Maternal–fetal transfer rates of PCBs, OCPs, PBDEs, and dioxin-like compounds predicted through quantitative structure–activity relationship modeling

The present study aims to predict the maternal–fetal transfer rates of the polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), and polybrominated diphenyl ethers (PBDEs), and dioxin-like compounds using a quantitative structure–activity relationship model. The relation between the maternal–fetal transfer rate and the contaminants’ physicochemical properties was investigated by multiple linear regression (MLR), partial least square regression (PLS), and random forest regression (RF). The 10-fold cross-validation technique estimated low predictive performances for both MLR and PLS models (R 2 CV = 0.425 ± 0.0964 for MLR and R 2 CV = 0.492 ± 0.115 for PLS) and is in agreement with an external test (R 2 pred = 0.129 for MLR and R 2 pred = 0.123 for PLS). In contrast, the RF model exhibits good predictive performance, estimated through 10-fold cross-validation (R 2 CV = 0.566 ± 0.0885) and an external test set (R 2 pred = 0.519). Molecular weight and polarity were selected in all models as important parameters that may predict the ability of a molecule to cross the placenta to the fetus.