JSPS. Modeling & Simulation: A Tool to Enable Efficient Clinical Drug Development. March 29, 2005

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Dr. Kunihiro Sasahara, Ph.D.

Dr. Russell Wada, Ph.D.

Dr. Yuying Gao, M.D., Ph.D.

Modeling & Simulation: A

Tool to Enable Efficient

Clinical Drug Development

JSPS

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Agenda

Overview of Clinical Trial Simulations

The Pharsight Trial Simulator

software

Application to the efficient approval of etanercept in pediatric patients with

rheumatoid arthritis

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Population pharmacokinetic modeling and exposure-response

modeling are essential ingredients in drug development today.

PK PD

Dose Efficacy/Safety

Pop PK (FDA Guidance: 1999)

Exposure-Response (FDA Guidance: 2003)

Cp

How can we incorporate these models into clinical trial

simulations?

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In 1999, the CDDS developed “good practices” for simulations in drug

development, with three guiding principles: clarity, completeness, and

parsimony.

Simulation in Drug Development: Good Practices

Draft Publication of the Center for Drug Development Science (CDDS) Draft version 1.0, July 23, 1999

Copyright: CDDS, 1999

Editors: Holford NHG, Hale M, Ko HC, Steimer J-L, Sheiner LB, Peck CC Contributors : Bonate P, Gillespie WR, Ludden T, Rubin DB, Stanski D

• CLARITY: The report of the simulation should be understandable in terms of scope and conclusions by intended users such as those responsible for committing

resources to a clinical trial.

• COMPLETENESS: The assumptions, methods and critical results should be described in sufficient detail to be reproduced by an independent team.

• PARSIMONY: The complexity of the models and simulation procedures should be no more than necessary to meet the objectives of the simulation project…

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Here’s an example using clinical trial simulations to avoid a

Phase 3 trial studying dose-intensification of docetaxel in

NSCLC patients with high

α

1-acid glycoprotein levels.

Veyrat-Follet CPT 2000; 68:677-87

Docetaxel was approved at a dose of 100 mg/m2 in patients with non-small-cell lung cancer.

Patients with high α1-acid glycoprotein levels (AAG) had shorter time to

progression and death.

Exposure-response analysis of 151

Phase 2 patients showed that cumulative dose, first cycle AUC and AAG were

predictive of progression and survival.

125 mg/m2 was selected as the optimal

dose because of dose-limiting hematologic toxicity.

Clinical trial simulations demonstrated a slight survival benefit that had a 6% likelihood to be significant in Phase 3.

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Here’s an example of using Phase 3 data to demonstrate

similar clinical outcome for weight-based vs. fixed-dose

regimens of ARANESP, an erythropoietin stimulating protein.

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Agenda

Overview of Clinical Trial Simulations

The Pharsight Trial Simulator software

Application to the efficient approval of etanercept in pediatric patients with

rheumatoid arthritis

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Before using Trial Simulation, equations describing population

variability must be determined using modeling.

30% Variability 1.071 (m2) Population Mean BSA Clearance/F 29% Variability 0.077 ± 0.005 (L/h) 0.058 ± 0.003 (L/h) Population Mean ± SE Male Female

CL/F = (Population Mean ± SE) × (BSA/1.071)1.41 × exp(η)

Variability due to BSA Variability(unexplained) Variability due to

uncertain parameter estimates

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The trial simulator software organizes the clinical trial

simulation into seven sections.

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The drug model is organized using a graphical model editor.

Formulations

Responses

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Demographic variables and pharmacokinetic parameters are

viewed by “opening” the blocks.

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Monte-Carlo simulations generate distributions of patient

characteristics. These virtual patients are enrolled into a

clinical trials, and drug response is determined.

ID SEX BSA (m2) CLF (L/h) 1 F 0.84 0.057 2 M 0.82 0.03 3 F 1.62 0.114 4 M 1.64 0.124 5 F 0.91 0.058 6 F 0.9 0.029 7 M 1.04 0.056 8 F 0.8 0.028 9 M 0.72 0.035 10 F 0.91 0.041 0.0 0.05 0.10 0.15 0.20 0 1500 Clearance (L/h) Count Male, BSA=1.071 m2 0.0 0.05 0.10 0.15 0.20 0 1500 Clearance (L/h) Count Male 0.0 0.05 0.10 0.15 0.20 0 1500 Clearance (L/h) Count Female 0.0 0.05 0.10 0.15 0.20 0 1500 Clearance (L/h) Count All Children 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 500 1000 1500 BSA (m2) Count

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Dosing schedules are specified and displayed in a graphical

user interface.

