A cross-species assessment of in silico prediction methods of steady-state volume of distribution using Simcyp simulators

Predicting steady-state volume of distribution (V_ss) is a key component of pharmacokinetic predictions and often guided using preclinical data. However, when bottom-up prediction from physiologically-based pharmacokinetic (PBPK) models and observed V_ss misalign in preclinical species, or predicted V_ss from different models varies significantly, no consensus exists for selecting models or preclinical species to improve the prediction. Through systematic analysis of V_ss prediction across rat, dog, monkey, and human, using common methods, a practical strategy for predicting human V_ss, with or without integration of preclinical PK information is warranted. In this analysis, we curated a dataset of 57 diverse compounds with measured physicochemical and protein binding data, together with observed V_ss in these species. Using a bottom-up approach, prediction performance was consistent across species for each method. Although no method consistently outperformed others for all compound types and across species, M2 (Rodgers-Rowland method) performed marginally better for acids. Comparable compound-specific global tissue Kp scalars were needed to match observed V_ss for both, human and preclinical species. Consequently, application of geometric mean values of preclinical Kp scalar to human V_ss prediction improved accuracy. We propose a decision tree for human V_ss prediction using PBPK methods with or without integrating preclinical PK information.