Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.

Eugene V Radchenko,Vladimir A Palyulin,Alina S Dyabina

doi:10.3390/molecules25245901

Eugene V Radchenko, Vladimir A Palyulin + Show 1 more

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https://doi.org/10.3390/molecules25245901

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Abstract

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (, ) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

Highlights

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds in humans and animals
The double cross-validation procedure generates an ensemble of neural network models based on different subsets of data that can be used to improve prediction quality and evaluate the model applicability
We have developed a predictive in silico blood–brain barrier permeability (LogBB) model based on extensive and verified dataset (529 compounds) and applicable to diverse drugs and drug-like compounds

Summary

Introduction

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds in humans and animals. Relevant and accurate in vitro models are being developed [6,7,8], including cell-free methods such as the widely used parallel artificial membrane permeability assay (PAMPA) or immobilized artificial membrane (IAM) chromatography, brain slices, isolated capillaries, and various cell culture models. All these approaches obviously require significant amounts of a physical substance, while achieving physiological relevance in a model may be challenging. The need for robust in silico techniques for the prediction of the BBB

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