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Agenda

Overview of Clinical Trial Simulations

The Pharsight Trial Simulator software

Application to the efficient approval of etanercept in pediatric patients with

rheumatoid arthritis

– This example is based on a recent publication by Yim et al, Journal Clinical Pharmacology, 2005; 45:246-256.

– It was selected because it illustrates clearly how clinical trial simulations bridge from data to new trials using models.

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Development strategy

Tumor necrosis factor alpha is elevated in synovial joints in rheumatoid

arthritis.

Etanercept inactivates tumor necrosis factor alpha

– Received FDA approval in adults for RA, psoriatic arthritis, ankylosing

spondylitis and polyarticular course of juvenile rheumatoid arthritis

and psoriasis in adults.

– Recommended dose in JRA patients was 0.4 mg/kg SQ twice weekly

Etanercept has a 5-day half-life, so a doubled dose of a once-weekly

injection was sought.

Key Question

– Using PK data from twice-weekly SQ injections in JRA patients, could

population PK models and clinical trial simulations be used to support

the approval of the once-weekly regimen?

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Description of patients studied with twice-a-week dosing used

in the modeling.

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Simulation strategy

Develop demographic model and population PK based on 69 juvenile RA

patients.

Trial simulations

– 100 subjects per BIW and QIW regimen.

– 100 trial replications of the trial

– Display graphically the mean, median, 5

th

and 95

th

percentile

concentration values at each time point.

– Compare BIW vs. QIW regimens

Utilize previously developed exposure-response model for efficacy in

adults, and dose-response information for safety in juveniles and adults.

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Population PK model parameters

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Here is an overlay of measured and predicted concentrations.

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Simulations of BIW and QIW dosing show 11% greater peak

concentrations and 18% lower trough concentrations on QIW

dosing.

Yim et al, Journal Clinical Pharmacology, 2005; 45:246-256.

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Conclusions of the clinical trial simulation

“Concentration simulations for the 0.4 mg/kg twice-weekly and 0.8 mg/kg once weekly dose regimens showed overlapping profiles that support interchangeability between the dosing regimens.”

It is possible that the increased peak concentrations by 11% may cause a safety risk.

“On the basis of simulation analysis, FDA approved the dosing regimen of

etanercept 0.8 mg/kg SC once weekly for pediatric patients with JRA along with 50 mg SC once weekly in adults.”

Key “Bridging” Assumptions

– Twice-weekly pharmacokinetics is predictive of once-weekly pharmacokinetics in pediatrics

– Exposure-response relationships in JRA are similar between pediatrics and adults

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Agenda

Overview of Clinical Trial Simulations

The Pharsight Trial Simulator software

Application to the efficient approval of etanercept in pediatric patients with

rheumatoid arthritis

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Clinical trial simulation bridges from existing data to new trials

using models. The predictability depends on the strength of

the key assumptions.

Phase 2 NSCLC to Phase 3 NSCLC for docetaxel

– Phase 2 population is similar to phase 3 population

– Exposure-response relationship extends to higher doses

Phase 3 weight-based vs. fixed doses of ARANESP

– The effects of weight on hemoglobin response are captured in pharmacokinetics

Twice-a-week to once-a-week dosing of etanercept in juvenile RA – Once-a-week PK predicts twice-a-week PK

– Exposure-response similar in adults and children

When assumptions are strong, clinical trial simulations can be used to support regulatory interactions. When assumptions are weaker, clinical trial simulations support internal decisions.

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Other “bridging” applications of clinical trial simulations

From immediate release to extended release formulations

From approved statins to new statins in hypercholesteremia

– Similar relationship between LDL reduction and cardiovascular risk

reduction

For PPAR gammas to PPAR alphas in diabetes

– Animal models for glucose lowering are relevant to humans

For novel neuroprotective Parkinson’s drugs

– Animal models of neuroprotection are relevant to humans

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Model-based drug development was broadly discussed by

industry and FDA at the 2005 American Society of Clinical

Pharmacology and Therapeutics Meeting.

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Conclusions

Modeling and clinical trial simulation is a tool that is being used by

pharmaceutical companies and FDA to improve the efficiency of drug

development.

Clinical trial simulation bridges from existing data to new trials using

models.

Pharsight’s Trial Simulator™ is capable of implementing most Clinical

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