonary SA (i.e., inhalation). Studies have shown that the

pharmacokinetics and metabolism of THC following intravenous injection are essentially

the same as inhalation with both routes producing rapid attainment of peak plasma

concentrations and a rapid distribution phase at similar time courses (Hollister et al.,

1981). Moreover, both routes produced a similar time course and magnitude of self-

reported ratings of feeling “high” (Hollister et al., 1981). Thus, the pharmacokinetic

parameters of THC and its positive subjective effects (e.g., subjective “high”) do not

appear to be different between both routes of administration. However, studies have also

shown that many adverse effects are reported by subjects when administered

intravenous THC, as well as smoked THC, such as dysphoria, anxiety, paranoia, and

nausea (D’Souza et al., 2008; Carbuto et al., 2012). In one study directly comparing the

psychotropic effects of both routes in humans at doses that produced similar plasma

concentrations, the adverse effects of intravenous THC were much more pronounced

158

compared to pulmonary administration (Naef et al., 2004). For instance, intravenous

administration produced more subjective reports of feeling irritated, anxious, tenseness

and aggressiveness, confusion and disorientation, nausea, headache, difficulties

breathing, and vertigo (Naef et al., 2004). Significantly lower amounts of the

psychoactive metabolite 11-OH-THC were formed following pulmonary administration

due to the absence of first-pass metabolism, which may partly explain the differences in

psychotropic side effects compared to intravenous administration. Thus, the more

pronounced adverse effects of intravenous THC may be overriding its reinforcing effects

when delivered through this route of administration, which could be playing a role in

preventing persistent THC SA in preclinical animal models. As a result, pulmonary

administration may increase the likelihood of THC functioning as a reinforcer in animal

models because the adverse effects that may be overriding the reinforcing effects are

more limited. Indeed, the development of inhaled delivery systems of THC have been

recently validated (Manwell et al., 2014; Nguyen et al., 2016); however, future studies

should continue to develop these technologies and incorporate them into operant

procedures to allow the study of factors mediating THC’s reinforcing effects.

Interactions of THC with other cannabinoids

Moving forward, it is important to consider that the Cannabis sativa plant contains

hundreds of known compounds and at least 70 cannabinoids in addition to THC (Elsohly

and Slade 2005). Although the present study provides no evidence of interactions

between THC and other marijuana constituents, it does not preclude the possibility that

such interactions may influence other mechanisms that are not reflected in the

behavioral endpoints studied, such as possible activity at non-CB1 sites or at CB1

receptors that are part of neural circuits distinct from those involved with reinforcement

or rate-decreasing effects. Cannabidiol (CBD), for instance, is another major

159

cannabinoid found in cannabis, and has opposing effects on CB1 and CB2 receptors

compared to THC. Although hashish may contain equal parts CBD and THC (ElSohly et

al, 2003; Hardwick and King, 2008; Potter et al, 2008), CBD is typically present in low

concentrations in recreational cannabis (<0.1%), based on samples seized by law

enforcement. There are multiple reports of CBD and THC interacting to modify each

other’s behavioral effects with some studies showing enhancement (Karniol and Carlini,

1973; Takahashi and Karniol, 1975) or antagonism (Karniol and Carlini, 1973; Borgen

and Davis, 1974; Karniol et al., 1974), depending on the endpoint of interest. In

particular, antagonism was previously reported in rhesus monkeys performing operant

conditioning and cognitive-based tasks (Brady and Balster, 1980; Wright et al., 2013). It

has also been shown that CBD significantly enhanced the potency of THC to produce

discriminate stimulus effects (McMahon, 2016), albeit at a dose 100 times greater than

THC, which far exceeds the amount obtained from marijuana.

Furthermore, there is evidence that CBD may reduce anxiety or transient

psychosis-like side-effects of THC observed in infrequent cannabis smokers or when

administered alone to patients with anxiety or psychosis (Bhattacharyya et al, 2010;

Crippa et al, 2011; Bergamaschi et al, 2011; Leweke et al, 2012; Niesink and van Laar,

2013). Older studies report that CBD changes the type of psychological reaction induced

by THC in infrequent cannabis smokers, reducing their anxiety and thereby rendering

THC more enjoyable (e.g., Karniol et al, 1974). Taken together, the addition of CBD to

THC may be a potential strategy for enhancing the reinforcing effects of THC compared

to just studying the drug by itself. The key will be finding an appropriate concentration of

CBD in combination with THC that attenuates THC’s rate-decreasing effects without

simultaneously cancelling out its reinforcing effects.

160

Relationship between THC and polysubstance use

Finally, one additional factor that deserves mention includes interactions of

cannabis with other substances. For example, co-use of marijuana and alcohol

increases risk for motor vehicle crashes (Hingson, et al., 2005), short- and long-term

memory impairment (Pope & Yurgelun-Todd, 1996), psychological disorders (Grech et

al., 2005; Hall 2009), and lower educational performance and attainment (Brook et al.,

1999; Lynskey and Hall, 2000; Brook et al., 2011; Arriaet al., 2013;). Tobacco and

marijuana use increases the risk for adverse respiratory and cardiovascular effects

(Tashkin, 1990; Polen, et al.,1993; Zhang et al., 1999; Mittleman et al., 2001; Aryana

and Williams, 2007) and increased susceptibility to cancer (Hashibe et al., 2005).

Additionally, we have shown that THC, in combination with cocaine, produced an

increase in the relative reinforcing strength of cocaine compared to cocaine alone as

assessed in a cocaine-food choice procedure (Figure 2A). In contrast to the low-efficacy

partial agonist THC, the full CB receptor agonist CP55,940 shifted the cocaine dose-

response curve to the right (Figure 2B), perhaps suggesting a novel treatment for

cocaine abuse involving full agonists at CB receptors. A better understanding of the

behavioral and neuropharmacological interactions of marijuana and other substances

will have important implications for the treatment of co-abuse.

161

Conclusions from studies in Chapter 2

Across all conditions, cannabinoid agonists (i.e., CP55,940 or THC) functioned

as a reinforcer in 7 of 13 monkeys studied (54%). Although our hypothesis that

attenuating the rate-decreasing effects of THC would enhance its reinforcing effects was

not supported considering reinforcement was not obtained in every subject, it should not

go unnoticed that these experiments have provided the most successful demonstration

of cannabinoid reinforcement in nonhuman primates in terms of number of subjects

acquiring SA and level of SA, besides only the group at NIDA-IRP using squirrel

monkeys (Tanda et al., 2000). Thus, these studies provide an important foundation for

which future investigations to build on and highlight the presence of individual

differences involved in the abuse-related effects of cannabinoids. Future research

Figure 2. Effects of THC (A) and CP55,940 (B) pretreatment on cocaine self-

administration under a food-drug choice procedure in rhesus monkeys (n = 4). Both drugs were administered intravenously one minute before the session. Abscissae: Unit dose of cocaine available for self-administration in mg/kg/injection. Left ordinate: Percent cocaine choice relative to food reinforcement. Right ordinate: Percent food choice relative to cocaine reinforcement. Data represent mean of at least two determinations per dose of THC for each monkey. *, p < 0.05. Unpublished data from John, Martin, and Nader, 2016.

162

should focus, rather than disregard, these individual differences in order to identify

behavioral, environmental, or biological determinants of cannabinoid self-administration

that can be further targeted to optimize the model. As discussed earlier,

recommendations for future studies examining the behavioral mechanisms underlying

THC reinforcement might include the use of intermittent or choice schedules of

reinforcement coupled with an initial induction procedure such as non-contingent drug

exposure and/or fixed-time schedules of food-delivery.

Cognitive effects of THC

The other goal of the research within this dissertation was to characterize the

effects of chronic THC treatment on cognitive performance, including the residual effects

(i.e., 22 hours after daily administration during chronic treatment) and during several

weeks of abstinence. Cognitive-enhancement is an emerging area for the treatment of

cannabis use disorder (Sofuoglu et al., 2013); however, it is not clear what specific

cognitive functions are most strongly related to improved treatment outcome. Much of

this discrepancy comes from the inconsistent results in human studies examining the

effects of long-term marijuana use on cognitive performance. For instance, the

interpretation of many studies are complicated by several variables such as the

heterogeneity of the participants, differences in drug histories and social variables, as

well as concurrent use of other substances, all of which are difficult to experimentally

control. In addition, studies in humans cannot distinguish whether marijuana abusers

exhibit innate cognitive differences prior to initiating drug taking. Therefore, it is difficult

to clearly establish a causal role of long-term THC exposure in producing cognitive

deficits. However, the use of preclinical animal models can control for these variables in

a systematic manner but have unfortunately been underutilized as it relates to

longitudinal, within-subject assessments of THC on cognition.

163

In Chapter 3, the effects of chronic THC exposure on four cognitive domains

were examined: working memory, discrimination learning, reversal learning, and

attentional set-shifting. Each of these cognitive domains are mediated by a different

neurobiological substrate and are integral to facilitating the behavioral change necessary

for positive treatment response (Rezapour et al., 2016). However, studies in chronic,

heavy cannabis users show that impairment within these domains is equivocal

depending on the time since last use (Crean et al. 2011; Crane et al., 2013). Thus, in

order to better clarify the specificity and temporal ordering of effects, the studies in

Chapter 3 examined the acute effects of THC on cognition before and during chronic

THC treatment (1.0-2.0 mg/kg/day, s.c.) and the residual effects of daily THC treatment

(i.e., cognition assessed 22 hrs after daily THC administration) as well as the

persistence of effects during abstinence.

It was found that the acute effects of THC were task-specific and produced

deficits to working memory in a delay-dependent manner, as well as deficits in

compound discrimination learning, and extradimensional set-shifting; tolerance

developed to those effects during chronic treatment. Furthermore, during chronic

treatment, THC produced persistent impairments 22 hours after administration to

working memory when the cognitive load was high but not to discrimination and reversal

learning or attentional set-shifting performance. Impairments to working memory

recovered within 2 weeks of abstinence. The implications of these findings will be

discussed in the following sections.

Task Specificity

One of the major findings of this work involves the task specificity on which THC

produced both its acute and residual effects. This finding is an important contribution to

the overall theme of this dissertation involving the behavioral mechanisms of THC - the

164

notion that the behavioral effects of THC cannot be predicted based on its inherent

pharmacological properties. Consistent with the findings in Chapter 3, other studies in

nonhuman primates have also found task-specific effects of THC on cognitive

performance. For instance, Winsauer et al. (1999) found that THC impaired the

acquisition of a novel stimulus-response chain but not the expression of an already-

learned chain of response. Furthermore, Kangas et al. (2016) found that short-term

memory assessed under the DMS task was most vulnerable to impairment by acute

THC pretreatment in squirrel monkeys, followed by discrimination reversal while simple

discrimination learning was not affected. Moreover, in the only other study in nonhuman

primates that has assessed the residual effects of THC on cognition, Verrico et al.

