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

被引:20
作者
Tuerkova, Alzbeta [1 ,2 ]
Bongers, Brandon J. [3 ]
Norinder, Ulf [4 ,5 ]
Ungvari, Orsolya [6 ,7 ]
Szekely, Virag [6 ]
Tarnovskiy, Andrey [8 ]
Szakacs, Gergely [6 ,9 ]
Ozvegy-Laczka, Csilla [6 ]
van Westen, Gerard J. P. [3 ]
Zdrazil, Barbara [1 ]
机构
[1] Univ Vienna, Dept Pharmaceut Sci, Div Pharmaceut Chem, A-1090 Vienna, Austria
[2] Uppsala Univ, Dept Cell & Mol Biol, Sci Life Lab, Box 596, S-75124 Uppsala, Sweden
[3] Leiden Univ, Leiden Acad Ctr Drug Res, Div Drug Discovery & Safety, NL-2300 RA Leiden, Netherlands
[4] Uppsala Univ, Dept Pharmaceut Biosci, S-75124 Uppsala, Sweden
[5] Orebro Univ, MTM Res Ctr, Sch Sci & Technol, S-70182 Orebro, Sweden
[6] Eotvos Lorand Res Network, Inst Enzymol, Drug Resistance Res Grp, RCNS, H-1117 Budapest, Hungary
[7] Eotvos Lorand Univ, Inst Biol, Doctoral Sch Biol, H-1117 Budapest, Hungary
[8] Enamine Ltd, UA-02094 Kiev, Ukraine
[9] Med Univ Vienna, Inst Canc Res, Comprehens Canc Ctr, Dept Med 1, A-1090 Vienna, Austria
基金
奥地利科学基金会;
关键词
BINDING POCKET; PREDICTION; OATP1B1; DRUG; IDENTIFICATION; MRP2;
D O I
10.1021/acs.jcim.1c01460
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
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 OATPIB1 (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 mu M), three OATP1B1 inhibitors (2.69 to 10 mu M), and five OATP1B3 inhibitors (1.53 to 10 mu M) 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.
引用
收藏
页码:6323 / 6335
页数:13
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