Applications of Model-Based Meta-Analysis in Drug Development.

Area of Science:

• Pharmacometrics and Statistics
• Quantitative Pharmacology
• Drug Development Sciences

Background:

• Model-based meta-analysis (MBMA) is a quantitative method integrating internal and published data.
• It supports critical drug development decisions, including benefit-risk assessment and optimal dose determination.
• MBMA offers a flexible framework for analyzing aggregated data from historical studies.

Purpose of the Study:

• To highlight the value of MBMA as a standard tool within the model-informed drug development (MIDD) framework.
• To demonstrate MBMA's utility in selecting optimal doses and supporting comparator selection for investigational drugs.
• To showcase MBMA's application in informing drug development decisions through case studies.

Main Methods:

• MBMA leverages published summary data alongside internal data.
• It incorporates longitudinal data, dose-response relationships, and combines individual and summary-level data.
• Covariates are routinely incorporated into the analysis.

Main Results:

• MBMA facilitates optimal dose and regimen selection for internal molecules against external benchmarks.
• Case studies demonstrated MBMA's application in biologics (safety) and small molecules (efficacy) in oncology and rheumatoid arthritis.
• The approach supports crucial drug development decision-making processes.

Conclusions:

• MBMA should be a standard tool in the model-informed drug development (MIDD) framework.
• Future directions include increased stakeholder engagement, enhanced collaboration, expanded data access, and machine learning integration.
• Timely and cost-effective MBMA application provides an integrated view for MIDD.