Abstract

Model-based meta-analysis (MBMA) is a valuable component of the quantitative pharmacology toolkit for model-informed drug discovery and development. It enables principled decision making with a totality of evidence mindset through integration of internal and external data across multiple dimensions (e.g., targets/mechanisms, molecules/drugs, doses/regimens, diseases/indications, populations, endpoints, and clinical trial designs). MBMA distinguishes itself from traditional meta-analysis by infusing pharmacologic plausibility into the statistical rigor that typifies meta-analytic data integration. This is possible through mechanism-informed formulation of pharmacologically inspired cause-effect and dose-response relationships, time course of treatment effects, and interrelationships between proximal and distal outcomes of modulation of disease biology and pathophysiology. In this review, we offer a question-based approach to enhance appreciation of the value of MBMA across the continuum from drug discovery and translational research through clinical development, comparative effectiveness research, and postapproval optimization of therapeutics using illustrative examples across therapeutic areas.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.