Dataset Information

Annotating Human P-Glycoprotein Bioassay Data.

ABSTRACT: Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases. Furthermore, the ability of data, determined in different assays, to be combined with each other is explored. As a result of this study, it is suggested that for inhibitors of human P-glycoprotein it is possible to combine data coming from the same assay type, if the cell lines used are also identical and the fluorescent or radiolabeled substrate have overlapping binding sites. In addition, it demonstrates that there is a need for larger chemical diverse datasets that have been measured in a panel of different assays. This would certainly alleviate the search for other inter-correlations between bioactivity data yielded by different assay setups.

SUBMITTER: Zdrazil B

PROVIDER: S-EPMC3531886 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Annotating Human P-Glycoprotein Bioassay Data.

Zdrazil Barbara B Pinto Marta M Vasanthanathan Poongavanam P Williams Antony J AJ Balderud Linda Zander LZ Engkvist Ola O Chichester Christine C Hersey Anne A Overington John P JP Ecker Gerhard F GF

Molecular informatics 20120807 8

Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases. Furthermore, the ability of data, determined ...[more]

PMID: 23293680

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Project description:BackgroundThere are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets.ResultsPharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of cross-referencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance.ConclusionsUnderstandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset.

Dataset Information

Annotating Human P-Glycoprotein Bioassay Data.

Publications

Annotating Human P-Glycoprotein Bioassay Data.

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