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Xiong J, Cui R, Li Z, Zhang W, Zhang R, Fu Z, Liu X, Li Z, Chen K, Zheng M. Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application. Acta Pharm Sin B 2024; 14:623-634. [PMID: 38322350 PMCID: PMC10840476 DOI: 10.1016/j.apsb.2023.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/07/2023] [Accepted: 10/11/2023] [Indexed: 02/08/2024] Open
Abstract
Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.
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Affiliation(s)
- Jiacheng Xiong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rongrong Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojun Li
- College of Computer and Information Engineering, Dezhou University, Dezhou 253023, China
- AI Department, Suzhou Alphama Biotechnology Co., Ltd., Suzhou 215000, China
| | - Wei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Runze Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zunyun Fu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohong Liu
- AI Department, Suzhou Alphama Biotechnology Co., Ltd., Suzhou 215000, China
| | - Zhenghao Li
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210023, China
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2
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Zhang Y, Liu X, Li F, Yin J, Yang H, Li X, Liu X, Chai X, Niu T, Zeng S, Jia Q, Zhu F. INTEDE 2.0: the metabolic roadmap of drugs. Nucleic Acids Res 2024; 52:D1355-D1364. [PMID: 37930837 PMCID: PMC10767827 DOI: 10.1093/nar/gkad1013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 11/08/2023] Open
Abstract
The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.
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Affiliation(s)
- Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xingang Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- The Children's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310052, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Clinical Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Hao Yang
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xuedong Li
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xinyu Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xu Chai
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Tianle Niu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Su Zeng
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Qingzhong Jia
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Feng Zhu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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3
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Levine DS, Jacobson LD, Bochevarov AD. Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase. J Chem Theory Comput 2023; 19:9302-9317. [PMID: 38085599 DOI: 10.1021/acs.jctc.3c00913] [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: 12/27/2023]
Abstract
Aldehyde oxidase (AOX) and other related molybdenum-containing enzymes are known to oxidize the C-H bonds of aromatic rings. This process contributes to the metabolism of pharmaceutical compounds and, therefore, is of vital importance to drug pharmacokinetics. The present work describes an automated computational workflow and its use for the prediction of intrinsic reactivity of small aromatic molecules toward a minimal model of the active site of AOX. The workflow is based on quantum chemical transition state searches for the underlying single-step oxidation reaction, where the automated protocol includes identification of unique aromatic C-H bonds, creation of three-dimensional reactant and product complex geometries via a templating approach, search for a transition state, and validation of reaction end points. Conformational search on the reactants, products, and the transition states is performed. The automated procedure has been validated on previously reported transition state barriers and was used to evaluate the intrinsic reactivity of nearly three hundred heterocycles commonly found in approved drug molecules. The intrinsic reactivity of more than 1000 individual aromatic carbon sites is reported. Stereochemical and conformational aspects of the oxidation reaction, which have not been discussed in previous studies, are shown to play important roles in accurate modeling of the oxidation reaction. Observations on structural trends that determine the reactivity are provided and rationalized.
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Affiliation(s)
- Daniel S Levine
- Schrödinger, Inc., 1540 Broadway, Floor 24, New York, New York 10036, United States
| | - Leif D Jacobson
- Schrödinger, Inc., 101 SW Main Street, Suite 1300, Portland, Oregon 97204, United States
| | - Art D Bochevarov
- Schrödinger, Inc., 1540 Broadway, Floor 24, New York, New York 10036, United States
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4
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Huang M, Zhu K, Wang Y, Lou C, Sun H, Li W, Tang Y, Liu G. In Silico Prediction of Metabolic Reaction Catalyzed by Human Aldehyde Oxidase. Metabolites 2023; 13:metabo13030449. [PMID: 36984889 PMCID: PMC10059660 DOI: 10.3390/metabo13030449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Aldehyde oxidase (AOX) plays an important role in drug metabolism. Human AOX (hAOX) is widely distributed in the body, and there are some differences between species. Currently, animal models cannot accurately predict the metabolism of hAOX. Therefore, more and more in silico models have been constructed for the prediction of the hAOX metabolism. These models are based on molecular docking and quantum chemistry theory, which are time-consuming and difficult to automate. Therefore, in this study, we compared traditional machine learning methods, graph convolutional neural network methods, and sequence-based methods with limited data, and proposed a ligand-based model for the metabolism prediction catalyzed by hAOX. Compared with the published models, our model achieved better performance (ACC = 0.91, F1 = 0.77). What's more, we built a web server to predict the sites of metabolism (SOMs) for hAOX. In summary, this study provides a convenient and automatable model and builds a web server named Meta-hAOX for accelerating the drug design and optimization stage.
