Modeling the transplacental transfer of small molecules using machine learning: A case study on per- and polyfluorinated substances (PFAS)

BACKGROUND: Despite their large numbers and widespread use, very little is known about the extent to which per- and polyfluoroalkyl substances (PFAS) can cross the placenta and expose the developing fetus. OBJECTIVE: The aim of our study is to develop a computational approach that can be used to evaluate the of extend of which small molecules, and in particular PFAS, can cross to cross the placenta and partition to cord blood. METHODS: We collected experimental values of the central tendency of concentration ratio between cord and maternal blood (R(CM)) for 260 chemical compounds and calculated their physicochemical descriptors using the cheminformatics package Mordred. We developed and tested an artificial neural network (ANN) and used the compiled database to train the model. We then applied our best performing model to make predictions of R(CM) for a large dataset of PFAS chemicals (n=7,982). We, finally, used the calculated descriptors of the chemicals to identify which properties correlated significantly with R(CM). RESULTS: We determined that 7855 compounds were within the applicability domain and 127 compounds are outside the applicability domain of our model. Our predictions of R(CM) for PFAS suggested that 3623 compounds had a log R(CM) > 0 indicating preferable partitioning to cord blood. Some examples of these compounds were bisphenol AF, 2,2-bis(4-aminophenyl)hexafluoropropane and nonafluoro-tert-butyl 3-methylbutyrate. SIGNIFICANCE: These observations have important public health implications as many PFAS have been shown to interfere with fetal development.