pragmatic approaches to using computational methods to predict xenobiotic metabolism
Open accessAbstract
In this study the performance of a selection of computational models for the prediction of metabolites and/or sites of metabolism was investigated. These included models incorporated in the MetaPrint2D-React, Meteor, and SMARTCyp software. The algorithms were assessed using two data sets: one a homogeneous data set of 28 Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and paracetamol (DS1) and the second a diverse data set of 30 top-selling drugs (DS2). The prediction of metabolites for the diverse data set (DS2) was better than for the more homogeneous DS1 for each model, indicating that some areas of chemical space may be better represented than others in the data used to develop and train the models. The study also identified compounds for which none of the packages could predict metabolites, again indicating areas of chemical space where more information is needed. Pragmatic approaches to using metabolism prediction software have also been proposed based on the results described here. These approaches include using cutoff values instead of restrictive reasoning settings in Meteor to reduce the output with little loss of sensitivity and for directing metabolite prediction by preselection based on likely sites of metabolism.
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Some commercial software exists for the pharmaceutical area and covers many phase I and II reactions.
“Some commercial software exists for the pharmaceutical area (192,193) and covers many phase I and II reactions.”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [193]
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The food metabolome: a window over dietary exposure
Scalbert, A., Brennan, L., Manach, C., Andres-Lacueva, C., Dragsted, L. O., Draper, J., Rappaport, S. M., van der Hooft, J. J., & Wishart, D. S. (2014) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.076133 · Cited by 519 · see citing works
- Authors
- Piechota, P., Cronin, M. T. D., Hewitt, M., & Madden, J. C.
- Affiliations
- Liverpool John Moores University (GB)
- Corresponding author
- Madden, Judith C.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-06-11
- Volume
- 53
- Issue
- 6
- Pages
- 1282-1293
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2025744234
- Cited by
- 30
- References
- 13
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2019: 1 · 2018: 1 · 2017: 8 · 2016: 3 · 2015: 1 · 2014: 7 · 2013: 4
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- other-oa
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Keywords
- metabolite prediction, meteors, nonsteroidal anti-inflammatory drugs, paracetamol, top-selling drugs, chemical space, cut-off values
- MeSH terms
- Acetaminophen, Algorithms, Anti-Inflammatory Agents, Non-Steroidal, Computer Simulation, Pharmaceutical Preparations, Humans, Models, Biological, Software, Analgesics, Non-Narcotic, Databases, Pharmaceutical
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