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Affiliation(s)
- Mengting Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Keyun Zhu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Huimin Sun
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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5
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Zheng S, Wang L, Xiong J, Liang G, Xu Y, Lin F. Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods. J Chem Inf Model 2022; 62:1078-1099. [PMID: 35156807 DOI: 10.1021/acs.jcim.1c00948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.
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Affiliation(s)
- Suqing Zheng
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China.,Chemical Biology Research Center, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Lei Wang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Jun Xiong
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Guang Liang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China.,Chemical Biology Research Center, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Yong Xu
- Center of Chemical Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, P.R. China
| | - Fu Lin
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
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6
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Mota C, Diniz A, Coelho C, Santos-Silva T, Esmaeeli M, Leimkühler S, Cabrita EJ, Marcelo F, Romão MJ. Interrogating the Inhibition Mechanisms of Human Aldehyde Oxidase by X-ray Crystallography and NMR Spectroscopy: The Raloxifene Case. J Med Chem 2021; 64:13025-13037. [PMID: 34415167 DOI: 10.1021/acs.jmedchem.1c01125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Human aldehyde oxidase (hAOX1) is mainly present in the liver and has an emerging role in drug metabolism, since it accepts a wide range of molecules as substrates and inhibitors. Herein, we employed an integrative approach by combining NMR, X-ray crystallography, and enzyme inhibition kinetics to understand the inhibition modes of three hAOX1 inhibitors-thioridazine, benzamidine, and raloxifene. These integrative data indicate that thioridazine is a noncompetitive inhibitor, while benzamidine presents a mixed type of inhibition. Additionally, we describe the first crystal structure of hAOX1 in complex with raloxifene. Raloxifene binds tightly at the entrance of the substrate tunnel, stabilizing the flexible entrance gates and elucidating an unusual substrate-dependent mechanism of inhibition with potential impact on drug-drug interactions. This study can be considered as a proof-of-concept for an efficient experimental screening of prospective substrates and inhibitors of hAOX1 relevant in drug discovery.
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Affiliation(s)
- Cristiano Mota
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Ana Diniz
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Catarina Coelho
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Teresa Santos-Silva
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Mariam Esmaeeli
- Department of Molecular Enzymology, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany
| | - Silke Leimkühler
- Department of Molecular Enzymology, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany
| | - Eurico J Cabrita
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Filipa Marcelo
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Maria João Romão
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
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7
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Dhuria NV, Haro B, Kapadia A, Lobo KA, Matusow B, Schleiff MA, Tantoy C, Sodhi JK. Recent developments in predicting CYP-independent metabolism. Drug Metab Rev 2021; 53:188-206. [PMID: 33941024 DOI: 10.1080/03602532.2021.1923728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
As lead optimization efforts have successfully reduced metabolic liabilities due to cytochrome P450 (CYP)-mediated metabolism, there has been an increase in the frequency of involvement of non-CYP enzymes in the metabolism of investigational compounds. Although there have been numerous notable advancements in the characterization of non-CYP enzymes with respect to their localization, reaction mechanisms, species differences and identification of typical substrates, accurate prediction of non-CYP-mediated clearance, with a particular emphasis with the difficulties in accounting for any extrahepatic contributions, remains a challenge. The current manuscript comprehensively summarizes the recent advancements in the prediction of drug metabolism and the in vitro to in vitro extrapolation of clearance for substrates of non-CYP drug metabolizing enzymes.
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Affiliation(s)
- Nikhilesh V Dhuria
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Bianka Haro
- School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA
| | - Amit Kapadia
- California Poison Control Center, University of California San Francisco, San Diego, CA, USA
| | | | - Bernice Matusow
- Department of Drug Metabolism and Pharmacokinetics, Plexxikon Inc, Berkeley, CA, USA
| | - Mary A Schleiff
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Christina Tantoy
- Department of Drug Metabolism and Pharmacokinetics, Plexxikon Inc, Berkeley, CA, USA
| | - Jasleen K Sodhi
- Department of Drug Metabolism and Pharmacokinetics, Plexxikon Inc, Berkeley, CA, USA.,Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, CA, USA
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