The document discusses a clinical trial simulation tool designed for Alzheimer's disease development, highlighting the reasons for failure in drug development and the importance of trial design and optimization. It outlines the integration of data from various studies and the predictive modeling capabilities to assess disease progression and drug effects, including cognitive assessments and demographic variables. Final conclusions indicate that the tool successfully captures essential clinical trial aspects and received regulatory recognition from the FDA and EMA.

1. Clinical Trial Simulation Tool for Alzheimer’s Disease
Klaus Romero MD MS FCP
Director of Clinical Pharmacology
Critical Path Institute

2. Why do drug development programs fail?
2
0
10
20
30
40
50
60
70
80
90
100
Preclinical Phase I Phase II Phase III Registration
%Failures
Development stage
Reason by phase
Clinical safety
Efficacy
Formulation
Market potential
PK/Bioavailability
Strategic
Resources
Toxicology
COGS
Unknown
Other

3. Clinical Trial Simulations
3
Evaluate different design options
Design selection
Drug-Disease-Trial
Models
Simulated results
Trial optimization
through
simulations
𝜃𝑖𝑝𝑘 = E
𝐴𝐷𝐴𝑆𝑖𝑝𝑘
70 patient 𝑝
𝑇𝑝𝑘~Weibull 𝛼, ℎ 𝑝𝑘
Trial Execution
•X dose
•N
•Frequency of observations
•Inclusion/exclusion criteria
Statistical Analysis

4. 4
Data integration for model development
ADNI:
• 224 patients
• Natural history
• Inter-patient variability
Competitive Information:
• Pre-clinical
• Class effects
• Individual effects
CAMD:
• 3179 patients
• Inter-study variability
• Inter-patient variability
• Dropouts
• Placebo effect
Literature:
• Inter-patient variability
• Dropouts
• Drug effects
Model

5. 5
• Longitudinal cognitive instrument:
- ADAS-Cog: 11items, 0-70points
• Basal cognitive instrument:
- MMSE: 8items, 30-0points
• Demographics:
- Baseline age and gender
• Genetics:
- APOE4 allele
Relevant variables

6. 6Rogers JA, et al. Alzheimer's disease modeling and simulation. J Pharmacokinet Pharmacodyn. 2012 Oct;39(5):479-98.

7. 7
Disease progression:
75year-old men, by APOE4 and baseline severity

8. Balancing power, sample size and duration,
given varying effect magnitudes
8
Crossover Parallel
91Week Crossover
Versus
78 Week Parallel
By effect magnitude

9. Regulatory conclusions
9
This model adequately captures relevant information regarding disease
progression, drug effects and clinical trial aspects (placebo effect and
dropouts)
Clinical Trial Simulations based on this tool allows the objective,
prospective and realistic evaluation of the operating characteristics of
different trial designs.
FDA fit-for-purpose decision on CAMD CTS tool. 2013
EMA qualification opinion on CAMD CTS tool. 2013

10. Thank You
www.c-path.org