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PRESENTED BY Jaspreet Singh Deepika (M.Pharm I)

Pharmacokinetic and Pharmacodynamic Modeling

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The presentation gives you a bird eye's view regarding basics of PK-PD modeling, its applications, types, limitations and various softwares used for the same.

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Page 1: Pharmacokinetic and Pharmacodynamic Modeling

PRESENTED BY

Jaspreet Singh

Deepika

(M.Pharm I)

Page 2: Pharmacokinetic and Pharmacodynamic Modeling

As defined by F.H. Dost in 1953, Pharmacokinetics is a

science dealing with study of biological fate of drug &/or its

metabolite(s) during its sojourn within the body of a man or

animal, with the help of mathematical modeling.

In simple words it is the study of what body does to the drug.

The term Pharmacokinetics was coined by Torston Teorell.

It involves the study of ADME.

Page 3: Pharmacokinetic and Pharmacodynamic Modeling

DOSE

DRUG IN

TISSUES

DRUG IN

SYSTEMIC

CIRCULATION

EXCRETION

AND

METABOLISM

SCHEMATIC REPRESENTATION ADME

ABSORPTION

ELIMINATION

DISTRIBUTION

Page 4: Pharmacokinetic and Pharmacodynamic Modeling

It refers to the relationship between drug concentration at the

site of action and the resulting effect, including the time

course and intensity of therapeutic and adverse effects.

In simple words it is the study of what drug does to the body.

IUPAC definition : Branch of pharmacology concerned with

pharmacological actions on living systems, including

reactions with and binding to cell constituents, and the

biochemical and physiological consequences of these

actions.

Page 5: Pharmacokinetic and Pharmacodynamic Modeling

RECEPTOR OCCUPANCY MODEL

Given by Langley, hill and Clarke.

Based on law of Mass Action.

Drug effect is related to proportion of receptors occupied.

[DRUG] + [RECEPTOR] [DRUG][RECEPTOR]

RESPONSE

K1

K2

Page 6: Pharmacokinetic and Pharmacodynamic Modeling

Any drug that binds to a receptor and stimulates the

functional activities

Has both affinity as well as intrinsic activity.

e.g. Ach

Receptor

Acetylcholine

A Cell

Some Effect

Page 7: Pharmacokinetic and Pharmacodynamic Modeling

It has affinity to receptor but no intrinsic activity.

It prevents binding of agonist to receptor.

e.g. atropine

Acetylcholine

Atropine

Dude, you’re

in my way!

Page 8: Pharmacokinetic and Pharmacodynamic Modeling

Any drug that binds to a receptor and produces an

opposite effect as that of an agonist.

Receptor

Inverse agonist

A Cell

Effect opposite

to that of

the true agonist

Page 9: Pharmacokinetic and Pharmacodynamic Modeling

Produces a sub maximal response.

Affinity is there but intrinsic activity is less than agonist.

True agonist

Partial agonist

Oh!!!, I should

Have been here

Submaximal

effect

Page 10: Pharmacokinetic and Pharmacodynamic Modeling

RATE THEORY

Pharmacological response is not dependent on drug-receptor

complex concentration but rather depends upon rate of

association of drug and receptor.

LOCK AND KEY MODEL

Only a drug of specific chemical structure can bind with the

receptor.

INDUCED FIT MODEL

When the drug binds to the receptor, it produces some

conformational change in the receptor which helps in better

fitting of the drug inside active site of receptor.

Page 11: Pharmacokinetic and Pharmacodynamic Modeling

PK/PD modeling is a scientific mathematical tool which

integrates PK model to that of PD model.

PK model - describes the time course of drug concentration

in the plasma or blood.

PD model - describes the relationship between drug

concentration at site of action and effect.

PK/PD models use data derived from plasma drug

concentration vs. time profile and from the time course of

pharmacological effect to predict the Pharmacodynamics of

the drug.

Result is summation of Pharmacodynamics and

pharmacokinetics effect.

Page 12: Pharmacokinetic and Pharmacodynamic Modeling

ADVANCED/NON STEADY-STATE/TIME DEPENDENT MODELS

SIMPLE DIRECT EFFECT/STEADY-

STATE/TIME INVARIANT MODELS

Page 13: Pharmacokinetic and Pharmacodynamic Modeling

Linear model

Log-linear model

Emax model

Sigmoidal Emax model

Biophase distribution

model

Signal transduction

model

Tolerance model

Mechanism based indirect response model

Simple direct effect

models

Nonsteady-state & time-

dependent models

Page 14: Pharmacokinetic and Pharmacodynamic Modeling

Linear model

Log-Linear Model

Emax ModelSigmoidal Emax Model

Effect of drug is direct.

