Pharmacokinetic-pharmacodynamic

integration in drug development

1

Pierre-Louis Toutain,

Ecole Nationale Vétérinaire

INRA & National veterinary School of Toulouse, France

Wuhan 10/10/2015

The campus of the national

Veterinary school at Toulouse

1-What is PK/PD?

• PK-PD modeling is a scientific tool to

quantify, in vivo, the key PD

parameters of a drug, which allows to

predict the time course of drug effects

under physiological and pathological

conditions (intensity and duration)

What is the main goal of a PK/PD trial

It is an alternative to dose-titration

studies to discover an optimal

dosage regimen

PK/PD to support decision making

and strategic applications during

drug development

Objectives of the presentation

• Overview on the concept of PK/PD

• PK/PD and extrapolation from in vitro to in vivo

• The dose-titration approach for dose determination and its limits

• The case of NSAID

2-An overview on the

concept of PK/PD

Clinical trials (Dose titration)

vs.

PK/PD trials

Dose titration

Dose Response Parasite killing

Black box

PK/PD

Dose

PK PD

Plasma

concentration

Surrogate

Response

9

PK/PD: mechanistic approach

PK/PD

Dose Response

PK PD

Plasma

concentration

Plasma

concentration

Drug receptor

interaction Transduction

Dose Response

Drug specificity, affinity &

intrinsic efficacy

System specificity

Kineticists can be viewed

as the first “engineers” of

clinicians

3-Why is plasma concentration

profile a better explicative

(independent) variable than dose for

determining a dosage regimen ?

Dose vs. plasma concentration profile

as independent variable

Dose

Mass (no biological

information)

Dose

Concentration profile (biological information)

X F%

Clearance

Time

4-Why to prefer a PK/PD

approach to a classical

dose-titration?

Why to prefer a PK/PD approach to

a classical dose-titration?

To separate PK and PD

variability

Dose effect vs. concentration

effect relationship

15

DOSE AUC = (Dose/Cl)

EFFECT EFFECT

Less variance must be expected in the AUC/effect

than in the dose/effect relationship

External dose Internal dose

Dose-effect vs. exposure-

effect relationship for GnRH

A

0

10

20

30

40

50

60

70

0 100 200 300

GnRH dose (µg in toto)

AU

C L

H (

ng.h

.mL

-1)

B

0

10

20

30

40

50

60

70

0 50 100 150 200 250 300

AUC GnRH (pg.h.mL-1

)

AU

C L

H (n

g.h

.mL-1

)

Monnoyer et a.l J vet pharmaco ther 2004

LH response

PK and PD variability

well documented – species

– food

– age

– sex

– diseases

PK PD

Dose

Plasma

concentration

Effect

BODY Receptor

Generally ignored

but usually more pronounced than

PK variabilities (for a given species)

PK/PD variability

• Consequence for dosage adjustment

PK PD

Dose

Plasma

concentration

Effect BODY

Receptor

Parasite

Kidney function

Liver function

...

Clinical covariables

• disease severity or duration

• Parasites susceptibility (MEC)

PK/PD population approach

5-When to use PK/PD in drug

development

Preclinical drug development Clinical drug development

Learning

Dru

g d

isc

ove

ry

Ap

pro

va

l

Confirming

1. To acquire basic knowledge

on drug

2. Extrapolation from in vitro to

in vivo

3. To be an alternative to dose-

titration studies to discover

an optimal dosage regimen

• To adjust dosage regimen to

different subgroups of animals

(age, sex, breed, disease)

Predictive PK/PD • Simulations

• Trial forecasting

• Bioequivalence

Preclinical PK/PD •Integrated information supporting go/no go

decision

Predicting

Clinical PK/PD

Population PK/PD

PK/PD applications

1. identify key PD parameters (efficacy,

potency, selectivity, affinity…)

2. predict dosage regimen

3. in vitro to in vivo extrapolation

4. interspecies extrapolation

5. sources (PK or PD) of intra- and inter-

individual variability in drug response

6. drug-drug interactions

7. influence of pathological conditions

• etc.

