1
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Wang S, Ballard TE, Christopher LJ, Foti RS, Gu C, Khojasteh SC, Liu J, Ma S, Ma B, Obach RS, Schadt S, Zhang Z, Zhang D. The Importance of Tracking "Missing" Metabolites: How and Why? J Med Chem 2023; 66:15586-15612. [PMID: 37769129 DOI: 10.1021/acs.jmedchem.3c01293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Technologies currently employed to find and identify drug metabolites in complex biological matrices generally yield results that offer a comprehensive picture of the drug metabolite profile. However, drug metabolites can be missed or are captured only late in the drug development process. This could be due to a variety of factors, such as metabolism that results in partial loss of the molecule, covalent bonding to macromolecules, the drug being metabolized in specific human tissues, or poor ionization in a mass spectrometer. These scenarios often draw a great deal of attention from chemistry, safety assessment, and pharmacology. This review will summarize scenarios of missing metabolites, why they are missing, and associated uncovering strategies from deeper investigations. Uncovering previously missed metabolites can have ramifications in drug development with toxicological and pharmacological consequences, and knowledge of these can help in the design of new drugs.
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Affiliation(s)
- Shuai Wang
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - T Eric Ballard
- Takeda Development Center Americas, Inc., 35 Landsdowne St, Cambridge, Massachusetts 02139, United States
| | - Lisa J Christopher
- Department of Clinical Pharmacology, Pharmacometrics, Disposition & Bioanalysis, Bristol-Myers Squibb, Route 206 & Province Line Road, Princeton, New Jersey 08543, United States
| | - Robert S Foti
- Preclinical Development, Merck & Co., Inc., 33 Avenue Louis Pasteur, Boston, Massachusetts 02115, United States
| | - Chungang Gu
- Drug Metabolism and Pharmacokinetics, Biogen Inc., 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - S Cyrus Khojasteh
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Joyce Liu
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Shuguang Ma
- Drug Metabolism and Pharmacokinetics, Pliant Therapeutics, 260 Littlefield Avenue, South San Francisco, California 94080, United States
| | - Bin Ma
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - R Scott Obach
- Pharmacokinetics, Dynamics, and Metabolism, Pfizer, Inc., Eastern Point Road, Groton, Connecticut 06340, United States
| | - Simone Schadt
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacher Strasse 124, 4070 Basel, Switzerland
| | - Zhoupeng Zhang
- DMPK Oncology R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Donglu Zhang
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
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2
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Flynn NR, Swamidass SJ. Message Passing Neural Networks Improve Prediction of Metabolite Authenticity. J Chem Inf Model 2023; 63:1675-1694. [PMID: 36926871 DOI: 10.1021/acs.jcim.2c01383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously developed models help understand how these enzymes modify molecule structure by predicting sites of metabolism or characterizing formation of metabolite-biomolecule adducts. However, the majority of reactive metabolites are formed by multiple metabolic steps, and understanding the progenitor molecule's network-level behavior necessitates an integrative approach that blends multiple site of metabolism and structure inference models. Our previously developed tool, XenoNet 1.0, generates metabolic networks, where nodes are molecules and weighted edges are metabolic transformations. We extend XenoNet with a bidirectional message passing neural network that integrates edge feature information and local network structure using edge-conditioned graph convolutions and jumping knowledge to predict the authenticity of inferred Phase I metabolite structures. Our model significantly outperformed prior work and algorithmic baselines on a data set of 311 networks and 6606 intermediates annotated using a chemically diverse set of 20 736 individual in vitro and in vivo reaction records accounting for 92.3% of all human Phase I metabolism in the Accelrys Metabolite Database. Cross-validated predictions resulted in area under the receiver operating characteristic curves of 88.5% and 87.6% for separating experimentally observed and unobserved metabolites at global and network levels, respectively. Further analysis verified robustness to networks of varying depth and breadth, accurate detection of metabolites, such as d,l-methamphetamine, that are experimentally observed or unobserved in different network contexts, extraction of important metabolic subnetworks, and identification of known bioactivation pathways, such as for nimesulide and terbinafine. By exploiting network structures, our approach accurately suggests unreported metabolites for experimental study and may rationalize modifications for avoiding deleterious pathways antecedent to reactive metabolite formation.
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Affiliation(s)
- Noah R Flynn
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
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3
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Wu X, Zhou Q, Mu L, Hu X. Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives. JOURNAL OF HAZARDOUS MATERIALS 2022; 438:129487. [PMID: 35816807 DOI: 10.1016/j.jhazmat.2022.129487] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.
