Generating Model Integrated Evidence for Generic Drug Development and Assessment.

Area of Science:

• Pharmacometrics and computational toxicology
• Drug regulatory science
• Pharmaceutical sciences

Background:

• Quantitative methods and modeling (QMM) encompass diverse tools crucial for generic drug development.
• Physiologically based models and quantitative clinical pharmacology are key components of QMM.
• The US Food and Drug Administration (FDA) increasingly utilizes QMM to streamline generic drug development and review processes.

Purpose of the Study:

• To highlight the critical role of QMM in modernizing bioequivalence (BE) assessment for generic drugs.
• To illustrate how QMM facilitates the development of novel BE methods, including in vitro-only approaches and risk-based evaluations.
• To explore the future of QMM in generic drug development, focusing on model-integrated evidence and virtual BE studies.

Main Methods:

• Review of FDA's application of QMM in generic drug development and regulatory decision-making.
• Analysis of QMM's impact on bioequivalence (BE) assessment for various drug product types.
• Examination of emerging QMM applications, such as virtual BE studies.

Main Results:

• QMM has been instrumental in modernizing BE assessment, particularly for locally acting, complex, and modified-release drug products.
• QMM has supported the creation of innovative BE methodologies and risk-based regulatory evaluations.
• Model-integrated evidence and virtual BE studies represent the future trajectory of QMM in regulatory science.

Conclusions:

• Quantitative methods and modeling (QMM) are indispensable for advancing generic drug development and regulatory science.
• QMM enhances bioequivalence (BE) assessment, enabling more efficient and scientifically robust evaluations.
• The integration of QMM into regulatory frameworks is crucial for modernizing drug approval processes and addressing complex product challenges.