6-PK/PD and extrapolation

from in vitro to in vivo

Antiparastic (tick) efficacy of

fipronil

Question: how to quickly and roughly

predict a antiparastic (tick) dose from in

vitro or from in vivo test system?

PK/PD: in vitro vs. in vivo

Response Plasma

concentration Body

Medium

concentration Test

system

Response

In vivo

In vitro

Extrapolation

in vitro in vivo

Mechanism-

based PK/PD

The tick blood meal: From a living animal or from a

silicone membrane?

Cumulative mortality of Ixodes ricinus females

feeding on bovine blood through a silicone

membrane with dimethylsulfoxide (DMSO)

added (placebo) and with increasing doses of

fipronil in DMSO.

Extrapolation from In vitro by directly

incorporating the in vivo efficacious

concentration in the equation giving the dose

Dose =

Dose - is a hybrid parameter (PK and PD)

-

Clearance x target concentration

Bioavailability

PD

PK

What is a Fipronil dose in

sheep

• In vitro efficacious concentration : about 100 ng/mL over 7 days

• Plasma clearance : about 2 mL/kg/min or 2.88 L/kg/day

• Daily dose : 2.8L/kg/day x 0.1 µg/L= 288 µg/kg /day

• Total dose: Daily dose x 7 days≈2mg/kg

7-PK/PD applications

1. in vitro to in vivo extrapolation

2. Estimate key PD parameters

(efficacy, potency, selectivity,

affinity…)

3. predict dosage regimen

4. sources (PK or PD) variability in

drug response (antibiotics)

The 3 PD parameters of a dose-

effect relationship

Emax ED50 Slope

stiff

ED502

Emax 1

Efficacy Potency

• Sensitivity

• Range of useful

concentrations

• Selectivity

Emax 2

1

2

1 2

ED501

Emax/2

Slope of the dose-effect

relationship & selectivity

ECVPT Toulouse 2009 -

30

therapeutic effect

side effect

A more potent than B

A = B for efficacy

B is preferable to A in a clinical context for its selectivity

A B

Concentration

Eff

ec

t 100

80

The most useful drug is not

necessarily the most potent one

8-PK/PD and the discovery of

an optimal dosage regimen

Components of an optimal

dosage regimen

Components Tools for investigation

Dose dose-titration or PK/PD

Interval of adm. PK/PD

Duration of adm. Clinics

Route, site and

conditions of adm. PK (bioavailability)

The discovery of an (optimal)

dosage regimen

• Should be acquired early in the

development process (preclinical studies)

for an efficient drug development

– to ensure succesful clinical trials

9-The dose-effect relationship

The two kinds of Dose/Effet

relationship

• There are 2 fundamental types of

endpoints that can be used to quantify the

dose-effect relationship, graded and

quantal.

35

Dose-Effect Endpoints

Graded

Quantal

• Continuous scale

(blood pressure, body temperature..)

• Measured in a single biologic unit

• Relates dose to intensity of effect

• All-or-none pharmacologic effect

dead/alive; pain/no pain

• Population studies

• Relates dose to frequency of effect

Anemia and erythropietin (a graded dose/effect relationship)

Erythropoietin Dose [units/kg]

[Peak hematocrit

increment %]

Eschbach et al. NEJM 316:73-8, 1987

Graded Dose-Effect Curve

% of

Maximal

Effect

[Concentration] EC50

Maximal effect

Potency

Efficacy

Log Dose-Effect Curve

% of

Maximal

Effect

[Drug]

EC50

Quantal Dose-Effect

relationship

Quantal Dose-Effect Distribution

• Dose is related to the frequency (probability) of the

all-or-none effect, such as the % of subjects who

survived.