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Affiliation(s)
- Xiaotong Wu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Qixing Zhou
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Li Mu
- Tianjin Key Laboratory of Agro-environment and Safe-product, Key Laboratory for Environmental Factors Control of Agro-product Quality Safety (Ministry of Agriculture and Rural Affairs), Institute of Agro-environmental Protection, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China.
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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4
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Laux J, Forster M, Riexinger L, Schwamborn A, Guezguez J, Pokoj C, Kudolo M, Berger LM, Knapp S, Schollmeyer D, Guse J, Burnet M, Laufer SA. Pharmacokinetic Optimization of Small Molecule Janus Kinase 3 Inhibitors to Target Immune Cells. ACS PHARMACOLOGY & TRANSLATIONAL SCIENCE 2022; 5:573-602. [DOI: 10.1021/acsptsci.2c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Julian Laux
- Synovo GmbH, Paul-Ehrlich-Straße 15, 72076 Tübingen, Germany
| | - Michael Forster
- Department of Pharmaceutical/Medicinal Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 8, 72076 Tübingen, DE Germany
| | - Laura Riexinger
- Synovo GmbH, Paul-Ehrlich-Straße 15, 72076 Tübingen, Germany
| | - Anna Schwamborn
- Synovo GmbH, Paul-Ehrlich-Straße 15, 72076 Tübingen, Germany
| | - Jamil Guezguez
- Synovo GmbH, Paul-Ehrlich-Straße 15, 72076 Tübingen, Germany
| | - Christina Pokoj
- Synovo GmbH, Paul-Ehrlich-Straße 15, 72076 Tübingen, Germany
| | - Mark Kudolo
- Department of Pharmaceutical/Medicinal Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 8, 72076 Tübingen, DE Germany
| | - Lena M. Berger
- Structural Genomics Consortium, Institute for Pharmaceutical Chemistry, Johann Wolfgang Goethe University, Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany
| | - Stefan Knapp
- Structural Genomics Consortium, Institute for Pharmaceutical Chemistry, Johann Wolfgang Goethe University, Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany
| | - Dieter Schollmeyer
- Institute for Organic Chemistry, Johannes Gutenberg University Mainz, Duesbergweg 10-14, 55099 Mainz, Germany
| | - Jan Guse
- Synovo GmbH, Paul-Ehrlich-Straße 15, 72076 Tübingen, Germany
| | - Michael Burnet
- Synovo GmbH, Paul-Ehrlich-Straße 15, 72076 Tübingen, Germany
| | - Stefan A. Laufer
- Department of Pharmaceutical/Medicinal Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 8, 72076 Tübingen, DE Germany
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Germany
- Tübingen Center for Academic Drug Discovery and Development (TüCAD2), 72076 Tübingen, Germany
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5
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Guo J, Li F, Cheng F, Ma L, Liu X, Durairaj P, Zhang G, Tang D, Long X, Zhang W, Du L, Zhang X, Li S. Bacterial Biosynthetic P450 Enzyme PikC D50N: A Potential Biocatalyst for the Preparation of Human Drug Metabolites. J Org Chem 2021; 86:14563-14571. [PMID: 34662127 DOI: 10.1021/acs.joc.1c01407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Human drug metabolites (HDMs) are important chemicals widely used in drug-related studies. However, acquiring these enzyme-derived and regio-/stereo-selectively modified compounds through chemical approaches is complicated. PikC is a biosynthetic P450 enzyme involved in pikromycin biosynthesis from the bacterium Streptomyces venezuelae. Here, we identify the mutant PikCD50N as a potential biocatalyst, with a broad substrate scope, diversified product profile, and high catalytic efficiency, for preparation of HDMs. Remarkably, PikCD50N can mediate the drug-metabolizing reactions using the low-cost H2O2 as a direct electron and oxygen donor.
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Affiliation(s)
- Jiawei Guo
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266237, China
| | - Fengwei Li
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Fangyuan Cheng
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Li Ma
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Xiaohui Liu
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Pradeepraj Durairaj
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Gang Zhang
- Fujian Universities and Colleges Engineering Research Center of Marine Biopharmaceutical Resources, Xiamen Medical College, Xiamen, Fujian 361023, China
| | - Dandan Tang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Xiangtian Long
- Tianjin Hankang Pharmaceutical Biotechnology Co. Ltd., Tianjin 300409, China
| | - Wei Zhang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Lei Du
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Xingwang Zhang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China
| | - Shengying Li
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266237, China
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6
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Conan M, Théret N, Langouet S, Siegel A. Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA. BMC Bioinformatics 2021; 22:450. [PMID: 34548010 PMCID: PMC8454073 DOI: 10.1186/s12859-021-04363-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 08/28/2021] [Indexed: 12/22/2022] Open
Abstract
Background The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A\documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}αC). Results We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures. Conclusions Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04363-6.