Fast mechanism of action.

Rapid equilibrium exists

between site of action and

the sampling biofluids.

PD parameters are time

invariant.

Page 15: Pharmacokinetic and Pharmacodynamic Modeling

Drug effect is directly proportional to drug concentration.

Pharmacodynamically it is explained as:

E∝ C …..(1)

E = S×C …..(2)

where,

E = Effect of drug

C = Drug concentration

S = Slope obtained from E vs C graph

In case of baseline effect (E0), when the drug is absent, model

may be represented as:

E = E0 + S*C …..(3)

Page 16: Pharmacokinetic and Pharmacodynamic Modeling

so,

slope = S

intercept= E0

Eff

ect

E0

Concentration

SE = E0 + S*C

y = c + mx

Page 17: Pharmacokinetic and Pharmacodynamic Modeling

Advantages

Model is simple and parameter estimation can be easily

performed by linear regression.

Limitations

Applicable at low drug concentrations only

excludes the prediction of maximum effect

Example

Relationship between central activity of diazepam and

plasma drug concentration

Page 18: Pharmacokinetic and Pharmacodynamic Modeling

When the effect of drug is measured over a large range, the

relationship between concentration and effect is not linear

and may be curvilinear and log transformation is needed.

The log concentration-Effect is roughly linear in

concentration range of 20-80% of maximum Effect.

It is given by:

E = E0 + S*log C …(4)

where,

E = effect

E0=Baseline effect

S = slope

C= concentration

Page 19: Pharmacokinetic and Pharmacodynamic Modeling

E

Log C

It expands the initial part of the curve where response is

slowly making progression before it accelerates

It contracts the latter part of the curve where a large change

in concentration produces a slight change in response.

In middle part relationship is linear.

Page 20: Pharmacokinetic and Pharmacodynamic Modeling

Advantage

Unlike linear model it is applicable over large concentration

range.

Limitations

Pharmacological effect cannot be estimated when the

concentration is zero because of the logarithmic function.

Maximum effect cannot be predicted.

Example

This model has been successfully used in predicting the

pharmacological activities of beta blockers and

anticoagulants.

Page 21: Pharmacokinetic and Pharmacodynamic Modeling

This model incorporates the observation known as the law

of diminishing returns.

This law shows that an increase in drug concentration near

the maximum pharmacological response produces a

disproportionately smaller increase in the pharmacological

response.

This model describes the drug action in the terms of :

E max (maximum effect)

EC50 ( the drug concentration that produces 50%

maximum pharmacological effect)

….(5)

CEC

CEE

50

max

Page 22: Pharmacokinetic and Pharmacodynamic Modeling

It mimics the hyperbolic shape of pharmacologic response

vs. drug concentration curve.

After maximum response (Emax) has reached, no further

increase in pharmacologic response is seen on increase in

concentration of the drug.

EC50 is useful for determining drug concentration that lies

within the therapeutic range.

E

C

EC50

Emax

Page 23: Pharmacokinetic and Pharmacodynamic Modeling

It is a saturable process and resembles the Michaelis-Menton

equation.

In case, there is a baseline effect i.e. the measured

pharmacologic effect has some value in absence of drug (e.g.

blood pressure, heart rate, respiratory rate) then the equation

becomes:

….(6)

where,

E0 = Pharmacologic effect (baseline activity) at zero

drug concentration in the body

CEC

CEEE o

50

max

Contd…

Page 24: Pharmacokinetic and Pharmacodynamic Modeling

A double-reciprocal plot of equation is used to linearize the

relation, similar to Lineweaver-Burke equation.

…(7)maxmax

50 11

ECE

EC

E

-1/ EC50 1/C

1/ Emax

slope = EC50 / Emax

Contd…

1/E

Page 25: Pharmacokinetic and Pharmacodynamic Modeling

Advantages

Maximum pharmacological response can be found out.

EC50 can be calculated (i.e., concentration needed to

produce half maximum response).

Limitations

In case of highly potent drugs it is not possible to find the

maximum effect because test organisms die long before the

maximum effect is attained.

The method can be time consuming if maximum effect is

obtained at a very high concentration.

Example

Bronchodilator activity of Theophylline is studied by this

model.

Page 26: Pharmacokinetic and Pharmacodynamic Modeling

Given by Hill.

It describes the pharmacologic response versus drug

concentration curve for many drugs that appear to be S-

shaped (i.e. Sigmoidal) rather than hyperbolic as

described by more simple Emax model.

The equation for the sigmoid Emax Model is an extension

of the Emax Model:

…(8) n

n

CEC

CEE

50

max

n is an exponent describing the number of drug molecules

that combine with each receptor molecule.