• Notion of threshold

– This is the minimal concentration to observe an

effect

– This threshold concentration is different amongst

different subjects and a distribution exist in the

population

– Thus it will be possible to associate a given

concentration to the probability of an effect

• E.g : adverse effect of drugs

Quantal Dose-Effect Distribution

Threshold dose to trigger the

effect of interest

# of

Subjects

ED5

0

Cumulative Dose-Effect Curve

Dose

Cumulative %

of Subjects

Doxorubicin Cardiotoxicity

Total Doxorubicin Dose [mg/m2]

Probability

of CHF

0

0.20

0.40

0.60

0.80

1.0

0 200 400 600 800 1000

von Hoff et al. Ann Intern Med 91:710-7, 1979

Modeling quantal dose-effect

relationship:

The logistic model

45

Probability of cure (POC)

• Logistic regression can be used to link measures of drug exposure to the probability of a clinical success

MICAUCbfaePOC

1

1

Dependent variable

(from 0 to 1) Placebo

effect

sensitivity Independent

continuous

variable

(here for a

for antibiotic

) 2 parameters:

a (placebo effect) & b (slope of the exposure-effect

curve)

11-The dose-titration

Dose ranging designs

• Parallel randomized dose design

– generally recommended by Regulatory

Authorities

• Cross-over design

The parallel design: Statistical model

• The null hypothesis

– placebo = D1 = D2 = D3

• The statistical linear model

– Yj = wj + j

• Conclusion

– D3 = D2 > D1 > Placebo

ECVPT Toulouse 2009 -

49

Placebo Dose

Response

1 2 3

*

*

NS

Selected dose

The parallel design

• Advantages

– easy to execute

– total study lasts over one period

– approved by Authorities

• Disadvantages

– "local information" (response at a given dose does not

provide any information about another dose)

– no information about the distribution of the individual

patient's dose response.

Parallel design: a population analysis

• The structural model

•Conclusion – Evaluation of 3 parameters:

– Emax (maximum response): efficacy

– Dose50 (dose producing half Emax): potency

– n (shape factor): selectivity

nn

n

DoseED

DoseEE

50

max

ECVPT Toulouse 2009 - 51

0 1 2 3

Dose, AUC

i (dose)

Re

sp

on

se

3 Subjects per dose

What is exactly an ED50 ?

What is exactly an ED50 ?

ED50 - is a hybrid parameter (PK and PD)

- is not a genuine PD drug parameter

𝑫𝒐𝒔𝒆 =𝑪𝒍𝒆𝒂𝒓𝒂𝒏𝒄𝒆 × 𝒆𝒇𝒇𝒊𝒄𝒂𝒄𝒊𝒐𝒖𝒔 𝒑𝒍𝒂𝒔𝒎𝒂 𝒄𝒐𝒏𝒄𝒆𝒏𝒕𝒓𝒂𝒕𝒊𝒐𝒏

𝑩𝒊𝒐𝒂𝒗𝒂𝒊𝒍𝒂𝒃𝒊𝒍𝒊𝒕𝒚

PK PD

PK

ED50 vs EC50

A variable vs. a parameter

ED50 - is a hybrid variable (PK and PD)

- is not a genuine PD drug parameter

ECVPT Toulouse 2009 -

54

PD

ilityBioavailab

ECclearancePlasmaED 50

50

_

PK

EC50 is a PD parameter allowing extrapolation

•Between formulations

•Between physiological status (renal failure)

•Between species

12-Measuring exposure and

response in PK/PD trial

Measuring variables in PK/PD

trial

• Full concentration time curve

• AUC

• Cmax , Cmin

• Biomarkers

• Surrogate

• Clinical outcomes

Measuring response Measuring exposure

Independent variable for

PK/PD modelling • Any concentration

in any matrix can

be used for

PK/PD modeling

The case

of diuretics

Clinical endpoint vs.

surrogate/biomarkers

• True clinical endpoints are patient

feeling, wellbeing, survival rate etc.