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Affiliation(s)
- Mael Conan
- Institut de Recherche en Santé, Environnement et Travail, Univ Rennes, Inserm, EHESP, IRSET, Rennes, France.,Institut de Recherche en Informatique et Systèmes Aléatoires, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | - Nathalie Théret
- Institut de Recherche en Santé, Environnement et Travail, Univ Rennes, Inserm, EHESP, IRSET, Rennes, France.,Institut de Recherche en Informatique et Systèmes Aléatoires, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | - Sophie Langouet
- Institut de Recherche en Santé, Environnement et Travail, Univ Rennes, Inserm, EHESP, IRSET, Rennes, France.
| | - Anne Siegel
- Institut de Recherche en Informatique et Systèmes Aléatoires, Univ Rennes, Inria, CNRS, IRISA, Rennes, France.
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7
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Huang X, Zhao X, Zhang X, Wang P, Zhu K, Shao B. Chlorinated disinfection byproducts of diazepam perturb cell metabolism and induce behavioral toxicity in zebrafish larvae. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 220:112416. [PMID: 34119928 DOI: 10.1016/j.ecoenv.2021.112416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 06/12/2023]
Abstract
Numerous byproducts resulting from chlorinated disinfection are constantly being generated during water treatment processes. The potential risks of these new emerging pollutions remain largely unknown. Here, we determined the risks of chlorinated disinfection byproducts of diazepam (DZP) in the cellular and zebrafish exposure experiments. The cytotoxicity of disinfection byproducts (MACB and MBCC) was greater than DZP in macrophage raw 264.7 cells at 10 mg/L. We further found that the effects of MBCC on the metabolism of glycine, serine, threonine and riboflavin were far greater than DZP by the targeted metabolomics methods. Moreover, MBCC significantly decreased the peak amplitude of neuronal action potential in primary embryonic rat (Spragu-Dawley SD) hippocampal neurons. We finally determined behavioral toxicity of DZP and byproducts in zebrafish larvae. MBCC significantly decreased the maximal swim-activity and peak duration of zebrafish after 72 h exposure. Altogether, these findings indicate the MBCC pose serious pressures on public health.
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Affiliation(s)
- Xiaoyong Huang
- Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China; College of Science, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing 100193, China; College of Veterinary Medicine, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing 100193, China
| | - Xiaole Zhao
- College of Veterinary Medicine, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing 100193, China
| | - Xin Zhang
- Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China
| | - Peng Wang
- College of Science, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing 100193, China
| | - Kui Zhu
- College of Veterinary Medicine, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing 100193, China
| | - Bing Shao
- Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China.
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8
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Zhang Y, Li X, Sun Y, Liu X, Wang W, Tian J. Pharmacokinetics of S-epacadostat, an indoleamine 2,3-dioxygenase 1 inhibitor, in dog plasma and identification of its metabolites in vivo and in vitro. Biomed Chromatogr 2021; 35:e5226. [PMID: 34388261 DOI: 10.1002/bmc.5226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/30/2021] [Accepted: 08/06/2021] [Indexed: 11/06/2022]
Abstract
S-epacadostat (S-EPA) is an efficient and selective small-molecule inhibitor of indoleamine 2,3-dioxygenase 1. It is an EPA analog with a sulfur atom instead of a nitrogen atom at the furazan C3 position. This study documents the pharmacokinetics of S-EPA in dogs and its metabolic pathway. After an oral administration of 15 mg/kg of S-EPA in dogs, the time to peak concentration was 0.80 h, the mean elimination half-life was 7.3 h, and the absolute bioavailability was 55.8%. Furthermore, we identified S-EPA metabolites in dog plasma and dog liver microsomes by UPLC-Q Exactive Orbitrap HRMS. In dog plasma, we found five metabolites, which came from glucuronidation (M1 and M2), deoxygenation (the amidine M4), glucuronidation of M4 (M3), and desulfonamidation and oxidation of M4 (the carboxylic acid M5). In dog liver microsomes, we identified three major metabolites, namely, the glucuronide conjugate (M6), a mono-oxidation product (M7), and a desulfonamidation and oxidation product (M8). Gut microbiota may cause the differences between in vivo and in vitro oxidation metabolisms. Contrary to EPA, S-EPA did not undergo dealkylation, suggesting that substituting the nitrogen with sulfur affects the metabolism of the adjacent alkyl side chain.
