AUTHOR=Kato Rintaro , Zeng Wenyu , Siramshetty Vishal B. , Williams Jordan , Kabir Md , Hagen Natalie , Padilha Elias C. , Wang Amy Q. , Mathé Ewy A. , Xu Xin , Shah Pranav TITLE=Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates JOURNAL=Frontiers in Pharmacology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1291246 DOI=10.3389/fphar.2023.1291246 ISSN=1663-9812 ABSTRACT=
Efficiently circumventing the blood-brain barrier (BBB) poses a major hurdle in the development of drugs that target the central nervous system. Although there are several methods to determine BBB permeability of small molecules, the Parallel Artificial Membrane Permeability Assay (PAMPA) is one of the most common assays in drug discovery due to its robust and high-throughput nature. Drug discovery is a long and costly venture, thus, any advances to streamline this process are beneficial. In this study, ∼2,000 compounds from over 60 NCATS projects were screened in the PAMPA-BBB assay to develop a quantitative structure-activity relationship model to predict BBB permeability of small molecules. After analyzing both state-of-the-art and latest machine learning methods, we found that random forest based on RDKit descriptors as additional features provided the best training balanced accuracy (0.70 ± 0.015) and a message-passing variant of graph convolutional neural network that uses RDKit descriptors provided the highest balanced accuracy (0.72) on a prospective validation set. Finally, we correlated