Prediction of the endocrine disruption profile of pesticides

Priority list of potential endocrine-disrupting chemicals in food chemical contaminants: a docking study and in vitro/epidemiological evidence integration

Diet is an important exposure route of endocrine-disrupting chemicals (EDCs), but many unfiltered potential EDCs remain in food. The in silico prediction of EDCs is a popular method for preliminary screening. Potential EDCs in food were screened using Endocrine Disruptome, an open-source platform for inverse docking, to predict the binding probabilities of 587 food chemical contaminants with 18 human nuclear hormone receptor (NHR) conformations. In total, 25 contaminants were bound to multiple NHRs such as oestrogen receptor α/β and androgen receptor. These 25 compounds mainly include pesticides and per- and polyfluoroalkyl substances (PFASs). The prediction results were validated with the in vitro data. The structural features and the crucial amino acid residues of the four NHRs were also validated based on previous literature. The findings indicate that the screening has good prediction efficiency. In addition, the epidemic evidence about endocrine interference of PFASs in food on children was further validated through this screening. This study provides preliminary screening results for EDCs in food and a priority list for in vitro and in vivo research.

Prediction of the endocrine-disrupting ability of 49 per- and polyfluoroalkyl substances: In silico and epidemiological evidence

The toxic effects of per- and polyfluoroalkyl substances (PFASs) on humans are mediated by nuclear hormone receptors (NHRs). However, data on the interaction of PFASs and NHRs is limited. Endocrine Disruptome, an inverse docking tool, was used in this study to simulate the docking of 49 common PFASs with 14 different types of human NHRs. According to the findings, 25 PFASs have a high or moderately high probability of binding to more than five NHRs, with androgen receptor (AR) and mineralocorticoid receptor (MR) being the most likely target NHRs. Molecular docking analyses revealed that the binding modes of PFASs with the two NHRs were similar to those of their corresponding co-crystallized ligands. PFASs, in particular, may disrupt the endocrine system by binding to MR. This finding is consistent with epidemiological research that has linked PFASs to MR-related diseases. Our findings may contribute to a better understanding of the health risks posed by PFASs.

In Silico, In Vitro, and In Vivo Evaluation of the Developmental Toxicity, Estrogenic Activity, and Mutagenicity of Four Natural Phenolic Flavonoids at Low Exposure Levels

Flavonoids are bioactive phenolic compounds widely present in plant food and used in various nutraceutical, pharmaceutical, and cosmetic products. However, recent studies showed rising concerns of endocrine disruptions and developmental toxicities for many flavonoids. To understand the impacts of flavonoid structure on toxicity, we used a new multitiered platform to investigate the toxicities of four common flavonoids, luteolin, apigenin, quercetin, and genistein, from flavone, flavonol, and isoflavone. Weak estrogenic activity was detected for four flavonoids (genistein, apigenin, quercetin, and luteolin) at 10^-12 to 10^-7 M by the MCF-7 cell proliferation assay, which agreed with the molecular docking results. Consistent with the simulation results of Toxicity Estimation Software Tool, genistein and luteolin showed high developmental toxicity in the chicken embryonic assay (45-477 μg/kg) with mortality rate up to 50%. Luteolin, quercetin, and apigenin showed signs of mutagenicity at 5 × 10^-3 pmol/plate. The findings showed nonmonotonic dose responses for the chemicals.

In Silico Analysis to Identify Molecular Targets for Chemicals of Concern: The Case Study of Flocoumafen, an Anticoagulant Pesticide

Rodenticides are pesticides used worldwide, with little information available regarding health consequences in wildlife and humans. The aim of the present study was to use virtual screening to identify potential targets for flocoumafen, a superwarfarin rodenticide. Blind docking of more than 841 human proteins was carried out employing AutoDock Vina. The strength of the ligand interaction with the proteins was quantified based on the binding affinity score (kcal/mol). Results indicate that flocoumafen could be a promiscuous ligand for diversity of cellular protein targets. The best complexes were obtained for prostaglandin F synthase (-14.2 kcal/mol) and serum albumin (-14.0 kcal/mol) followed by glucocorticoid receptor 2, matrix metalloproteinase-9, nuclear receptor ROR-alpha, and activin receptor type-1, all with values equal or better than -13.5 kcal/mol. Docking method validation based on the root-mean-square deviation showed that flocoumafen had good capability to predict corresponding co-crystallized poses; and molecular dynamics simulations suggested the complex with greater binding affinity was thermodynamically stable. Protein-protein interaction networks built with main protein targets revealed that protein kinase B (AKT1), ribosomal protein S6 kinase B1 (RPS6KB1), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), retinoid X receptor alpha (RXRA), and protein phosphatase 2 catalytic subunit alpha (PPP2CA) were major hub proteins, whereas the gene ontology analysis reported that cellular response to endogenous stimulus, protein binding, and the TOR complex were the biological processes, molecular function, and cell component enrichments, respectively. These results should motivate more ecotoxicity testing for flocoumafen and other superwarfarins, as well as precautionary legislation to minimize exposure to these highly toxic chemicals. Environ Toxicol Chem 2021;40:2034-2043. © 2021 SETAC.

