Physiologically based pharmacokinetic (PBPK) models provide a mechanistic description of the absorption, distribution, metabolism, and elimination of chemicals and drugs across biological compartments. These models are governed by coupled systems of ordinary and partial differential equations that encode physiological structure, transport processes, and biochemical interactions.
Our PBPK work focuses on using mechanistic structure to enable inference, extrapolation, and decision-making under uncertainty, rather than on producing nominal best-fit simulations.
PBPK models are inherently high-dimensional and only partially identifiable from available data. Rather than forcing identifiability through ad hoc assumptions, our approach explicitly characterizes what aspects of the system are constrained by data and physiology, and what remain uncertain.
Key principles include:
mechanistic formulation grounded in physiology and transport theory,
identifiability-aware parameterization,
Bayesian inference that preserves parameter correlations and degeneracies, and
prediction as propagation of admissible model sets rather than single trajectories.
This perspective is essential when PBPK models are used for extrapolation across formulations, populations, dosing regimens, or study designs.
A major focus of our PBPK work is dermal and transdermal exposure modeling, where transport through layered skin structures couples diffusion, partitioning, clearance, and vehicle effects.
Our models integrate:
partial differential equations for transport across skin layers,
coupling between formulation dynamics and tissue uptake,
in vitro and in vivo data through IVIVE frameworks, and
uncertainty propagation to systemic and local exposure metrics.
These models are used to support chemical risk assessment, formulation development, and comparative evaluations of topical products.
We apply Bayesian and optimization-based inference to calibrate PBPK models using heterogeneous data sources, including in vitro experiments, clinical pharmacokinetic measurements, and literature constraints.
This approach enables:
transparent quantification of uncertainty and non-identifiability,
sensitivity analysis to identify data-informative parameters, and
principled guidance on experimental and study design to improve inferential power.
Rather than treating calibration as an end point, inference is used to clarify the limits of model-based prediction.
PBPK models are increasingly used in regulatory decision-making, where transparency, reproducibility, and reuse are essential. Our work contributes to the development of Model Master File (MMF) concepts for PBPK models, with particular emphasis on dermal applications. Within this context, PBPK models are treated as reusable scientific assets, whose validity is defined by explicit context of use, documented assumptions, and characterized uncertainty. This perspective supports consistent application of PBPK models across regulatory submissions, comparative assessments, and lifecycle decision-making.
Our PBPK modeling activities have supported:
virtual bioequivalence and in silico trial design,
assessment of formulation and vehicle effects on exposure,
population variability analysis, and
integration of mechanistic modeling into regulatory workflows.
An Open-Source Framework for Virtual Bioequivalence Modeling and Clinical Trial Design, CPT: Pharmacometrics & Systems Pharmacology, 2025
Enhancement of Skin Permeability Prediction through PBPK Modeling, Bayesian Inference, and Experimental Design, Pharmaceutics, 2023
A Mechanistic Bayesian Inferential Workflow for Estimation of In Vivo Skin Permeation from In Vitro Measurements, Journal of Pharmaceutical Sciences, 2022
Mechanistic Skin Modeling of Plasma Concentrations of Sunscreen Active Ingredients Following Facial Application, Journal of Pharmaceutical Sciences, 2024