Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

Abstract

The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein–ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein–ligand interactions.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-017-0021-x) contains supplementary material, which is available to authorized users.

Discovery of Potent Inhibitors for the Large Neutral Amino Acid Transporter 1 (LAT1) by Structure-Based Methods

“…With the increasing availability of the X-ray structures of transmembrane transporters, homology modeling, in combination with molecular docking, has been successfully used in the discovery of novel ligands for various protein targets of the SLC family [66,67,68,69,70,71]. Additionally, these computational models have shown their significance in the elucidation of the molecular basis of drug transporter interaction for targets of therapeutic interest [51,52,53,72]. We describe here a successful structure-based design approach that led to the identification of potent new inhibitors of LAT1, with an unprecedented hit-rate.…”

Bsep expression in hilar cholangiocarcinoma of rat model

“…Drugs in the research and development stages do not directly target the genes that contribute to hilar cholangiocarcinoma. The data presented here may enhance our understanding of the molecular basis of hilar cholangiocarcinoma 28 – 30 . Moreover, our study illuminates that Bsep may be a target for new and more effective treatment strategies.…”

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

“…In case of P‐glycoprotein, SAR guided docking led to a docking protocol which allowed to perform structure‐based classification of a large set of P‐gp inhibitors and noninhibitors . Also for the BSEP, an ABC‐transporter linked to cholestasis, structure‐based classification was successfully implemented . Finally, we would like to mention the off‐target safety assessment (OTSA) framework, which combines a number of 2D target prediction methods with 3D protein‐based approaches.…”