When n=1, the Sigmoid Emax Model reduces to the Emax Model

Page 27: Pharmacokinetic and Pharmacodynamic Modeling

A value of n>1 influences the slope of the curve and the

model fit.

In the Sigmoid Emax Model, the slope is influenced by the

number of drug molecules bound to the receptor.

A very large n value may indicate allosteric or cooperative

effects in the interaction of the drug molecules with the

receptor.

Cooperativity is the case when binding of substrate at on

binding site affects the affinity of other sites to their

substrates.

Page 28: Pharmacokinetic and Pharmacodynamic Modeling

E

CONCENTRATION

EMAX

EC50

Graphical representation

n > 1

n = 1

n < 1E

CONCENTRATION

Page 29: Pharmacokinetic and Pharmacodynamic Modeling

Biophase distribution model

Mechanism-based indirect response

model

Signal transduction

model

Tolerance model

Page 30: Pharmacokinetic and Pharmacodynamic Modeling

Indirect effect of the drug.

The effect is not immediate.

Distribution of the drug is the rate limiting step.

Slow association and dissociation of drug with the

receptors.

Page 31: Pharmacokinetic and Pharmacodynamic Modeling

For some drugs, the pharmacologic response produced by

the drug may be observed before or after the plasma drug

concentration has peaked. Such drugs may produce

indirect or delayed response.

Drug distribution to the effect site may represent a rate-

limiting step for drugs in exerting their pharmacological

effect.

To account for this indirect or delayed response, a

hypothetical effect compartment has been postulated by

Holford and Sheiner.

Page 32: Pharmacokinetic and Pharmacodynamic Modeling

EFFECT COMPARTMENT

It is not part of the pharmacokinetic model but is a

hypothetical pharmacodynamic compartment that links to the

plasma compartment containing drug.

It is because amount of drug entering this compartment is

considered to be negligible and is therefore not reflected in

pharmacokinetics of the drug.

Page 33: Pharmacokinetic and Pharmacodynamic Modeling

V C1 Ve CeEffect

k1e keo

Plasma

Compartment Effect

Compartment

Drug transfer from plasma to hypothetical effect compartment

takes place with first order rate constant.

Only free drug can diffuse into the effect compartment.

The pharmacological response of the drug depends on the rate

constant ke0 and the drug concentration in the effect

compartment.

k1

Page 34: Pharmacokinetic and Pharmacodynamic Modeling

The amount of drug in the effect compartment after i.v. bolus

dose may be given by:

...(9)

where,

De = amount of drug in effect compartment

D1 = amount of drug in central compartment

ke0 = rate constant for drug transfer out of the effect

compartment

K1e = rate constant for drug transfer from plasma to effect

compartment

dt

DkDkdD eeee 011

Page 35: Pharmacokinetic and Pharmacodynamic Modeling

Integrating the equation we get:

…(10)

Dividing by Ve ,

…(11)

The above equation is not very useful as parameters Ve and

k1e are both unknown and cannot be obtained from plasma

drug concentrations. Therefore assumptions are made.

)()(

0

0

10 tkkt

e

e eeekk

kDDe

)()(

0

0

10 tkkt

ee

ee

eeekkV

kDC

Page 36: Pharmacokinetic and Pharmacodynamic Modeling

Even though an effect compartment is present in addition to the plasma compartment, this hypothetical effect compartment takes up only a negligible amount of the drug dose.

So plasma drug level still follows a one-compartment equation.

After an IV bolus dose, the rate of drug entering and leaving the effect compartment is controlled by k1e and ke0.

At steady state,

input = output

k1eD1 = keoDe …(12)

Rearranging,

…(13)e

ee

k

DkD

1

01

Assumptions

Page 37: Pharmacokinetic and Pharmacodynamic Modeling

Dividing by VD yields the steady state plasma drug concentration

C1

…(14)

from eq.…(10)

substituting De in equation (14)

…(16)

…(17)

De

ee

Vk

DkC

1

01

)()(

0

0

10 tkkt

e

ee

eeekk

kDD

)()(

0

01

1001

tkkt

eDe

ee eeekkVk

kDkC

)()(

0

0

00

1

tkkt

eD

eeee

kkV

kDkC

Page 38: Pharmacokinetic and Pharmacodynamic Modeling

At steady state, C1 is unaffected by k1e but depends on

k and ke0.

C1 is the steady state concentration and has been used

to relate the pharmacokinetic effect of many drugs,

including some of delayed equilibrium between plasma

and effect compartment.

k and ke0 jointly determine the pharmacodynamic

profile of the drug.

Page 39: Pharmacokinetic and Pharmacodynamic Modeling

Dynamic flexibility and adaptability.

