Comparing in silico and in vitro methods for classification of BCS II and CYP3A4 and MDR-1 substrate specificity

Background Previous work has shown considerable laboratory variability of Biopharmaceutics Classification System (BCS) classification, efflux ratio in intestinal cell lines and cytochrome P450 (CYP450)-metabolism pathways. Such variability and inconsistency create uncertainty in predictions of human clinical pharmacokinetics and the pharmacokinetic optimization process and is a problem when developing corresponding in silico methods. Objectives and Methodology One objective of the study was to quantify the degree of laboratory inconsistency for BCS II-classing, MDR-1 and CYP3A4 substrate specificity (substrate/non-substrate). Another objective was to predict BCS II-classing, MDR-1 and CYP3A4 substrate specificity using in silico methodology and compare results to laboratory data/classifications. Results and Discussion 27 BCS II-classified drugs (with non-contradictory BCS-classing in various sources) were found. 17 (63 %) had an in vivo fraction absorbed (fa) of ≥90 % and belong to in vivo BCS I. With in silico methodology, 74 % correct BCS-classing was reached for the same set of compounds. The mean prediction error for fa was 1.2-fold. MDR-1 and CYP3A4 substrate specificities were collected for 346 and 808 compounds, respectively. For MDR-1, 143 of the compounds had reported data in at least two studies, and out of these, 49 (34 %) and 18 (13 %) had contradictory (reported as both substate and non-substrate) and uncertain substrate specificities, respectively. For CYP3A4, 42 (9.8 %) out of 427 compounds showed inconsistency between laboratories. With in silico methodology, MDR-1 and CYP3A4 classification predictions were incorrect for 13 and 15 % of compounds. Conclusion The results show considerable variability/inconsistency for BCS II-classing (63 % inconsistency between BCS II-classing and in vivo fa) and MDR-1 (34 % inconsistency between sources) and CYP3A4 (10 % inconsistency between sources) substrate specificities. Corresponding estimates obtained with in silico methodology are 22, 13 and 15 %, respectively, demonstrating the power and applicability of such technology.