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Wishart DS, Tian S, Allen D, Oler E, Peters H, Lui V, Gautam V, Djoumbou-Feunang Y, Greiner R, Metz T. BioTransformer 3.0-a web server for accurately predicting metabolic transformation products. Nucleic Acids Res 2022; 50:W115-W123. [PMID: 35536252 PMCID: PMC9252798 DOI: 10.1093/nar/gkac313] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/11/2022] [Accepted: 05/04/2022] [Indexed: 11/15/2022] Open
Abstract
BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.
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Affiliation(s)
- David S Wishart
- To whom correspondence should be addressed. Tel: +1 780 492 8574;
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dana Allen
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vicki W Lui
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | | | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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2
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Bassan A, Alves VM, Amberg A, Anger LT, Auerbach S, Beilke L, Bender A, Cronin MT, Cross KP, Hsieh JH, Greene N, Kemper R, Kim MT, Mumtaz M, Noeske T, Pavan M, Pletz J, Russo DP, Sabnis Y, Schaefer M, Szabo DT, Valentin JP, Wichard J, Williams D, Woolley D, Zwickl C, Myatt GJ. In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 20:100187. [PMID: 35340402 PMCID: PMC8955833 DOI: 10.1016/j.comtox.2021.100187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.
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Affiliation(s)
- Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Vinicius M. Alves
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | - Andreas Bender
- AI and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW
| | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | | | - Jui-Hua Hsieh
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Nigel Greene
- Data Science and AI, DSM, IMED Biotech Unit, AstraZeneca, Boston, USA
| | - Raymond Kemper
- Nuvalent, One Broadway, 14th floor, Cambridge, MA, 02142, USA
| | - Marlene T. Kim
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, 20993, USA
| | - Moiz Mumtaz
- Office of the Associate Director for Science (OADS), Agency for Toxic Substances and Disease, Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Tobias Noeske
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Julia Pletz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Daniel P. Russo
- Department of Chemistry, Rutgers University, Camden, NJ 08102, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Yogesh Sabnis
- UCB Biopharma SRL, Chemin du Foriest – B-1420 Braine-l’Alleud, Belgium
| | - Markus Schaefer
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | | | - Joerg Wichard
- Bayer AG, Genetic Toxicology, Müllerstr. 178, 13353 Berlin, Germany
| | - Dominic Williams
- Functional & Mechanistic Safety, Clinical Pharmacology & Safety Sciences, AstraZeneca, Darwin Building 310, Cambridge Science Park, Milton Rd, Cambridge CB4 0FZ, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, USA
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215. USA
- Corresponding author. (G.J. Myatt)
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Zhang Q, Yu C, Fu L, Gu S, Wang C. New Insights in the Endocrine Disrupting Effects of Three Primary Metabolites of Organophosphate Flame Retardants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:4465-4474. [PMID: 32150676 DOI: 10.1021/acs.est.9b07874] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Despite the ubiquity of organophosphate flame retardants (OPFRs) metabolites in the biota, the endocrine disrupting potency has not been well examined. Herein, we chose three primary metabolites of OPFRs (BCIPP, BDCIPP, and DPHP) to investigate their potential endocrine disrupting effects by in vitro, in vivo, and in silico assays. Three metabolites were agonistic to rat estrogenic receptor alpha (ERα) and antagonists to human mineralocorticoid receptor (MR). BCIPP exerted endocrine disrupting effect contrasting to the negative response of its parental compound. It also poses the strongest binding capacity to ERα among the tested compounds. Both BCIPP and BDCIPP upregulated the genes encoded for estrogenic synthesis enzymes in H295R cells, including 17βHSD and CYP19. All three compounds stimulated the transcription of CYP11B1, whereas BCIPP and DPHP also triggered CYP11B2, encoding for corticoid production. BDCIPP inhibits genes for progesterone synthesis including CYP11A1, STAR, and 3-βHSD. The induction of mortality and low hatchability of zebrafish embryo were ranked as BCIPP ≥ BDCIPP > DPHP. All compounds lead to malformation of zebrafish larvae. Both of the hypothalamic-pituitary-adrenocortical and hypothalamic-pituitary-gonadal axes were disrupted, with the highest impact by BCIPP. Altogether, the data clarified OPFRs metabolites may produce comparable or even higher endocrine disrupting effects than OPFRs.
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Affiliation(s)
- Quan Zhang
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, China
| | - Chang Yu
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, China
| | - Lili Fu
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, China
| | - Sijia Gu
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, China
| | - Cui Wang
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
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Tarasova O, Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. QNA-Based Prediction of Sites of Metabolism. Molecules 2017; 22:molecules22122123. [PMID: 29194399 PMCID: PMC6149875 DOI: 10.3390/molecules22122123] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 11/28/2017] [Indexed: 01/25/2023] Open
Abstract
Metabolism of xenobiotics (Greek xenos: exogenous substances) plays an essential role in the prediction of biological activity and testing for the subsequent research and development of new drug candidates. Integration of various methods and techniques using different computational and experimental approaches is one of the keys to a successful metabolism prediction. While multiple structure-based and ligand-based approaches to metabolism prediction exist, the most important problem arises at the first stage of metabolism prediction: detection of the sites of metabolism (SOMs). In this paper, we describe the application of Quantitative Neighborhoods of Atoms (QNA) descriptors for prediction of the SOMs using potential function method, as well as several different machine learning techniques: naïve Bayes, random forest classifier, multilayer perceptron with back propagation and convolutional neural networks, and deep neural networks.
