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Drug Metabolism & Disposition

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Classification of Metabolites with Kernel-Partial Least Squares (K-PLS)

Mark J. Embrechts and Sean Ekins
Drug Metabolism and Disposition March 2007, 35 (3) 325-327; DOI: https://doi.org/10.1124/dmd.106.013185
Mark J. Embrechts
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Sean Ekins
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Abstract

Numerous experimental and computational approaches have been developed to predict human drug metabolism. Since databases of human drug metabolism information are widely available, these can be used to train computational algorithms and generate predictive approaches. In turn, they may be used to assist in the identification of possible metabolites from a large number of molecules in drug discovery based on molecular structure alone. In the current study we have used a commercially available database (MetaDrug) and extracted a fraction of the human drug metabolism data. These data were used along with augmented atom descriptors in a predictive machine learning model, kernel-partial least squares (K-PLS). A total of 317 molecules, including parent drugs and their primary and secondary (sequential) metabolites, were used to build these models corresponding to individual metabolism rules, representing the formation of discrete metabolites, e.g., N-dealkylation. Each model was internally validated to assess the capability to classify other molecules that were left out. Using receiver operator curve statistics models for N-dealkylation, O-dealkylation, aromatic hydroxylation, aliphatic hydroxylation, O-glucuronidation, and O-sulfation gave area under the curve values from 0.75 to 0.84 and were able to predict between 61 and 79% active molecules upon leave-one-out testing. This preliminary study indicates that K-PLS and possibly other similar machine learning methods (such as support vector machines) can be applied to predicting human drug metabolite formation in a classification manner. Improvements can be achieved using considerably larger datasets that contain more positive examples for the less frequently occurring metabolite rules, as well as the external evaluation of novel molecules.

Footnotes

  • The development of MetaDrug was supported by National Institutes of Health Grants 1-R43-GM069124-01 and 2-R44-GM069124-02 “In silico Assessment of Drug Metabolism and Toxicity”.

  • Competing Financial Interest: MetaDrug is a proprietary tool developed and licensed by GeneGo, Inc.

  • Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

  • doi:10.1124/dmd.106.013185.

  • ABBREVIATIONS: QSAR, quantitative structure-activity relationship; K-PLS, kernel-partial least squares; AUC, area under the curve.

    • Received October 2, 2006.
    • Accepted November 29, 2006.
  • The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 35 (3)
Drug Metabolism and Disposition
Vol. 35, Issue 3
1 Mar 2007
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OtherShort Communication

Classification of Metabolites with Kernel-Partial Least Squares (K-PLS)

Mark J. Embrechts and Sean Ekins
Drug Metabolism and Disposition March 1, 2007, 35 (3) 325-327; DOI: https://doi.org/10.1124/dmd.106.013185

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OtherShort Communication

Classification of Metabolites with Kernel-Partial Least Squares (K-PLS)

Mark J. Embrechts and Sean Ekins
Drug Metabolism and Disposition March 1, 2007, 35 (3) 325-327; DOI: https://doi.org/10.1124/dmd.106.013185
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