(2014) found that adolescent rhesus monkeys were impaired by THC (0.2-0.3 mg/kg,

i.v.; 23 hours after drug administration) on a spatial working memory task (i.e. location-

recall) but not an object working memory task (i.e. object-recall). Overall, these findings

suggest cognitive performance may be most susceptible to impairment by THC when the

retention of information over time or the flexible alteration of responses based on

ongoing within-trial contingencies are required.

As discussed in Chapter 3, the basis for THC to affect cognition in a task-specific

manner is unclear but may be related to CB1 receptor function in different brain regions

that mediate such tasks. For example, the control of discrimination reversal performance

appears to be highly localized in the orbital prefrontal cortex (Chudasama, 2011),

whereas object working memory is thought to be controlled by dorsolateral prefrontal

systems (Hampson et al., 2009) and hippocampal mechanisms (Eichenbaum et al.,

1992) while spatial working memory likely involves more frontal-temporal circuitry

(Browning and Gaffan, 2008). As such, CB1 receptors are widely expressed throughout

the brain (Freund et al., 2003); however, recent studies have indicated that CB1 receptor

dynamics (e.g., desensitization and downregulation) differ across brain regions (Lazenka

165

et al., 2013). For example, Sim-Selley et al. (2006) treated mice chronically with THC or

the CB1 full agonist WIN55,212-2 and showed that after cessation of treatment,

cannabinoid-induced decreases in CB1 receptor function persisted for two weeks longer

in the hippocampus compared to the striatum. These findings are consistent with recent

data from positron emission tomography imaging studies in human marijuana users that

showed slower recovery of CB1 receptors in hippocampus than in other brain regions

(Hirvonen et al., 2012). Considering the significant hippocampal mechanism involved in

working memory performance, these data support the task specificity of THC to impair

DMS performance in the present study and in others.

Alternatively, it may be that differences in the relative potencies of THC across

tasks most directly reflect differences in the difficulty of the tasks, i.e., more difficult tasks

may be more vulnerable to cannabinergic drug action, resulting in higher potency and

larger effects. For example, Branch et al. (1980) showed that squirrel monkeys trained to

emit a 5-key response sequence were more sensitive to acute doses of THC than when

emitting a 2-key sequence. In this regard, performances under all tasks used in the

present study required discriminative behavior but differed in complexity. That is, simple

discrimination trials are simple stimulus-response associations, compound discrimination

adds complexity with the presentation of another stimulus dimension, and discrimination

reversal, intradimensional/extradimensional shift is made even more complex due to the

unsignaled shift in contingency. DMS is perhaps the most complex task among the ones

used here because accuracy depends on a conditional discrimination (i.e., S+ and S-

contingencies are conditional on the previously presented sample) and, moreover, a

delay between the presentation of sample and comparison stimuli. Thus, although the

extent to which the potency of THC depends on task complexity may be difficult to

determine a priori, the present results support the view that this factor at least

contributes to the behavioral mechanisms of THC cognition-related studies.

166

Implications for chronic THC exposure to produce tolerance to its acute cognitive

effects

These findings for both the acute and persistent effects of THC on cognitive

function have several important implications. First, as touched on briefly in Chapter 3,

the differential acute effects of THC on cognitive performance as a function of chronic

THC exposure may bear an important implication for establishing thresholds of THC that

can be used to determine impairment for driving or other tasks. Ideally, a crash risk ratio

of THC would be calculated similar to alcohol in which there is a direct comparison

between the level of THC in the blood and impairment for driving. To this end, Colorado

recently enacted a law stipulating a threshold of 5 ng/ml of THC in blood for driving

under the influence, based on other published reports (Hartman and Huestis, 2013;

Walker et al., 2013). Another study indicated that blood levels below 10 ng/ml were not

associated with increased accident risk, and recommended a threshold of 7–10 ng/ml for

driving under the influence (Grotenhermen et al., 2007).

However, based on recent research, what these thresholds don’t take into

account is the level of tolerance chronic THC exposure produces to the acute

intoxicating effects of THC. For instance, one study reported that in occasional (≤1

time/week) cannabis users, smoked cannabis (500 μg/kg) produced significant

impairment of tracking performance, divided attention, and inhibitory control but only

modestly affected inhibitory control in heavy (>4 days/week) users (Ramaekers et al.,

2009). Similarly, in a sample of heavy cannabis users (>4 days/week for more than 2

years), smoked cannabis (700 μg/kg) produced no impairment on tasks measuring

tracking errors or reaction time, and only produced modest effects on a divided attention

task (Schwope et al., 2012). Levels of THC and its major metabolites were measured in

that study, and remained above the legal threshold of 5.0 ng/ml for 2–4 h after cannabis

167

consumption. These findings are also consistent with a study in nonhuman primates

showing that monkeys with a chronic history of THC (1.0 mg/kg/12h for several years)

compared with monkeys intermittently exposed to THC (0.1 mg/kg every 3-4 days)

resulted in greater tolerance to the hypothermic and rate-decreasing effects following

administration of 3.2 mg/kg THC (Ginsburg et al., 2014). THC blood levels were also

measured in this study, however, no differences in blood levels of THC or its metabolites

were present between the groups. These findings are consistent with others showing

that tolerance does not develop to the pharmacokinetics of THC despite tolerance to its

behavioral effects. Interestingly, although maximal THC blood levels occurred 20

minutes after injection for both groups (457.0 and 355.2 ng/ml for the intermittent and

chronic groups, respectively), maximal effects of THC to decrease response rates and to

produce hypothermia in the intermittent group did not occur until 120 minutes after

injection, after THC blood levels had substantially decreased. By 12 hours and 24 hours,

effects on temperature and response rate was not different from control levels although

blood THC levels for both groups were approximately 20 ng/ml, well above the legal

threshold of 5 ng/ml. Taken together, these studies indicate that blood levels of THC do

not necessarily reflect the behavioral effects of the drug when drug history is not taken

into account.

The findings in Chapter 3 further add to this literature by using a within-subject

study design to show that chronic THC exposure produced tolerance to the acute

cognitive impairing effects of THC. Moreover, tolerance to the cognitive effects of THC

such as working memory, that involves attentional processes and the active processing

and manipulation of information, may more closely parallel the demanding task of driving

a vehicle. Although the experiments in Chapter 3 were not designed to directly address a

blood threshold that generally reflects impairment to the acute effects of THC, these

findings add to the body of literature demonstrating that a chronic history of THC results

168

in tolerance to its acute behavioral effects, even when that behavior is complex and

under strict stimulus control. It can be speculated based on previous studies in humans,

however, that THC blood levels were above the legal threshold of 5 ng/ml following

acute administration. For instance, i.v. administration of 5.0 mg THC (approximately 0.07

mg/kg) in a group of frequent cannabis users produced an average blood level of 20

ng/ml at 30 minutes post administration, the same time that cognitive assessment

occurred in Chapter 3. Furthermore, THC levels declined rapidly to about 3 ng/ml at 4

hours post injection. These data offer a good comparison because the same route of

administration was used and the dose range is within the range (0.03-0.056 mg/kg) to

which tolerance developed during chronic treatment in Chapter 3. Taken together, these

data suggest that the current threshold of 5 ng/ml may be too low in many cases and

that drug history needs to be a critical component for determining impairment.

Pre-existing vs. drug-induced cognitive impairment

The studies in Chapter 3 also have implications regarding the extent to which

cognitive impairments are caused by chronic drug use itself. For instance, while there

may be a causal relationship between chronic drug use and cognitive impairment,

individuals with innate cognitive deficits may also be more vulnerable to initiating drug

use and/or becoming drug dependent (Wagner et al., 2012). In order to address this

issue, the studies in Chapter 3 used a within-subjects study design that allowed for the

assessment of THC on cognition from initial to chronic exposure and during abstinence.

Thus, it was demonstrated that the residual effects of THC to impair cognitive function

were a consequence of the drug exposure itself as opposed to innate differences in

baseline cognitive functioning. Such an assessment is more difficult in studies with

human subjects as group designs comparing marijuana users with healthy control

subjects are typically used. As a result, there is always the looming question of whether

169

cognitive deficits associated with chronic cannabis use predated the initiation of drug use

and were instead, a factor that rendered individuals more vulnerable to chronic use in

the first place. Some studies, however, compare heavy cannabis users against a control

group of infrequent users rather than a control group of nonusers (e.g., Pope et al.,

1996; Whitlow et al., 2004). To some extent, this approach controls for some

confounding factors associated with using a healthy control group; however, with each

subject serving as its own control, the present results unequivocally demonstrate that

THC-induced cognitive deficits were from the drug itself and not a result of baseline

differences between control groups.

The implications of these findings bears particular importance in terms of the

feasibility of cognitive enhancement as a treatment approach. For instance, if innate

cognitive impairment contributed to the initiation of cannabis use then cognitive

rehabilitation as a treatment strategy may be more challenging because of a potential

ceiling effect in cognitive capabilities. However, the present study shows that cognitive

deficits may be produced by the drug itself and in that case, there may be more room for

cognitive enhancement to be an effective treatment option because baseline function

would be the endpoint as opposed to the starting point.

Of course, in a treatment context it may be very difficult to discern whether

cognitive impairments were from the drug itself or a trait variable that contributed to the

initiation of compulsive use. One strategy would be to compare cognitive performance at

the time of treatment to scholastic measures that have been taken prior to the initiation

of use such as test scores, IQ tests, behavioral assessments etc. in order to obtain some

sort of within subject comparison. However, the practicality of having access to the

necessary resources for such an approach is uncertain. Nevertheless, if cognitive

enhancement increases general functioning, it has the potential to influence substance

use outcomes regardless of whether substance abuse arises secondary to cognitive

170

impairment or vice versa. In reality, cognitive impairments in most cannabis users are

likely a combination of pre-existing vulnerability factors, long-term drug effects,

withdrawal effects, and acute drug effects; however, a better understanding of these

factors will provide clinicians with a better understanding of when a treatment works and

will allow clinicians to maximize the effectiveness of cognition-based treatments.

Working memory as a treatment target for cannabis use disorder

Furthermore, the results in Chapter 3 show that working memory was the only

cognitive domain examined that showed impairment 22 hrs after THC administration

during chronic treatment with deficits persisting until two weeks of abstinence. Therefore,

these findings suggest that working memory, in particular, may represent a

neurocognitive domain that may be predictive of treatment outcome or one that is

particularly sensitive to therapeutic interventions in cannabis dependent patients.

Several studies have suggested that working memory function is linked to

inhibitory control in that high working memory demand or impairments to working

memory function may facilitate drug craving or relapse during abstinence (Chambers et

al., 2009). For instance, Hester and Garavan (2004) showed that under high working

memory demand, cocaine users have reduced response inhibition, as measured by a

Go-No/Go task, compared to healthy controls. Poorer working memory performance has

also been shown to be predictive of relapse in abstinent smokers (Patterson et al.,

2010). Surprisingly, the impact of cognitive function on treatment outcomes for cannabis

use disorder has not been well-studied. The only such study to date evaluated cognitive

functioning in 20 marijuana-dependent patients at treatment entry and its relation to

retention and drug use outcome in motivational enhancement therapy and cognitive

behavioral therapy (Aharanovich et al. 2008). It was found that cognitive impairments in

abstract reasoning, spatial processing, and processing accuracy were predictive of poor

171

treatment retention but not rates of abstinence. There was no association between

performance on a memory task and treatment retention or outcome, although this study

was limited by small sample size and it is unclear whether the particular task was

sensitive enough to detect impairments. However, given the evidence of working

memory and other cognitive functions to predict the success of behavioral treatment

programs in cocaine and cigarette users, future studies are likely to find cognitive

functions that are predictive of treatment outcomes in marijuana users as well. The

selection of validated cognitive tests that include high levels of cognitive demand that

would be most sensitive to detecting impairments (i.e. in order to avoid false negatives)

will be a crucial step.