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Affiliation(s)
- Olga Tarasova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia.
| | - Anastassia Rudik
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia.
| | - Alexander Dmitriev
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia.
| | - Alexey Lagunin
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia.
- Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., 117997 Moscow, Russia.
| | - Dmitry Filimonov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia.
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia.
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5
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Portychová L, Schug KA. Instrumentation and applications of electrochemistry coupled to mass spectrometry for studying xenobiotic metabolism: A review. Anal Chim Acta 2017; 993:1-21. [PMID: 29078951 DOI: 10.1016/j.aca.2017.08.050] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 08/21/2017] [Accepted: 08/26/2017] [Indexed: 01/03/2023]
Abstract
The knowledge of metabolic pathways and biotransformation of xenobiotics, artificial substances foreign to the entire biological system, is crucial for elucidation of degradation routes of potentially toxic substances. Nowadays, there are many methods to simulate xenobiotic metabolism in the human body in vitro. In this review, the metabolism of various substances in the human body is described, followed by a summary of methods used for prediction of metabolic pathways and biotransformation. Above all, focus is placed on the coupling of electrochemistry to mass spectrometry, which is still a relatively new technique. This promising tool can mimic both oxidative phase I and conjugative phase II metabolism. Different experimental arrangements, with or without a separation step, and various applications of this technique are illustrated and critically reviewed.
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Affiliation(s)
- Lenka Portychová
- Research Institute for Organic Synthesis, Inc., 533 54 Rybitví, Czech Republic; Department of Analytical Chemistry, Palacký University, 771 46 Olomouc, Czech Republic
| | - Kevin A Schug
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, TX 76019, USA.
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Tyzack JD, Hunt PA, Segall MD. Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations. J Chem Inf Model 2016; 56:2180-2193. [PMID: 27753488 DOI: 10.1021/acs.jcim.6b00233] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We describe methods for predicting cytochrome P450 (CYP) metabolism incorporating both pathway-specific reactivity and isoform-specific accessibility considerations. Semiempirical quantum mechanical (QM) simulations, parametrized using experimental data and ab initio calculations, estimate the reactivity of each potential site of metabolism (SOM) in the context of the whole molecule. Ligand-based models, trained using high-quality regioselectivity data, correct for orientation and steric effects of the different CYP isoform binding pockets. The resulting models identify a SOM in the top 2 predictions for between 82% and 91% of compounds in independent test sets across seven CYP isoforms. In addition to predicting the relative proportion of metabolite formation at each site, these methods estimate the activation energy at each site, from which additional information can be derived regarding their lability in absolute terms. We illustrate how this can guide the design of compounds to overcome issues with rapid CYP metabolism.
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Affiliation(s)
- Jonathan D Tyzack
- Optibrium Ltd. , 7221 Cambridge Research Park, Beach Drive, Cambridge CB25 9TL, U.K
| | - Peter A Hunt
- Optibrium Ltd. , 7221 Cambridge Research Park, Beach Drive, Cambridge CB25 9TL, U.K
| | - Matthew D Segall
- Optibrium Ltd. , 7221 Cambridge Research Park, Beach Drive, Cambridge CB25 9TL, U.K
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7
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Bianchini LF, Arruda MFC, Vieira SR, Campelo PMS, Grégio AMT, Rosa EAR. Microbial Biotransformation to Obtain New Antifungals. Front Microbiol 2015; 6:1433. [PMID: 26733974 PMCID: PMC4689855 DOI: 10.3389/fmicb.2015.01433] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 12/01/2015] [Indexed: 12/30/2022] Open
Abstract
Antifungal drugs belong to few chemical groups and such low diversity limits the therapeutic choices. The urgent need of innovative options has pushed researchers to search new bioactive molecules. Literature regarding the last 15 years reveals that different research groups have used different approaches to achieve such goal. However, the discovery of molecules with different mechanisms of action still demands considerable time and efforts. This review was conceived to present how Pharmaceutical Biotechnology might contribute to the discovery of molecules with antifungal properties by microbial biotransformation procedures. Authors present some aspects of (1) microbial biotransformation of herbal medicines and food; (2) possibility of major and minor molecular amendments in existing molecules by biocatalysis; (3) methodological improvements in processes involving whole cells and immobilized enzymes; (4) potential of endophytic fungi to produce antimicrobials by bioconversions; and (5) in silico research driving to the improvement of molecules. All these issues belong to a new conception of transformation procedures, so-called "green chemistry," which aims the highest possible efficiency with reduced production of waste and the smallest environmental impact.