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Affiliation(s)
- Yumu Zhang
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, Shandong, China
| | - Xin Li
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, Shandong, China
| | - Yufei Sun
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, Shandong, China
| | - Xinghua Liu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, Shandong, China
| | - Wenyan Wang
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, Shandong, China
| | - Jingwei Tian
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, Shandong, China
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9
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Hughes TB, Flynn N, Dang NL, Swamidass SJ. Modeling the Bioactivation and Subsequent Reactivity of Drugs. Chem Res Toxicol 2021; 34:584-600. [PMID: 33496184 DOI: 10.1021/acs.chemrestox.0c00417] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.
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Affiliation(s)
- Tyler B Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - Noah Flynn
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - Na Le Dang
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
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10
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Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ. Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J Chem Inf Model 2020; 60:4702-4716. [PMID: 32881497 PMCID: PMC8716321 DOI: 10.1021/acs.jcim.0c00360] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.
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Affiliation(s)
- Tyler B Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - Na Le Dang
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - Ayush Kumar
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - Noah R Flynn
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
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11
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Sarullo K, Matlock MK, Swamidass SJ. Site-Level Bioactivity of Small-Molecules from Deep-Learned Representations of Quantum Chemistry. J Phys Chem A 2020; 124:9194-9202. [PMID: 33084331 DOI: 10.1021/acs.jpca.0c06231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Atom- or bond-level chemical properties of interest in medicinal chemistry, such as drug metabolism and electrophilic reactivity, are important to understand and predict across arbitrary new molecules. Deep learning can be used to map molecular structures to their chemical properties, but the data sets for these tasks are relatively small, which can limit accuracy and generalizability. To overcome this limitation, it would be preferable to model these properties on the basis of the underlying quantum chemical characteristics of small molecules. However, it is difficult to learn higher level chemical properties from lower level quantum calculations. To overcome this challenge, we pretrained deep learning models to compute quantum chemical properties and then reused the intermediate representations constructed by the pretrained network. Transfer learning, in this way, substantially outperformed models based on chemical graphs alone or quantum chemical properties alone. This result was robust, observable in five prediction tasks: identifying sites of epoxidation by metabolic enzymes and identifying sites of covalent reactivity with cyanide, glutathione, DNA and protein. We see that this approach may substantially improve the accuracy of deep learning models for specific chemical structures, such as aromatic systems.
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Affiliation(s)
- Kathryn Sarullo
- Department of Pathology and Immunology, School of Medicine, Washington University in St. Louis, Saint Louis, Missouri 63110, United States
| | - Matthew K Matlock
- Department of Pathology and Immunology, School of Medicine, Washington University in St. Louis, Saint Louis, Missouri 63110, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, School of Medicine, Washington University in St. Louis, Saint Louis, Missouri 63110, United States
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12
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Zhang Z, Qiu C, Xu Y, Han Q, Tang J, Loh KP, Su C. Semiconductor photocatalysis to engineering deuterated N-alkyl pharmaceuticals enabled by synergistic activation of water and alkanols. Nat Commun 2020; 11:4722. [PMID: 32948764 PMCID: PMC7501254 DOI: 10.1038/s41467-020-18458-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/11/2020] [Indexed: 12/23/2022] Open
Abstract
Precisely controlled deuterium labeling at specific sites of N-alkyl drugs is crucial in drug-development as over 50% of the top-selling drugs contain N-alkyl groups, in which it is very challenging to selectively replace protons with deuterium atoms. With the goal of achieving controllable isotope-labeling in N-alkylated amines, we herein rationally design photocatalytic water-splitting to furnish [H] or [D] and isotope alkanol-oxidation by photoexcited electron-hole pairs on a polymeric semiconductor. The controlled installation of N-CH3, -CDH2, -CD2H, -CD3, and -13CH3 groups into pharmaceutical amines thus has been demonstrated by tuning isotopic water and methanol. More than 50 examples with a wide range of functionalities are presented, demonstrating the universal applicability and mildness of this strategy. Gram-scale production has been realized, paving the way for the practical photosynthesis of pharmaceuticals.