Chicken embryonic toxicity and potential in vitro estrogenic and mutagenic activity of carvacrol and thymol in low dose/concentration

Thymol and carvacrol are phenolic isomers with the potential developmental toxicity and endocrine disruptions (ED) at low concentrations. However, few reports estimated their toxicity and ED below 10^-6 M (150 μg/L) (MW of thymol and carvacrol: 150 g/mol). In this study, both chemicals were determined for the developmental toxicity and potential ED at 500 μg/kg and 50 μg/kg using the chicken embryonic assay, potential estrogenic activity (EA) at 10^-12 to 10^-7 M (1.5 × 10^-4 to 15 μg/L) by the MCF-7 cell proliferation assay, mutagenicity at 10^-12 to 10^-6 M (1.5 × 10^-4 to 150 μg/L) by the Ames test, and an in silico method for ED. Carvacrol showed mutagenic risks at 10^-7, 10^-8, and 10^-11 M (15, 1.5, and 0.0015 μg/L) while thymol at 10^-6 and 10^-8 M (150 and 1.5 μg/L). Carvacrol negatively impacted embryonic growth at 50 μg/kg, with weak EA at 10^-8 M (1.5 μg/L). Carvacrol but not thymol had weak EA at 10^-12 M (1.5 × 10^-4 μg/L). Molecular docking to 14 types of hormone-related receptors revealed that carvacrol had higher binding affinities to two estrogen receptors and the mineralocorticoid receptor than those to thymol. Carvacrol and thymol varied in toxicities due to a different location of one phenolic hydroxyl group.

Toxicity profiling and prioritization of plant-derived antimalarial agents

Human malaria is the most widespread mosquito-borne life-threatening disease worldwide. In the absence of effective vaccines, prevention and treatment of malaria only depend on prophylaxis and drug-based therapy either in monotherapy or in combination. Unfortunately, the number of available antimalarial drugs presenting different mechanisms of action is rather limited. In addition, the appearance of drug-resistance in the parasite strains impacts the efficacy of the treatments. As a result, there is a crucial need to find new drugs to circumvent resistance problems. In the quest to identify new antimalarial agents a huge number of plant-derived compounds (PDCs) have been investigated. Surprisingly in the in silico PDC screening programs, toxicity filters are either never used or so simple that their interest is limited. In this context, the goal of this study was to show how to take advantage of validated toxicity QSAR models for refining the selection of PDCs. From an original data set of 507 PDCs collected from the literature, the use of toxicity filters for endocrine disruption, developmental toxicity, and hepatotoxicity in conjunction with classical pharmacokinetic filters allowed us to obtain a list of 31 compounds of potential interest. The pros and cons of such a strategy have been discussed.

Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge

A suite of models is proposed for estimating the risk of pesticides against the grey partridge (Perdix perdix) and their clutches. Radio-tracked data of females, description and location of the clutches, and data on the pesticide treatments during the laying periods of the partridges were used as basic information. Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modelling allowed us to characterize the pesticides by their 1-octanol/water partition coefficient (log P), vapour pressure, primary and ultimate biodegradation potential, acute toxicity (LD₅₀) on P. perdix, and endocrine disruption potential. From these physicochemical and toxicological data, the system of integration of risk with interaction of scores (SIRIS) method was used to design scores of risk for pesticides, alone or in mixture. A program, written in R (version 3.1.1), called Simulation of Toxicity in Perdix perdix (SimToxPP), was designed for estimating the risk of substances, considered alone or in mixture, against the grey partridge during breeding. The software tool is flexible enough to simulate realistic in situ scenarios. Different examples of applications are shown. The advantages and limitations of the approach are briefly discussed.

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.