Vasyuchenko Ekaterina P, Orekhov Philipp S, Armeev Grigoriy A, Bozdaganyan Marine E
School of Biology, Lomonosov Moscow State University, 119234 Moscow, Russia.
Institute of Personalized Medicine, Sechenov University, 119991 Moscow, Russia.
Pharmaceutics. 2021 Jan 7;13(1):66. doi: 10.3390/pharmaceutics13010066.
The cutaneous delivery route currently accounts for almost 10% of all administered drugs and it is becoming more common. Chemical penetration enhancers (CPEs) increase the transport of drugs across skin layers by different mechanisms that depend on the chemical nature of the penetration enhancers. In our work, we created a chemical penetration enhancer database (CPE-DB) that is, to the best of our knowledge, the first CPE database. We collected information about known enhancers and their derivatives in a single database, and classified and characterized their molecular diversity in terms of scaffold content, key chemical moieties, molecular descriptors, etc. CPE-DB can be used for virtual screening and similarity search to identify new potent and safe enhancers, building quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models, and other machine-learning (ML) applications for the prediction of biological activity.
目前,经皮给药途径占所有给药药物的近10%,且越来越普遍。化学渗透促进剂(CPEs)通过不同机制增加药物跨皮肤层的转运,这些机制取决于渗透促进剂的化学性质。在我们的工作中,我们创建了一个化学渗透促进剂数据库(CPE-DB),据我们所知,这是首个CPE数据库。我们将有关已知促进剂及其衍生物的信息收集到一个单一数据库中,并根据支架含量、关键化学基团、分子描述符等对其分子多样性进行分类和表征。CPE-DB可用于虚拟筛选和相似性搜索,以识别新的强效且安全的促进剂,构建定量构效关系(QSAR)和定量构性关系(QSPR)模型,以及用于预测生物活性的其他机器学习(ML)应用。