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Affiliation(s)
- Zhaofei Zhang
- International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology of Ministry of Education, Engineering Technology Research Center for 2D Materials Information Functional Devices and Systems of Guangdong Province, Institute of Microscale Optoeletronics, Shenzhen University, 518060, Shenzhen, China
| | - Chuntian Qiu
- International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology of Ministry of Education, Engineering Technology Research Center for 2D Materials Information Functional Devices and Systems of Guangdong Province, Institute of Microscale Optoeletronics, Shenzhen University, 518060, Shenzhen, China
| | - Yangsen Xu
- International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology of Ministry of Education, Engineering Technology Research Center for 2D Materials Information Functional Devices and Systems of Guangdong Province, Institute of Microscale Optoeletronics, Shenzhen University, 518060, Shenzhen, China
| | - Qing Han
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
- Key Laboratory of Photoelectronic/Electrophotonic, Conversion Materials, Key Laboratory of Cluster Science, Ministry of Education, School of Chemistry, Beijing Institute of Technology, 100081, Beijing, China
| | - Junwang Tang
- Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK
| | - Kian Ping Loh
- Department of Chemistry and Centre for Advanced 2D Materials (CA2DM), National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore
| | - Chenliang Su
- International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology of Ministry of Education, Engineering Technology Research Center for 2D Materials Information Functional Devices and Systems of Guangdong Province, Institute of Microscale Optoeletronics, Shenzhen University, 518060, Shenzhen, China.
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13
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Zhang C, Lu M, Lin L, Huang Z, Zhang R, Wu X, Chen Y. Riboflavin Is Directly Involved in N-Dealkylation Catalyzed by Bacterial Cytochrome P450 Monooxygenases. Chembiochem 2020; 21:2297-2305. [PMID: 32243060 DOI: 10.1002/cbic.202000071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/01/2020] [Indexed: 11/09/2022]
Abstract
Like a vast number of enzymes in nature, bacterial cytochrome P450 monooxygenases require an activated form of flavin as a cofactor for catalytic activity. Riboflavin is the precursor of FAD and FMN that serves as indispensable cofactor for flavoenzymes. In contrast to previous notions, herein we describe the identification of an electron-transfer process that is directly mediated by riboflavin for N-dealkylation by bacterial P450 monooxygenases. The electron relay from NADPH to riboflavin and then via activated oxygen to heme was proposed based on a combination of X-ray crystallography, molecular modeling and molecular dynamics simulation, site-directed mutagenesis and biochemical analysis of representative bacterial P450 monooxygenases. This study provides new insights into the electron transfer mechanism in bacterial P450 enzyme catalysis and likely in yeasts, fungi, plants and mammals.
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Affiliation(s)
- Chengchang Zhang
- Laboratory of Chemical Biology and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu Province, 211198, P. R. China
| | - Meiling Lu
- Laboratory of Chemical Biology and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu Province, 211198, P. R. China
| | - Lin Lin
- National Center for Protein Science and Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 333 Haike Road, Shanghai, 201210, P. R. China
| | - Zhangjian Huang
- Laboratory of Chemical Biology and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu Province, 211198, P. R. China
| | - Rongguang Zhang
- National Center for Protein Science and Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 333 Haike Road, Shanghai, 201210, P. R. China
| | - Xuri Wu
- Laboratory of Chemical Biology and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu Province, 211198, P. R. China
| | - Yijun Chen
- Laboratory of Chemical Biology and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu Province, 211198, P. R. China
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14
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Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model 2020; 60:3431-3449. [PMID: 32525671 PMCID: PMC8716322 DOI: 10.1021/acs.jcim.0c00361] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation, both experimental and computational screening assays are utilized. Experimental assays for assessing the formation of reactive metabolites are low throughput and expensive to perform, so they are often reserved until later stages of the drug development pipeline when the drug candidate pools are already significantly narrowed. In contrast, computational methods are high throughput and cheap to perform to screen thousands to millions of compounds for potentially toxic molecules during the early stages of the drug development pipeline. Commonly used computational methods focus on detecting and structurally characterizing reactive metabolite-biomolecule adducts or predicting sites on a drug molecule that are liable to form reactive metabolites. However, such methods are often only concerned with the structure of the initial drug molecule or of the adduct formed when a biomolecule conjugates to a reactive metabolite. Thus, these methods are likely to miss intermediate metabolites that may lead to subsequent reactive metabolite formation. To address these shortcomings, we create XenoNet, a metabolic network predictor, that can take a pair of a substrate and a target product as input and (1) enumerate pathways, or sequences of intermediate metabolite structures, between the pair, and (2) compute the likelihood of those pathways and intermediate metabolites. We validate XenoNet on a large, chemically diverse data set of 17 054 metabolic networks built from a literature-derived reaction database. Each metabolic network has a defined substrate molecule that has been experimentally observed to undergo metabolism into a defined product metabolite. XenoNet can predict experimentally observed pathways and intermediate metabolites linking the input substrate and product pair with a recall of 88 and 46%, respectively. Using likelihood scoring, XenoNet also achieves a top-one pathway and intermediate metabolite accuracy of 93.6 and 51.9%, respectively. We further validate XenoNet against prior methods for metabolite prediction. XenoNet significantly outperforms all prior methods across multiple metrics. XenoNet is available at https://swami.wustl.edu/xenonet.
