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WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound

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Abstract

In the development of novel pharmaceuticals, the knowledge of how many, and which, Cytochrome P450 isoforms are involved in the phase I metabolism of a compound is important. Potential problems can arise if a compound is metabolised predominantly by a single isoform in terms of drug–drug interactions or genetic polymorphisms that would lead to variations in exposure in the general population. Combined with models of regioselectivities of metabolism by each isoform, such a model would also aid in the prediction of the metabolites likely to be formed by P450-mediated metabolism. We describe the generation of a multi-class random forest model to predict which, out of a list of the seven leading Cytochrome P450 isoforms, would be the major metabolising isoforms for a novel compound. The model has a 76% success rate with a top-1 criterion and an 88% success rate for a top-2 criterion and shows significant enrichment over randomised models.

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Acknowledgements

This research has received funding from the Union Seventh Framework Programme 2013 under the Grant agreement no. 602156.

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Authors and Affiliations

  1. Optibrium Ltd., F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road, Cambridge, CB25 9PB, UK

    Peter A. Hunt & Matthew D. Segall

  2. The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK

    Jonathan D. Tyzack

Authors
  1. Peter A. Hunt
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  2. Matthew D. Segall
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  3. Jonathan D. Tyzack
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Correspondence to Peter A. Hunt.

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Conflict of interest

Matthew Segall and Peter Hunt are current employees of Optibrium Ltd., which develops the StarDrop software in which the methods described herein are implemented. Jonathan Tyzack is a former employee of Optibrium Ltd.

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Hunt, P.A., Segall, M.D. & Tyzack, J.D. WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound. J Comput Aided Mol Des 32, 537–546 (2018). https://doi.org/10.1007/s10822-018-0107-0

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  • DOI: https://doi.org/10.1007/s10822-018-0107-0

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