The model accommodates the aggregate effects of drug

elimination, binding, partitioning and distribution.

Model represent in vivo pharmacologic event relating to

plasma drug concentration that clinician can monitor and

adjust.

This model has been used to characterize the PK/PD of several

drugs (e.g. midazolam, pancuronium, alprazolam, etc.) whose

plasma concentrations could not be correlated with the effect

being produced.

Page 40: Pharmacokinetic and Pharmacodynamic Modeling

The indirect response model is based on the premise that the

drug response is indirectly mediated by either inhibition or

stimulation of the factors controlling either the production

(Kin) or the dissipation of response (Kout).

EXAMPLES:

Indirect response modeling was first introduced by

Nagashima et al. for the anticoagulant effect of warfarin.

These models may be appropriate for various classes of

drugs, including histamine H2-receptor antagonists (such

as cimetidine) and oral hypoglycemic agents (such as

tolbutamide).

Page 41: Pharmacokinetic and Pharmacodynamic Modeling

Response

[DRUG] [DRUG]

KinKout

Stimulation

Or

Inhibition

Stimulation

Or

Inhibition

Page 42: Pharmacokinetic and Pharmacodynamic Modeling

In the absence of drug, the rate of change in response over time (dR/dt) can be described by a differential equation as follows:

…(18)

where,

R = response

kin = zero-order rate constant for the production of response

kout = first order rate constant for the dissipation of response

Used in cases where endogenous mediators are involved in the expression of the response.

Rkkdt

dRoutin

Page 43: Pharmacokinetic and Pharmacodynamic Modeling

TYPES OF INDIRECT RESPONSE MODELS

II. Inhibition of Kout

III. Stimulation of Kin

(Stimulation of production)

IV. Stimulation of Kout

(Dissipation of response)

RKtSKdt

dRoutin

RKtIKdt

dRoutin

RtSKKdt

dRoutin

RtIKKdt

dRoutin

S(t), I(t) – Stimulation and inhibition functions

I. Inhibition of Kin

(Inhibition of production)

(Stimulation of response)

Page 44: Pharmacokinetic and Pharmacodynamic Modeling
Page 45: Pharmacokinetic and Pharmacodynamic Modeling

1. H2-receptor antagonist: Inhibition of gastric secretion.

Page 46: Pharmacokinetic and Pharmacodynamic Modeling

which MODEL is it representing?

Model I

Page 47: Pharmacokinetic and Pharmacodynamic Modeling

2. Induction of MX protein synthesis: Interferon α-2a

Page 48: Pharmacokinetic and Pharmacodynamic Modeling

Now which model is it?

MODEL III

Page 49: Pharmacokinetic and Pharmacodynamic Modeling

SIGNAL TRANSDUCTION MODEL

The pharmacological effects

of drugs may be mediated by a

time-dependent signal

transduction process, in

which the response measured

clinically involves multiple

steps removed from the initial

biochemical effect of the drug.

Page 50: Pharmacokinetic and Pharmacodynamic Modeling

CONTD…

There are two major classes of receptors involved in signal

transduction process:

1.cell membrane receptors

2.cytosolic/nuclear receptors

Since cascade of steps is involved in signal transduction,

theoretically there should be delay between each step.

Owing to technical and research limitations at cellular and

molecular level, PD response vs. time relationship for every

step is difficult to obtain.

To characterize such delayed effects stochastic models with

transit compartments and transit times are employed.

This model has been used to characterize the

parasympathomimetic activity of scopolamine and atropine

in rats.

Page 51: Pharmacokinetic and Pharmacodynamic Modeling

1 2 3 Nτ τ τ

D + R DR

Page 52: Pharmacokinetic and Pharmacodynamic Modeling

TOLERANCE MODEL

Tolerance is characterized by a reduction in pharmacological

response after repeated or continuous drug exposure.

For some drugs, pharmacodynamic parameters like Emax and

EC50 may appear to vary over time, resulting in changes in

pharmacological response despite the presence of constant

concentrations at the effect site.

The complex mechanism of tolerance may involve:

receptor pool depletion

decrease in receptor affinity

Page 53: Pharmacokinetic and Pharmacodynamic Modeling

The development of tolerance can have a significant impact

on the exposure-response relationship and, if not

recognized, can contribute to poor clinical outcome.

Pharmacokinetic/ pharmacodynamic modeling can be a

very useful tool to characterize the time course and

magnitude of tolerance development.

53

Page 54: Pharmacokinetic and Pharmacodynamic Modeling

An increase in EC50 over time for Terbutaline which is

likely attributed to a decrease in the receptor number’

Development of tolerance to the acid inhibitory effect of

ranitidine. The derived model indicated that ranitidine

developed tolerance with increased EC50 by 100% within 6 –

10 hr after prolonged IV administration.

