RT Journal Article
SR Electronic
T1 Towards Predicting Drug-Induced Liver Injury (DILI): Parallel Computational Approaches to Identify MRP4 and BSEP Inhibitors
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP dmd.114.062539
DO 10.1124/dmd.114.062539
A1 Matthew A. Welch
A1 Kathleen Kock
A1 Thomas J. Urban
A1 Kim L. Brouwer
A1 Peter W. Swaan
YR 2015
UL http://dmd.aspetjournals.org/content/early/2015/03/03/dmd.114.062539.abstract
AB Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of MRP4, in addition to BSEP, might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and non-inhibitors in the training set. Models were externally validated and used to predict the affinity of compounds towards BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of 9 well-characterized MRP4 inhibitors and 9 potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/non-inhibition were developed with cross-validated Receiver Operator Curve (ROC) values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements; similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.