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Peroxisome proliferator-activated receptors (PPARs), with the α , β / δ and γ isoforms, are nuclear receptors that control the expression of genes involved in glucose and lipids' metabolism and into inflammatory processes and play a central role in metabolic syndrome. PPARs are a particular class of nuclear receptors because of their larger and more flexible ligand-binding domain with a particular Y shape. As nuclear receptors, PPARs are sensitive to exposure to xenobiotic compounds, called endocrine disruptions, even at low concentrations that could alter their homeostasis. Among these, food contact materials (FCMs), like phthalates, are synthetic compounds able to migrate from packaging to food and represent a significant source of exposure because of the increased use of plastic in the packaging in the last years. Through multiple docking and consensus scoring, we can analyse the ligand-binding domain's chemical and physical features, understand the mechanism of activation and predict the interaction with possible endocrine disruptions with an evaluation of their effects.

According to Eurostat, the EU production of chemicals hazardous to health reached 211 million tonnes in 2019. Thus, the possibility that some of these chemical compounds interact negatively with the human endocrine system has received, especially in the last decade, considerable attention from the scientific community. It is obvious that given the large number of chemical compounds it is impossible to use in vitro/in vivo tests for identifying all the possible toxic interactions of these chemicals and their metabolites. In addition, the poor availability of highly curated databases from which to retrieve and download the chemical, structure, and regulative information about all food contact chemicals has delayed the application of in silico methods. To overcome these problems, in this study we use robust computational approaches, based on a combination of highly curated databases and molecular docking, in order to screen all food contact chemicals against the nuclear receptor family in a cost and time-effective manner., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Mycotoxins are secondary metabolites produced by pathogenic fungi. They are found in a variety of different products, such as spices, cocoa, and cereals, and they can contaminate fields before and/or after harvest and during storage. Mycotoxins negatively impact human and animal health, causing a variety of adverse effects, ranging from acute poisoning to long-term effects. Given a large number of mycotoxins (currently more than 300 are known), it is impossible to use in vitro/in vivo methods to detect the potentially harmful effects to human health of all of these. To overcome this problem, this work aims to present a new robust computational approach, based on a combination of in silico and statistical methods, in order to screen a large number of molecules against the nuclear receptor family in a cost and time-effective manner and to discover the potential endocrine disruptor activity of mycotoxins. The results show that a high number of mycotoxins is predicted as a potential binder of nuclear receptors. In particular, ochratoxin A, zearalenone, α- and β-zearalenol, aflatoxin B1, and alternariol have been shown to be putative endocrine disruptors chemicals for nuclear receptors., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Within the framework of reduction, refinement and replacement of animal experiments, new approaches for identification and characterization of chemical hazards have been developed. Grouping and read across has been promoted as a most promising alternative approach. It uses existing toxicological information on a group of chemicals to make predictions on the toxicity of uncharacterized ones. In the present work, the feasibility of applying in vitro and in silico techniques to group chemicals for read across was studied using the food mycotoxin zearalenone (ZEN) and metabolites as a case study. ZEN and its reduced metabolites are known to act through activation of the estrogen receptor α (ERα). The ranking of their estrogenic potencies appeared highly conserved across test systems including binding, in vitro and in vivo assays. This data suggests that activation of ERα may play a role in the molecular initiating event (MIE) and be predictive of adverse effects and provides the rationale to model receptor-binding for hazard identification. The investigation of receptor-ligand interactions through docking simulation proved to accurately rank estrogenic potencies of ZEN and reduced metabolites, showing the suitability of the model to address estrogenic potency for this group of compounds. Therefore, the model was further applied to biologically uncharacterized, commercially unavailable, oxidized ZEN metabolites (6α-, 6β-, 8α-, 8β-, 13- and 15-OH-ZEN). Except for 15-OH-ZEN, the data indicate that in general, the oxidized metabolites would be considered a lower estrogenic concern than ZEN and reduced metabolites.

Foodstuff could be a vector for naturally occurring and/or unwanted dangerous substances that can act either as they are or after their bioactivation. The scientific community agrees that the metabolic activity of chemicals should be taken into account for proper risk assessment. Unfortunately, the in vitro evaluation of a metabolic panel and analytical/biochemical detection in food-safety assessment are very expensive and challenging because of the abundance of data to analyze. In this context, properly validated computational protocols could be a useful tool for making metabolic and binding/activity predictions. This strategy has been applied to thioxanthone photoinitiators (TX), identified as food contaminants, especially in infant formulas, as reported by the European Food Safety Authority in 2005. Their lipophilicity suggests rapid hepatic metabolism, but the currently available data only concern 2-ITX. We have predicted phase I metabolites for the TX class of compounds and defined their binding affinity for the AR ligand-binding pocket using a local model based on available information about metabolism and AR activity. Some metabolites should undergo further in vitro or/and in vivo toxicological evaluations because they have proved to be suitable as ligands for AR.

Background. Poisoning from pesticides can be extremely hazardous for non-invasive species, such as bees, and humans causing nearly 300,000 deaths worldwide every year. Several pesticides are recognized as endocrine disruptors compounds that alter the production of the normal hormones mainly by acting through their interaction with nuclear receptors (NRs). Among the insecticides, one of the most used is pyriproxyfen. As analogous to the juvenile hormone, the pyriproxyfen acts in the bee's larval growth and creates malformations at the adult organism level. Methods. This work aims to investigate the possible negative effects of pyriproxyfen and its metabolite, the 4′-OH-pyriproxyfen, on human and bee health. We particularly investigated the mechanism of binding of pyriproxyfen and its metabolite with ultraspiracle protein/ecdysone receptor (USP-EcR) dimer of A. mellifera and the relative heterodimer farnesoid X receptor/retinoid X receptor alpha (FXR-RXRα) of H. sapiens using molecular dynamic simulations. Results. The results revealed that pyriproxyfen and its metabolite, the 4′-OH- pyriproxyfen, stabilize each dimer and resulted in stronger binders than the natural ligands. Conclusion. We demonstrated the endocrine interference of two pesticides and explained their possible mechanism of action. Furthermore, in vitro studies should be carried out to evaluate the biological effects of pyriproxyfen and its metabolite. [ABSTRACT FROM AUTHOR]