Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set

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dc.contributor.authorBrito-Sanchez, Yoan
dc.contributor.authorMarrero-Ponce, Yovani
dc.contributor.authorBarigye, Stephen J.
dc.contributor.authorPerez, Carlos Morell
dc.contributor.authorCherkasov, Artem
dc.date.accessioned2023-02-09T10:47:05Z
dc.date.available2023-02-09T10:47:05Z
dc.date.issued2015
dc.description.abstractIn the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analysis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit classification and correlation functions. The best LDA-based model showed overall accuracies over 85 % and 83 % for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69 % of the variance in the experimental log BB was developed. A brief and general interpretation of proposed models allowed the estimation on how ‘near’ our computational approach is to the factors that determine the passage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion regarding to these compounds is provided. Finally, our results were compared with methodologies previously reported in the literature showing comparable to better results. The results could represent useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.en_US
dc.identifier.citationBrito‐Sánchez, Y., Marrero‐Ponce, Y., Barigye, S. J., Yaber‐Goenaga, I., Morell Perez, C., Le‐Thi‐Thu, H., & Cherkasov, A. (2015). Towards better BBB passage prediction using an extensive and curated data set. Molecular informatics, 34(5), 308-330.https://doi.org/10.1002/minf.201400118en_US
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7663
dc.language.isoenen_US
dc.publisherMolecular informaticsen_US
dc.subjectLinear discriminant analysisen_US
dc.subjectMultiple linear regressionen_US
dc.subjectDragon descriptoren_US
dc.subjectBBB endpointen_US
dc.subjectP-glycoproteinen_US
dc.subjectQuantitative structure pharmacokinetic (property) relationshipen_US
dc.subjectBlood¢brain barrieren_US
dc.titleTowards Better BBB Passage Prediction Using an Extensive and Curated Data Seten_US
dc.typeArticleen_US
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