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Research ArticleArticle

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Rishi R. Gupta, Eric M. Gifford, Ted Liston, Chris L. Waller, Moses Hohman, Barry A. Bunin and Sean Ekins
Drug Metabolism and Disposition November 2010, 38 (11) 2083-2090; DOI: https://doi.org/10.1124/dmd.110.034918
Rishi R. Gupta
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Eric M. Gifford
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Ted Liston
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Chris L. Waller
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Moses Hohman
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Barry A. Bunin
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Sean Ekins
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Abstract

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

Footnotes

  • M.H., B.A.B., and S.E. are employees or consultants of Collaborative Drug Discovery, Inc.

  • This work was supported by Collaborative Drug Discovery, Inc. funding from the Bill and Melinda Gates Foundation [Grant 49852] (“Collaborative Drug Discovery for TB through a Novel Database of SAR Data Optimized to Promote Data Archiving and Sharing”).

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

    doi:10.1124/dmd.110.034918.

  • ABBREVIATIONS:

    ADME/Tox
    absorption, distribution, metabolism, excretion, and toxicity
    QSAR
    quantitative structure-activity relationship
    PDM
    Pharmacokinetics, Dynamics and Metabolism
    HLM
    human liver microsomes
    RRCK
    Russ Ralph canine kidney
    P-gp
    P-glycoprotein
    MDR
    multidrug resistance
    Clint
    intrinsic clearance
    Papp
    passive apparent permeability
    A
    apical
    B
    basolateral
    MOE2D
    Molecular Operating Environment 2D
    CDK
    chemistry development kit
    SVM
    support vector machine
    RP
    Recursive Partitioning
    RMSE
    root mean squared error
    PCA
    principal component analysis
    PC
    principal component
    SMARTS
    Smiles Arbitrary Target Specification.

  • Received June 9, 2010.
  • Accepted August 3, 2010.
  • Copyright © 2010 by The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 38 (11)
Drug Metabolism and Disposition
Vol. 38, Issue 11
1 Nov 2010
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Research ArticleArticle

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Rishi R. Gupta, Eric M. Gifford, Ted Liston, Chris L. Waller, Moses Hohman, Barry A. Bunin and Sean Ekins
Drug Metabolism and Disposition November 1, 2010, 38 (11) 2083-2090; DOI: https://doi.org/10.1124/dmd.110.034918

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Research ArticleArticle

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Rishi R. Gupta, Eric M. Gifford, Ted Liston, Chris L. Waller, Moses Hohman, Barry A. Bunin and Sean Ekins
Drug Metabolism and Disposition November 1, 2010, 38 (11) 2083-2090; DOI: https://doi.org/10.1124/dmd.110.034918
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