54

EXAMPLES

Page 55: Pharmacokinetic and Pharmacodynamic Modeling

Many pharmacological responses are complex and do not show a direct relationship between pharmacologic effect and plasma drug concentration.

Some drugs have a plasma drug concentration and response that resembles hysteresis loop.

Hysteresis is defined as ‘the retardation or lagging of an effect behind the cause of the effect’.

An alternative definition would be ‘failure of one of two related phenomena to keep pace with the other’.

.

Page 56: Pharmacokinetic and Pharmacodynamic Modeling

Identical drug concentration can result in different

pharmacological response, depending on whether the plasma

drug concentration is on ascending or descending phase of the

loop.

Hysteresis

Clockwise Anticlockwise

Page 57: Pharmacokinetic and Pharmacodynamic Modeling

Here response decreases with time.

If we take a concentration say (C1), it can be clearly seen that the response at this concentration decreases from E2 to E1 with passage of time

C

E

C1

E2

E1

Page 58: Pharmacokinetic and Pharmacodynamic Modeling

1.Fentanyl and Alfentanil

Explanation: These are opioid analgesics and have high lipid solubility. Initially, with increase in plasma concentration effect is increasing proportionally but after some times effect decreases due to redistribution of drug.

2.Isoprterenol

Explanation: The diminished response is due to result of cellular response and physiologic adaptation to intense stimulation of drug.

3.Acetazolamide

Explanation: physical adaptation.

Page 59: Pharmacokinetic and Pharmacodynamic Modeling

4.Amphetamine

Explanation: Exhaustion of mediators.

5. Anticonvulsants

Explanation: Increased metabolism.

6. Benzodiazepenes

Explanation: Loss of modulator binding site.

Page 60: Pharmacokinetic and Pharmacodynamic Modeling

In the counterclockwise hysteresis loop, response increases with time.

If we take a concentration say (C1), it can be clearly seenthat the response at this concentration increases from E1toE2 with passage of time.

E

C

E2

E1

C1

Page 61: Pharmacokinetic and Pharmacodynamic Modeling

1.Ajmaline

Explanation: Drug is highly bound to α1-AGP and

initial diffusion of drug into effect compartment is

slow.

2.Pancuronium

Explanation: Slow movement of ionized compound

from capillaries to NMJ.

3. Morphine

Explanation: Slow entry into CNS due to low lipid

solubility .

Page 62: Pharmacokinetic and Pharmacodynamic Modeling

POPULATION PK/PD MODELLING

This includes the search for covariates such as patient weight,

age, renal function & disease status which contribute to

interindividual variability, affecting PK/PD relationship.

It is a useful tool during drug development.

OBJECTIVE : Characterisation of interindividual variability

in PK/PD parameters.

The detection and quantification of covariate effects may

influence the dosage regimen design.

Page 63: Pharmacokinetic and Pharmacodynamic Modeling

METHODS USED IN PK/PD MODELING

Two Stage Approach

Naive Pooled Approach

Hierarchical Non-linear Mixed

Effects Modeling

1.

2.

3.

Page 64: Pharmacokinetic and Pharmacodynamic Modeling

TWO STAGE APPROACH

The standard two-stage approach can be used to estimate

population model parameters:

STAGE 1: Individual parameters are estimated for each subject.

STAGE 2: Using these estimates, in the secondstage, population mean values andinterindividual variability of parameters arecalculated

Page 65: Pharmacokinetic and Pharmacodynamic Modeling

ADVANTAGE :

• Simplicity

LIMITATIONS :

• Requires extensive sampling for each individual in order to

estimate individual parameters.

• It has been shown from simulation studies that the standard

two stage approach tends to overestimate parameter

dispersion.

CONTD….

65

Page 66: Pharmacokinetic and Pharmacodynamic Modeling

Naive Pooled Approach

It was proposed by Sheiner and Beal.

Method involves pooling all the data from all individuals

as if they were from a single individual to obtain population

parameter estimates.

Generally, the naïve pooled approach performs well in

estimating population pharmacokinetic parameters from

balanced pharmacokinetic data with small between-

subject variations.

Page 67: Pharmacokinetic and Pharmacodynamic Modeling

Tends to confound individual differences and diverse sources

of variability, and it generally performs poorly when dealing

with imbalanced data.

Caution is warranted when applying the naïve pooled

approach for PD data analysis because it may produce a

distorted picture of the exposure–response relationship and

thereby could have safety implications when applied to the

treatment of individual patients.

