Translational CNS Steady-State Drug Disposition Model in Rats, Monkeys, and Humans for Quantitative Prediction of Brain-to-Plasma and Cerebrospinal Fluid-to-Plasma Unbound Concentration Ratios.

Capturing unbound drug exposure in the brain is crucial to evaluate pharmacological effects for drugs acting on the central nervous system. However, to date, there are no reports of validated prediction models to determine the brain-to-plasma unbound concentration ratio (K) as well as the cerebrospinal fluid (CSF)-to-plasma unbound concentration ratio (K) between humans and other species. Here, we developed a translational CNS steady-state drug disposition model to predict K and K across rats, monkeys, and humans by estimating the relative activity factors (RAF) for MDR1 and BCRP in addition to scaling factors (γ and σ) using the molecular weight, logD, CSF bulk flow, and in vitro transport activities of these transporters. In this study, 68, 26, and 28 compounds were tested in the rat, monkey, and human models, respectively. Both the predicted K and K values were within the 3-fold range of the observed values (71, 73, and 79%; 79, 88, and 78% of the compounds, respectively), indicating successful prediction of K and K in the three species. The overall predictivity of the RAF approach is consistent with that of the relative expression factor (REF) approach. As the established model can predict K and K using only in vitro and physicochemical data, this model would help avoid ethical issues related to animal use and improve CNS drug discovery workflow.