Exposure-response modeling of clinical end points using latent variable indirect response models
C Hu1
1Janssen Research & Development, LLC, Spring House, Pennsylvania, USA.
CPT: Pharmacometrics & Systems Pharmacology
|June 5, 2014
Summary
This study presents a framework for exposure-response modeling using mechanism-based models for clinical drug development. It focuses on indirect response models for disease scores and effective dosing regimen selection.
Area of Science:
- Pharmacometrics
- Clinical Pharmacology
- Drug Development
Background:
- Exposure-response modeling is crucial for selecting optimal dosing regimens in clinical trials.
- Clinical endpoints are frequently disease scores, posing unique modeling challenges compared to physiological variables.
- Models must align with pharmacological principles and be identifiable from patient data.
Purpose of the Study:
- To introduce a general framework for applying mechanism-based models to diverse clinical endpoints.
- To detail the parameterization, interpretation, and assessment of placebo and drug models.
- To emphasize the application of indirect response models within this framework.
Main Methods:
- Development of a general framework for mechanism-based exposure-response modeling.
- Application to various clinical endpoint types, including disease scores.
- Focus on indirect response models for parameterization and assessment.
Main Results:
- Demonstrated a consistent approach to modeling clinical endpoints using mechanism-based models.
- Provided methods for parameterization, interpretation, and assessment of drug effects.
- Highlighted the utility of indirect response models for understanding drug action.
Conclusions:
- The proposed framework enables robust exposure-response modeling for clinical endpoints.
- Mechanism-based and indirect response models are valuable tools for drug development.
- Effective dosing regimens can be selected using these pharmacologically consistent models.
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