Pharmacometric Tools in The Pharmaceutical Industry: Concepts

and Application in Drug Development

Serge Guzy; PhDPresident, CEO, POP-PHARM; Inc.

Pharmacokinetics• What the body does to the drug

– Distribute in circulation– Distribute in tissue– Eliminate drug by chemical degradation or filtering via

kidneys

Pharmacodynamics

• What the drug does to the body– Interact with target protein in circulation or on a cell

surface– Reduce or enhance activity of circulating protein or cell– Mitigate disease condition

Definition of Half-Life

• The time required for the concentration of drug to decline by ½

• Example:– Drug is 20 ug/mL at 1 hour after dosing– Drug is 10 ug/mL at 5 hours after dosing– Drug is 5 ug/mL at 9 hours after dosing– ½ drug cleared every 4 hours.– Half-life is therefore 4 hours

Pharmacometrics

• Pharmacometrics – analysis and interpretation of data produced in

pre-clinical and clinical trials.– Inter-disciplinary field

• Biostatistics• Computational methods• Pharmacokinetic/Pharmacodynamic modeling.

Pharmacometric components• Population pharmacokinetic and pharmacodynamic

modeling

• Disease progression modeling

• Clinical trial simulation

Population pharmacokinetic and pharmacodynamic modeling

• Population modeling involves the analysis of data from a group (population) of individuals, with all their data analyzed simultaneously to provide information about the variability of the model's parameters.

Disease progression modeling

• Mathematical models to describe, explain, investigate and predict the changes in disease status as a function of time. It incorporates– functions of natural disease progression– Drug action which reflects the effect of a drug on

disease status

Clinical Trial Simulation

• Simulation of a clinical trial can provide a data set that will resemble the results of an actual trial.

• Multiple replications of a clinical trial simulation can be used to make statistical inferences– Estimate the power of the trial– Predicting p-value – Estimate the expected % of the population that should fall

within a predefined therapeutic range

Impact of Pharmacometrics on Drug Approval and Labeling Decisions: Example 1

• Drug nesiritide for the treatment of acute decompensated congestive heart failure – Acute decompensated heart failure (ADHF) is a common and potentially serious

cause of acute respiratory distress.– PD marker used to measure severity of the disease: The Pulmonary wedge pressure

(PWP) is the pressure measured in a pulmonary artery distal to an occlusion of that artery

• High in the presence of ADHF• Drug for ADHF should decrease PWP

– Side effect: Hypotension• In April 1999, the FDA issued a nonapprovable letter to the sponsor.• A subsequent Pharmacometric analysis was performed to optimize dosing regimen of

nesiritide to achieve a faster decrease in PCWP (benefit) and minimize undesired hypotension (risk)?– Exposure (PK) and response (PD) data from the original submission were modeled.

The developed model was used to explore various alternative dosing scenarios.– Evidently, 2 µg/kg followed by 0.01 µg/min/kg infusion seems to offer a reasonable

benefit-risk profile.– The sponsor submitted the results in support of a revised dosing regimen.– The FDA approved nesiritide for acute CHF in May 2001.

Impact of Pharmacometrics on Drug Approval and Labeling Decisions: Example 2

• The sponsor sought approval of apomorphine (Apokyn), subcutaneous injection) for acute use in patients with Parkinson’s disease.

– Along with the registration studies, the sponsor submitted results from a dose-finding (2 to 10 mg) study with a suggested maximum recommended dose.

• In the renal-impaired apomorphine demonstrated a 50% increase in exposure. • The FDA conducted exposure-response analysis to aid in evaluating the appropriate dosing instructions

for labeling.• Regulatory Questions

– Is the maximum recommended dose and the titration strategy proposed by the sponsor appropriate?

– Is there a need for adjusting dose in the renal impaired?• Role of Pharmacometric Analysis

– The data from the dose-finding study indicated a concentration-dependent effect on Unified Parkinson’s Disease Rating Scale, which is desired, and blood pressure, which is undesired.

• Simulations using the exposure-response model suggested only minor additional benefits beyond 6 mg.

• The starting dose for patients with renal impairment was recommended to be 1 mg.• Regulatory Action

– The dosing recommendations suggested by the Pharmacometric exposure-response analysis were incorporated in the labeling after discussions with the sponsor.

