Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters-An in Silico Modeling Approach.
Cholestasis represents one out of three types of drug induced liver injury (DILI), which comprises a major challenge in drug development. In this study we applied a two-class classification scheme based on k-nearest neighbors in order to predict cholestasis, using a set of 93 two-dimensional (2D) physicochemical descriptors and predictions of selected hepatic transporters' inhibition (BSEP, BCRP, P-gp, OATP1B1, and OATP1B3). In order to assess the potential contribution of transporter inhibition, we compared whether the inclusion of the transporters' inhibition predictions contributes to a significant increase in model performance in comparison to the plain use of the 93 2D physicochemical descriptors. Our findings were in agreement with literature findings, indicating a contribution not only from BSEP inhibition but a rather synergistic effect deriving from the whole set of transporters. The final optimal model was validated via both 10-fold cross validation and external validation. It performs quite satisfactorily resulting in 0.686 ± 0.013 for accuracy and 0.722 ± 0.014 for area under the receiver operating characteristic curve (AUC) for 10-fold cross-validation (mean ± standard deviation from 50 iterations).
University of Vienna , Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria.
Eleni
0000-0002-9965-5917
E
Kotsampasakou
University of Vienna , Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria.
Gerhard F
0000-0003-4209-6883
GF
Ecker
D002779:Cholestasis / Q000139:chemically induced* / Q000378:metabolism*; D003198:Computer Simulation; D057809:HEK293 Cells; D006801:Humans; D008099:Liver / Q000187:drug effects* / Q000378:metabolism*; D026901:Membrane Transport Proteins / Q000378:metabolism*; D008954:Models, Biological
D026901:Membrane Transport Proteins