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Affiliation(s)
- Noah R Flynn
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Na Le Dang
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Michael D Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, Missouri 63110, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
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15
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Driscoll JP, Sadlowski CM, Shah NR, Feula A. Metabolism and Bioactivation: It's Time to Expect the Unexpected. J Med Chem 2020; 63:6303-6314. [PMID: 32267691 DOI: 10.1021/acs.jmedchem.0c00026] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Improvements in in vitro ADME tools and pharmacokinetic prediction models have helped to shift attrition rates in early clinical trials from poor exposure to drug safety concerns, such as drug-induced liver injury (DILI). Assessing a new chemical entity's potential for liver toxicity is an important consideration for the likely success of new drug candidates. Reactive intermediates produced during drug metabolism have been implicated as a cause of DILI, and their formation has been correlated to the addition of a black box warning on a drug label. In this work, we will present contemporary examples of the bioactivation of atypical structures usually regarded as benign and often used by medicinal chemists when attempting to avoid bioactivation. Medicinal chemistry strategies used to derisk bioactivation will be discussed, and an emphasis will be placed on the necessity of a multidisciplinary approach.
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Affiliation(s)
- James P Driscoll
- MyoKardia, Inc., 333 Allerton Avenue, South San Francisco, California 94080, United States
| | - Corinne M Sadlowski
- MyoKardia, Inc., 333 Allerton Avenue, South San Francisco, California 94080, United States
| | - Nina R Shah
- MyoKardia, Inc., 333 Allerton Avenue, South San Francisco, California 94080, United States
| | - Antonio Feula
- MyoKardia, Inc., 333 Allerton Avenue, South San Francisco, California 94080, United States
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16
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Dang NL, Matlock MK, Hughes TB, Swamidass SJ. The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors. J Chem Inf Model 2020; 60:1146-1164. [DOI: 10.1021/acs.jcim.9b00836] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Na Le Dang
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Matthew K. Matlock
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Tyler B. Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - S. Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
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17
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Matlock MK, Tambe A, Elliott-Higgins J, Hines RN, Miller GP, Swamidass SJ. A Time-Embedding Network Models the Ontogeny of 23 Hepatic Drug Metabolizing Enzymes. Chem Res Toxicol 2019; 32:1707-1721. [PMID: 31304741 DOI: 10.1021/acs.chemrestox.9b00223] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Pediatric patients are at elevated risk of adverse drug reactions, and there is insufficient information on drug safety in children. Complicating risk assessment in children, there are numerous age-dependent changes in the absorption, distribution, metabolism, and elimination of drugs. A key contributor to age-dependent drug toxicity risk is the ontogeny of drug metabolism enzymes, the changes in both abundance and type throughout development from the fetal period through adulthood. Critically, these changes affect not only the overall clearance of drugs but also exposure to individual metabolites. In this study, we introduce time-embedding neural networks in order to model population-level variation in metabolism enzyme expression as a function of age. We use a time-embedding network to model the ontogeny of 23 drug metabolism enzymes. The time-embedding network recapitulates known demographic factors impacting 3A5 expression. The time-embedding network also effectively models the nonlinear dynamics of 2D6 expression, enabling a better fit to clinical data than prior work. In contrast, a standard neural network fails to model these features of 3A5 and 2D6 expression. Finally, we combine the time-embedding model of ontogeny with additional information to estimate age-dependent changes in reactive metabolite exposure. This simple approach identifies age-dependent changes in exposure to valproic acid and dextromethorphan metabolites and suggests potential mechanisms of valproic acid toxicity. This approach may help researchers evaluate the risk of drug toxicity in pediatric populations.