In addition to the prognostic significance of working memory, the basic research

in Chapter 3 also has implications for working memory to serve as a cognitive domain

that could be targeted to improve the effectiveness of treatment in cannabis abusers.

Working memory has been recognized as one dimension of inhibitory control so in

theory, therapeutic approaches aimed at improving working memory could also help

restore impairment within inhibitory control functions, which are important for overriding

pre-potent responses, such as drug-taking behavior in response to drug cues (Sarter et

al., 2006). A growing amount of empirical evidence also supports working memory as a

treatment target for addiction (Klingberg, 2010). For instance, it has been shown that

working-memory training in alcoholics reduced alcohol use for more than one month

after training (Houben et al., 2011). Consistent with the association between working

memory and inhibitory control, it was also found that training had an effect on alcohol

use for those with strong automatic preferences for alcohol, indicating working memory

training may increase control over the underlying automatic processes that drive alcohol

use. Furthermore, Bickel et al. (2011) showed that working memory training improved

delay discounting amongst stimulant users, suggesting that such training may lead to a

172

greater ability to attend to future consequences and thus reduce impulsive decision

making that could contribute to relapse during abstinence. Cognitive rehabilitation

approaches aimed at targeting working memory specifically have not yet been evaluated

among patients with cannabis use disorder. However, the results in Chapter 3

demonstrating that chronic THC exposure produces persistent effects within this domain

combined with promising evidence in alcoholics and stimulant abusers indicate that the

incorporation of working memory training into behavioral treatments should be explored.

THC-induced alteration to brain activity independent of cognitive performance

Although working memory was the only cognitive domain impaired during chronic

THC treatment, the lack of impairment during performance within the other domains (i.e.,

discrimination learning, behavioral flexibility, executive function) should not be

completely disregarded in terms of clinical significance. First, as discussed previously,

the complexity of the tasks assessing these domains of cognition may not have been

sensitive enough to detect impairments during chronic THC treatment. Second,

functional neuroimaging studies showed that despite similar performance on certain

cognitive measures, the underlying neural mechanisms mediating such performance

may still be affected (Batalla et al., 2013). These findings indicate that neuroadaptations

may occur during chronic THC exposure in which additional brain regions are recruited

in order to maintain normal cognitive functioning and compensate for the THC-induced

deficits.

As it relates to neuroadaptations in human marijuana users, Chang et al. (2006)

used fMRI to compare a visual attention task in current and abstinent cannabis users

with healthy controls. Despite all groups showing normal task performance, both active

and abstinent chronic cannabis users demonstrated decreased activation in the right

prefrontal, medial and dorsal parietal cortices and medial cerebellar regions but greater

173

activation in several smaller regions throughout the frontal, posterior parietal, occipital

and cerebellum. The authors suggested that their findings may reflect a compensatory

role for these regions in mitigating the effects of abnormal attentional and visual

processing following chronic cannabis exposure. Furthermore, in a group of abstinent

cannabis users, Tapert et al. (2007) compared the activation pattern on a go/no-go task

during fMRI with seventeen healthy subjects. Despite similar level of task performance,

cannabis users showed greater activation during inhibitory trials in the right dorsolateral

prefrontal, bilateral medial frontal, bilateral inferior and superior parietal lobules and right

occipital gyrus compared to the healthy subjects. During the non-inhibitory trials,

differences were located in right prefrontal, insular and parietal cortices, with cannabis

users showing greater activation in these areas compared to the controls. Similar

findings have also been observed in adults (Hester et al., 2009), and suggest a greater

neurocognitive effort during the task in cannabis users, even after the abstinence period.

In this regard, the brain seems able to achieve some degree of reorganization

during chronic THC exposure by activating brain regions not usually needed to perform

the cognitive task in response to an impaired ability of the normally engaged task

network. Thus, it is possible that despite control-level performance during tasks other

than DMS in Chapter 3, THC-induced alterations in the primary regions controlling

performance on these tasks were present but were compensated for by the recruitment

of additional regions. However, it is not clear how long such compensatory mechanisms

can maintain normal functioning and whether impairments over time may become

apparent. The impact of these subtle brain alterations on treatment outcome remains to

be determined.

Age of onset

It is also important to acknowledge that the cognition-related studies in Chapter 3

174

were conducted in adult monkeys. Cannabis use, however, is often initiated in

adolescence, and recent data show that a decline in perceived risk of marijuana has

been accompanied by a simultaneous increase in rates of use among adolescents

(Johnston et al., 2014). Adolescence is a critical period of neurodevelopment, especially

for frontal regions (Giedd et al., 1999; Gogtay et al., 2004), with synaptic pruning and

refinement leading to the development of complex cognitive domains such as working

memory, executive functions, decision making and emotional/motivational processes

(Yurglun-Todd, 2007). Thus, chronic cannabis use during this time may disrupt normal

neuromaturation (Bava and Tapert, 2010), and in turn, lead to long-lasting impairments

in neurocognitive functioning more so than if use was initiated as an adult.

Indeed, studies that have examined the relationship between age of onset of

regular cannabis use and cognitive function have reported long-lasting alterations on a

range of tasks. Ehrenreich and colleagues (1999) reported that early onset cannabis use

(prior to age 16) predicted significantly longer reaction times on a task of visual scanning

and attention, while those who began use at age 16 or later performed similarly to

controls. Pope and colleagues (2003) analyzed cognitive data from long-term heavy

cannabis users following a 28-day period of abstinence, and compared early onset users

(prior to age 17) with late onset users (use at age 17 or later). After adjusting for age,

gender, ethnicity and family background, late onset cannabis users were no different

from control subjects; however, early onset users demonstrated reduced performance

relative to control subjects on measures of verbal learning, fluency and overall verbal

intelligence quotient. Consistent with these data with human subjects, repeated THC

administration in adolescent rats was shown to produce more profound effects on

working memory using a novel object test than in adult rats (Cha et al., 2006; Quinn et

al., 2008).

Given these data, it is possible that if the same study as in Chapter 3 were

175

conducted in adolescent monkeys, dramatically different results would have been

generated. In fact, the largest reduction in working memory performance during chronic

THC treatment in Chapter 3 was approximately 25% of baseline performance. Although

this was a significant decrease that persisted for several weeks, a 25% reduction in

baseline performance does not appear all that substantial. This effect also occurred at

the long delay, which was individualized to produce < 60% accuracy (chance accuracy

was 25-33% depending on the number of distractors), therefore, it is possible that a floor

effect prevented any further decreases in performance. Nevertheless, it would be

hypothesized that persistent THC-induced cognitive impairment would be even greater

in adolescent monkeys (e.g., impairments on other tasks and/or further decrease in DMS

percent accuracy at the long delay as well as at other delays) than what was seen in

adult monkeys in the present study. These results would support current evidence

showing the marijuana-induced cognitive impairments are generally more persistent

when use was initiated during adolescent compared to adulthood (Chang et al. 2006;

Jager et al. 2006; Fisk and Montgomery 2008; Becker et al. 2010; Grant et al. 2011;

Gruber et al. 2012;). Future studies should continue to characterize the specific domains

of cognitive function that are impaired from chronic THC exposure in adolescents and

the time course of effects. The persistence of cognitive impairments in adolescent

cannabis users supports the need to address these impairments early in treatment and

continually during abstinence.

Relationships between behavioral endpoints

One advantage of this dissertation was that the studies across both chapters

were conducted within-subject, that is, the majority of the same monkeys used for the

experiments in Chapter 2 were also used for the ones in Chapter 3. Thus, it was

possible to compare the potency and efficacy of THC across various behavioral

176

endpoints (i.e., food-maintained responding, hypothermia, self-administration, and

cognitive performance) in order to provide some insight on the behavioral mechanisms

of THC. Although differences in THC pretreatment times and the inability to calculate an

ED50 for some effects made potency comparisons across tasks difficult, examination of

the smallest dose of THC that produced a significant deficit (or the highest dose that had

no effect) offers some indication of relative potency. In this regard, the rank order of

THC’s potency in the behavioral measures within this dissertation was: working memory

< FR10 food-maintained responding = extradimensional set shifting < compound

discrimination < simple discrimination = reversal learning = intradimensional set-shifting

= hypothermia.

The fact that THC was more potent to decrease rates of responding under the

FR 10 schedule of food presentation than to decrease body temperature is consistent

with the pattern of effects seen in other studies with rodents (e.g. De Vry et al., 2004;

Singh et al., 2011). It should be noted that for some subjects, no hypothermic effects

were seen up to doses that abolished all operant behavior therefore higher doses were

not tested because that was not the primary endpoint. However, it is likely that

hypothermic effects would have become apparent if higher doses were tested, based on

previous studies (McMahon, 2005; Taffe, 2012). Thus, the difference in potency for

producing the two effects might reflect differential binding to multiple receptor subtypes

that differentially mediate the two effects. However, the CB1 receptor antagonist

rimonabant antagonized the rate-decreasing and hypothermic effects of THC (Giuffrida

and McMahon 2010; Singh et al., 2011), albeit antagonism of rate-decreasing effects

was less orderly and of a lesser magnitude than antagonism of hypothermia. Less

orderly antagonism of rate-decreasing effects could reflect actions of rimonabant at a

subset of brain cannabinoid receptors that are more prominently involved in rate-

177

decreasing effects than hypothermic effects; however, mechanisms underlying the

effects of rimonabant are not known.

Interestingly, a study in rhesus monkeys reported the opposite relationship than

observed in this dissertation regarding the potency of THC to alter these behavioral

effects, i.e., THC was more potent to decrease body temperature than to decrease food-

maintained responding (McMahon, 2005). In that study, however, an FR10, FR10

multiple schedule of food presentation and stimulus-shock termination was used to study

the effects of THC on operant behavior. Therefore, additional variables including

schedule of reinforcement, environmental context and the maintaining event should also

be considered with regard to the potency of THC to produce rate-decreasing effects on

operant behavior. Moreover, rate dependency has also been shown to be a determinant

of THC’s potency to affect operant behavior (McMillan, 1977; Brady and Balster, 1980).

Supporting this notion, the overall rate of responding in the rhesus monkeys used for this

dissertation under the FR10 schedule of food presentation was negatively related to the

potency of THC to produce rate-decreasing effects under that schedule (Figure 3).

