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Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools.

ABSTRACT: ADME/Tox (absorption, distribution, metabolism, elimination and toxicity) technology is traditionally associated as a tool in the drug discovery process which is often used to predict the efficiency of drug adsorption, distribution, metabolic pathways, and elimination. For the past four years we have been involved in an effort to evaluate readily available Food and Drug Administration (FDA) consumer drug profiles and pharmacological data. Portable Document Format (PDF) data from drug profiles available on the FDA Drug Information website were used to create a searchable FDA Consumer Drug Database© using Bio-Rad's KnowItAll® platform which includes ADME/Tox in silico predictors. 14 pertinent pharmaceutical and pharmacological properties were collected for 75 structurally diverse consumer prescription drugs, and for several drugs, not all properties were completely populated. The major objective of this investigation was to validate the platforms prediction models for plasma protein binding (PPB) and bioavailability (BIO).

SUBMITTER: D'Souza MJ

PROVIDER: S-EPMC4605434 | biostudies-literature | 2009 Sep-Oct

REPOSITORIES: biostudies-literature

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Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools.

D'Souza Malcolm J MJ Koyoshi Fumie F Everett Lynn M LM

Pharmaceutical reviews 20090901 5

ADME/Tox (absorption, distribution, metabolism, elimination and toxicity) technology is traditionally associated as a tool in the drug discovery process which is often used to predict the efficiency of drug adsorption, distribution, metabolic pathways, and elimination. For the past four years we have been involved in an effort to evaluate readily available Food and Drug Administration (FDA) consumer drug profiles and pharmacological data. Portable Document Format (PDF) data from drug profiles av ...[more]

PMID: 26478759

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Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.

Dataset Information

Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools.

Publications

Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools.

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