Integrating <i>in Silico</i> and <i>in Vitro</i> Approaches To Predict Drug Accessibility to the Central Nervous System

Estimation of uptake across the blood–brain barrier (BBB) is key to designing central nervous system (CNS) therapeutics. <i>In silico</i> approaches ranging from physicochemical rules to quantitative structure–activity relationship (QSAR) models are utilized to predict potential for CNS penetration of new chemical entities. However, there are still gaps in our knowledge of (1) the relationship between marketed human drug derived CNS-accessible chemical space and preclinical neuropharmacokinetic (neuroPK) data, (2) interpretability of the selected physicochemical descriptors, and (3) correlation of the <i>in vitro</i> human P-glycoprotein (P-gp) efflux ratio (ER) and <i>in vivo</i> rodent unbound brain-to-blood ratio (<i>K</i>_p,uu), as these are assays routinely used to predict clinical CNS exposure, during drug discovery. To close these gaps, we explored the CNS druglike property boundaries of 920 market oral drugs (315 CNS and 605 non-CNS) and 846 compounds (54 CNS drugs and 792 proprietary GlaxoSmithKline compounds) with available rat <i>K</i>_p,uu data. The exact permeability coefficient (<i>P</i>_exact) and P-gp ER were determined for 176 compounds from the rat <i>K</i>_p,uu data set. Receiver operating characteristic curves were performed to evaluate the predictive power of human P-gp ER for rat <i>K</i>_p,uu. Our data demonstrates that simple physicochemical rules (most acidic p<i>K</i>_a ≥ 9.5 and TPSA < 100) in combination with P-gp ER < 1.5 provide mechanistic insights for filtering BBB permeable compounds. For comparison, six classification modeling methods were investigated using multiple sets of <i>in silico</i> molecular descriptors. We present a random forest model with excellent predictive power (∼0.75 overall accuracy) using the rat neuroPK data set. We also observed good concordance between the structural interpretation results and physicochemical descriptor importance from the <i>K</i>_p,uu classification QSAR model. In summary, we propose a novel, hybrid <i>in silico</i>/<i>in vitro</i> approach and an <i>in silico</i> screening model for the effective development of chemical series with the potential to achieve optimal CNS exposure