Detailed Case Study: TV1102 Phase 2a Study Design

• Objective of the study– prove the therapeutic concept and to determine the pharmacokinetic profile of ATL 1102 by

subcutaneous injections in patients with multiple sclerosis– Develop a PK/PD/Efficacy model that will allow optimally designing the next Phase 2 b study

• Study Design– Cohort 1 – 40 patients to be s.c administered three ‘induction’ doses of 200 mg ATL 1102 each on

days 1, 4, and 7 of study and then a ‘maintenance’ dose regimen of 200 mg twice a week (days 4 and 7 of the week) for seven weeks.

– Cohort 2 – 40 patients to be administered placebo injections s.c. according to the schedule of Cohort 1

• Duration– 16 weeks, 8 weeks treatment period (first week induction phase followed by seven weeks

maintenance period) followed by 8 weeks without treatment.• Efficacy variable modeled

– Cumulative Number of T1 Gd-Enhancing Lesions• PK measurements

– 6 samples on Day 1, 28, 56 and 112 as follows:• 1 sample before administration of medication and after 1, 2, 3, 4 and 6 hours

• MRI measurements– Day 28,56,84 and 112

Population PK/T1 Modeling• The observed T1 lesions data suggest a significant

difference between the Placebo and Treated group (see next slide)

• We modeled the average T1 lesion time profile using a Poisson (response is categorical) regression (T1 lesion changes with time) model and linked it to the PK model. This model can therefore simulate Placebo T1 time profile as well as treated T1 time profile. – The model accounts also for the patients that did not show

any active lesions during the course of the Phase 2a trial

Observed T1 enhancing lesions over time: Placebo vs. Treated Group

PK/T1 Modeling: Processes

Dose

ka

k12

k21

Kmet,i (i=1,7) k10

Extravascular Compartment

Kout,i

Vm,Km

Linear Clearance

k13

ka

T1

Poisson (C)

c

Tissue 2

k31

SC

PK/T1 Modeling: Mathematical Model

(1)- 01. (1)

(2)01. (1). - 10. (2) - 12. (2) 21. (3) - 13. (2) 31. (4)

- . (2) / ( (2) . 2)

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eripher

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A(5) is the logarithm of the Poisson mean.

The rate of change of A(5) is assumed t

o be a constant ( ) for Placebo (constan

balance for the number of T1 lesions:

t slope) w

dAK A K A

dMatss

0 0

0

hile the slope

is affected by the drug through a Michael Menten equation type.

(5).(1- max.( (2) / ( (2) 50. 2)) )

, (5) . int

is the first recorded time for each patient

int

dAe A A EC V

dtt t A t ercept

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ercep

is the value of A(5) at t=0

t

Fitting Results

Parameter Value Meaningslope (placebo) 0.000218611 change in the log(average number of T1 lesions) per unit of timeintercept -0.372133322 log(average number of T1 lesions) at t =t0emax 3.601925822 maximum change relative to Placebo in the log(average number of T1 lesions)ec50 0.095389776 drug concentration at which the log(average number of T1 lesions) is half emax

Knowing the Efficacy parameters will help predicting the efficacy time profile for the upcoming Phase 2b Trial

Population PK/Platelet Modeling

• An indirect response model was linked to the PK model in order to quantify the correlation between Platelet and PK time profile

PK/PD Modeling: Processes

Dose

ka

k12

k21

Kmet,i (i=1,7) k10

Extravascular Compartment

Kout,i

Vm,Km

Linear Clearance

k13

ka

Platelets

kin(1-C/(C+ED50))

kout

c

Tissue 2

k31

SC

PK/Platelet Modeling: Mathematical Model

(1)- 01. (1)

(2)01. (1). - 10. (2) - 12. (2) 21. (3) - 13. (2) 31. (4)

- . (2) / ( (2) . 2)

(3)12. (2) - 21.

Tissue

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dAK A K A

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Periphe

eripher

Plasm

ald

a

S

r l

a

C

t

(4)13. (2) - 31. (4)

A(5) is the platelet number.

(5) (2).(1 max. ) . (5)

(2) 50.

0, (5)

is the Volume of distribution associated with the

balance for Plat

P

e

la

el ts:

dAK A K A

dt

dA Akin E kout A

dt A ED V

kint A

Mas

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s

sma compartment

Average Population PD estimates

Value unitskin 1.284273 (platelet/hour)kout 0.005264 /hourED50 0.351775 ug/ml

Knowing the PD parameters will help predicting the PD time profile for the upcoming Phase 2b Trial

Phase 2b Trial design and Goal• The Phase-2b tentative design is a 6 month treatment period, with 2-3 ATL dose

groups and placebo. MRI would be observed once a month with the primary endpoint being the percent reduction in the average cumulative (starting on Month 4 and cumulated every month until Month 7) number of T1 lesions, relative to Placebo. The optimal regimen would have preferably the following characteristics.

