Prediction of the Extent of Blood–Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model

Abstract

Introduction The unbound brain-to-plasma partition coefficient (K p,uu,BBB ) is an essential parameter for predicting central nervous system (CNS) drug disposition using physiologically-based pharmacokinetic (PBPK) modeling. K p,uu,BBB values for specific compounds are however often unavailable, and are moreover time consuming to obtain experimentally. The aim of this study was to develop a quantitative structure–property relationship (QSPR) model to predict the K p,uu,BBB and to demonstrate how QSPR-model predictions can be integrated into a physiologically-based pharmacokinetic model for the CNS. Methods Rat K p,uu,BBB values were obtained for 98 compounds from literature or in house historical data. For all compounds, 2D and 3D physico-chemical and structural properties were derived using the Molecular Operating Environment (MOE) software. Multiple machine learning (ML) regression models were compared for prediction of the K p,uu,BBB , including random forest, support vector machines, K-nearest neighbors, and (sparse-) partial least squares. Finally, we demonstrate how the developed QSPR model predictions can be integrated into a CNS PBPK modeling workflow. Results Among all ML algorithms, a random forest showed the best predictive performance for K p,uu,BBB on test data with R 2 value of 0.61 and 61% of all predictions were within twofold error. The obtained K p,uu,BBB were successfully integrated into the LeiCNS-PK3.0 CNS PBPK model. Conclusions The developed random forest QSPR model for K p,uu,BBB prediction was found to have adequate performance, and can support drug discovery and development of novel investigational drugs targeting the CNS in conjunction with CNS PBPK modeling.

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