A Framework of Modeling and Simulation in Regulatory Decisions

ACPS

Nov 16, 2000

Peter Lee, Stella Machado, and Larry Lesko

OCPB & OB/CDER

Terminology

• Modeling: determining the mathematical equations that appropriately describe the data (mechanism of action or smoothness).

• Simulation: predict the outcomes under specified conditions based on models.

• Clinical Trial Simulation: A specific type of simulation that predict outcomes of clinical trials.

•It is not possible to review simulation without evaluating modeling process

Topics for Discussion

• What is the trend of modeling and simulation (M&S) in regulatory submissions?

• What are regulatory experience in decision-making based on M&S ?

• What are the potential applications of clinical trial simulation (CTS), specifically ?

• What are the directions and next steps for evaluating the applications of simulation ?

How good is the current drug development process?

• 354/499 approved NME, 1980-1999– 22% required a post-market dose change (79)– 80% were dose reduction (64)

• Pre-market drug development is improvable regarding safe dose (C. Peck, CR AC, Oct 2000)

• 12 year, $350-600 million (CMR Internation, 1999)

• 30% NDAs non-approvable; 15% phase III failed (S. Arlington, April 2000)

Pharma 2005 Vision for Simulation - at the centre of drug development process

Protocol design Study design

Data analysis

Reporting

Data capture

InvestigationKnowledge extractionSimulation

… but can be applied more widely

Simulation - a rapidly emerging technology

Discovery PreClinical Clinical Outcomes

Molecular Structure Activity

Subcellular

Whole Body (animals/humans)

Clinical Trials

Clinical Programs

Drug Portfolios

Cellular

Tissues/Organs

Medical Care Systems

Not currently addressed

Under Development

Products Available

Not appropriate

Current Environment

• Computer aided trial design (CATD) used by 17 out of top 20 PhRMA companies, and over 1200 users.

• Over 15 different software packages.

• Past experience with modeling & simulation to support regulatory decisions

• Emerging submissions using simulation to support trial designs.

Number of CTS

• Over 100 (C. Peck, 10/12/00)

• Therapeutic areas (D. Weiner, 9/11/00)

0%

5%

10%

15%

20%

25%

Pain

Cardio

vasc

ular

Infec

tious

dise

ase

CNS

Urolog

y/GI

Diabet

es

Cance

r

Other

s

Past Experience in M&S

• PD Simulation- Albuterol BE

• Population PK- Viagra

•PK/PD Simulation- Remifentanil- Saquinavir Dose Selection

• PK Simulation- Cisapride 20 mg- Oxaliplatin Toxicity

•New indication with new formulation•Single dose PK study•Simulate multiple dose PK for the new formulation based on single dose PK

•BE based on PD end point (FEV)•Single dose, 4-way crossover, nasal spray•PD model parameter estimation•BE test on PD model parameter

•Identify sub-population & DDI•Single and multiple doses•Multiple studies•Demographic information•1 structure and ~10 covariate models

•Support the dose selection•Randomized , non-blind, multi-center, dose ranging study•400, 600, 800, 1200 mg tid•Simulate distribution of response as a function of dose

New Experiencein CTS

• Physiological/Disease Models– Alzheimer’s– QTc prolongation– Diabetic

• Clinical Trial Simulation– Neuropharm drug

•Design phase III trial•Based on PK & phase II study•PK and PK/PD model, covariate model, assay model, drop-off, severity, statistics

An Example: Drug X

• Drug X showed marginal efficacy in phase II studies

• Apply CTS to optimize phase III design for maximum success rate

Backgrounds

• Dose Regimen– Continuous IV infusion

• Reason for marginal results in phase II– Drug concentration may not be optimal

• Goal– Optimize the concentration in phase III

Concentration-Effect Relationship

0102030405060708090

place

bo 1 2 3 4 5 >5

Plasma Conc

Eff

ect

(pro

b%

)

N = ~0%

32%

9%

12%32%

15%

Adjust Infusion Rate

Overdose (44%)

0

5

10

15

0 2 4 6 8 10 12 14

Time (hr)

Con

c

Overdose(44%)

Loading Dose+Infusion

Underdose (15%)

02468

10

0 2 4 6 8 10 12 14

Time (hr)

Con

c Underdose(15%)

Study Design/Conduct Factors

• Responder/Non-responder• P450 2D6 genotype• Patient demographic• Number of patients• Timing of assay• Amount of dose adjustment• Amount of loading dose• Drop-off

Three Best Designs: Number of Patients

79808182838485868788

125/150 100/200 110/220

N placebo/treated patients

Pro

b s

ucc

ess

(%)

N=275

N=300

N=330

Utilities of Simulation

• Predict PK under conditions not studied.

• Select the optimal dose.

• Study design: pop PK, exposure-response.

• Evaluate change in PD due to change in formulation, dose regimen, or dosing route.

• Provide bridging information for sub-populations.

• Develop informative labeling language.

Additional (Potential) Utilities of Simulations

• Integrate preclinical, clinical pharmacology, and biopharmaceutics study results into late-phase clinical trials to ensure safe and effective study design.

• Design unbiased, powered, and robust studies to maximize the treatment benefits/risk ratio in the patients.

• Explore “what if” scenarios, and compare different study designs

• Combine multi-discipline expertise in reviewing IND/NDA.

Key Factors to Successful Simulation Projects

• Prospective planning

• Well-understood MOA

• Robust model that are not overly sensitive to assumptions

• Disease progression model

• Availability of exposure-response data

• Balanced inputs from relevant disciplines

• How far dose it extrapolate ?

Issues

• No consistent approach for CDER reviewers to assure quality of M/S projects.

• Other FDA guidance recommend simulation technique but not address “best practice”

• Proper review of M/S submissions may require FDA standard for industry

Goals of MPCC M&S WG

• Assess current “state of art” of M/S

• Explore potential for regulatory applications

• Determine standards to assess suitability

• Develop standards for M/S outputs

• Develop a guidance as standards for reviewing and critiquing M&S reports

• Prepare a guidance for industry for reporting M&S results

Questions To ACPS Committee

1. How does industry use simulation to help the drug development process ?

2. Are modeling and simulation appropriate for drug development and regulatory decisions ?

3. What are the important attributes for a meaningful simulation practice ?

Questions To ACPS Committee (cont.)

4. Do we need a FDA guidance to industry regarding the best practice of modeling and simulation for regulatory applications ?

5. If yes to # 4, what are the important information should the guidance include ?

6. If no to #4, what are the critical issues that need to be addressed before move forward to developing a guidance ?