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Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening

[Image: see text] 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 w...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795544/
https://www.ncbi.nlm.nih.gov/pubmed/35274943
http://dx.doi.org/10.1021/acs.jcim.1c01460
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author 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
author_facet 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
author_sort Tuerkova, Alzbeta
collection PubMed
description [Image: see text] 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 (IC(50) 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 (IC(50) values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC(50) = 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.
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spelling pubmed-97955442022-12-29 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 J Chem Inf Model [Image: see text] 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 (IC(50) 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 (IC(50) values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC(50) = 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. American Chemical Society 2022-03-11 2022-12-26 /pmc/articles/PMC9795544/ /pubmed/35274943 http://dx.doi.org/10.1021/acs.jcim.1c01460 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle 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
Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
title Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
title_full Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
title_fullStr Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
title_full_unstemmed Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
title_short Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening
title_sort identifying novel inhibitors for hepatic organic anion transporting polypeptides by machine learning-based virtual screening
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795544/
https://www.ncbi.nlm.nih.gov/pubmed/35274943
http://dx.doi.org/10.1021/acs.jcim.1c01460
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