化学物质永远可以通过与胎盘酶结合而使人类胎儿暴露于异种生物:来自分子对接、DFT和机器学习的先见之明

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2023-05-01 DOI:10.1016/j.comtox.2023.100274
Chidi Edbert Duru
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引用次数: 0

摘要

有毒的全氟烷基和多氟烷基物质(PFAS)也被称为永久化学物质,在脐带血中积累,迫切需要探索PFAS在母胎界面胎盘中的动力学。因此,本研究模拟了10种PFAS对两种酶(谷胱甘肽s -转移酶(GST)和n -乙酰转移酶(NAT2))的可能影响,这两种酶在胎盘中具有活性,可以保护胎儿免受外源物的影响。分子对接是用来确定一些常见的PFAS在两个胎盘酶靶点的结合亲和力。利用密度泛函理论(DFT)和人工神经网络(ANN)分别对PFAS的化学反应性描述符和最重要的负责结合的描述符进行识别。分子对接研究表明,全氟辛烷磺酰胺(PFOSA)和全氟烷酸(PFDA)对两种胎盘酶的结合亲和力始终高于对照物谷胱甘肽和辅酶a。DFT显示,在所分析的10种PFAS中,PFDA的结合亲和力和化学柔软度最低,是该组中活性最强、毒性最强的PFAS。在归一化重要性为80%时,ANN分析预测分子量和总能量是PFAS与GST结合的主要反应性描述符。相反,它们的结合能负责在NAT2上结合。这些模拟结果表明,PFAS,特别是PFDA,具有抑制人类胎盘酶活性的潜力。这可能会对胎盘功能和胎儿发育产生深远的影响,这需要在未来的研究中加以澄清。
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Forever chemicals could expose the human fetus to xenobiotics by binding to placental enzymes: Prescience from molecular docking, DFT, and machine learning

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.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
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
期刊最新文献
Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review From model performance to decision support – The rise of computational toxicology in chemical safety assessments Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment The OECD (Q)SAR Assessment Framework: A tool for increasing regulatory uptake of computational approaches A developmental and reproductive toxicity adverse outcome pathway network to support safety assessments
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