In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

Quantitative Structure–Property Relationship models (QSPR) based on in vivo blood–brain permeation data (logBB) of 88 diverse compounds, 324 descriptors and a systematic variable selection method, namely ‘Variable Selection and Modeling method based on the prediction (VSMP)’, are reported. Of all the models developed using VSMP, the best three-descriptors model is based on Atomic type E-state index (SsssN), AlogP98 and Van der Waal’s surface area (r = 0.8425, q = 0.8239, F = 68.49 and SE = 0.4165); the best four-descriptors model is based on Kappa shape index of order 1, Atomic type E-state index (SsssN), Atomic level based AI topological descriptor (AIssssC) and AlogP98 (r = 0.8638, q = 0.8472, F = 60.982 and SE = 0.3919). The performance of the models on three test sets taken from the literature is illustrated and compared with the results from other reported computational approaches. Test set III constitutes 91 compounds from the literature with known qualitative BBB indication and is used for virtual screening studies. The success rate of the reported models is 82% in the case of BBB+ compounds and a similar success rate is observed with BBB− compounds. Finally, as the models reported herein are based on computed properties, they appear as a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Predictive models for blood–brain barrier permeation were derived using 116 diverse compounds, 324 molecular descriptors, VSMP, a systematic variable selection method and multiple linear regression. Validation tests demonstrate that the models possess excellent predictive power and can be applied to virtual screening studies.Figure optionsDownload as PowerPoint slide