Developing a predictive model for blood-brain-barrier permeability to explore relevance of neurotoxicity data for risk assessment.

INTRODUCTION

Despite recent rapid advancements in toxicology, its application to whole-body health outcomes remains limited. Incorporating factors like internal exposure, such as permeability across biomembranes, could improve its relevance. Notably, there is a lack of data and predictive models for blood-brain barrier (BBB) permeability, a proxy for the exposure of target organs to neurotoxicity. We developed a predictive model for BBB permeability to investigate whether it can strengthen the correlation between and neurotoxicity data.

METHODS

We collected permeability data from parallel artificial membrane permeability assays for brain membranes (PAMPA-BBB) for 106 compounds with varied physicochemical properties. This was utilized to develop an empirical model to expand the potential coverage of chemicals. A list of 23 chemicals with available and neurotoxicity data from EPA IRIS and ToxCast was curated to analyze the correlation in toxicity rankings with the Spearman correlation coefficient, with and without the consideration of permeability from our predictive model.

RESULTS

The PAMPA-BBB predictive model showed promising results, with an R2 of 0.71 (measured vs predicted permeabilities). Considering permeability did not improve the correlation between and neurotoxicity (0.01 vs -0.11).

DISCUSSION

This weak correlation may stem from model uncertainty and the exclusion of other toxicokinetic processes, along with interspecies toxicodynamics differences. Our results indicate more detailed information on how neurotoxic substances behave inside the body is essential to better utilize the neurotoxicity data for predicting toxicity and assessing the risk to the central nervous system.