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Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
Örebro University, School of Science and Technology. Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. (MTM Research Centre)ORCID iD: 0000-0003-3107-331X
Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Budapest, Hungary; Doctoral School of Biology and Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary.
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2022 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 62, no 24, p. 6323-6335Article in journal (Refereed) Published
Abstract [en]

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 μM), three OATP1B1 inhibitors (2.69 to 10 μM), and five OATP1B3 inhibitors (1.53 to 10 μM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2022. Vol. 62, no 24, p. 6323-6335
National Category
Pharmacology and Toxicology
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URN: urn:nbn:se:oru:diva-98150DOI: 10.1021/acs.jcim.1c01460ISI: 000840962400001PubMedID: 35274943Scopus ID: 2-s2.0-85126619146OAI: oai:DiVA.org:oru-98150DiVA, id: diva2:1645935
Available from: 2022-03-21 Created: 2022-03-21 Last updated: 2024-01-16Bibliographically approved

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