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Hirte S, Burk O, Tahir A, Schwab M, Windshügel B, Kirchmair J. Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR. Cells 2022; 11:cells11081253. [PMID: 35455933 PMCID: PMC9029776 DOI: 10.3390/cells11081253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/30/2022] [Indexed: 02/04/2023] Open
Abstract
The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model’s training and validation performance. On a challenging test set, this technique led to improvements in Matthew correlation coefficients (MCCs) by up to 0.21. Using these regularized ML models, we selected 31 compounds that are structurally distinct from known PXR ligands for experimental validation. Twelve of them were confirmed as active in the cellular PXR ligand-binding domain assembly assay and more hits were identified during follow-up studies. Comprehensive analysis of key features of PXR biology conducted for three representative hits confirmed their ability to activate the PXR.
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Affiliation(s)
- Steffen Hirte
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
| | - Oliver Burk
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (O.B.); (M.S.)
| | - Ammar Tahir
- Division of Pharmacognosy, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (O.B.); (M.S.)
- Departments of Clinical Pharmacology and Biochemistry and Pharmacy, University of Tuebingen, 72074 Tübingen, Germany
- Cluster of Excellence IFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72074 Tübingen, Germany
| | - Björn Windshügel
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Discovery Research Screening Port, 22525 Hamburg, Germany;
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
| | - Johannes Kirchmair
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
- Correspondence: ; Tel.: +43-1-4277-55104
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Bower MJ, Aronov AM, Cleveland T, Hariparsad N, McGaughey GB, McMasters DR, Zhang X, Goldman B. Smallest Maximum Intramolecular Distance: A Novel Method to Mitigate Pregnane Xenobiotic Receptor Activation. J Chem Inf Model 2020; 60:2091-2099. [DOI: 10.1021/acs.jcim.9b00692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Michael J. Bower
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Alex M. Aronov
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Thomas Cleveland
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Niresh Hariparsad
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Georgia B. McGaughey
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Daniel R. McMasters
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Xiaodan Zhang
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Brian Goldman
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
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Kato H. Computational prediction of cytochrome P450 inhibition and induction. Drug Metab Pharmacokinet 2019; 35:30-44. [PMID: 31902468 DOI: 10.1016/j.dmpk.2019.11.006] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/27/2019] [Accepted: 11/17/2019] [Indexed: 12/14/2022]
Abstract
Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.
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Affiliation(s)
- Harutoshi Kato
- DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Aoba-ku, Yokohama-shi, 227-0033, Japan.
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Nagai M, Konno Y, Satsukawa M, Yamashita S, Yoshinari K. Establishment of In Silico Prediction Models for CYP3A4 and CYP2B6 Induction in Human Hepatocytes by Multiple Regression Analysis Using Azole Compounds. ACTA ACUST UNITED AC 2016; 44:1390-8. [PMID: 27208383 DOI: 10.1124/dmd.115.068619] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2015] [Accepted: 05/18/2016] [Indexed: 11/22/2022]
Abstract
Drug-drug interactions (DDIs) via cytochrome P450 (P450) induction are one clinical problem leading to increased risk of adverse effects and the need for dosage adjustments and additional therapeutic monitoring. In silico models for predicting P450 induction are useful for avoiding DDI risk. In this study, we have established regression models for CYP3A4 and CYP2B6 induction in human hepatocytes using several physicochemical parameters for a set of azole compounds with different P450 induction as characteristics as model compounds. To obtain a well-correlated regression model, the compounds for CYP3A4 or CYP2B6 induction were independently selected from the tested azole compounds using principal component analysis with fold-induction data. Both of the multiple linear regression models obtained for CYP3A4 and CYP2B6 induction are represented by different sets of physicochemical parameters. The adjusted coefficients of determination for these models were of 0.8 and 0.9, respectively. The fold-induction of the validation compounds, another set of 12 azole-containing compounds, were predicted within twofold limits for both CYP3A4 and CYP2B6. The concordance for the prediction of CYP3A4 induction was 87% with another validation set, 23 marketed drugs. However, the prediction of CYP2B6 induction tended to be overestimated for these marketed drugs. The regression models show that lipophilicity mostly contributes to CYP3A4 induction, whereas not only the lipophilicity but also the molecular polarity is important for CYP2B6 induction. Our regression models, especially that for CYP3A4 induction, might provide useful methods to avoid potent CYP3A4 or CYP2B6 inducers during the lead optimization stage without performing induction assays in human hepatocytes.
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Affiliation(s)
- Mika Nagai
- Pharmacokinetics and Safety Department, Drug Research Center, Kaken Pharmaceutical, Kyoto, Japan (M.N., Y.K., M.S.); Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan (M.N., K.Y.); and Faculty of Pharmaceutical Sciences, Setsunan University, Osaka, Japan (S.Y.)
| | - Yoshihiro Konno
- Pharmacokinetics and Safety Department, Drug Research Center, Kaken Pharmaceutical, Kyoto, Japan (M.N., Y.K., M.S.); Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan (M.N., K.Y.); and Faculty of Pharmaceutical Sciences, Setsunan University, Osaka, Japan (S.Y.)
| | - Masahiro Satsukawa
- Pharmacokinetics and Safety Department, Drug Research Center, Kaken Pharmaceutical, Kyoto, Japan (M.N., Y.K., M.S.); Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan (M.N., K.Y.); and Faculty of Pharmaceutical Sciences, Setsunan University, Osaka, Japan (S.Y.)
| | - Shinji Yamashita
- Pharmacokinetics and Safety Department, Drug Research Center, Kaken Pharmaceutical, Kyoto, Japan (M.N., Y.K., M.S.); Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan (M.N., K.Y.); and Faculty of Pharmaceutical Sciences, Setsunan University, Osaka, Japan (S.Y.)
| | - Kouichi Yoshinari
- Pharmacokinetics and Safety Department, Drug Research Center, Kaken Pharmaceutical, Kyoto, Japan (M.N., Y.K., M.S.); Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan (M.N., K.Y.); and Faculty of Pharmaceutical Sciences, Setsunan University, Osaka, Japan (S.Y.)
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