Quantitative Structure-Activity Relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta

The increasing diversity of environmental chemicals in the environment, some of which may be developmental toxicants, is a public health concern. The aim of this work was to contribute to the development of rapid and effective methods to assess prenatal exposure. Quantitative structure-activity relationships (QSAR) modeling has emerged as a promising method in the development of a predictive model for the placental transfer of con-taminants. Cord to maternal plasma or serum concentration ratios for 105 chemicals were extracted from the literature, and 214 molecular descriptors were generated for each of these chemicals. Ten predictive models were built using Molecular Operating Environment (MOE) software, and the Python and R programming languages. Training and test datasets were used, respectively, to build and validate the models. The Applicability Domain Tool v1.0 was used to determine the applicability domain. Models developed with the partial least squares regression method in MOE and SuperLearner in R showed the best precision and predictivity, with internal coefficients of determination (R2) of 0.88 and 0.82, cross-validated R2s of 0.72 and 0.57, and external R2s of 0.73 and 0.74, respectively. All test chemicals were within the domain of applicability. The results obtained in this study suggest that QSAR modeling can help estimate the placental transfer of environmental chemicals.

Automated summary

This study aimed to develop a rapid and effective method for assessing prenatal exposure. QSAR modeling was used to predict the placental transfer of environmental chemicals, showing good precision and predictivity.