From Prediction to Decision Making: PBPK and QSP as Regulatory-Grade NAMs

New approach methodologies (NAMs) encompass a diverse and rapidly evolving set of experimental and computational tools designed to generate human-relevant mechanistic data for use in drug development and regulatory decision making. Experimental NAMs provide insights into drug disposition, pharmacological activity, and disease biology that are difficult or impossible to obtain from traditional animal-based models. Physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models represent a complementary class of computational NAMs. Together, experimental and computational NAMs form an integrated translational framework that converts mechanistic biological data into quantitative predictions of human exposure, efficacy, and safety across the drug development continuum. In this state-of-the-art review, we present our perspective on the current and emerging role of PBPK and QSP as computational NAMs, supported by case studies spanning a range of regulatory and clinical applications. Across all case studies, the integration of human-relevant experimental data into mechanistic models is shown to be the critical determinant of translational success. We also discuss the evolving regulatory landscape for NAMs, including the recent FDA draft guidance on QSP-based MABEL determination, and the ICH M15 framework for model-informed drug development. Collectively, these developments signal a fundamental shift in how mechanistic models are positioned within drug development and regulatory decision making: not as alternatives to animal testing alone, but as quantitative decision-support frameworks that generate the human-relevant evidence needed to support safer, more effective, and more equitable medicines.