Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, (M.P.), India.
SAR QSAR Environ Res. 2010 Jul;21(5-6):481-94. doi: 10.1080/1062936X.2010.501819.
Skin provides passage for the delivery of drugs. The in vitro and in vivo testing of chemicals for estimation of dermal absorption is very time consuming, costly and has many ethical difficulties related to human and animal testing. The solution to the problem is Quantitative structure-permeability relationships. This method relates dermal penetration properties of a range of chemical compounds to their physicochemical parameters. In the present study, an effort has been made to develop models for the accurate prediction of skin permeability using a large, diverse dataset through the combination of various regression methods coupled with the Genetic Algorithm (GA)/Interval Partial Least-Squares Algorithm (iPLS). The descriptors were calculated using e-DRAGON and ADME Pharma Algorithms-Abrahams descriptors. The original dataset was divided into a training set and a testing set using the Kennard-Stone Algorithm. The selection of descriptors was made by the GA and iPLS. The model applicability domain was determined. The results showed that a three-parameter model built through Partial Least-squares Regression was most accurate with r(2) of 0.936.
皮肤提供了药物输送的途径。为了评估皮肤吸收，对化学品进行体外和体内测试非常耗时、昂贵，并且在人体和动物测试方面存在许多伦理难题。解决这个问题的方法是定量构效关系。这种方法将一系列化学化合物的皮肤渗透特性与其物理化学参数联系起来。在本研究中，通过结合各种回归方法和遗传算法（GA）/区间偏最小二乘法（iPLS），使用大量多样的数据集，努力开发出一种准确预测皮肤渗透性的模型。使用 e-DRAGON 和 ADME Pharma Algorithms-Abrahams 描述符计算描述符。原始数据集使用 Kennard-Stone 算法分为训练集和测试集。通过 GA 和 iPLS 选择描述符。确定模型的适用域。结果表明，通过偏最小二乘回归建立的三参数模型最为准确，其 r(2)为 0.936。
