In silico identification and in vitro validation of potential cholestatic compounds through 3D ligand-based pharmacophore modeling of BSEP inhibitors
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SourceChemical Research in Toxicology, 27, 5, (2014), pp. 873-881
Article / Letter to editor
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Chemical Research in Toxicology
SubjectRadboudumc 11: Renal disorders RIMLS: Radboud Institute for Molecular Life Sciences; Radboudumc 19: Nanomedicine RIMLS: Radboud Institute for Molecular Life Sciences; Radboudumc 4: lnfectious Diseases and Global Health RIMLS: Radboud Institute for Molecular Life Sciences; Radboudumc 6: Metabolic Disorders RIMLS: Radboud Institute for Molecular Life Sciences
Drug-induced cholestasis is a frequently observed side effect of drugs and is often caused by an unexpected interaction with the bile salt export pump (BSEP/ABCB11). BSEP is the key membrane transporter responsible for the transport of bile acids from hepatocytes into bile. Here, we developed a pharmacophore model that describes the molecular features of compounds associated with BSEP inhibitory activity. To generate input and validation data sets, in vitro experiments with membrane vesicles overexpressing human BSEP were used to assess the effect of compounds (50 muM) on BSEP-mediated (3)H-taurocholic acid transport. The model contains two hydrogen bond acceptor/anionic features, two hydrogen bond acceptor vector features, four hydrophobic/aromatic features, and exclusion volumes. The pharmacophore was validated against a set of 59 compounds, including registered drugs. The model recognized 9 out of 12 inhibitors (75%), which could not be identified based on general parameters, such as molecular weight or SlogP, alone. Finally, the model was used to screen a virtual compound database. A number of compounds found via virtual screening were tested and displayed statistically significant BSEP inhibition, ranging from 13 +/- 1% to 67 +/- 7% of control (P < 0.05). In conclusion, we developed and validated a pharmacophore model that describes molecular features found in BSEP inhibitors. The model may be used as an in silico screening tool to identify potentially harmful drug candidates at an early stage in drug development.
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