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Affiliation(s)
- Luiz F. Bianchini
- Xenobiotics Research Unit, School of Health and Biosciences, The Pontifical Catholic University of ParanaCuritiba, Brazil
| | - Maria F. C. Arruda
- Xenobiotics Research Unit, School of Health and Biosciences, The Pontifical Catholic University of ParanaCuritiba, Brazil
| | - Sergio R. Vieira
- Faculty of Dentistry, School of Health and Biosciences, The Pontifical Catholic University of ParanaCuritiba, Brazil
| | - Patrícia M. S. Campelo
- Xenobiotics Research Unit, School of Health and Biosciences, The Pontifical Catholic University of ParanaCuritiba, Brazil
| | - Ana M. T. Grégio
- Xenobiotics Research Unit, School of Health and Biosciences, The Pontifical Catholic University of ParanaCuritiba, Brazil
| | - Edvaldo A. R. Rosa
- Xenobiotics Research Unit, School of Health and Biosciences, The Pontifical Catholic University of ParanaCuritiba, Brazil
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Groh KJ, Carvalho RN, Chipman JK, Denslow ND, Halder M, Murphy CA, Roelofs D, Rolaki A, Schirmer K, Watanabe KH. Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology. CHEMOSPHERE 2015; 120:764-77. [PMID: 25439131 DOI: 10.1016/j.chemosphere.2014.09.068] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Revised: 09/11/2014] [Accepted: 09/19/2014] [Indexed: 05/02/2023]
Abstract
To elucidate the effects of chemicals on populations of different species in the environment, efficient testing and modeling approaches are needed that consider multiple stressors and allow reliable extrapolation of responses across species. An adverse outcome pathway (AOP) is a concept that provides a framework for organizing knowledge about the progression of toxicity events across scales of biological organization that lead to adverse outcomes relevant for risk assessment. In this paper, we focus on exploring how the AOP concept can be used to guide research aimed at improving both our understanding of chronic toxicity, including delayed toxicity as well as epigenetic and transgenerational effects of chemicals, and our ability to predict adverse outcomes. A better understanding of the influence of subtle toxicity on individual and population fitness would support a broader integration of sublethal endpoints into risk assessment frameworks. Detailed mechanistic knowledge would facilitate the development of alternative testing methods as well as help prioritize higher tier toxicity testing. We argue that targeted development of AOPs supports both of these aspects by promoting the elucidation of molecular mechanisms and their contribution to relevant toxicity outcomes across biological scales. We further discuss information requirements and challenges in application of AOPs for chemical- and site-specific risk assessment and for extrapolation across species. We provide recommendations for potential extension of the AOP framework to incorporate information on exposure, toxicokinetics and situation-specific ecological contexts, and discuss common interfaces that can be employed to couple AOPs with computational modeling approaches and with evolutionary life history theory. The extended AOP framework can serve as a venue for integration of knowledge derived from various sources, including empirical data as well as molecular, quantitative and evolutionary-based models describing species responses to toxicants. This will allow a more efficient application of AOP knowledge for quantitative chemical- and site-specific risk assessment as well as for extrapolation across species in the future.
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Affiliation(s)
- Ksenia J Groh
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland; ETH Zürich, Department of Chemistry and Applied Biosciences, 8093 Zürich, Switzerland.
| | - Raquel N Carvalho
- European Commission, Joint Research Centre, Institute for Environment and Sustainability, Water Resources Unit, 21027 Ispra, Italy
| | | | - Nancy D Denslow
- University of Florida, Department of Physiological Sciences, Center for Environmental and Human Toxicology and Genetics Institute, 32611 Gainesville, FL, USA
| | - Marlies Halder
- European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Systems Toxicology Unit, 21027 Ispra, Italy
| | - Cheryl A Murphy
- Michigan State University, Fisheries and Wildlife, Lyman Briggs College, 48824 East Lansing, MI, USA
| | - Dick Roelofs
- VU University, Institute of Ecological Science, 1081 HV Amsterdam, The Netherlands
| | - Alexandra Rolaki
- European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Systems Toxicology Unit, 21027 Ispra, Italy
| | - Kristin Schirmer
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland; ETH Zürich, Department of Environmental Systems Science, 8092 Zürich, Switzerland; EPF Lausanne, School of Architecture, Civil and Environmental Engineering, 1015 Lausanne, Switzerland
| | - Karen H Watanabe
- Oregon Health & Science University, Institute of Environmental Health, Division of Environmental and Biomolecular Systems, 97239-3098 Portland, OR, USA
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Tyzack JD, Mussa HY, Williamson MJ, Kirchmair J, Glen RC. Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers. J Cheminform 2014; 6:29. [PMID: 24959208 PMCID: PMC4047555 DOI: 10.1186/1758-2946-6-29] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 05/12/2014] [Indexed: 02/02/2023] Open
Abstract
Background The prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository. Results It is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively. Conclusions 2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme.
