Lindh Martin, Karlén Anders, Norinder Ulf
Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry, BMC, Uppsala University , Box 574, SE-751 23 Uppsala, Sweden.
Swetox, Karolinska Institutet , Unit of Toxicology Sciences, Forskargatan 20, SE-151 36 Södertälje, Sweden.
Mol Pharm. 2017 May 1;14(5):1571-1576. doi: 10.1021/acs.molpharmaceut.7b00007. Epub 2017 Apr 17.
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) 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.
皮肤可作为一种给药途径，化学品的皮肤渗透性在制药和化妆品行业中备受关注。使用聚合共形预测（ACP）框架来构建预测化合物通过人体皮肤的渗透速率（log K）的模型。共形预测方法针对每种化合物给出在给定置信水平下的预测范围，这使得用户在例如建议制备下一种化合物时能够做出更明智的决策。使用随机森林和支持向量机方法构建预测模型，并基于211种不同化合物的实验得出的渗透性数据。与早期发表的模型相比，所推导的模型具有相似的预测质量，但具有额外的优势，即不仅为每种化合物呈现单个预测值，还提供可靠的、单独分配的预测范围。这些模型使用计算得出的描述符，能够快速预测新化合物的皮肤渗透速率。