Page 68: Pharmacokinetic and Pharmacodynamic Modeling

HIERARCHICAL NON-LINEAR MIXED-

EFFECT MODELLING

Can handle both sparse and intensive sampleddata, making it a powerful tool to study PK/PDin special populations, such as neonates, theelderly, and AIDS patients, where the numberof samples to be collected per subject islimited due to ethical and/or medical concerns.

Page 69: Pharmacokinetic and Pharmacodynamic Modeling

Contd…

Influence of patient demographics (e.g., weight, gender, age, etc.) and pathophysiological factors (e.g., hepatic function, renal function, disease status, etc.) on drug PK and PD disposition may be assessed.

Analyzes the data of all individuals at once, estimating individual and population parameters, as well as the interindividual, intraindividual residual, and interoccasionvariabilities.

It also allows the evaluation and quantification of potential sources of variability in pharmacokinetics and pharmacodynamics in the target population.

Page 70: Pharmacokinetic and Pharmacodynamic Modeling

Contd…

Useful in the design of dosing regimens and therapeutic drug monitoring.

The non-linear mixed-effects model is the most widely used method and has proven to be very useful for continuous measures of drug effect, categorical response data, and survival-type data.

The non-linear mixed-effects modeling software (NONMEM) introduced by Sheiner and Beal is one of the most commonly used programs for population analysis.

Page 71: Pharmacokinetic and Pharmacodynamic Modeling

NIH (National Institute of Health) defines biomarkers as, an indicator of a biological state.

It is a characteristic that is measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic responses to a therapeutic intervention.

Detection of biomarker

Quantitative

a link between quantity of the marker and disease .

Qualitative

a link between existence of a marker and disease.

An Ideal Marker should have great sensitivity, specificity, and accuracy in reflecting total disease burden. A tumor marker should also be prognostic of outcome and treatment

Page 72: Pharmacokinetic and Pharmacodynamic Modeling

ANTECEDENT BIOMARKERS : Identifying the risk of developing an illness. e.g. amyloidal plaques start forming before the symptoms of AD appear.

SCREENING BIOMARKERS: Screening for subclinical disease. E.g. abnormal lipid profile is a screening marker of heart disease.

DIAGNOSTIC BIOMARKERS: Recognizing overt disease. E.g. Diagnostic kits for various diseases.

STAGING BIOMARKERS : Categorizing disease severity.

PROGNOSTIC BIOMARKERS: Predicting future disease course, including recurrence and response to therapy and monitoring efficacy of therapy.

Page 73: Pharmacokinetic and Pharmacodynamic Modeling

APPLICATIONS OF BIOMARKERS

• Use in early-phase clinical trials to establish “proof of

concept”.

• Diagnostic tools for identifying patients with a specific

disease.

•As tools for characterizing or staging disease processes.

•As an indicator of disease progress.

• For predicting and monitoring the clinical response to

therapeutic intervention.

Page 74: Pharmacokinetic and Pharmacodynamic Modeling

APPLICATIONS

OF PK/PD

MODELING

Page 75: Pharmacokinetic and Pharmacodynamic Modeling

1.PK/PD STUDIES IN DRUG DEVELOPMENT

• Pharmacokinetic (PK) and pharmacodynamic (PD) modelling

and simulation (M&S) are well-recognized powerful tools

that enable effective implementation of the learn-and confirm

paradigm in drug development.

• M&S methodologies can be used to capture uncertainty and

use the expected variability in PK/PD data generated in

preclinical species for projection of the plausible range of

clinical dose.

Page 76: Pharmacokinetic and Pharmacodynamic Modeling

Clinical trial simulation can be used to forecast the

probability of achieving a target response in patients

based on information obtained in early phases of

development.

Contd…

Framing the right question and capturing the key

assumptions are critical components of the learn-and-

confirm paradigm in the drug development process and

are essential to delivering high-value PK/PD M&S

results.

Page 77: Pharmacokinetic and Pharmacodynamic Modeling

LEARN AND CONFIRM DRUG-DEVELOPMENT

PARADIGM

Contd…

Page 78: Pharmacokinetic and Pharmacodynamic Modeling

PRECLINICAL PHASE:

Contd…

OVERALL OBJECTIVE:

• Demonstration of biologic activity in experimental models.

•Accrual of toxicology data to support initial dosing in

humans.

• Identify the lead candidates based on desired attributes.

QUESTIONS:

• Efficacy and safety of NCE?

• Dose range to be studied in early clinical trials given

the uncertainty in the predicted dose required for

efficacy and safety?

Page 79: Pharmacokinetic and Pharmacodynamic Modeling

MODELING AND SIMULATION TASKS

To understand mechanism of action PK/PD assist in the

identification of potential surrogates or biomarkers.

PK/PD assists in identification of the appropriate animal

model.