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Affiliation(s)
- Jonathan D Tyzack
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
| | - Hamse Y Mussa
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
| | - Mark J Williamson
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
| | - Johannes Kirchmair
- ETH Zurich, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, HCI G 474.2, Vladimir-Prelog-Weg 1-5/10, 8093 Zurich, Switzerland
| | - Robert C Glen
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
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10
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Tyzack JD, Williamson MJ, Torella R, Glen RC. Prediction of cytochrome P450 xenobiotic metabolism: tethered docking and reactivity derived from ligand molecular orbital analysis. J Chem Inf Model 2013; 53:1294-305. [PMID: 23701380 DOI: 10.1021/ci400058s] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.
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Affiliation(s)
- Jonathan D Tyzack
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
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11
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Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software. Future Med Chem 2013; 4:1907-32. [PMID: 23088273 DOI: 10.4155/fmc.12.150] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.
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Scientific Opinion on Exploring options for providing advice about possible human health risks based on the concept of Threshold of Toxicological Concern (TTC). EFSA J 2012. [DOI: 10.2903/j.efsa.2012.2750] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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Kirchmair J, Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, Glen RC. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012; 52:617-48. [PMID: 22339582 PMCID: PMC3317594 DOI: 10.1021/ci200542m] [Citation(s) in RCA: 187] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
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Metabolism of xenobiotics remains a central challenge
for the discovery
and development of drugs, cosmetics, nutritional supplements, and
agrochemicals. Metabolic transformations are frequently related to
the incidence of toxic effects that may result from the emergence
of reactive species, the systemic accumulation of metabolites, or
by induction of metabolic pathways. Experimental investigation of
the metabolism of small organic molecules is particularly resource
demanding; hence, computational methods are of considerable interest
to complement experimental approaches. This review provides a broad
overview of structure- and ligand-based computational methods for
the prediction of xenobiotic metabolism. Current computational approaches
to address xenobiotic metabolism are discussed from three major perspectives:
(i) prediction of sites of metabolism (SOMs), (ii) elucidation of
potential metabolites and their chemical structures, and (iii) prediction
of direct and indirect effects of xenobiotics on metabolizing enzymes,
where the focus is on the cytochrome P450 (CYP) superfamily of enzymes,
the cardinal xenobiotics metabolizing enzymes. For each of these domains,
a variety of approaches and their applications are systematically
reviewed, including expert systems, data mining approaches, quantitative
structure–activity relationships (QSARs), and machine learning-based
methods, pharmacophore-based algorithms, shape-focused techniques,
molecular interaction fields (MIFs), reactivity-focused techniques,
protein–ligand docking, molecular dynamics (MD) simulations,
and combinations of methods. Predictive metabolism is a developing
area, and there is still enormous potential for improvement. However,
it is clear that the combination of rapidly increasing amounts of
available ligand- and structure-related experimental data (in particular,
quantitative data) with novel and diverse simulation and modeling
approaches is accelerating the development of effective tools for
prediction of in vivo metabolism, which is reflected by the diverse
and comprehensive data sources and methods for metabolism prediction
reviewed here. This review attempts to survey the range and scope
of computational methods applied to metabolism prediction and also
to compare and contrast their applicability and performance.
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Affiliation(s)
- Johannes Kirchmair
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom
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Chen H, Engkvist O, Blomberg N, Li J. A comparative analysis of the molecular topologies for drugs, clinical candidates, natural products, human metabolites and general bioactive compounds. MEDCHEMCOMM 2012. [DOI: 10.1039/c2md00238h] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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15
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Fernández-Ballester G, Fernández-Carvajal A, González-Ros JM, Ferrer-Montiel A. Ionic channels as targets for drug design: a review on computational methods. Pharmaceutics 2011; 3:932-53. [PMID: 24309315 PMCID: PMC3857065 DOI: 10.3390/pharmaceutics3040932] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2011] [Revised: 10/26/2011] [Accepted: 11/30/2011] [Indexed: 01/21/2023] Open
Abstract
Ion channels are involved in a broad range of physiological and pathological processes. The implications of ion channels in a variety of diseases, including diabetes, epilepsy, hypertension, cancer and even chronic pain, have signaled them as pivotal drug targets. Thus far, drugs targeting ion channels were developed without detailed knowledge of the molecular interactions between the lead compounds and the target channels. In recent years, however, the emergence of high-resolution structures for a plethora of ion channels paves the way for computer-assisted drug design. Currently, available functional and structural data provide an attractive platform to generate models that combine substrate-based and protein-based approaches. In silico approaches include homology modeling, quantitative structure-activity relationships, virtual ligand screening, similarity and pharmacophore searching, data mining, and data analysis tools. These strategies have been frequently used in the discovery and optimization of novel molecules with enhanced affinity and specificity for the selected therapeutic targets. In this review we summarize recent applications of in silico methods that are being used for the development of ion channel drugs.