178

The higher potency of THC to disrupt working memory performance compared to

hypothermia and other unconditioned behaviors is consistent with at least one other

study in rodents that included these endpoints (Varvel et al., 2001). As discussed above,

if both THC-induced deficits to cognition and the other unconditioned behaviors are

mediated by CB1 receptors, then the differences in potency might be due to a difference

in the magnitude of CB1 receptor stimulation (i.e., the efficacy requirement) required for

the effects (e.g., hypothermia > working memory). This would imply that the receptor

Figure 3. Rate-dependent effects of THC on food-

maintained responding (r = -0.85, p < 0.05). Abscissa: ED50 (mg/kg) of THC to decrease food-maintained responding under an FR10 schedule of food presentation. Ordinate: Mean overall rate of responding (responses/second) under an FR10 schedule of food presentation. Unpublished data from John, Martin, and Nader, 2016.

0.001 0.01 0.1 1

0.0

0.5

1.0

1.5

2.0

2.5

3.0

ED50 (mg/kg)

Overa

ll R

esp

on

se R

ate

(resp

on

ses/s

ec)

179

reserve level is higher for the CB1 receptor population in the brain mediating the effects

of cannabinoids on working memory than for the CB1 receptor population mediating the

hypothermic, rate-decreasing effects, and other cognitive domains assessed in this

study. Collectively, these data indicate that the potency of THC is determined by multiple

factors potentially involving pharmacological and behavioral mechanisms. In particular,

these potency relationships across various behavioral endpoints have implications for

establishing legal limits for THC in the blood when driving; however, it remains to be

determined which behavioral endpoints are most predictive of driving performance. For

instance, if the effects of THC on working memory performance are most predictive of its

effects on driving, then a relatively lower THC blood limit might be the most suitable. On

the other hand, if compound discrimination and reversal learning are more predictive,

lower blood levels could result in false positives with regard to driving impairment.

Moreover, as discussed previously, the potency of THC to impair behavioral

performance differs as function of drug history, which should be considered along with

differential levels of tolerance between behavioral endpoints.

Conclusions

Overall, the research conducted in this dissertation provided insight on the

behavioral mechanisms underlying the reinforcing, unconditioned, and cognitive effects

of THC. These data indicate that the behavioral effects of THC cannot be predicted

solely on its pharmacological mechanism of action. Instead, a complex set of

environmental variables must be considered including behavioral sensitivity, drug

history, and the contingencies governing behavior. The adoption of a more

comprehensive perspective that includes the interaction of environmental variables with

neuropharmacology will be vital for the development of the most effective approaches to

treat substance abuse problems.

180

REFERENCES

Agrawal A, Wetherill L, Dick DM, Xuei X, Hinrichs A, Hesselbrock V, Kramer J,

Nurnberger Jr. JI, Schuckit M, Bierut LJ, Edenberg HJ, Foroud T (2009)

Evidence for association between polymorphisms in the cannabinoid receptor 1

(CNR1) gene and cannabis dependence. Am. J. Med. Genet. B Neuropsychiatr.

Genet. 150B: 736-740.

Aharonovich E, Nunes EV, Hasin D (2003) Cognitive impairment, retention and

abstinence among cocaine abusers in cognitive- behavioral treatment. Drug

Alcohol Depend. 71: 207–211.

Aharonovich E, Hasin DS, Brooks AC, Liu X, Bisaga A, Nunes EV (2006) Cognitive

deficits predict low treatment retention in cocaine dependent patients. Drug

Alcohol Depend 81: 313-322.

Aharonovich E, Brooks AC, Nunes EV, et al. (2008) Cognitive deficits in marijuana

users: effects on motivational enhancement therapy plus cognitive behavioral

therapy treatment outcome. Drug Alcohol Depend. 95:279–83.

Arria AM, Garnier-Dykstra LM, Caldeira KM, Vincent KB, Winick AR, O'Grady KE

(2013). Drug use patterns and continuous enrollment in college: Results from a

longitudinal study. Journal of Studies on Alcohol and Drugs, 74, 71–83.

Aryana A, Williams MA (2007). Marijuana as a trigger of cardiovascular events:

Speculation or scientific certainty? International Journal of Cardiology, 118: 141–

144.

Ator NA (2002) Relation between discriminative and reinforcing effects of midazolam,

pentobarbital, chlordiazepoxide, zolpidem, and imidazenil in baboons.

Psychopharmacology 163: 477–487.

Ator NA and Griffiths RR (1983) Nicotine self-administration in baboons. Pharmacol

181

Biochem Behav 19: 993-1003.

Ator NA, Griffiths RR (2003) Principles of drug abuse liability assessment in laboratory

animals. Drug and Alcohol Dependence. 70: S55–S72.

Balster BL and Schuster CR (1973) Fixed-interval schedule of cocaine reinforcement:

effect of dose and infusion duration. Journal of the Experimental Analysis of

Behavior, 20: 119-129.

Batalla A, Bhattacharyya S, Yücel M, Fusar-Poli P, Crippa JA, Nogué S, Torrens M,

Pujol J, Farré M, Martin-Santos R (2013) Structural and functional imaging

studies in chronic cannabis users: a systematic review of adolescent and adult

findings. PLoS One. 8: e55821.

Bava S, Tapert SF (2010) Adolescent brain development and the risk for alcohol and

other drug problems. Neuropsychol Rev. 20: 398-413.

Beardsley PM, Balster RL (1993) The effects of delay of reinforcement and dose on the

self-administration of cocaine and procaine in rhesus monkeys. Drug Alcohol

Depend. 34: 37-43.

Becker B, Wagner D, Gouzoulis-Mayfrank E, Spuentrup E, Daumann J (2010). The

impact of early-onset cannabis use on functional brain correlates of working

memory. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 34:

837–845.

Bergamaschi M, Queiroz R, Chagas M, Oliveira D, Martinis B, Kapczinski F et al (2011).

Cannabidiol reduces the anxiety induced by simulated public speaking in

treatment-naïve social phobia patients. Neuropsychopharmacology 36: 1219–

1226.

Bhattacharyya S, Morrison PD, Fusar-Poli P, Martin-Santos R, Borgwardt S, Winton-

Brown T et al (2010). Opposite effects of Δ-9-tetrahydrocannabinol and

cannabidiol on human brain function and psychopathology.

182

Neuropsychopharmacology 35: 764–774.

Bickel W, Yi R, Landes R, et al. (2011) Remember the future: working memory training

decreases delay discounting among stimulant addicts. Biol Psychiatry. 69: 260–5.

Bolla KI, Brown K, Eldreth D, Tate K, Cadet JL (2002): Dose-related neurocognitive

effects of marijuana use. Neurology. 59: 1337-1343.

Borgen LA, Davis WM (1974) Cannabidiol interaction with delta-9-tetrahydrocannabinol.

Res. Commun. Chem. Pathol. Pharmacol. 7: 663–670.

Brady KT, Balster RL (1980) The effects of delta 9-tetrahydrocannabinol alone and in

combination with cannabidiol on fixed-interval performance in rhesus monkeys.

Psychopharmacology (Berl). 72: 21-6.

Branch MN, Dearing ME, and Lee DM (1980) Acute and chronic effects of Δ9-

tetrahydrocannabinol on complex behavior of squirrel monkeys.

Psychopharmacology (Berl) 71: 247–256.

Brook JS, Kessler RC, and Cohen P (1999). The onset of marijuana use from

preadolescence and early adolescence to young adulthood. Development and

Psychopathology, 11: 901–914.

Brook JS, Zhang C, and Brook DW (2011). Developmental trajectories of marijuana use

from adolescence to adulthood: Personal predictors. Archives of Pediatrics &

Adolescent Medicine. 165: 55–60.

Browning PG, Gaffan D (2008) Impairment in object-in-place scene learning after

uncinate fascicle section in macaque monkeys. Behav Neurosci. 122: 477-82.

Carbuto M, Sewell RA, Williams A, Forselius-Bielen K, Braley G, Elander J, Pittman B,

Schnakenberg A, Bhakta S, Perry E, Ranganathan M, D'Souza DC; Yale THC

Study Group (2012) The safety of studies with intravenous Δ⁹-

tetrahydrocannabinol in humans, with case histories. Psychopharmacology

183

(Berl). 219: 885-896.

Carroll KM, Kiluk BD, Nich C, Babuscio TA, Brewer JA, Potenza MN, et al. (2011)

Cognitive function and treatment response in a randomized clinical trial of

computer-based training in cognitive-behavioral therapy. Substance use and

misuse 46: 23–34.

Carter LP, Griffiths RR (2009) Principles of laboratory assessment of drug abuse liability

and implications for clinical development. Drug and Alcohol Dependence.

105(supplement 1): S14–S25.

Cha YM, White AM, Kuhn CM, Wilson WA, Swartzwelder HS (2006). Differential effects

of delta9-THC on learning in adolescent and adult rats. Pharmacol Biochem

Behav 83: 448–455.

Chait LD (1989) Delta-9-tetrahydrocannabinol content and human marijuana self-

administration. Psychopharmacology (Berl). 98: 51–55.

Chait LD, Burke KA (1994) Preference for high- versus low-potency marijuana.

Pharmacol Biochem Behav. 49: 643–647.

Chait LD, Zacny JP (1992) Reinforcing and subjective effects of oral delta 9-THC and

smoked marijuana in humans. Psychopharmacology (Berl) 107: 255 – 62.

Chambers CD, Garavan H, Bellgrove MA 2009. Insights into the neural basis of

response inhibition from cognitive and clinical neuroscience. Neurosci. Bio-

behav. Rev. 33: 631-646.

Chang L, Yakupov R, Cloak C, Ernst T (2006) Marijuana use is associated with a

reorganized visual-attention network and cerebellar hypoactivation. Brain 129:

1096–1112.

Chudasama Y (2011) Animal models of prefrontal-executive function. Behav Neurosci

125: 327–343.

184

Crane NA, Schuster RM, Fusar-Poli P, et al. (2013) Effects of cannabis on

neurocognitive functioning: recent advances, neurodevelopmental influences,

and sex differences. Neuropsychol Rev. 23: 117–137.

Crean, RD., Crane, NA, and Mason, BJ (2011) An evidence based review of acute and

long-term effects of cannabis Use on executive cognitive functions. Journal of

Addiction Medicine. 5: 1–8.

Crippa J, Derenusson GN, Ferrari TB, Wichert-Ana L, Dura F, Marti N-Santos RO et al

(2011) Neural basis of anxiolytic effects of cannabidiol (CBD) in generalized

social anxiety disorder-a preliminary report. J Psychopharmacol 25: 121–130.

De Vry J, Jentzsch KR, Kuhl E, Eckel G (2004). Behavioral effects of cannabinoids show

differential sensitivity to cannabinoid receptor blockade and tolerance

development. Behav Pharmacol 15: 1–12.

Deneau GA and lnoki R(1967) Nicotine self-administration in monkeys. Ann NY Acad Sci

142: 227-279.

