A novel in silico approach for predicting unbound brain-to-plasma ratio using machine learning-based support vector regression.

The blood-brain barrier (BBB) functions as a vital protective mechanism, restricting the entry of substances and xenobiotics into the central nervous system (CNS). Consequently, BBB penetration is a critical aspect of absorption, distribution, metabolism, elimination, and toxicity (ADME/Tox) considerations in drug discovery and development as it is essential to minimize CNS-associated side effects in systemically targeted drugs and to enhance efficacy in CNS-targeted therapeutics. In this study, an in silico model utilizing the novel machine learning-based hierarchical support vector regression (HSVR) scheme was developed to predict the unbound brain-to-plasma concentration ratio (K) values using a diverse dataset of compounds with known BBB penetration properties. The HSVR model leverages a hierarchical framework to capture the complex relationships between molecular descriptors and BBB penetration mechanisms that can otherwise be insurmountably difficult for traditional methods or other machine learning algorithms. These complexities arise from the fact that BBB penetration can be governed by various factors, including passive diffusion and active influx and efflux transport processes. The accuracy, predictivity, robustness of HSVR were rigorously validated using comprehensive valuation metrics and stringent validation criteria. Its practical application was further substantiated through a mock test. Comparative analyses revealed that the HSVR model outperforms existing published models both quantitatively and qualitatively, providing a reliable tool for early-stage drug discovery and development. The adoption of this model has the potential to significantly streamline BBB penetration assessments, minimizing reliance on in vivo studies while expediting the identification of viable CNS drug candidates or systemic drug candidates prone to CNS-related challenges. This approach aligns seamlessly with the "fail early and fail fast" paradigm of modern drug discovery, enhancing both efficiency and cost-effectiveness.