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Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.
Tuerkova, Alzbeta; Bongers, Brandon J; Norinder, Ulf; Ungvári, Orsolya; Székely, Virág; Tarnovskiy, Andrey; Szakács, Gergely; Özvegy-Laczka, Csilla; van Westen, Gerard J P; Zdrazil, Barbara.
Afiliação
  • Tuerkova A; Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, A-1090 Vienna, Austria.
  • Bongers BJ; Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands.
  • Norinder U; Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-75124 Uppsala, Sweden.
  • Ungvári O; MTM Research Centre, School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden.
  • Székely V; Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Magyar tudósok krt. 2, H-1117 Budapest, Hungary.
  • Tarnovskiy A; Doctoral School of Biology and Institute of Biology, ELTE Eötvös Loránd University, Pázmány P. stny. 1/C, H-1117 Budapest, Hungary.
  • Szakács G; Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Magyar tudósok krt. 2, H-1117 Budapest, Hungary.
  • Özvegy-Laczka C; Enamine Ltd., 78 Chervonotkatska Street, 02094 Kyiv, Ukraine.
  • van Westen GJP; Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Magyar tudósok krt. 2, H-1117 Budapest, Hungary.
  • Zdrazil B; Department of Medicine I, Institute of Cancer Research, Comprehensive Cancer Center, Medical University of Vienna, A-1090 Vienna, Austria.
J Chem Inf Model ; 62(24): 6323-6335, 2022 12 26.
Article em En | MEDLINE | ID: mdl-35274943
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.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Transportadores de Ânions Orgânicos Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Áustria

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Transportadores de Ânions Orgânicos Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Áustria