– because therapeutic endpoints may be

unavailable, impossible to evaluate, time

taking…

biomarkers & surrogates

Relation of serum cholesterol to coronary heart disease death*

* From Gotto AM Jr, et al. Circulation 81:1721-1733, 1990

Measuring response

e.g.: ACE inhibitors

biomarker

surrogate

Clinical outcome

Binding affinity

ACE inhibition

Renin/angiotensin

aldosterone

modulation

Blood pressure

Survival time

Well-being

Continuity Objectivity

Sensitivity

reproducibility

Validity +++

13-PK/PD modeling

Modelling issues:

Need professional skill

14-A working example: the

case of NSAIDs

Step 1: selection of an

appropriate inflammatory

model

As for a conventional dose titration,

PK/PD investigations generally require a

relevant experimental model (here a

kaolin inflammation model)

Possibility to perform PK/PD in patient

Step 2: selection of

endpoints

• As for a conventional dose

titration, PK/PD

investigations require to

measure some relevant

endpoints

• To measure the vertical forces, a corridor of walk is used with a force plate placed in its center.

• The cat walks on the force plate on leach.

Video

Measure of vertical forces exerted on force

plate

• The measure of vertical force and video control are recorded

Vertical forces (Kg)

Video

Measure of vertical forces

exerted on force plate

Measure of pain with

analgesiometer

• Cat is placed in a Plexiglas

box.

• A light ray is directed to its

paw to create a thermal

stimulus.

• The time for the cat to

withdraw its paw of the ray is

measured.

withdrawal time of the

paws (second) Video

0

1

2

3

4

5

0.0 0.6 1.5 2.5 3.5 4.6 6.1 8.1 10.1 12.1 23.5

Lo

co

mo

tio

n s

co

re

Time after robenacoxib administration (h)

Follow-up of mean locomotion score

Results: locomotion score

2 mg/kg

38.0

38.5

39.0

39.5

40.0

40.5

41.0

0.0 0.5 1.5 2.5 3.5 4.5 6.0 7.0 8.0 10.0 12.0 23.4

Re

cta

l te

mp

era

ture

(°C

)

Time after robenacoxib administration (h)

Follow-up of mean rectal temperature

Results: body temperature

2 mg/kg Robenacoxib

0

2

4

6

8

10

12

14

16

18

20

22

0.0 0.8 1.7 2.7 3.7 4.8 6.3 8.3 10.3 12.3 23.7

Wit

hd

raw

al ti

me

(s

)

Time after robenacoxib administration (h)

Follow-up of mean paw withdrawal time

Results: Pain (withdrawal time)

2 mg/kg

Step 5: modelling

IC50 40.0 ng/mL

ID50 = 0.59

mg/kg/24h

Robenacoxb : analgesic effect

0

200

400

600

800

1000

1200

1400

1600

1800

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12C

on

ce

ntr

ati

on

s (n

g/m

L)

Pa

in (

%)

Time (h)

𝒅𝑹

𝒅𝑻= 𝑲𝒊𝒏 𝟏 −

𝑰𝒎𝒂𝒙+𝑪𝒏

𝑰𝑪𝟓𝟎𝒏 +𝑪𝒏 -𝑲𝒐𝒖𝒕 × 𝑹

Step 6 : simulations

Simulated dose-response:

Robenacoxib: analgesic

effect

-250

-200

-150

-100

-50

0

50

100

0 4 8 12 16 20 24

Time (h)

Pain

sco

re (

%)

0.1 mg/kg

0.2 mg/kg

0.3 mg/kg

0.4 mg/kg

0.5 mg/kg

1 mg/kg

Simulations Robenacoxib: once vs. twice a day

Mean effect 32

% Mean effect 52

%

Simulated time course of pain

0

10

20

30

40

50

60

70

80

90

100

0 4 8 12 16 20 24

Time (h)

Pa

in (

%)

5 mg/kg

2 x 2.5 mg/kg

5 mg/kg split in 12

Mean effect 96 %

PK / PD modeling

CONCLUSION

• A powerful tool for many applications

• Requires clear understanding of

theoretical background and computer

software

• Veterinary pharmacologists should be

encouraged to consider PD, and not only

PK.