Development of mechanism-based PK/PD models for

efficacy and toxicity early in the drug development process is

very useful and preferred over the development of empirical

models.

Unlike empirical models, mechanism-based PK/PD models

take into account the physiological processes behind the

observed pharmacological response, likely making it more

‘‘predictive’’ for future study outcome.

Page 80: Pharmacokinetic and Pharmacodynamic Modeling

Contd…

Understanding and developing the PK/PD relationship early in the

discovery stage can also provide a quantitative way of selecting

the best candidate. In the anticancer area, a typical way of

selecting the most potent candidate within a series of anticancer

drug candidates is to measure tumor volumes from in vivo

evaluation of the antitumor effect.

For initial dose selection and the subsequent escalation scheme in

Phase 1 studies, there are many examples in which PK/PD models

enabled the successful extrapolation of preclinical results in order

to predict the effective and toxicologic drug concentrations for

clinical investigations.

Assessing and predicting drug–drug interaction potential as well

as formulation development.

Page 81: Pharmacokinetic and Pharmacodynamic Modeling

Contd…

Combination of M&S approaches, including population analysis

of sparse preclinical PK data, allometric scaling to predict human

PK, and empirical efficacy scaling, can be used to project the

anticipated human dose and/or dosing regimen.

This can be explained by a case study:

A NCE, possessing a high amount of prior information from

other drugs in the therapeutic class, was to be evaluated as a

treatment for hypertension. The main M&S objective was to

project the clinical dose range based on the preclinical PK/PD

properties of the NCE. The preclinical and clinical PK/PD

properties of a comparator drug were well known.

Page 82: Pharmacokinetic and Pharmacodynamic Modeling

Contd…

The main assumptions of these analyses were as follows:

The relative efficacy and potency observed in the rat

hypertension model between the comparator and the NCE

were predictive of the relative efficacy and potency in

humans.

Allometric scaling provided a reasonable estimate of the

clearance of the NCE in humans.

Page 83: Pharmacokinetic and Pharmacodynamic Modeling

The concentration-response parameters for the NCE in clinical

hypertension were calculated using an empirical scaling

approach by combining the results of the rat hypertension Emax

model parameters and the clinical Emax model parameters of the

comparator.

Contd…

Page 84: Pharmacokinetic and Pharmacodynamic Modeling

CLINICAL DRUG DEVELOPMENT:

In clinical drug development, PK/PD modeling and simulation

can potentially impact both internal and regulatory decisions in

drug development.

PHASE 1:

•Assist in characterizing PK, safety, and tolerability of the

drug candidate.

•Provide information for the rational design of all

subsequent clinical trials.

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Contd…

Phase 1 starts with dose escalating studies in normal

volunteers with rigorous sampling. In addition, one may

establish an initial dose–concentration–effect relationship

that offers the opportunity to predict and assess drug

tolerance and safety in early clinical development.

Quantitative dose–concentration–effect relationships

generated from PK/PD modeling in Phase1 can be utilized in

Phase 2 study design.

PK/PD modeling is an important tool in assessing drug-

drug interaction potential.

Dosage form improvements often occur based on the PK

properties of the drug candidate.

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Phase 2 trials are typically divided into two stages, each with

some specific objectives.

Phase 2A : is to test the efficacy hypothesis of a drug

candidate, demonstrating the proof of concept.

Phase 2B : is to develop the concentration–response

relationship in efficacy and safety by exploring a large

range of doses in the target patient population.

The PK/PD relationship that has evolved from the

preclinical phase up to Phase 2B is used to assist in

designing the Phase 3 trial.

Phase 2

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PHASE 3:

OBJECTIVE:

To provide confirmatory evidence that demonstrates

an acceptable benefit/risk in a large target patient

population.

This period provides the ideal condition for final

characterization of the PK/PD in patients as well as for

explaining the sources of interindividual variability in

response, using population PK/PD approaches.

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NDA REVIEW:

PK/PD modeling plays an important role during the NDA

submission and review phase by integrating information from

the preclinical and development phases.

Existence of a well defined PK/PD model furthermore

enables the reviewer to perform PK/PD simulations for various

scenarios.

This ability helps the reviewer gain a deeper understanding of

the compound and provides a quantitative basis for dose

selection.

Thus, PK/PD modeling can facilitate the NDA review

process and help resolve regulatory issues.

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POST MARKETING PHASE:

Post-marketing strategy, population modeling approaches

can provide the clinician with relevant information regarding

dose individualization by:

Characterizing the variability associated with PK and

PD.

Identifying subpopulations with special needs.

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PK/PD STUDIES IN DOSAGE REGIMEN

OPTIMISATION:

PK/PD modeling is a scientific tool to help developers

select a rational dosage regimen for confirmatory clinical

testing.