D'Souza DC, Perry E, MacDougall L, Ammerman Y, Cooper T, Wu YT, Braley G,

Gueorguieva R, Krystal JH (2004) The psychotomimetic effects of intravenous

delta-9-tetrahydrocannabinol in healthy individuals: implications for psychosis.

Neuropsychopharmacology. 29:1558-1572.

Ehrenreich H, Rinn T, Kunert HJ, Moeller MR, Poser W, Schilling L, et al. (1999).

Specific attentional dysfunction in adults following early start of cannabis use.

Psychopharmacology, 142: 295–301.

Eichenbaum H, Otto T, and Cohen NJ (1992) The hippocampus—what does it do?

Behav Neural Biol 57: 2–36.

ElSohly M, Wachtel S, Wit H (2003). Cannabis versus THC: response to Russo and

McPartland. Psychopharmacology 165: 433–434.

Elsohly MA, Slade D (2005) Chemical constituents of marijuana: the complex mixture of

185

natural cannabinoids. Life Sci. 78: 539-48.

Falk JL (1961) Production of polydipsia in normal rats by an intermittent food schedule.

Science 133: 195-196.

Falk JL (1964) Studies on schedule-induced polydipsia. In: Thirst. Proceedings of the

First International Symposium on Thirst in the Regulation of Body Water, edited

by M. J. Wayner. Oxford, England: Pergamon Press.

Falk JL (1966) Schedule-induced polydipsia as a function of fixed interval length. J Exp

Anal Behav. 9: 37–38.

Falk, JL (1969). Conditions producing psychogenic polydipsia in animals. Ann. N. Y.

Acad. Sci. 157: 569-593.

Falk JL, Samson HH, Tang M (1972) Chronic ingestion techniques for the production of

physical dependence on ethanol, in Alcohol Intoxication and Withdrawal

(GrossMM ed) 197–211. Plenum, New York.

Fisk JE, and Montgomery C (2008). Real-world memory and executive processes in

cannabis users and non-users. Journal of Psychopharmacology, 22: 727–736.

Ford MM (2014) Applications of schedule-induced polydipsia in rodents for the study of

an excessive ethanol intake phenotype. Alcohol. 48: 265-76.

Freund TF, Katona I, and Piomelli D (2003) Role of endogenous cannabinoids in

synaptic signaling. Physiol Rev 83: 1017–1066.

Ghozland S, Mathews H, Simonin F, Filliol D, Kieffer BL, Maldonado R (2002)

Motivational effects of cannabinoids are mediated by μ- and k-opioid receptors. J

Neurosci. 22:1146–1154.

Goldberg SR, Spealman RDand Goldberg DM (1981) Persistent behavior at high rates

maintained by intravenous self-administration of nicotine. Science. 214: 573-575.

Goldberg SR and Spealman RD (1982) Maintenance and suppression of behavior by

intravenous nicotine injections in squirrel monkeys. Fed Proc 41: 216-220.

186

Goldberg SR and Henningfield JE (1988) Reinforcing effects of nicotine in humans and

experimental animals responding under intermittent schedules of i.v. drug

injection. Pharmacol Biochem Behav.1: 227-234.

Grant KA and Johanson CE (1988) The nature of the scheduled reinforcer and

adjunctive drinking in nondeprived rhesus monkeys. Pharmacol Biochem Behav

29: 295–301.

Grant JD, Scherrer, JF, Lyons MJ, Tsuang M, True WR, Bucholz KK (2005) Subjective

reactions to cocaine and marijuana are associated with abuse and dependence.

Addict. Behav., 30: 1574–1586

Grant KA, Leng X, Green HL, Szeliga KT, Rogers LS, Gonzales SW (2008) Drinking

typography established by scheduled induction predicts chronic heavy drinking in

a monkey model of ethanol self-administration. Alcohol Clin Exp 32: 1824-1838.

Grant JE, Chamberlain SR, Schreiber L, and Odlaug BL (2011). Neuropsychological

deficits associated with cannabis use in young adults. Drug and Alcohol

Dependence, 121: 159–162.

Grech, A, Van Os J, Jones PB, Lewis SW, and Murray RM (2005) Cannabis use and

outcome of recent onset psychosis. European Psychiatry, 20: 349–353.

Grotenhermen F, Leson G, Berghaus G, Drummer OH, Krüger HP, Longo M, Moskowitz

H, Perrine B, Ramaekers JG, Smiley A, Tunbridge R, (2007) Developing limits for

driving under cannabis. Addiction 102: 1910–1917.

Gruber SA, Sagar KA, Dahlgren MK, Racine M, and Lukas SE (2012) Age of onset of

marijuana use and executive function. Psychology of Addictive Behaviors, 26:

496–506.

Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., et

al. (1999) Brain development during childhood and adolescence: a longitudinal

MRI study. Nature Neuroscience, 2: 861–863.

187

Giuffrida A, McMahon LR (2010) In vivo pharmacology of endocannabinoids and their

metabolic inhibitors: therapeutic implications in Parkinson’s disease and abuse

liability. Prostaglandins Other Lipid Mediat 91: 90–103.

Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., et al.

(2004) Dynamic mapping of human cortical development during childhood

through early adulthood. Proceed- ings of the National Academy of Sciences of

the United States of America 101: 8174–8179.

Gorriti MA, Ferrer B, del Arco I, Bermúdez-Silva FJ, de Diego Y, Fernandez-Espejo E,

Navarro M, Rodríguez de Fonseca F (2005) Acute delta9-tetrahydrocannabinol

exposure facilitates quinpirole-induced hyperlocomotion. Pharmacol Biochem

Behav 81:71-77.

Hall, W. (2009) The adverse health effects of cannabis use: What are they, and what are

their implications for policy? The International Journal on Drug Policy 20: 458–

466.

Hampson RE, España RA, Rogers GA, Porrino LJ, Deadwyler SA (2009) Mechanisms

underlying cognitive enhancement and reversal of cognitive deficits in nonhuman

primates by the ampakine CX717. Psychopharmacology (Berl) 202: 355-69.

Haney M, Comer SD, Ward AS, Foltin RW, Fischman MW (1997) Factors influencing

marijuana self-administration by humans. Behav Pharmacol. 8: 101–112.

Hanson, H. M,, C. A. Ivester and B. R. Morton (1979) Nicotine self-administration in rats.

In: Cigarette Smoking as a Dependence Process, edited by N. A. Krasnegor.

National Institute on Drug Abuse Research Monograph Series No. 23. U.S.

Department of Health, Education and Welfare, Washington, DC: U.S.

Government Printing Office. 70-90.

Hardwick S, King LA (2008) Home Office Cannabis Potency Study. Home Office

188

Scientific Development Branch: St Albans. Report.

Hart CL, Haney M, Vosburg SK, Comer SD, Foltin RW (2005) Reinforcing effects of oral

Delta9-THC in male marijuana smokers in a laboratory choice procedure.

Psychopharmacology (Berl). 181: 237-243.

Hartman RL, Huestis MA( 2013) Cannabis effects on driving skills. Clin. Chem. 59: 478–

492.

Hashibe M, Straif K, Tashkin DP, Morgenstern H, Greenland S, and Zhang ZF (2005)

Epidemiologic review of marijuana use and cancer risk. Alcohol, 35: 265–275.

Hasin DS, Saha TD, Kerridge BT, Goldstein RB, Chou SP, Zhang H et al (2015)

Prevalence of marijuana use disorders in the United States between 2001–2002

and 2012–2013. JAMA Psychiatry 72: 1–9

Hello NK (1973) A review of methods to induce alcohol addiction in animals. Pharmacol

Biochem Behav. 1: 89-101.

Hester R, Garavan H (2004) Executive dysfunction in cocaine addiction: evidence for

discordant frontal, cingulate, and cerebellar activity. J. Neurosci. 24: 11017-

11022.

Hester R, Nestor L, Garavan H (2009) Impaired error awareness and anterior cingulate

cortex hypoactivity in chronic cannabis users. Neuropsychopharmacology 34:

2450–2458.

Hill KP (2015) Medical Marijuana for Treatment of Chronic Pain and Other Medical and

Psychiatric Problems: A Clinical Review. JAMA. 313: 2474-2483.

Hingson R, Heeren T, Winter M, and Wechsler H (2005). Magnitude of alcohol-related

mortality and morbidity among U.S. college students ages 18–24: Changes from

1998 to 2001. Annual Review of Public Health, 26: 259–279.

Hirvonen J, Goodwin RS, Li CT, Terry GE, Zoghbi SS, Morse C, Pike VW, Volkow ND,

189

Huestis MA, and Innis RB (2012) Reversible and regionally selective

downregulation of brain cannabinoid CB1 receptors in chronic daily cannabis

smokers. Mol Psychiatry 17: 642–649.

Hoffmeister F (1988) A comparison of the stimulus effects of codeine in rhesus monkeys

under the contingencies of a two lever discrimination task and a cross self-

administration paradigm: tests of generalization to pentazocine, buprenorphine,

tilidine, and different doses of codeine. Psychopharmacology 94: 315–320.

Hollister LE, Gillespie HK, Ohlsson A, Lindgren JE, Wahlen A, Agurell S (1981) Do

plasma concentrations of delta 9-tetrahydrocannabinol reflect the degree of

intoxication? J Clin Pharmacol. 21: 171S-177S.

Hollister LE (1986) Health aspects of cannabis. Pharmacol Rev 38: 1–20

Holtzman SG (1985) Drug discrimination studies. Drug Alcohol Depend 14: 263–282.

Hopfer CJ, Young SE, Purcell S, Crowley TJ, Stallings, MC, Corley RP, Rhee SH,

Smolen A, Krauter K, Hewitt JK, Ehringer MA, (2006) Cannabis receptor

haplotype associated with fewer cannabis dependence symptoms in

adolescents. Am. J. Med. Genet. B Neuropsychiatr. Genet. 141B: 895-901.

Houben K, Wiers RW, Jansen A (2011) Getting a grip on drinking behavior: training

working memory to reduce alcohol abuse. Psychol Sci. 22: 968–975.

Hurd YL, Michaelides M, Miller ML, Jutras-Aswad D (2014) Trajectory of adolescent

cannabis use on addiction vulnerability. Neuropharmacology. 76 Pt B: 416-424.

Jager G, Kahn RS, Van Den Brink W, Van Ree JM, and Ramsey NF (2006). Long-term

effects of frequent cannabis use on working memory and attention: an fMRI

study. Psychopharmacology. 185: 358–368.

Johnson LA, Johnson RL, Portier RB (2013) Current “legal highs.” J Emerg Med 44:

1108–1115.

190

Johnston LD, et al (2014). Monitoring the Future: National Survey Results on Drug Use,

1975 2014-Overview, Key Findings on Adolescent Drug Use. (Institute for Social

Research, University of Michigan, Ann Arbor) Available at

http://monitoringthefuture.org//pubs/monographs/mtf-vol1_2014.pdf.

Kangas BD, Leonard MZ, Shukla VG, Alapafuja SO, Nikas SP, Makriyannis A, Bergman

J (2016) Comparisons of Δ9-Tetrahydrocannabinol and Anandamide on a Battery

of Cognition-Related Behavior in Nonhuman Primates. J Pharmacol Exp Ther.

