Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V(D,ss)) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V(D,ss), accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V(D,ss) directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V(D,ss) directly and to predict input parameters required for mechanistic and empirical V(D,ss) predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V(D,ss) predictions was also investigated. In predicting V(D,ss) directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V(D,ss) demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r(2) = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V(D,ss) of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V(D,ss) predictions significantly (81% within 3-fold, r(2) = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V(D,ss) but was limited in estimating the compounds with low V(D,ss). SIGNIFICANCE STATEMENT: This work advances the in silico prediction of V(D,ss) directly from structure and with the aid of in vitro data. Rigorous and comprehensive evaluation of various methods using a large set of clinical compounds (n = 956) is presented. The scale of techniques evaluated is far beyond any previously presented. The novel data set (n = 254) generated using a single protocol for each in vitro assay reported in this study could further aid in advancing V(D,ss) prediction methodologies.