Applied to individual dose optimization.

Time course and variability in the effect versus time

relationship can be predicted for different dosage-regimen

scenarios.

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EXAMPLE:

FOR DEVELOPMENT OF A NEW ANTIMICROBIAL

AGENT:

• Serial concentration-time data were available from 19 healthy,

male and female subjects administered NCE in doses ranging

from

1 to 200 mg in the first single-dose-multiple-dose study in

humans.

A 2-compartmental population PK model best described the

data.

• For the first efficacy trial in patients, the target concentration

was defined based on the concentration required to kill

90% of the susceptible bacterial strains, or IC90, determined

from an Emax model fit of in vitro exposure-kill data.

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The clinical target concentration was 1.7 mcg (mcg)/mL

(calculated by dividing in vitro IC90, or 0.05 mcg/mL, by

plasma bound fraction of 0.03).

Given the target exposure, the population PK model, and

margin of safety based on preliminary preclinical safety the

objective of M&S for the first efficacy trial was to select one

dose level to be studied as a once-a-day regimen that would

maintain concentrations >1.7 mcg/mL for the entire dosing

period in 85% of the patients.

Based on historical information on comparator compounds, it

is known that disease and protein binding can contribute to

differences in PK properties of an NCE between healthy

subjects and patients.

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To minimize the risk of underpredicting the dose, a 20%

higher clearance (lower exposure) was assumed, and an

additional 10% variability was added to the between-subject

variability in

clearance and volume for patients. Concentration-time data

were simulated for 500 patients administered daily doses

ranging from 100 to 300 mg for 14 days. Eighty-five percent

of patients maintained the 24-hour trough concentrations

above the target at doses >200mg.

The 200-mg dose, therefore, met the criteria as the lowest

dose, which maintains persistent drug exposure for the entire

dosing interval in 85% of the patient population.

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3.PK/PD MODELING IN INTERSPECIES

EXTRAPOLATION:

Primary source of between-species variability is often

attributable to variability that is mainly of PK origin.

Drug plasma concentration required to elicit a given

response is rather similar between species, whereas the

corresponding dose for eliciting the same effect can differ

widely.

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4. EXTRAPOLATION FROM in vitro to in vivo:

If an efficacious concentration (EC for stimulation, IC for

inhibition) is obtained on the basis of an in vitro assay, then

a dose can be proposed by incorporating the in vitro EC

directly into equation:

ED 50 = Cl x EC 50/Bioavailability

As in vitro concentrations are generally equivalent to free

drug concentrations, corrections for drug binding to plasma

protein might be needed to estimate the corresponding in-

vivo plasma

EC or IC.

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4. SELECTION OF ANTIBACTERIAL

AGENT:

PK/PD parameters correlate the bacteriological and clinical

outcome in animal models and in humans.

PK/PD parameters (AUC/MIC, Cmax/MIC) can be used to

select agents with maximum potential for bacterial

eradication.

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5. APPLICATIONS OF PK/PD METHODS

STUDY DRUG INTERACTIONS:

Drug interactions study protocols often incorporate

pharmacodynamic endpoints to allow estimating the clinical

consequences of drug interactions along with the usual

pharmacokinetic outcome measures.

Example:

Co-administration of triazolam and erythromycin produced a

large increase in plasma concentration of triazolam.

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Drug Development process

Discovery (3years)

Preclinical (3.5 years)

Phase 1 (1 year)

Phase 2 (2 years)

Phase 3 (3 years)

Thus it takes a molecule around 12-13 years to come

into market where it further faces the challenge of

Phase 4 trials.

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CTS refers to computer modeling approaches that replicate

actual human trials using predictive equations and virtual

subject.

It is relatively fast and inexpensive as compared to cost of

actual clinical trials.

It can provide insight into both efficacy and cost

effectiveness, even with limited data.

Project team members from various disciplines utilize the

CTS to explore a series of scenarios, from different trial

designs, to alternative ways of analyzing the generated

data.

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Optimize design of Phase 2 to phase 4 human trials (set

inclusion and exclusion criteria, give statistically significant

results by accounting for variation in compliance and inter-

patient variability.

Help in making in-licensing decisions based on predictions

of effectiveness.

Optimize target selection for a therapeutic indication.

Formulating strategies for competitive differentiation of

novel drugs based on predicted effectiveness in clinical and

post market populations.

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SOFTWARES USED IN PK/PD MODELING

•WinNonlin

•NONMEM

•XLMEM

•Boomer

• JGuiB (Java Graphic User Interface for Boomer)

•TOPFIT

•ADAPT II

•BIOPAK

•MULTI

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