Forever chemicals could expose the human fetus to xenobiotics by binding to placental enzymes: Prescience from molecular docking, DFT, and machine learning
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引用次数: 0
Abstract
The accumulation of toxic perfluoroalkyl and polyfluoroalkyl substances (PFAS), also known as forever chemicals, in umbilical cord blood calls for an urgent need to explore PFAS kinetics at the maternal-fetal interface placenta. Therefore, this study modeled the possible effects of ten PFAS on two enzymes (glutathione S-transferase (GST) and N-acetyltransferase (NAT2) that are active in the placenta and can protect the fetus from xenobiotics. Molecular docking was used to determine the binding affinities of some common PFAS at two placental enzyme targets. Density functional theory (DFT) analysis and artificial neural networks (ANN) on the PFAS were performed to identify their chemical reactivity descriptors and the most important one responsible for binding, respectively. The molecular docking studies showed that perfluorooctanesulphonamide (PFOSA) and perfluorodecanoic acid (PFDA) consistently had higher binding affinities on the two placental enzymes than the controls, glutathione, and coenzyme A. DFT revealed that out of the ten PFAS analyzed, PFDA had the lowest binding affinity and chemical softness, making it the most reactive and as such toxic PFAS in the group. At normalized importance of >80 %, the ANN analysis predicted that the molecular weight and total energy were the primary reactivity descriptors of the PFAS responsible for their binding on the GST. In contrast, their binding energy was responsible for binding at the NAT2. The results from these simulations indicate that PFAS, especially PFDA, have the potential to inhibit placental enzyme activity in humans. This may have far-reaching consequences for placental functions and fetal development, which needs to be clarified in future studies.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs