Prediction of Pharmacokinetics Prior to In Vivo Studies. 1. Mechanism‐Based Prediction of Volume of Distribution

Abstract

In drug discovery and nonclinical development the volume of distribution at steady state (V(ss)) of each novel drug candidate is commonly determined under in vivo conditions. Therefore, it is of interest to predict V(ss) without conducting in vivo studies. The traditional description of V(ss) corresponds to the sum of the products of each tissue:plasma partition coefficient (P(t:p)) and the respective tissue volume in addition to the plasma volume. Because data on volumes of tissues and plasma are available in the literature for mammals, the other input parameters needed to estimate V(ss) are the P(t:p)'s, which can potentially be predicted with established tissue composition-based equations. In vitro data on drug lipophilicity and plasma protein binding are the input parameters used in these equations. Such a mechanism-based approach would be particularly useful to provide first-cut estimates of V(ss) prior to any in vivo studies and to explore potential unexpected deviations between sets of predicted and in vivo V(ss) data, when the in vivo data become available during the drug development process. The objective of the present study was to use tissue composition-based equations to predict rat and human V(ss) prior to in vivo studies for 123 structurally unrelated compounds (acids, bases, and neutrals). The predicted data were compared with in vivo data obtained from the literature or at Roche. Overall, the average ratio of predicted-to-experimental rat and human V(ss) values was 1.06 (SD = 0.817, r = 0.78, n = 147). In fact, 80% of all predicted values were within a factor of two of the corresponding experimental values. The drugs can therefore be separated into two groups. The first group contains 98 drugs for which the predicted V(ss) were within a factor of two of those experimentally determined (average ratio of 1.01, SD = 0.39, r = 0.93, n = 118), and the second group includes 25 other drugs for which the predicted and experimental V(ss) differ by a factor larger than two (average ratio of 1.32, SD = 1.74, r = 0.42, n = 29). Thus, additional relevant distribution processes were neglected in predicting V(ss) of drugs of the second group. This was true especially in the case of some cationic-amphiphilic bases. The present study is the first attempt to develop and validate a mechanistic distribution model for predicting rat and human V(ss) of drugs prior to in vivo studies.