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 ?
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