357: 125-133.

Karniol IG, Carlini EA (1973) Pharmacological interaction between cannabidiol and delta

9-tetrahydrocannabinol. Psychopharmacologia. 33: 53-70.

Karniol IG, Shirakawa I, Kasinski N, Pfeferman A, Carlini EA, (1974) Cannabidiol

interferes with the effects of delta 9

-tetrahydrocannabinol in man. Eur. J.

Pharmacol. 28: 172–177.

Kato S, Wakasa Y, Yanagita T (1987) Relationship between minimum reinforcing doses

and injection speed in cocaine and pentobarbital self-administration in crab-

eating monkeys. Pharmacol Biochem Behav. 28: 407-410.

Kelleher RT and Morse WH (1968) Determinants of the specificity of the behavioral

effects of drugs. Ergeb Physiol Biol Chem Exp Pharmakol. 60: 1-56.

Kelly TH, Foltin RW, Emurian CS, Fischman MW (1994a) Effects of delta 9-THC on

marijuana smoking, dose choice, and verbal report of drug liking. J Exp Anal

Behav. 61: 203–211.

Kelly TH, Foltin RW, Mayr MT, Fischman MW (1994b) Effects of delta 9-

tetrahydrocannabinol and social context on marijuana self-administration by

humans. Pharmacol Biochem Behav. 49: 763–768.

Kelly TH, Foltin RW, Emurian CS, Fischman MW (1997) Are choice and self-

administration of marijuana related to delta 9-THC content? Exp Clin

191

Psychopharmacol. 5: 74–82.

Klingberg T (2010) Training and plasticity of working memory. Trends Cogn Sci. 14:

317–324.

Lang, WJ, Latiff AA, McQueen A, and Singer G (1977) Self-administration of nicotine

with and without a food delivery schedule. Pharmacol Biochem Behav. 7: 65-

70.

Lazenka MF, Selley DE, and Sim-Selley LJ (2013) Brain regional differences in CB1

receptor adaptation and regulation of transcription. Life Sci 92: 446–452.

Lepore M, Vorel SR, Lowinson J, Gardner EL (1995) Conditioned place preference

induced by Δ9-tetrahydrocannabinol: comparison with cocaine, morphine, and

food reward. Life Sci 56: 2073–2080.

Leweke FM, Piomelli D, Pahlisch F, Muhl D, Gerth CW, Hoyer C et al (2012).

Cannabidiol enhances anandamide signaling and alleviates psychotic symptoms

of schizophrenia. Transl Psychiatry 2: E94.

Lyons MJ, Toomey R, Meyer JM, Green AI, Eisen SA, Goldberg J, True WR, and

Tsuang MT (1997). How do genes influence marijuana use? The role of

subjective effects. Addiction, 92: 409–417

Lynskey M, and Hall W (2000). The effects of adolescent cannabis use on educational

attainment: A review. Addiction, 95: 1621–1630.

Mansbach RS, Nicholson KL, Martin BR, Balster RL (1994) Failure of Δ9-

tetrahydrocannabinol and CP 55,940 to maintain intravenous self-administration

under a fixed-interval schedule in rhesus monkeys. Behav Pharmacol 5: 219–

225.

Manwell LA, Charchoglyan A, Brewer D, Matthews BA, Heipel H, Mallet PE (2014) A

vapourized Δ(9)-tetrahydrocannabinol (Δ(9)-THC) delivery system part I:

192

development and validation of a pulmonary cannabinoid route of exposure for

experimental pharmacology studies in rodents. J Pharmacol Toxicol Methods.

70:120-127.

Martelle JL, Nader MA (2009) A within-subject assessment of the discriminative stimulus

and reinforcing effects of self-administered cocaine in rhesus monkeys.

Psychopharmacology (Berl). 203: 343-353.

Martelle SE, Nader SH, Czoty PW, John WS, Duke AN, Garg PK, Garg S, Newman AH,

Nader MA (2014) Further characterization of quinpirole-elicited yawning as a

model of dopamine D3 receptor activation in male and female monkeys. J

Pharmacol Exp Ther. 350: 205-211.

McMahon LR, Amin MR, France CP (2005) SR 141716A differentially attenuates the

behavioral effects of delta9-THC in rhesus monkeys. Behav Pharmacol. 16: 363-

372.

McMahon LR (2006) Characterization of cannabinoid agonists and apparent pA2

analysis of cannabinoid antagonists in rhesus monkeys discriminating Delta9-

tetrahydrocannabinol. J Pharmacol Exp Ther. 319: 1211-1218.

McMahon LR (2011) Chronic Δ⁹-tetrahydrocannabinol treatment in rhesus monkeys:

differential tolerance and cross-tolerance among cannabinoids Br J Pharmacol.

162: 1060-1073.

McMahon LR (2016) Enhanced discriminative stimulus effects of Δ(9)-THC in the

presence of cannabidiol and 8-OH-DPAT in rhesus monkeys. Drug Alcohol

Depend. 165: 87-93.

McMillan DE (1977) Behavioral pharmacology of the tetrahydro- cannabinols. In:

Thompson T, Dews PB (eds) Advances in behavioral pharmacology, vol I.

Academic Press, New York, p 1.

Mello NK. A review of methods to induce alcohol addiction in animals (1973) Pharmacol

193

Biochem Behav. 1: 89-101.

Mello NK and Negus SS (1996) Preclinical evaluation of pharmacotherapies for

treatment of cocaine and opioid abuse using drug self-administration procedures.

Neuropsychopharmacology.14: 375-424.

Mendelson JH, Mello NK (1984) Reinforcing properties of oral delta 9-

tetrahydrocannabinol, smoked marijuana, and nabilone: influence of previous

marijuana use. Psychopharmacology (Berl) 83: 351–356.

Meisch RA (1977) Ethanol self-administration, infrahuman studies. Advances in

Behavioral Pharmacology. 1: 36–71.

Mittleman MA, Lewis RA, Maclure M, Sherwood JB, and Muller JE (2001) Triggering

myocardial infarction by marijuana. Circulation. 103: 2805–2809.

Moreno M, Lopez-Moreno JA, Rodríguez de Fonseca F, Navarro M (2005)

Behavioural effects of quinpirole following withdrawal of chronic treatment with

the CB1

agonist, HU-210, in rats. Behav Pharmacol. 16: 441-446.

Moskowitz H, Fiorentino D (2000) A Review of the Literature on the Effects of Low

Doses of Alcohol on Driving-Related Skills. National Highway Traffic Safety

Administration, Washington, DC.

Mumford GK, Holtzman SG (1991) Qualitative differences in the discriminative stimulus

effects of low and high doses of caffeine in the rat. J Pharmacol Exp Ther 258:

857–865.

Naef M, Russmann S, Petersen-Felix S, Brenneisen R (2004) Development and

pharmacokinetic characterization of pulmonal and intravenous delta-9-

tetrahydrocannabinol (THC) in humans. J Pharm Sci. 93: 1176-1184.

Nava F, Carta G, Gessa GL (2000) Permissive role of dopamine D(2) receptors in the

hypothermia induced by delta(9)-tetrahydrocannabinol in rats. Pharmacol

194

Biochem Behav. 66: 183-187.

Nelson ME, Bryant SM, Aks SE (2014) Emerging drugs of abuse. Emerg Med Clin North

Am 32: 1–28.

Nguyen JD, Aarde SM, Vandewater SA, Grant Y, Stouffer DG, Parsons LH, Cole M,

Taffe MA (2016) Inhaled delivery of Δ(9)-tetrahydrocannabinol (THC) to rats by

e-cigarette vapor technology. Neuropharmacology. 109: 112-20.

Niesink RJ, van Laar MW (2013). Does cannabidiol protect against adverse

psychological effects of THC? Front Psychiatry 4: 130.

Nordstrom BR, Levin FR (2007) Treatment of cannabis use disorders: A review of the

literature. Am J Addict. 16: 331–342.

Ohlsson A, Lindgren JE, Wahlen A, Agurell S, Hollister LE, Gillespie HK. Plasma delta-9

tetrahydrocannabinol concentrations and clinical effects after oral and

intravenous administration and smoking. Clin Pharmacol Ther 1980;28: 409–416.

Patterson F, Jepson C, Strasser AA, Loughead J, Perkins KA, Gur RC, Frey JM, Siegel

S, Lerman C, (2009) Varenicline improves mood and cognition during smoking

abstinence. Biol. Psychiatry 65: 144-149.

Patterson F, Jepson C, Loughead J, Perkins K, Strasser AA, Siegel S, Frey J, Gur R,

Lerman C, (2010) Working memory deficits predict short-term smoking

resumption following brief abstinence. Drug Alcohol Depend. 106: 61-64.

Polen MR, Sidney S, Tekawa IS, Sadler M, and Friedman GD (1993). Health care use

by frequent marijuana smokers who do not smoke tobacco. The Western Journal

of Medicine, 158: 596–601.

Pope HG Jr, Gruber AJ, Yurgelun-Todd D (1995) The residual neuropsychological

effects of cannabis: The current status of research. Drug Alcohol Depend. 38:

25–34.

195

Pope HG, Jr, and Yurgelun-Todd D (1996). The residual cognitive effects of heavy

marijuana use in college students. Journal of the American Medical Association,

275: 521–527.

Pope HG, Gruber Jr AJ, Hudson JI, Cohane G, Huestis MA, and Yurgelun-Todd, D.

(2003) Early-onset cannabis use and cognitive deficits: what is the nature of the

association? Drug and Alcohol Dependence, 69: 303–310.

Potter DJ, Peter C, Brown MB (2008). Potency of D9–THC and other cannabinoids in

cannabis in England in 2005: implications for psychoactivity and pharmacology. J

Forensic Sci 53: 90–94.

Prescott WR, Gold LH, Martin BR (1992) Evidence for separate neuronal mechanisms

for the discriminative stimulus and catalepsy induced by delta 9-THC in the rat.

Psychopharmacology (Berl). 107:117-124.

Quinn HR, Matsumoto I, Callaghan PD, Long LE, Arnold JC, Gunasekaran N et al.

(2008). Adolescent rats find repeated Delta(9)-THC less aversive than adult rats

but display greater residual cognitive deficits and changes in hippocampal protein

expression following exposure. Neuropsychopharmacology 33: 1113–1126.

Ramaekers JG, Kauert G, Theunissen, EL, Toennes SW, Moeller MR (2009)

Neurocognitive performance during acute THC intoxication in heavy and

occasional cannabis users. J. Psychopharmacol. Oxford 23: 266–277.

Rezapour T, DeVito EE, Sofuoglu M, Ekhtiari H (2016) Perspectives on neurocognitive

rehabilitation as an adjunct treatment for addictive disorders: From cognitive

improvement to relapse prevention. Prog Brain Res 224: 345-369.

Rosenbaum CD, Carreiro SP, Babu KM (2012) Here today, gone tomorrow, and back

again? A review of herbal marijuana alternatives (K2, Spice), synthetic

cathinones (bath salts), kratom, Salvia divinorum, methoxetamine, and

196

piperazines. J Med Toxicol 8: 15–32.