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McCollum CW, Ducharme NA, Bondesson M, Gustafsson JA. Developmental toxicity screening in zebrafish. ACTA ACUST UNITED AC 2011; 93:67-114. [DOI: 10.1002/bdrc.20210] [Citation(s) in RCA: 100] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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17
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Adler S, Basketter D, Creton S, Pelkonen O, van Benthem J, Zuang V, Andersen KE, Angers-Loustau A, Aptula A, Bal-Price A, Benfenati E, Bernauer U, Bessems J, Bois FY, Boobis A, Brandon E, Bremer S, Broschard T, Casati S, Coecke S, Corvi R, Cronin M, Daston G, Dekant W, Felter S, Grignard E, Gundert-Remy U, Heinonen T, Kimber I, Kleinjans J, Komulainen H, Kreiling R, Kreysa J, Leite SB, Loizou G, Maxwell G, Mazzatorta P, Munn S, Pfuhler S, Phrakonkham P, Piersma A, Poth A, Prieto P, Repetto G, Rogiers V, Schoeters G, Schwarz M, Serafimova R, Tähti H, Testai E, van Delft J, van Loveren H, Vinken M, Worth A, Zaldivar JM. Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010. Arch Toxicol 2011; 85:367-485. [PMID: 21533817 DOI: 10.1007/s00204-011-0693-2] [Citation(s) in RCA: 358] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2011] [Accepted: 03/03/2011] [Indexed: 01/09/2023]
Abstract
The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.
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Affiliation(s)
- Sarah Adler
- Centre for Documentation and Evaluation of Alternatives to Animal Experiments (ZEBET), Federal Institute for Risk Assessment (BfR), Berlin, Germany
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18
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Vaz RJ, Zamora I, Li Y, Reiling S, Shen J, Cruciani G. The challenges of in silico contributions to drug metabolism in lead optimization. Expert Opin Drug Metab Toxicol 2011; 6:851-61. [PMID: 20565339 DOI: 10.1517/17425255.2010.499123] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
IMPORTANCE OF THE FIELD The site of metabolism (SOM) predictions by CYP 3A4 are extremely important during the drug discovery process especially during the lead discovery or library design phases. With the ability to rapidly characterize metabolites from these enzymes, the challenges facing in silico contribution change during the drug optimization phase. Some of the challenges are addressed in this article. Some aspects of the SOM prediction software and methodology are discussed in this opinion article and examples of software utility in overcoming metabolic instability in drug optimization are shown. AREAS COVERED IN THIS REVIEW SOM prediction by various approaches is discussed. Two ways of overcoming metabolic instability, blocking the metabolic softspots and rational modification of the instable molecule to avoid interaction with the CYP pocket, are discussed. The contribution plot in MetaSite and its use are discussed. WHAT THE READER WILL GAIN The reader will gain an understanding of possible approaches to either blocking the metabolic softspot or rationally modifying the molecule using MetaSite software or docking approaches. Blocking metabolism using fluorination has risks especially introducing multifluorinated benzene rings in the molecule. TAKE HOME MESSAGE During the lead optimization phase of drug discovery, when metabolic instability is an issue in a series, in silico approaches can be used to modify the molecule in order to decrease clearance due to metabolism, even that due to CYP3A4.
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Affiliation(s)
- Roy J Vaz
- Structure, Design, Informatics, Sanofi-Aventis US, 1041 Rt 202/206N, Bridgewater, NJ 08807, USA.
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Bonn B, Leandersson C, Fontaine F, Zamora I. Enhanced metabolite identification with MS(E) and a semi-automated software for structural elucidation. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2010; 24:3127-3138. [PMID: 20941759 DOI: 10.1002/rcm.4753] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The identification of metabolites is almost exclusively done with liquid chromatography/tandem mass spectrometry (LC/MSMS) and despite the enormous progress in the development of these techniques and software for handling of data this is a time-consuming task. In this study the use of quadrupole time-of-flight (QTOF)-generated MS(E) and MS/MS data were compared with respect to rationalization of metabolites. In addition Mass-MetaSite, a semi-automated software for metabolite identification, was evaluated. The program combines the information from MS raw data, in the form of collision-induced dissociation spectra, with a prediction of the site of metabolism in order to assign the structure of a metabolite. The aim of the software is to mimic the rationalization of fragment ions performed by a biotransformation scientist in the process of structural elucidation. For this evaluation, metabolite identification in human liver microsomes was accomplished for 19 commercially available compounds and 15 in-house compounds. The results were very encouraging and for 96% of the metabolites the same structures were assigned using MS(E) compared with MSMS acquired data. The possibility of using MS(E) could considerably reduce the analysis time. Moreover, Mass-MetaSite performed well and the correct assigned structure, compared to manual inspection of the data, was picked in the first rank in ∼80% of the cases. In conclusion MS(E) could be successfully used for metabolite identification in order to reduce time of analysis and Mass-MetaSite could alleviate the work of a biotransformation scientist and decrease the workload by assigning the structure for a majority of the metabolites.