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Affiliation(s)
- Matthew K Matlock
- Department of Pathology and Immunology , Washington University in St. Louis , Saint Louis , Missouri 63110 , United States
| | - Abhik Tambe
- Department of Pathology and Immunology , Washington University in St. Louis , Saint Louis , Missouri 63110 , United States
| | - Jack Elliott-Higgins
- Department of Pathology and Immunology , Washington University in St. Louis , Saint Louis , Missouri 63110 , United States
| | - Ronald N Hines
- National Health and Environmental Effects Research Laboratory , United States Environmental Protection Agency , Research Triangle Park , North Carolina 27709 , United States
| | - Grover P Miller
- Department of Biochemistry and Molecular Biology , University of Arkansas for Medical Sciences , Little Rock , Arkansas 72205 , United States
| | - S Joshua Swamidass
- Institute for Informatics , Washington University in St. Louis , Saint Louis , Missouri 63110 , United States
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18
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Bai F, Hong D, Lu Y, Liu H, Xu C, Yao X. Prediction of the Antioxidant Response Elements' Response of Compound by Deep Learning. Front Chem 2019; 7:385. [PMID: 31214568 PMCID: PMC6554289 DOI: 10.3389/fchem.2019.00385] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 05/14/2019] [Indexed: 11/13/2022] Open
Abstract
The antioxidant response elements (AREs) play a significant role in occurrence of oxidative stress and may cause multitudinous toxicity effects in the pathogenesis of a variety of diseases. Determining if one compound can activate AREs is crucial for the assessment of potential risk of compound. Here, a series of predictive models by applying multiple deep learning algorithms including deep neural networks (DNN), convolution neural networks (CNN), recurrent neural networks (RNN), and highway networks (HN) were constructed and validated based on Tox21 challenge dataset and applied to predict whether the compounds are the activators or inactivators of AREs. The built models were evaluated by various of statistical parameters, such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and receiver operating characteristic (ROC) curve. The DNN prediction model based on fingerprint features has best prediction ability, with accuracy of 0.992, 0.914, and 0.917 for the training set, test set, and validation set, respectively. Consequently, these robust models can be adopted to predict the ARE response of molecules fast and accurately, which is of great significance for the evaluation of safety of compounds in the process of drug discovery and development.
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Affiliation(s)
- Fang Bai
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Ding Hong
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yingying Lu
- State Key Laboratory of Applied Organic Chemistry, Department of Chemistry, Lanzhou University, Lanzhou, China
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Cunlu Xu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaojun Yao
- State Key Laboratory of Applied Organic Chemistry, Department of Chemistry, Lanzhou University, Lanzhou, China
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19
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Davis MA, Barnette DA, Flynn NR, Pidugu AS, Swamidass SJ, Boysen G, Miller GP. CYP2C19 and 3A4 Dominate Metabolic Clearance and Bioactivation of Terbinafine Based on Computational and Experimental Approaches. Chem Res Toxicol 2019; 32:1151-1164. [PMID: 30925039 DOI: 10.1021/acs.chemrestox.9b00006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Lamisil (terbinafine) is an effective, widely prescribed antifungal drug that causes rare idiosyncratic hepatotoxicity. The proposed toxic mechanism involves a reactive metabolite, 6,6-dimethyl-2-hepten-4-ynal (TBF-A), formed through three N-dealkylation pathways. We were the first to characterize them using in vitro studies with human liver microsomes and modeling approaches, yet knowledge of the individual enzymes catalyzing reactions remained unknown. Herein, we employed experimental and computational tools to assess terbinafine metabolism by specific cytochrome P450 isozymes. In vitro inhibitor phenotyping studies revealed six isozymes were involved in one or more N-dealkylation pathways. CYP2C19 and 3A4 contributed to all pathways, and so, we targeted them for steady-state analyses with recombinant isozymes. N-Dealkylation yielding TBF-A directly was catalyzed by CYP2C19 and 3A4 similarly. Nevertheless, CYP2C19 was more efficient than CYP3A4 at N-demethylation and other steps leading to TBF-A. Unlike microsomal reactions, N-denaphthylation was surprisingly efficient for CYP2C19 and 3A4, which was validated by controls. CYP2C19 was the most efficient among all reactions. Nonetheless, CYP3A4 was more selective at steps leading to TBF-A, making it more effective in terbinafine bioactivation based on metabolic split ratios for competing pathways. Model predictions did not extrapolate to quantitative kinetic constants, yet some results for CYP3A4 and CYP2C19 agreed qualitatively with preferred reaction steps and pathways. Clinical data on drug interactions support the CYP3A4 role in terbinafine metabolism, while CYP2C19 remains understudied. Taken together, knowledge of P450s responsible for terbinafine metabolism and TBF-A formation provides a foundation for investigating and mitigating the impact of P450 variations in toxic risks posed to patients.