Samson HH, Falk JL (1975) Pattern of daily blood ethanol elevation and the

development of physical dependence. Pharmacol Biochem Behav 3: 1119–1123.

Sarter M, Bruno JP, Parikh V, Martinez V, Kozak R, Richards JB, (2006) Forebrain

dopaminergic-cholinergic interactions, attentional effort, psychostimulant

addiction and schizophrenia. EXS. 98: 65-86.

Scherrer JF, Grant JD, Duncan AE, Sartor CE, Haber JR, Jacob T, Bucholz KK (2009)

Subjective effects to cannabis are associated with use, abuse and dependence

after adjusting for genetic and environmental influences. Drug Alcohol Depend.

105: 76-82.

Schuster CR, Balster RL (1977) The discriminative stimulus properties of drugs. In:

Thompson T, Dews PB (eds) Advances in behavioral pharmacology. Academic,

New York. 85–138.

Schuster CR, Johanson CE (1988) Relationship between the discriminative stimulus

properties and subjective effects of drugs. Psychopharmacol. 4: 161-175.

Schwope DM, Bosker WM, Ramaekers JG, Gorelick DA, Huestis MA (2012)

Psychomotor performance, subjective and physiological effects and whole blood

9-tetrahydrocannabinol concentrations in heavy, chronic cannabis smokers

following acute smoked cannabis. J. Anal. Toxicol. 36: 405–412.

Shelton KL, Young JE, Grant KA (2001) A multiple schedule model of limited access

drinking in the cynomolgus macaque. Behav Pharmacol 12: 559–573.

Sim-Selley LJ, Schechter NS, Rorrer WK, Dalton GD, Hernandez J, Martin BR, and

Selley DE (2006) Prolonged recovery rate of CB1 receptor adaptation after

cessation of long-term cannabinoid administration. Mol Pharmacol 70: 986–996.

197

Singh H, Schulze DR, McMahon LR (2011) Tolerance and cross-tolerance to

cannabinoids in mice: schedule-controlled responding and hypothermia.

Psychopharmacology (Berl). 215: 665-675.

Simms JA, Steensland P, Medina B, Abernathy KE, Chandler LJ, Wise R, et al (2008)

Intermittent access to 20% ethanol induces high ethanol consumption in Long-

Evans and Wistar rats. Alcoholism: Clinical and Experimental Research, 32:

1816–1823.

Singer G, Oei TP, Wallace M (1982) Schedule-induced self-injection of drugs. Neurosci

Biobehav Rev. 6: 77-83.

Sim-Selley LJ, Schechter NS, Rorrer WK, Dalton GD, Hernandez J, Martin BR, and

Selley DE (2006) Prolonged recovery rate of CB1 receptor adaptation after

cessation of long-term cannabinoid administration. Mol Pharmacol 70: 986–996.

Slifer BL (1983) Schedule-induction of nicotine self-administration. Pharmacol Biochem

Behav. 19: 1005–1009.

Slifer BL, Balster RL (1985) Intravenous self-administration of nicotine: with and without

schedule-induction. Pharmacol Biochem Behav. 1: 61-69.

Sofuoglu M, DeVito EE, Waters AJ, and Carroll KM (2013): Cognitive Enhancement as a

Treatment for Drug Addictions. Neuropharmacology 64: 452-463.

Solowij N (1995) Do cognitive impairments recover following cessation of cannabis use?

Life Sciences 56: 2119-2126.

Solowij N, Michie PT, Fox AM (1995): Differential impairments of selective attention due

to frequency and duration of cannabis use. Biol Psychiatry 37:731-739.

Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, et al. (2002)

Cognitive functioning of long-term heavy cannabis users seeking treatment.

JAMA 287:1123-1131.

198

Stewart JL and McMahon LR (2010) Rimonabant-induced Δ9-THC withdrawal in rhesus

monkeys: discriminative stimulus effects and other withdrawal signs. J

Pharmacol Exp Ther. 334: 347-356.

Stretch R, Gerber GJ, Lane E (1976) Cocaine self-injection behaviour under schedules

of delayed reinforcement in monkeys. Can J Physiol Pharmacol. 54: 632-638.

Slifer BL (1983) Schedule-induction of nicotine self-administration Pharmacol Biochem

Behav 19: 1005-1009.

Spealman RD and Goldberg SR (1973) Drug self-administration by laboratory animals:

Control by schedules of reinforcement. Annu Rev Pharmaeol Toxieol 18: 313-

339.

Spealman RD and Goldberg SR (1982) Maintenance of schedule-controlled behavior by

intravenous injections of nicotine in squirrel monkeys. J Pharmacol Exp Ther.

223: 402- 408.

Taffe MA (2012) Δ9-Tetrahydrocannabinol attenuates MDMA-induced hyperthermia in

rhesus monkeys. Neuroscience. 201: 125-133.

Takahashi RN, Singer G (1979) Self-administration of delta 9-tetrahydrocannabinol by

rats. Pharmacol Biochem Behav. 11: 737-740.

Takahashi RN, Karniol IG (1975) Pharmacologic interaction between cannabinol and

delta 9-tetrahydrocannabinol. Psychopharmacologia. 41: 277–284.

Tanda G, Munzar P, Goldberg SR (2000) Self-administration behavior is maintained by

the psychoactive ingredient of marijuana in squirrel monkeys. Nat Neurosci. 3:

1073–1074.

Tanda G (2016) Preclinical studies on the reinforcing effects of cannabinoids. A tribute to

the scientific research of Dr. Steve Goldberg. Psychopharmacology (Berl). 233:

1845-1866.

199

Tapert SF, Schweinsburg AD, Drummond SP, Paulus MP, Brown SA, et al. (2007)

Functional MRI of inhibitory processing in abstinent adolescent marijuana users.

Psychopharmacology (Berl) 194: 173–183.

Tashkin DP (1990) Pulmonary complications of smoked substance abuse. The Western

Journal of Medicine, 152: 525–530.

Terry P, Witkin JM, Katz JL (1994) Pharmacological characterization of the novel

discriminative stimulus effects of a low dose of cocaine. J Pharmacol Exp Ther

270: 1041–1048.

Tyndale RF, Payne JI, Gerber AL, Sipe JC (2007) The fatty acid amide hydrolase C385A

(P129T) missense variant in cannabis users: studies of drug use and

dependence in Caucasians. Am. J. Med. Genet. B Neuropsychiatr. Genet. 144B:

660-666.

Valjent E, Maldonado R (2000) A behavioural model to reveal place preference to Δ9-

tetrahydrocannabinol in mice. Psychopharmacology 147: 436–438.

Varvel SA, Hamm RJ, Martin BR, Lichtman AH (2001) Differential effects of delta 9-THC

on spatial reference and working memory in mice. Psychopharmacology (Berl).

157: 142-150.

Verrico CD, Gu H, Peterson ML, Sampson AR, Lewis DA (2014) Repeated Δ9-

tetrahydrocannabinol exposure in adolescent monkeys: persistent effects

selective for spatial working memory. Am J Psychiatry. 171: 416-25.

Vivian JA, Green HL, Young JE, Majerksy LS, Thomas BW, Shively CA, Tobin JR,

Nader MA, Grant KA (2001) Induction and maintenance of ethanol self-

administration in cynomolgus monkeys (Macaca fascicularis): long-term

characterization of sex and individual differences. Alcohol Clin Exp Res 25:

1087–1097.

200

Wagner M, Schulze-Rauschenbach S, Petrovsky N, Brinkmeyer J, von der Goltz C,

Gründer G, Spreckelmeyer KN, Wienker T, Diaz-Lacava A, Mobascher A,

Dahmen N, Clepce M, Thuerauf N, Kiefer F, de Millas JW, Gallinat J, Winterer G

(2013) Neurocognitive impairments in non-deprived smokers--results from a

population-based multi-center study on smoking-related behavior. Addict Biol.

18:752-761.

Walker S, Fields R, King S, (2013) A bill for an act concerning penalties for persons who

drive while under the influence of alcohol or drugs and in connection therewith

making an appropriation. House Bill 114. Colorado State Assembly.

Ward AS, Comer SD, Haney M, Foltin RW, Fischman MW (1997) The effects of a

monetary alternative on marijuana self-administration. Behav Pharmacol. 8: 275-

286.

Wayner MJ, Greenberg I (1972) Effects of hypothalamic stimulation, acclimation and

periodic withdrawal on ethanol consumption. Physiol Behav 9: 737–740.

Winsauer PJ, Lambert P, and Moerschbaecher JM (1999) Cannabinoid ligands and their

effects on learning and performance in rhesus monkeys. Behav Pharmacol 10:

497–511.

Whitlow CT, Liguori A, Livengood LB, Hart SL, Mussat-Whitlow BJ, Lamborn CM, et al.

(2004): Long-term heavy marijuana users make costly decisions on a gambling

task. Drug Alcohol Depend 76: 107-111.

Wise RA (1973) Voluntary ethanol intake in rats following exposure to ethanol on various

schedules. Psychopharmacologia 29: 203–210.

Woolverton WL and Nader MA (1990) Experimental evaluation of the reinforcing effects

of drugs. in Modern Methods in Pharmacology: Testing and Evaluation of Drugs

of Abuse. (Adler MW and Cowans A eds) 165-192, Wiley-Liss, Inc., New York.

201

Wright MJ, Vandewater SA, Taffe MA (2013) Cannabidiol attenuates deficits of

visuospatial associative memory induced by Δ9-tetrahydrocannabinol. Br. J.

Pharmacol. 170: 1365–1373.

Young AM, Masaki MA, Geula C (1992) Discriminative stimulus effects of morphine:

effects of training dose on agonist and antagonist effects of mu opioids. J

Pharmacol Exp Ther 261:246–257.

Yurgelun-Todd D (2007) Emotional and cognitive changes during adolescence. Curr

Opin Neurobiol. 17: 251-257.

Zacny JP, de Wit H (1991) Effects of food deprivation on subjective effects and self-

administration of marijuana in humans. Psychol Rep. 68: 1263–1274.

Zhang ZF, Morgenstern H, Spitz MR, et al. (1999) Marijuana use and increased risk of

squamous cell carcinoma of the head and neck. Cancer Epidemiology,

Biomarkers & Prevention. 8: 1071–1078.

202

SCHOLASTIC VITAE

NAME: WILLIAM SCOTT JOHN CURRENT TITLE: Ph.D. Candidate ADDRESS: Department of Physiology & Pharmacology Wake Forest University School of Medicine Medical Center Boulevard, NRC 547 Winston-Salem, NC 27157-1083 Telephone: (336) 713-7166 Telefax: (336) 713-7180 E-mail: wjohn@wakehealth.edu

PERSONAL INFORMATION:

BIRTHPLACE: Mount Airy, NC, USA DATE OF BIRTH: February 11, 1988 MARITAL STATUS: Married: Joanna Elizabeth John

EDUCATION:

2012 - Present Wake Forest University School of Medicine (July, 2016 graduation) Ph.D. Integrative Physiology & Pharmacology

Dissertation: Behavioral mechanisms of Δ9-THC

in nonhuman primates Advisor: Michael A. Nader, Ph.D.