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Affiliation(s)
- Britta Bonn
- Department of Chemistry, Medicinal Chemistry, University of Gothenburg, SE-412 96 Gothenburg, Sweden
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20
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Fragkaki AG, Angelis YS, Tsantili-Kakoulidou A, Koupparis M, Georgakopoulos C. Schemes of metabolic patterns of anabolic androgenic steroids for the estimation of metabolites of designer steroids in human urine. J Steroid Biochem Mol Biol 2009; 115:44-61. [PMID: 19429460 DOI: 10.1016/j.jsbmb.2009.02.016] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2008] [Revised: 02/13/2009] [Accepted: 02/13/2009] [Indexed: 11/19/2022]
Abstract
Unified metabolism schemes of anabolic androgenic steroids (AAS) in human urine based on structure classification of parent molecules are presented in this paper. Principal components analysis (PCA) was applied to AAS molecules referred in the World Anti-Doping Agency (WADA) list of prohibited substances, resulting to their classification into six distinct groups related to structure features where metabolic alterations usually occur. The metabolites of the steroids participating to these six groups were treated using the Excel(c) classification filters showing that common metabolism routes are derived for each of the above PCA classes, leading to the proposed metabolism schemes of the present study. This rule-based approach is proposed for the prediction of the metabolism of unknown, chemically modified steroids, otherwise named as designer steroids. The metabolites of three known, in the literature, AAS are estimated using the proposed metabolism schemes, confirming that their use could be a useful tool for the prediction of metabolic pathways of unknown AAS.
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Affiliation(s)
- A G Fragkaki
- Olympic Athletic Center of Athens "Spyros Louis", Kifisias, Maroussi, Greece
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21
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Pelander A, Tyrkkö E, Ojanperä I. In silico methods for predicting metabolism and mass fragmentation applied to quetiapine in liquid chromatography/time-of-flight mass spectrometry urine drug screening. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2009; 23:506-514. [PMID: 19142846 DOI: 10.1002/rcm.3901] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Current in silico tools were evaluated for their ability to predict metabolism and mass spectral fragmentation in the context of analytical toxicology practice. A metabolite prediction program (Lhasa Meteor), a metabolite detection program (Bruker MetaboliteDetect), and a fragmentation prediction program (ACD/MS Fragmenter) were used to assign phase I metabolites of the antipsychotic drug quetiapine in the liquid chromatography/time-of-flight mass spectrometry (LC/TOFMS) accurate mass data from ten autopsy urine samples. In the literature, the main metabolic routes of quetiapine have been reported to be sulfoxidation, oxidation to the corresponding carboxylic acid, N- and O-dealkylation and hydroxylation. Of the 14 metabolites predicted by Meteor, eight were detected by LC/TOFMS in the urine samples with use of MetaboliteDetect software and manual inspection. An additional five hydroxy derivatives were detected, but not predicted by Meteor. The fragment structures provided by ACD/MS Fragmenter software confirmed the identification of the metabolites. Mean mass accuracy and isotopic pattern match (SigmaFit) values for the fragments were 2.40 ppm (0.62 mDa) and 0.010, respectively. ACD/MS Fragmenter, in particular, allowed metabolites with identical molecular formulae to be differentiated without a need to access the respective reference standards or reference spectra. This was well exemplified with the hydroxy/sulfoxy metabolites of quetiapine and their N- and O-dealkylated forms. The procedure resulted in assigning 13 quetiapine metabolites in urine. The present approach is instrumental in developing an extensive database containing exact monoisotopic masses and verified retention times of drugs and their urinary metabolites for LC/TOFMS drug screening.
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Affiliation(s)
- Anna Pelander
- Department of Forensic Medicine, PO Box 40, FI-00014 University of Helsinki, Finland.
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22
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Lohmann W, Karst U. Electrochemistry meets enzymes: instrumental on-line simulation of oxidative and conjugative metabolism reactions of toremifene. Anal Bioanal Chem 2009; 394:1341-8. [DOI: 10.1007/s00216-008-2586-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2008] [Revised: 12/11/2008] [Accepted: 12/15/2008] [Indexed: 01/01/2023]
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23
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Jamei M, Dickinson GL, Rostami-Hodjegan A. A Framework for Assessing Inter-individual Variability in Pharmacokinetics Using Virtual Human Populations and Integrating General Knowledge of Physical Chemistry, Biology, Anatomy, Physiology and Genetics: A Tale of ‘Bottom-Up’ vs ‘Top-Down’ Recognition of Covariates. Drug Metab Pharmacokinet 2009; 24:53-75. [DOI: 10.2133/dmpk.24.53] [Citation(s) in RCA: 269] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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25
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Smith J, Stein V. SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design. Comput Biol Chem 2008; 33:149-59. [PMID: 19157988 DOI: 10.1016/j.compbiolchem.2008.11.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2008] [Accepted: 11/12/2008] [Indexed: 10/21/2022]
Abstract
Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations. Examples of reaction centre detection in Metabolite are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10(-1) to 10(-5) and their order of magnitude reflected the known and experimentally observed metabolites. SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure-activity and physiochemical information.