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Affiliation(s)
- Mary A Davis
- Department of Biochemistry and Molecular Biology , University of Arkansas for Medical Sciences , Little Rock , Arkansas 72205 , United States
| | - Dustyn A Barnette
- Department of Biochemistry and Molecular Biology , University of Arkansas for Medical Sciences , Little Rock , Arkansas 72205 , United States
| | - Noah R Flynn
- Department of Pathology and Immunology , Washington University , St. Louis , Missouri 63130 , United States
| | - Anirudh S Pidugu
- Department of Neuroscience and Behavioral Biology , Emory University , Atlanta , Georgia 30322 , United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology , Washington University , St. Louis , Missouri 63130 , United States
| | - Gunnar Boysen
- Department of Environmental and Occupational Health , University of Arkansas for Medical Sciences , Little Rock , Arkansas 72205 , United States
| | - Grover P Miller
- Department of Biochemistry and Molecular Biology , University of Arkansas for Medical Sciences , Little Rock , Arkansas 72205 , United States
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20
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Finkelmann AR, Goldmann D, Schneider G, Göller AH. MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes. ChemMedChem 2018; 13:2281-2289. [PMID: 30184341 DOI: 10.1002/cmdc.201800309] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/31/2018] [Indexed: 12/20/2022]
Abstract
The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in-house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum-chemistry-derived molecular representations for reactivity prediction.
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Affiliation(s)
- Arndt R Finkelmann
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | - Daria Goldmann
- KNIME GmbH, Reichenaustrasse 11, 78467, Konstanz, Germany
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | - Andreas H Göller
- Bayer AG, Pharmaceuticals, Research & Development, 42096, Wuppertal, Germany
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21
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Barnette DA, Davis MA, Dang NL, Pidugu AS, Hughes T, Swamidass SJ, Boysen G, Miller GP. Lamisil (terbinafine) toxicity: Determining pathways to bioactivation through computational and experimental approaches. Biochem Pharmacol 2018; 156:10-21. [PMID: 30076845 PMCID: PMC6188815 DOI: 10.1016/j.bcp.2018.07.043] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 07/30/2018] [Indexed: 12/01/2022]
Abstract
Lamisil (terbinafine) may cause idiosyncratic liver toxicity through a proposed toxicological mechanism involving the reactive metabolite 6,6-dimethyl-2-hepten-4-ynal (TBF-A). TBF-A toxicological relevance remains unclear due to a lack of identification of pathways leading to and competing with TBF-A formation. We resolved this knowledge gap by combining computational modeling and experimental kinetics of in vitro hepatic N-dealkylation of terbinafine. A deep learning model of N-dealkylation predicted a high probability for N-demethylation to yield desmethyl-terbinafine followed by N-dealkylation to TBF-A and marginal contributions from other possible pathways. We carried out steady-state kinetic experiments with pooled human liver microsomes that relied on development of labeling methods to expand metabolite characterization. Those efforts revealed high levels of TBF-A formation and first order decay during metabolic reactions; actual TBF-A levels would then reflect the balance between those processes as well as reflect the impact of stabilizing adduction with glutathione and other biological molecules. Modeling predictions and experimental studies agreed on the significance of N-demethylation and insignificance of N-denaphthylation in terbinafine metabolism, yet differed on importance of direct TBF-A formation. Under steady-state conditions, the direct pathway was the most important source of the reactive metabolite with a Vmax/Km of 4.0 pmol/min/mg protein/µM in contrast to model predictions. Nevertheless, previous studies show that therapeutic dosing leads to accumulation of desmethyl-terbinafine in plasma, which means that likely sources for TBF-A would draw from metabolism of both the major metabolite and parent drug based on our modeling and experimental studies. Through this combination of novel modeling and experimental approaches, we are the first to identify pathways leading to generation of TBF-A for assessing its role in idiosyncratic adverse drug interactions.
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Affiliation(s)
- Dustyn A Barnette
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Mary A Davis
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Na L Dang
- Department of Pathology and Immunology, Washington University, St. Louis, MO 63130, United States
| | - Anirudh S Pidugu
- Department of Neuroscience and Behavioral Biology, Emory University, Atlanta, GA 30322, United States
| | - Tyler Hughes
- Department of Pathology and Immunology, Washington University, St. Louis, MO 63130, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University, St. Louis, MO 63130, United States
| | - Gunnar Boysen
- Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Grover P Miller
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States.
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22
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Fraser K, Bruckner DM, Dordick JS. Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies. Chem Res Toxicol 2018; 31:412-430. [PMID: 29722533 DOI: 10.1021/acs.chemrestox.8b00054] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.
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Affiliation(s)
- Keith Fraser
- Department of Chemical and Biological Engineering and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies , Rensselaer Polytechnic Institute , Troy , New York 12180 , United States
| | - Dylan M Bruckner
- Department of Chemical and Biological Engineering and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies , Rensselaer Polytechnic Institute , Troy , New York 12180 , United States
| | - Jonathan S Dordick
- Department of Chemical and Biological Engineering and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies , Rensselaer Polytechnic Institute , Troy , New York 12180 , United States
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