A Framework of Modeling and Simulation in Regulatory Decisions

1
A Framework of Modeling and Simulation in
Regulatory Decisions

• ACPS
• Nov 16, 2000
• Peter Lee, Stella Machado, and Larry Lesko
• OCPB OB/CDER

2
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

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Topics for Discussion

• What is the trend of modeling and simulation
  (MS) in regulatory submissions?
• What are regulatory experience in decision-making
  based on MS ?
• What are the potential applications of clinical
  trial simulation (CTS), specifically ?
• What are the directions and next steps for
  evaluating the applications of simulation ?

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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)

5
Pharma 2005 Vision for Simulation - at the
centre of drug development process
but can be applied more widely
6
Simulation - a rapidly emerging technology
Discovery
PreClinical
Clinical
Outcomes
Not appropriate
Not currently addressed
Under Development
Products Available
7
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.

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Number of CTS

• Over 100 (C. Peck, 10/12/00)
• Therapeutic areas (D. Weiner, 9/11/00)

9
Past Experience in MS

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

• PK Simulation
• - Cisapride 20 mg
• - Oxaliplatin Toxicity

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

• PD Simulation
• - Albuterol BE

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

• Population PK
• - Viagra

• 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

• PK/PD Simulation
• - Remifentanil
• - Saquinavir Dose Selection

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New Experiencein CTS

• Physiological/Disease Models
• Alzheimers
• 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

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An Example Drug X

• Drug X showed marginal efficacy in phase II
  studies
• Apply CTS to optimize phase III design for
  maximum success rate

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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

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Concentration-Effect Relationship
32
9
N 0
15
12
32
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Adjust Infusion Rate
15
Loading Dose? Infusion
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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

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Three Best Designs Number of Patients
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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.

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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.

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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 ?

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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

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Goals of MPCC MS 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 MS reports
• Prepare a guidance for industry for reporting MS
  results

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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 ?

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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 ?