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Affiliation(s)
- James Smith
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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26
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Stranz DD, Miao S, Campbell S, Maydwell G, Ekins S. Combined Computational Metabolite Prediction and Automated Structure-Based Analysis of Mass Spectrometric Data. Toxicol Mech Methods 2008; 18:243-50. [DOI: 10.1080/15376510701857189] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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27
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Combes R, Grindon C, Cronin MT, Roberts DW, Garrod JF. Integrated Decision-tree Testing Strategies for Acute Systemic Toxicity and Toxicokinetics with Respect to the Requirements of the EU REACH Legislation. Altern Lab Anim 2008; 36 Suppl 1:91-109. [DOI: 10.1177/026119290803601s08] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Liverpool John Moores University and FRAME conducted a joint research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for acute systemic toxicity and toxicokinetic testing. The paper reviews in vitro tests based on basal cytotoxicity and target organ toxicity, along with QSAR models and expert systems available for this endpoint. The use of PBPK modelling for the prediction of ADME properties is also discussed. These tests are then incorporated into a decision-tree style, integrated testing strategy, which also includes the use of refined in vivo acute toxicity tests, as a last resort. The implementation of the strategy is intended to minimise the use of animals in the testing of acute systemic toxicity and toxicokinetics, whilst satisfying the scientific and logistical demands of the EU REACH legislation.
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Affiliation(s)
| | | | - Mark T.D. Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, UK
| | - David W. Roberts
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, UK
| | - John F. Garrod
- Chemicals and Nanotechnologies Division, Defra, London, UK
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28
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Combes R, Grindon C, Cronin MTD, Roberts DW, Garrod JF. Integrated decision-tree testing strategies for acute systemic toxicity and toxicokinetics with respect to the requirements of the EU REACH legislation. Altern Lab Anim 2008; 36:45-63. [PMID: 18333714 DOI: 10.1177/026119290803600107] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Liverpool John Moores University and FRAME conducted a joint research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for acute systemic toxicity and toxicokinetic testing. The paper reviews in vitro tests based on basal cytotoxicity and target organ toxicity, along with QSAR models and expert systems available for this endpoint. The use of PBPK modelling for the prediction of ADME properties is also discussed. These tests are then incorporated into a decision-tree style, integrated testing strategy, which also includes the use of refined in vivo acute toxicity tests, as a last resort. The implementation of the strategy is intended to minimise the use of animals in the testing of acute systemic toxicity and toxicokinetics, whilst satisfying the scientific and logistical demands of the EU REACH legislation.
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29
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Dearden JC. In silico prediction of ADMET properties: how far have we come? Expert Opin Drug Metab Toxicol 2008; 3:635-9. [PMID: 17916052 DOI: 10.1517/17425255.3.5.635] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
There have been considerable advances in the last few years in both the quantity and the quality of in silico ADMET property predictions. Most ADMET properties are now computable, and the accuracy of some of the software predictions for physicochemical properties in particular is close to that of measured data. There is, however, universal agreement that more good experimental ADMET data are needed for use in in silico model development, for models are only as good as the data on which they are based. Many data remain confidential but it is to be hoped that, with projects such as the Vitic toxicity database, being developed by Lhasa Limited, pharmaceutical companies will be prepared to release data to an 'honest broker' on a confidential basis, so that better in silico models can be developed. Incorporation of calculated ADMET properties into drug discovery and development is a multi-factorial problem and really needs a multi-factorial solution. Some progress is being made in this direction and it is hoped that within the foreseeable future software will be available for this purpose.
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Affiliation(s)
- John C Dearden
- Liverpool John Moores University, School of Pharmacy and Chemistry, Byrom Street, Liverpool, L3 3AF, UK.
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30
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Kulkarni SA, Moir D, Zhu J. Influence of structural and functional modifications of selected genotoxic carcinogens on metabolism and mutagenicity - a review. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2007; 18:459-514. [PMID: 17654335 DOI: 10.1080/10629360701430090] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Alterations in molecular structure are responsible for the differential biological response(s) of a chemical inside a biosystem. Structural and functional parameters that govern a chemical's metabolic course and determine its ultimate outcome in terms of mutagenic/carcinogenic potential are extensively reviewed here. A large number of environmentally-significant organic chemicals are addressed under one or more broadly classified groups each representing one or more characteristic structural feature. Numerous examples are cited to illustrate the influence of key structural and functional parameters on the metabolism and DNA adduction properties of different chemicals. It is hoped that, in the event of limited experimental data on a chemical's bioactivity, such knowledge of the likely roles played by key molecular features should provide preliminary information regarding its bioactivation, detoxification and/or mutagenic potential and aid the process of screening and prioritising chemicals for further testing.
