ADME properties evaluation in drug discovery: in silico prediction of blood–brain partitioning

• Published in: Molecular diversity 2018-11, Vol.22 (4), p.979-990
• Main Authors: Zhu, Lu, Zhao, Junnan, Zhang, Yanmin, Zhou, Weineng, Yin, Linfeng, Wang, Yuchen, Fan, Yuanrong, Chen, Yadong, Liu, Haichun
• Format: Article
• Language: English
• Subjects: Absorption, Physicochemical; Biochemistry; Biomedical and Life Sciences; Blood; Blood-Brain Barrier - metabolism; Brain; Central nervous system; Computer Simulation; Drug Discovery; Excretion; Life Sciences; Metabolism; Models, Theoretical; Multivariate Analysis; Organic Chemistry; Original Article; Pharmacy; Polymer Sciences; Prediction models; Quantitative Structure-Activity Relationship
• ISSN: 1381-1991; 1573-501X
• DOI: 10.1007/s11030-018-9866-8

The absorption, distribution, metabolism and excretion properties are important for drugs, and prediction of these properties in advance will save the cost of drug discovery substantially. The ability to penetrate the blood–brain barrier is critical for drugs targeting central nervous system, which is represented by the ratio of its concentration in brain and in blood. Herein, a quantitative structure–property relationship study was carried out to predict blood–brain partitioning coefficient (logBB) of a data set consisting of 287 compounds. Four different methods including support vector machine, multivariate linear regression, multivariate adaptive regression splines and random forest were employed to build prediction models with 116 molecular descriptors selected by Boruta algorithm. The RF model had best performance in training set ( R 2  = 0.938), test set ( R 2  = 0.840) and tenfold cross-validation ( Q 2  = 0.788). Finally, we found that the polar surface area and octanol–water partition coefficient have the greatest influence on blood–brain partitioning. Results suggest that the proposed model is a useful and practical tool to predict the logBB values of drug candidates.