P18-107 Reducing Animal Use in Toxicological Research through Open-Source Individualized (PB)PK/QST Modeling: A Case Study with Phenobarbital in Dogs

Animal studies have long been central to toxicological research, yet they face growing scrutiny due to ethical concerns, limited translatability, financial burden and high inter-individual variability. In response, New Approach Methodologies (NAMs) – such as in vitro systems, computational modeling, and organ-on-chip technologies – are increasingly being developed, validated, and integrated into regulatory science frameworks worldwide. Reflecting this shift, the U.S. FDA recently announced its plan to phase out animal testing requirements for certain drug categories, underscoring the need for scientifically robust, non-animal approaches in drug development and toxicological assessments [1] . As part of ongoing efforts to advance NAMs in toxicological research, we present an endeavor to build an open-source modeling workflow using R and the Open Systems Pharmacology (OSP) Suite that integrates physiologically based pharmacokinetics (PBPK), classic pharmacokinetics (PK), and quantitative systems pharmacology (QSP). Our model focuses on phenobarbital in dogs and leverages data from [2] to explore how individualized modeling can enhance toxicological insights while minimizing animal use. The framework includes both PBPK and two-compartment PK models calibrated to single-dose phenobarbital data and validated against repeat dosing scenarios. The QSP component describes the dynamic life cycle of three common liver biomarkers- ALT, AST, and ALP- and links drug exposure to measurable physiological responses. An individualized fitting approach was implemented to account for the interanimal variability observed in the dataset. A genetic algorithm-based optimization strategy, incorporating lognormal variability in key pharmacokinetic and pharmacodynamic parameters identified via Sobol global sensitivity analysis, was applied to generate individualized parameter sets that reproduce the observed drug concentrations and biomarker trajectories of each dog in the study. The model successfully captured the diversity in pharmacokinetic profiles and biomarker responses and revealed a spectrum of sensitivity profiles consistent with the experimental data. The goal of our work is to demonstrate how mechanistic modeling can complement and reduce traditional animal use in toxicological research. By enabling deeper interpretation of existing data, supporting virtual study designs, and promoting model reuse and transparency, this approach provides a mechanistic framework to enhance the efficiency and reproducibility of toxicological studies while aligning with evolving ethical and regulatory expectations.