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Affiliation(s)
- S A Kulkarni
- Chemistry Research Division, Safe Environments Programme, Health Canada, AL: 0800C, Ottawa, Ontario, K1A 0L2, Canada
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31
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Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 2007; 152:9-20. [PMID: 17549047 PMCID: PMC1978274 DOI: 10.1038/sj.bjp.0707305] [Citation(s) in RCA: 383] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.
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Affiliation(s)
- S Ekins
- ACT LLC, 1 Penn Plaza, New York, NY 10119, USA.
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32
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Gupta S, Aires-de-Sousa J. Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness. Mol Divers 2007; 11:23-36. [PMID: 17447158 DOI: 10.1007/s11030-006-9054-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2006] [Accepted: 10/27/2006] [Indexed: 11/29/2022]
Abstract
The chemical space covered by compounds involved in metabolic reactions was compared with that of a random dataset of purchasable compounds by chemoinformatics techniques. The comparison was based on 3D structure, 2D structure, or descriptors of global properties, by means of self-organizing maps, random forests, and classification trees. The overlap between metabolites and non-metabolites was observed to be the least in the space defined by the global descriptors, the most discriminatory features being the number of OH groups, presence of aromatic systems, and molecular weight. Discrimination between the two datasets was achieved with accuracy up to 97%. Models were built to produce a metabolite-likeness parameter. A relationship between metabolite-likeness and ready biodegradability was observed.
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Affiliation(s)
- Sunil Gupta
- REQUIMTE, CQFB, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal.
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Caron G, Ermondi G, Testa B. Predicting the Oxidative Metabolism of Statins: An Application of the MetaSite® Algorithm. Pharm Res 2007; 24:480-501. [PMID: 17253156 DOI: 10.1007/s11095-006-9199-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2006] [Accepted: 11/29/2006] [Indexed: 02/07/2023]
Abstract
PURPOSE This study was undertaken to examine the MetaSite algorithm by comparing its predictions with experimentally characterized metabolites of statins produced by cytochromes P450 (CYPs). METHODS Seven statins were investigated, namely atorvastatin, cerivastatin, fluvastatin, pitavastatin and pravastatin which are (or were) used in their active hydroxy-acid form, and lovastatin and simvastatin which are used as the lactone prodrug. But given the fast lactone-hydroxy-acid equilibrium undergone by statins, both forms were investigated for each of the seven drugs. The MetaSite version 2.5.3 used here contains the homology 3D-models of CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4. In addition, we also used the crystallographic 3D-structure of human CYP2C9 and CYP3A4. To allow a better interpretation of results, the probability function PsMi calculated by MetaSite (namely the probability of atom i to be a site of metabolism) was explicitly decomposed into its two components, namely a recognition score Ei (the accessibility of atom i) and the chemical reactivity Ri of atom i toward oxidation reactions. RESULTS The current version of MetaSite is known to work best with prior experimental knowledge of the cytochrome(s) P450 involved. And indeed, experimentally confirmed sites of oxidation were correctly given a high priority by MetaSite. In particular 77% of correct predictions (including false positive but, as discussed, this is not necessarily a shortcoming) were obtained when considering the first five metabolites indicated by MetaSite. CONCLUSION To the best of our knowledge, this is the first independent report on the software. It is expected to contribute to the development of improved versions, but above all it demonstrates that the usefulness of such softwares critically depends on human experts.
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Affiliation(s)
- Giulia Caron
- Dipartimento di Scienza e Tecnologia del Farmaco, via Giuria 9, 10125 Torino, Italy.
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Abstract
Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.
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Affiliation(s)
- Larry J Jolivette
- Preclinical Drug Discovery, Cardiovascular and Urogenital Centre of Excellence in Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania, USA
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Madden JC, Cronin MTD. Structure-based methods for the prediction of drug metabolism. Expert Opin Drug Metab Toxicol 2006; 2:545-57. [PMID: 16859403 DOI: 10.1517/17425255.2.4.545] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
There is a tantalising possibility that we may be able to predict the metabolism of a drug directly from its structure, thus obviating the requirement for animal tests in this area. There are a number of techniques that can be used to estimate a range of events associated with metabolism, and may allow us to achieve this aim. This paper considers the role of (quantitative) structure-activity relationships, and pharmacophore and homology modelling in the prediction of metabolism. Examples are also presented where such approaches have been formalised into expert systems. Clearly, many advances have been made in this area in recent years. Discussed herein is the importance of fully integrating the diverse systems and approaches available to fulfil the aspiration to predict metabolism directly from structure.
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Affiliation(s)
- Judith C Madden
- Liverpool John Moores University, School of Pharmacy and Chemistry, UK
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