Karen Rowland Yeo

Clinical pharmacology and pharmacometrics teams are now being encouraged to implement the ICH M15 framework in drug development to support model‐informed decisions in regulatory submissions. Although model performance is an important component of model evaluation, regulatory confidence also depends on the intended context of use (CoU) and the supporting level of evidence. This is particularly relevant for physiologically based pharmacokinetic (PBPK) modeling, where increasing regulatory application has strengthened the need for structured approaches to evaluate predictive performance, extrapolation, uncertainty, and model applicability. The Simcyp Simulator (V19R1) was recently qualified by the EMA for prediction of CYP‐mediated drug–drug interactions (DDIs) involving six enzymes (CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4/5), competitive inhibition, and mechanism‐based inhibition for both victim and perpetrator drugs across three CoUs. Qualification data demonstrated consistent predictive performance across the three CoUs, with average fold errors close to unity, absolute average fold errors ranging from 1.23 to 1.25, and 98.8% of 246 predictions falling within 2‐fold of observed values. The opinion predates ICH M15; however, it does not provide either a mapping to its credibility concepts or an operational basis for extending platform‐level evidence to a specific investigational drug. We address this gap with an explicit mapping of the qualification opinion onto ICH M15 credibility concepts and an integrated approach linking qualification evidence, drug‐specific validation, predictive performance, uncertainty characterization, and prospective application, illustrating how confidence in PBPK‐supported regulatory decisions can be established within an ICH M15‐aligned framework.

Physiologically based pharmacokinetic (PBPK) models are increasingly used in pediatric drug development and matching such models to the target population is an important consideration. A US pediatric PBPK model was developed incorporating four sub‐groups describing physiology and enzymatic/transporter levels for White, African American, Hispanic/Latino and Asian subjects. Virtual populations could then be created reflecting the demographics of the clinical study design. Ten different drugs (14 studies) were used to verify the pediatric PBPK model performance. Predicted PK parameters showed reasonable agreement with observed data, 50% of the predicted: observed values were within 0.8–1.25‐fold, 74% within 0.67–1.5‐fold, and 97% within 0.5–2‐fold. There was minimal difference for most drugs in performance between the mixed US and North European pediatric populations. This was partly due to reported studies having low numbers of subjects and under‐representation of some ethnicities. Power calculations showed that to detect a pharmacokinetic difference for the CYP3A4/5 substrates midazolam, sirolimus and tacrolimus between African American and White American children with a power of 80%, 25, 18 and 12 subjects respectively, in each group would be needed. Developing adult and pediatric PBPK models to closely match study populations is important due to their increased regulatory use and with the move to their application in personalized medicine. Such models may also help to bridge some of the ethnic differences not captured in pediatric clinical studies due to the low numbers of subjects.

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.

Physiologically based pharmacokinetic (PBPK) modeling has emerged as a critical tool for predicting fetal drug exposure during pregnancy, accounting for the complex interplay of maternal physiological changes and placental transfer mechanisms. This review summarizes the current state of PBPK modeling in maternal‐fetal pharmacology, discussing various modeling approaches, including compartmental structures and mathematical formulations for placental transfer, as well as approaches to parameterize placental drug transfer in humans. Furthermore, a regulatory perspective on the use of maternal‐fetal PBPK models in submissions to the MHRA is provided. Despite advancements, significant gaps remain in understanding the variability of placental drug transfer across gestational stages and the implications of transporter activity. Future research should focus on generating robust clinical data and integrating innovative technologies, such as organ‐on‐a‐chip systems, to enhance model accuracy. Additionally, regulatory acceptance of PBPK models will depend on the establishment of comprehensive validation frameworks. By fostering collaboration between researchers, clinicians, and regulatory bodies, PBPK modeling can advance toward safer therapeutic options for pregnant women and their developing fetuses, ultimately improving maternal‐fetal health outcomes.