2006 - 2010 University of North Carolina at Greensboro B.S. Biology Graduated with Departmental Honors RESEARCH EXPERIENCE:

Jan. 2013 – Present Graduate Research Michael A. Nader, Ph.D. Department of Physiology & Pharmacology Wake Forest University School of Medicine Aug. 2012 – Dec. 2012 Graduate Research Rotation Christos Constantinidis, Ph.D. Department of Neurobiology & Anatomy Wake Forest University School of Medicine 2011 – 2012 Laboratory Technician II Larry Liao, Ph.D. Duke Human Vaccine Institute

Duke University Medical Center

203

2010 – 2011 Research Assistant Keith Erikson, Ph.D. Department of Nutrition University of North Carolina at Greensboro PROFESSIONAL MEMBERSHIPS:

2014 – Present International Study Group Investigating Drugs as Reinforcers (ISGIDAR)

2014-Present Western North Carolina Chapter of the Society

for Neuroscience

2013-Present The American Society for Pharmacology and Experimental Therapeutics (ASPET)

2008-2010 TriBeta National Biological Honor Society HONORS, AWARDS, AND FELLOWSHIPS:

2016 NIDA Director’s Travel Award to The College on Problems of Drug Dependence (CPDD)

2016 Ruth L. Kirschstein National Research Service

Award (NIDA)

2016 Predoctoral Travel Award Behavioral Pharmacology Society Meeting

2016 Graduate Student Travel Award ASPET Annual Meeting at Experimental

Biology

2015 Graduate Student Travel Award, Behavior, Biology and Chemistry: Translational Research in Addiction Annual Meeting

2014 Institutional Training Fellowship (T32)

Collaborative Research on Addiction initiative at NIH (CRAN)

2014 William L. Woolverton Memorial ISGIDAR Travel Award

2014, 2015 Alumni Student Travel Award WFU Graduate School of Arts and Sciences

204

2014 Graduate Student Travel Award, Behavior, Biology and Chemistry: Translational Research in Addiction Annual Meeting

2012-2013 Graduate Fellowship Department of Integrative Physiology and

Pharmacology, Wake Forest University School of Medicine

2008-2010 TriBeta National Biological Honor Society 2006-2010 NCAA Div. I Scholarship Student Athlete INSTITUTIONAL SERVICE:

2014-Present Matching Matriculates and Return Students (MMARS) program

GUEST REVIEWER:

Addiction Biology Biological Psychiatry Drug and Alcohol Dependence Human Brain Mapping Journal of Neuroscience Methods Neuropsychopharmacology Pharmacology, Biochemistry and Behavior

TEACHING EXPERIENCE: Wake Forest School of Medicine Fall 2015 Animal Models of Human Disease (COMD 706)

BIBLIOGRAPHY:

JOURNAL ARTICLES: Keck T, John WS, Nader MA, Newman AH (2015). Identifying medication targets

for psychostimulant addiction: Unraveling the dopamine D3 receptor hypothesis. Journal of Medicinal Chemistry: 58: 5361-80. John WS, Newman AH, Nader MA (2015). Differential effects of acute and

repeated dopamine D3 receptor antagonism in cocaine and methamphetamine self-administration in rhesus monkeys. Neuropharmacology: 92: 34-43.

John WS, Banala AK, Newman AH, Nader MA (2014). Effects of buspirone and

the dopamine D3 receptor compound PG 619 on cocaine and methamphetamine self-administration using the drug-food choice paradigm. Psychopharmacology: 232: 1279-89.

205

Martelle SE, Nader SH, Czoty PW, John WS, Duke AN, Garg PK, Garg S, and

Nader MA (2014). Further characterization of quinpirole-elicited yawning as a model of dopamine D3 receptor activation in male and female monkeys. J Pharmacol Exp Ther 350: 205-11. John, WS and Nader, MA (2016). Effects of acute ethanol administration on

cocaine self-administration under a second-order schedule of reinforcement. Under review. John, WS, Martin, TJ, Sai, KKS, Nader, SH, and Nader, MA (2016). Chronic Δ9-

THC administration in nonhuman primates: Effects on cognition, sleep, and dopamine D2 receptor availability. Under review. John, WS, Martin, TJ, and Nader, MA (2016). Determinants of cannabinoid self-

administration in Old-World monkeys. Under Review.

John, WS, Newman, AH, Hopkins, C, Nader, MA. Effects of dopamine D2, D3,

and D4 selective antagonists on cocaine self-administration in rhesus monkeys using a food-drug choice paradigm. In preparation. John, WS and Nader, MA. Cannabinoid modulation of cocaine-food choice in

rhesus monkeys. In preparation. Czoty PW, John WS, Newman AH, Nader MA. Ethanol/cocaine interactions:

conditioned and unconditioned behaviors in rhesus monkeys. In preparation. BOOK CHAPTERS: Nader MA, Lile JA, John WS, Czoty PW (2015). Nonhuman primate models of

abuse liability. In GF Weinbauer, F Vogel (Eds) “Primate Biologics Research at a Crossroads.” Waxmann Publishing Co: Muenster, Germany: 37-59.

ABSTRACTS:

John, WS and Nader, MA (2016). Characterization of the behavioral phenotypes

underlying the reinforcing effects of cannabinoid receptor agonists in rhesus monkeys. Experimental Biology (American Society for Pharmacology & Experimental Therapeutics). San Diego, CA. John, WS and Nader MA (2016). Effects of chronic Δ9-THC self-administration on cognition and the reinforcing effects of Δ9-THC in nonhuman primates. Wake

Forest University Graduate School Research Day. Winston-Salem, NC.

Czoty PW, John WS, Nader MA, Newman AH, Epperly P (2015).

Ethanol/cocaine interactions: conditioned and unconditioned behaviors in rhesus monkeys. American College of Neuropsychopharmacology. Hollywood, FL.

John, WS, Newman AH, Nader MA (2015). Individual differences in the efficacy

of dopamine D3 receptor antagonism in monkey models of psychostimulant

206

abuse. Wake Forest University Neuroscience Graduate School Research Day . Winston-Salem, NC.

John WJ, Newman AH, Nader MA (2015). Differential effects of the dopamine D3 receptor antagonist PG01037 on cocaine and methamphetamine self-administration in rhesus monkeys. Experimental Biology (American Society for Pharmacology & Experimental Therapeutics). Boston, MA.

John WS and Nader MA (2015). Self-administration of the cannabinoid agonist

CP 55,940 in Old-World monkeys. Behavior, Biology and Chemistry: Translational Research in Addiction. San Antonio, Texas.

Boateng, CA, Okunola-Bakare O, Keck TM, Burzynski C, Rais R, Slusher B, Donthamsetti P, Javitch JA, John WS, Czoty PW, Nader MA, Newman AH

(2014). The development of novel dopamine D3 receptor selective partial agonists as potential medications to treat psychostimulant abuse. Society for Neuroscience. Washington, D.C. John, WS, Newman AH, Nader MA (2014). Dopamine D2/D3 receptor modulation of food-drug choice in rhesus monkeys. Wake Forest University Neuroscience Graduate School Research Day. Winston-Salem, NC.

John WS, Newman AH, Nader MA (2014). Effects of buspirone and the

dopamine D2/D3 receptor antagonist PG 01037 on food-drug choice in rhesus monkeys. Behavior, Biology and Chemistry: Translational Research in Addiction. San Antonio, Texas.

John, WS, Newman AH, Nader MA (2014). Dopamine D2/D3 receptor modulation

of food-drug choice in rhesus monkeys. Wake Forest University Graduate School Research Day. Winston-Salem, NC.

John WS, Newman AH, Nader MA (2014). Dopamine D2/D3 receptor modulation

of food-drug choice in rhesus monkeys. The College on Problems of Drug Dependence. San Juan, Puerto Rico.

Martelle SE, Nader SH, Czoty PW, John WS, Newman AH, Nader MA (2014).

Dopamine D3 receptor availability: sex differences and effects of chronic drug exposure. The College on Problems of Drug Dependence. San Juan, Puerto Rico.

John WJ, Green HL, Newman AH, Nader MA (2014). Effects of selective dopamine D3 receptor compounds in nonhuman primate models of cocaine and methamphetamine abuse. Experimental Biology (American Society for Pharmacology & Experimental Therapeutics). San Diego, CA.

INVITED PRESENTATIONS: John, WS and Nader, MA (2016). Characterization of the behavioral phenotypes underlying the reinforcing effects of cannabinoid receptor agonists in rhesus monkeys. The College on Problems of Drug Dependence. Palm Springs, CA.

207

John WS and Nader, MA (2016). Behavioral determinants of cannabinoid

receptor agonist self-administration in nonhuman primates. Behavioral Pharmacology Society Meeting, San Diego, CA. John WS (2015). Behavioral and pharmacological determinants of cannabinoid

reinforcement and effects on cognition in rhesus monkeys. Student Seminar Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC. John WS, Nader MA (2015). Behavioral mechanisms of cannabinoid

reinforcement: Towards a model of Δ9-THC self-administration in rhesus monkeys. Wake Forest University-Emory Annual Lab Exchange. Atlanta, GA. John WS (2015). Role of Kappa Opioid Receptors in Stress. Student Seminar

Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC.

John WS (2014). Differential effects of acute and repeated dopamine D3 receptor antagonism on food-drug choice: A comparison between cocaine and methamphetamine. Wake Forest University-Emory Annual Lab Exchange. Winston-Salem, NC.

John WS (2014). Effects of buspirone and the dopamine D3 receptor partial

agonist PG 619 on cocaine and methamphetamine self-administration in monkeys. ISGIDAR, San Juan, Peurto Rico. John WS (2014). Behavioral effects of dopamine D2/D3 compounds in nonhuman

primate models of cocaine and methamphetamine abuse. Student Seminar Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC. John WS (2013). Effects of dopamine D2/D3 receptor ligands on cocaine and

methamphetamine self-administration, reinstatement, and discrimination in monkeys. Wake Forest University-Emory Annual Lab Exchange. Atlanta, GA.

John WS (2013). Effects of dopamine D2/D3 receptor ligands on cocaine and

methamphetamine self-administration in monkeys. Student Seminar Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC.

CURRENT RESEARCH SUPPORT:

F31 DA041825 03/1/16 – Present Mentor: Michael Nader, Ph.D. Ruth L. Kirschstein National Research Service Award (NIDA) Behavioral and pharmacological determinants of cannabinoid reinforcement and effects on cognition in rhesus monkeys

Role: PI

208

COMPLETED RESEARCH SUPPORT:

T32 AA007565 07/01/2014 – 2/29/16 Mentor: Michael A. Nader, Ph.D. Institutional Training Fellowship (T32), Collaborative Research on Addiction Initiative at NIH (CRAN) supplement (NIAAA) Ethanol/cocaine interactions in nonhuman primates

Role: Graduate student