Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction.

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  • Author(s): Ahmed SS;Ahmed SS; Ramakrishnan V
  • Source:
    PloS one [PLoS One] 2012; Vol. 7 (7), pp. e40654. Date of Electronic Publication: 2012 Jul 16.
  • Publication Type:
    Journal Article; Research Support, Non-U.S. Gov't
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Signal Transduction*; Biomarkers/*metabolism ; Systems Biology/*methods; Administration, Oral ; Algorithms ; Artificial Intelligence ; Biological Availability ; Caco-2 Cells ; Databases as Topic ; Humans ; Logistic Models ; Models, Biological ; Multivariate Analysis
    • Abstract:
      Background: Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.
      Results: The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.
      Conclusion: The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.
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    • Accession Number:
      0 (Biomarkers)
    • Publication Date:
      Date Created: 20120721 Date Completed: 20130321 Latest Revision: 20211021
    • Publication Date:
      20240628
    • Accession Number:
      PMC3398012
    • Accession Number:
      10.1371/journal.pone.0040654
    • Accession Number:
      22815781