Predicting Unbound Drug Bioavailability in The Brain Application of machine learning

In the last fifteen years, unbound brain bioavailability has become a major driver of central nervous system (CNS) drug discovery. An article from 2022 in Pharmaceutical Research by Loryan et al. describes the partition coefficient of unbound drug concentrations between the brain and plasma (Kp,uu,br) as a “game-changing parameter” in the field of CNS therapeutics. A survey published in that same article, administered to 14 representatives of major pharmaceutical companies (13 of whom belonged to their companies' DMPK department), confirmed that almost all companies have integrated the estimation of unbound brain bioavailability to establish in vitro-in vivo correlations, the PK/PD relationship for CNS effects, and/or the prediction of therapeutic doses for brain medications under development.

However, the most commonly used in vivo and in vitro methods to measure free brain bioavailability (brain/blood microdialysis, brain/blood sampling followed by binding correction, the brain slices assay, and the brain homogenate assay) present various limitations, from systematic errors in the determination (e.g., brain homogenate assay) to high cost and need for highly trained personnel (e.g., brain microdialysis). The greatest of these limitations is, possibly, their throughput, which is mid-to-high at best.

This possibly explains why more than half of companies resort to QSAR or physiologically based pharmacokinetic modelling (PBPK) models to determine brain tissue binding and uptake, and to predict the distribution of unbound drugs. Once such models are built, they can be applied with high throughput at no cost. Moreover, experts point out the establishment of a truly predictable QSAR model as one of the key developments for the implementation of the unbound brain bioavailability concept in the next 15 years.

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