(M-129) Automating Translational Physiologically Based Pharmacokinetic Modeling with R and Simcyp: An Innovative Approach

Xinxin Yang, N/A – Research Investigator, Bristol Myers Squibb; Mian Zhang, N/A – Principal Consultant, Certara UK Limited, Simcyp Division; Anthonia Onasanwo, N/A – Research Scientist, Certara UK Limited, Simcyp Division; Surabhi Talele, N/A – Scientist, Bristol Myers Squibb; Robert Walsky, N/A – Scientific Associate Director, Bristol Myers Squibb; Brian Schmidt, N/A – Executive Director, Bristol Myers Squibb; Lu Gaohua, N/A – Senior Director, Bristol Myers Squibb

Objectives: Prediction of human pharmacokinetics is essential for translating preclinical data to human, evaluating drug safety, and estimating first-in-human doses. This project aimed to develop an automated strategy using physiologically based pharmacokinetic (PBPK) modeling to predict human pharmacokinetics from preclinical data.

Methods: The approach combined a PBPK model for ADME (Absorption, Distribution, Metabolism, and Excretion) properties extrapolation in humans with an SimCYP-R based automated, standardized workflow. Model parameterization was conducted by optimizing effective permeability (Peff) using data from animal oral pharmacokinetics and adjusting the tissue plasma partition coefficients scalar (Kp scalar) to align with the observed volume of distribution at steady state (Vss). Hepatic extraction ratio-based methods were used for predicting clearance in human. The mean values of these parameters served as initial inputs for developing the human PBPK model. The SimCYP-R workflow consists of collecting physicochemical and animal in vivo data, automating model parameterization/simulations, and data visualization, using both the SimCYP simulator and Simcyp-R package, version 23.

Results: The described strategy has been implemented on four compounds with different physicochemical properties across different therapeutic areas. This approach significantly expedited PBPK model development for first-in-human predictions, minimizing bias in model development and parameterization, enhancing the reproducibility of the PBPK models, leading to more objective, consistent and accurate pharmacokinetic predictions.

Conclusions: The integration of PBPK models with animal data and an automated workflow demonstrates its efficiency in projecting human pharmacokinetics. This streamlined approach, requiring minimal human resources and preclinical in vivo pharmacokinetic data, shows substantial promise for expediting human pharmacokinetic predictions in the early phases of drug development.

Citations: N/A