QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

Abstract

Development of externally predictive Quantitative Structure-Activity Relationship (QSAR) models for Blood-Brain Barrier (BBB) permeability. Combinatorial QSAR analysis was carried out for a set of 159 compounds with known BBB permeability data. All six possible combinations of three collections of descriptors derived from two-dimensional representations of molecules as chemical graphs and two QSAR methodologies have been explored. Descriptors were calculated by MolconnZ, MOE, and Dragon software. QSAR methodologies included k-Nearest Neighbors and Support Vector Machine approaches. All models have been rigorously validated using both internal and external validation methods. The consensus prediction for the external evaluation set afforded high predictive power (R2 = 0.80 for 10 compounds within the applicability domain after excluding one activity outlier). Classification accuracies for two additional external datasets, including 99 drugs and 267 organic compounds, classified as permeable (BBB+) or non-permeable (BBB-) were 82.5% and 59.0%, respectively. The use of a fairly conservative model applicability domain increased the prediction accuracy to 100% and 83%, respectively (while naturally reducing the dataset coverage to 60% and 43%, respectively). Important descriptors that affect BBB permeability are discussed. Models developed in these studies can be used to estimate the BBB permeability of drug candidates at early stages of drug development.