• At least 60% reduction of cumulative T1 lesions compared to placebo• Platelets with not more than 10% of subjects <150 (10^9/L) at any time, 5% of

subjects <100 (10^9/L) at any time and no subject <50 (10^9/L) at any time• The dosing to be explored are 100 mg, 200 mg and 400 mg with dosage intervals

varying from weekly to every 4 weeks. The goal of the simulation exercise was to have insight to the following issues:

– What dosing regimens can provide the required outcome based on the boundaries of MRI and platelets?

– What is the outcome simulating the predefined regimens?– Can drug loading provide any advantage?

Calculation of the Cumulative number of T1 lesions

• The PK/PD model was used to predict on Month 4-5-6 and 7 the expected average number of T1 lesions

• Therefore, the cumulative number of T1 lesions for the treated group is simply calculated by just summing up the number of T1 lesions starting on month 4 until Month 7. We call it cum_T1_drug

• The same calculation proceeds for the Placebo group (same model with a zero dose). We call it cum_T1_Placebo – The % MRI improvement ( %MRI ) is directly computed using the following

formula%MRI=(cum_T1_Placebo-cum_T1_drug)/cum_T1_Placebo x 100

Phase 2b Trial Simulation Results: Percent MRI improvement

vs.Total Dosing per 4 weeks for dosage intervals between 1 and 4 weeks: MRI cumulated on Month 4-5-6 and 7

MRI Improvement is better for small Dosage intervals, given a fixed total dose

Interval Week

Phase 2b Trial Simulation Results: Estimation of the percent of Patients to reach platelet counts below a certain threshold

value• The PK/PD model that was developed and fit to the Phase 2a data lead to

an estimate of both the PK/PD average parameters as well as the parameters quantifying the variability across the population– The average parameter values were used to simulate the average PK/PD

profile using the Phase 2b dosing conditions (shown next slide) • Both average and variability information can be used to simulate

hypothetical patients that would behave similarly than the actual population for any specific dosage regimen

• We simulated a large number of patients and recorded their platelet counts for each of them at the expected measurements times (usually predose), based on the potential Phase 2b Trial designs.

• Each patient that had at least one recorded platelet count less than X (X being either 150,100 or 50) was considered as passing the threshold value

• The percent of patients passing at least once the specific threshold value was then plotted versus the total dosing per 4 weeks for different dosage intervals.

Example of simulation of both PK and Platelet average time profile: 200 mg TV1102 given weekly

The predictions are predose up to the last dose, then every day for 30 days after last dose. PK and Platelets are mirror projections (PK going down, PDgoing up)

Phase 2b Trial Simulation Results: Percent subjects with Platelet <150 versus total dose per 4 weeks and

dosage interval

Dosage interval

Phase 2b Trial Simulation Results: Percent subjects with Platelet <100 versus total dose per 4 weeks and

dosage interval

Dosage Interval

Phase 2b Trial Simulation Results: Percent subjects with Platelet <50 versus total dose per 4 weeks and

dosage interval

Conclusions

• PK and Platelet time profiles are highly correlated• The only safety concerns are with the percent of Patients expected to

have platelet counts less than 150 which is larger than 10% for a total dose per 4 weeks exceeding 400 mg

• However, 200 mg every week (800 mg per 4 weeks) should not lead to more than 5% of the population with platelet counts less than 100

• In Conclusion, for the tentative Phase2b Trial design, we have– 200mg every week: safety concerns only for Platelet counts less than 150

(about 20% of the population) but • less than 5% of the population will have platelet counts less than 100 for that

regimen• MRI reduction is expected to be about 60%

– 200 mg every two weeks (no safety concerns)• MRI reduction is expected to be about 45%

– 200 mg every three weeks (no safety concerns)• MRI reduction is expected to be about 35%

Conclusions• The proposed design of 200mg every week, two and three

weeks without a loading dose should lead to enough separation in the MRI response (60,45 and 35% MRI reduction) to model a dose response relationship– Characterize a dose response relationship will lead to an optimal

design of Phase 3.• The safety concern for a 200 mg Dose every week has been

addressed and quantified– About 20% of the population is expected to have platelet counts less

than 150 for that dosage regimen but only 2% would have platelet counts less than 100