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Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.ORCID iD: 0000-0002-4420-772X
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
Karolinska Inst, Unit Toxicol Sci, Swetox, Forskargatan 20, SE-15136 Sodertalje, Sweden.;Stockholm Univ, Dept Comp & Syst Sci, Forum 100, SE-16440 Kista, Sweden..
2017 (English)In: Molecular Pharmaceutics, ISSN 1543-8384, E-ISSN 1543-8392, Vol. 14, no 5, 1571-1576 p.Article in journal (Refereed) Published
Abstract [en]

Skin serves as a drug administration route, and skin permeability of chemicals is of significant interest in the pharmaceutical and cosmetic industries. An aggregated conformal prediction (ACP) framework was used to build models, for predicting the permeation rate (log K-p) of chemical compounds through human skin. The conformal prediction method gives as an output the prediction range at a given level of confidence for each compound, which enables the user to make a more informed decision when, for example, suggesting the next compound to prepare, Predictive models were built using;both the random forest and the support vector machine methods and were based on experimentally derived permeability data on 211 diverse compounds. The derived models were of similar predictive quality as compared to earlier published models but have the extra advantage of not only presenting a single predicted value for each, compound but also a reliable, individually assigned prediction range. The models use calculated descriptors and can quickly predict the skin permeation rate of new compounds.

Place, publisher, year, edition, pages
2017. Vol. 14, no 5, 1571-1576 p.
Keyword [en]
conformal prediction, skin penetration nonconformist, Scikit Learn, random forest, Support vector machines
National Category
Basic Medicine
Identifiers
URN: urn:nbn:se:uu:diva-323448DOI: 10.1021/acs.molpharmaceut.7b00007ISI: 000400633300024PubMedID: 28335598OAI: oai:DiVA.org:uu-323448DiVA: diva2:1119714
Available from: 2017-07-04 Created: 2017-07-04 Last updated: 2017-07-04Bibliographically approved

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CiteExportLink to record
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