Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships

Interaction of Bisphenol A and Its Analogs with Estrogen and Androgen Receptor from Atlantic Cod (Gadus morhua)

The widespread use of bisphenol A (BPA) in polycarbonate plastics and epoxy resins has made it a prevalent environmental pollutant in aquatic ecosystems. BPA poses a significant threat to marine and freshwater wildlife due to its documented endocrine-disrupting effects on various species. Manufacturers are increasingly turning to other bisphenol compounds as supposedly safer alternatives. In this study, we employed in vitro reporter gene assays and ex vivo precision-cut liver slices from Atlantic cod (Gadus morhua) to investigate whether BPA and 11 BPA analogs exhibit estrogenic, antiestrogenic, androgenic, or antiandrogenic effects by influencing estrogen or androgen receptor signaling pathways. Most bisphenols, including BPA, displayed estrogenic properties by activating the Atlantic cod estrogen receptor alpha (gmEra). BPB, BPE, and BPF exhibited efficacy similar to or higher than that of BPA, with BPB and BPAF being more potent agonists. Additionally, some bisphenols, like BPG, induced estrogenic effects in ex vivo liver slices despite not activating gmEra in vitro, suggesting structural modifications by hepatic biotransformation enzymes. While only BPC2 and BPAF activated the Atlantic cod androgen receptor alpha (gmAra), several bisphenols exhibited antiandrogenic effects by inhibiting gmAra activity. This study underscores the endocrine-disrupting impact of bisphenols on aquatic organisms, emphasizing that substitutes for BPA may pose equal or greater risks to both the environment and human health.

Haloketones: A class of unregulated priority DBPs with high contribution to drinking water cytotoxicity

Although unregulated aliphatic disinfection byproducts (DBPs) had a much higher concentration and cytotoxicity than known aromatic DBPs, a recent study indicated that seven classes of regulated and unregulated priority DBPs (one and two-carbon-atom DBPs) just accounted for 16.2% of disinfected water cytotoxicity in the U.S., meaning some of the highly toxic aliphatic DBPs may be overlooked. Haloketones (HKs) are an essential class of priority DBPs with a 1-100 µg/L concentration in drinking water but lack cytotoxicity data. This study investigated the cytotoxicity of seven HKs using Chinese hamster ovary (CHO) cells. The order for cytotoxicity of HKs from most to least toxic was: 1,3-dichloroacetone (LC50: 1.0 ± 0.20 μM) ≈ 1,3-dibromoacetone (1.5 ± 0.19 μM) ≈ bromoacetone (1.9 ± 0.49 μM) > chloroacetone (4.3 ± 0.22 μM) > 1,1,3-trichloropropanone (6.6 ± 0.46 μM) > 1,1,1-trichloroacetone (222 ± 7.7 μM) > hexachloroacetone (3269 ± 344 μM). The cytotoxicity of HKs was higher than most regulated and priority aliphatic DBPs in mono-halogenated, di-halogenated, and tri-halogenated categories. A prediction model of HK cytotoxicity was developed based on the quantitative structure-activity relationship (QSAR), optimizing structures and computing descriptors with Gaussian 09 W. The average concentrations of HKs in representative drinking water samples from South Carolina (U.S.) and Suzhou (China) were 12.4 and 0.9 μg/L, respectively, accounting for 18.8% and 1.7% of their specific total DBPs measured (i.e. not TOX). For South Carolina drinking water, their contributions to total calculated additive cytotoxicity of aliphatic DBPs and overall drinking water cytotoxicity were 86.7% and 14.0%, respectively, demonstrating that HKs are an essential class of overlooked DBPs with a high contribution to drinking water cytotoxicity. Our study can help to explain the conflict that why regulated and priority DBPs (except HKs) just accounted for 16% of chlorinated drinking water cytotoxicity even enough they had much higher concentration and cytotoxicity than known aromatic DBPs.

A combined in vitro-in silico method for assessing the androgenic activities of bisphenol A and its analogues

Interactions between endocrine-disruptor chemicals (EDCs) and androgen receptor (AR) have adverse effects on the endocrine system, leading to human reproductive dysfunction. Bisphenol A (BPA) is an EDC that can damage both the environment and human health. Although numerous BPA analogues have been produced as substitutes for BPA, few studies have evaluated their endocrine-disrupting abilities. We assessed the (anti)-androgenic activities of BPA and its analogues using a yeast-based reporter assay. The BPA analogues tested were bisphenol S (BPS), 4-phenylphenol (4PP), 4,4'-(9-fluorenyliden)-diphenol (BPFL), tetramethyl bisphenol F (TMBPF), and tetramethyl bisphenol A (TMBPA). We also conducted molecular docking and dynamics simulations to assess the interactions of BPA and its analogues with the ligand-binding domain of human AR (AR-LBD). Neither BPA nor its analogues had androgenic activity; however, all except BPFL exerted robust anti-androgenic effects. Consistent with the in vitro results, anti-androgenic analogues of BPA formed hydrogen bonding patterns with key residues that differed from the patterns of endogenous hormones, indicating that the analogues display in inappropriate orientations when interacting with the binding pocket of AR-LBD. Our findings indicate that BPA and its analogues disrupt androgen signaling by interacting with the AR-LBD. Overall, BPA and its analogues display endocrine-disrupting activity, which is mediated by AR.

Control list of high-priority chemicals based on 5-HT-RI functionality and the human health interference effects selective CNN-GRU deep learning model

The antidepressant drug known as 5-HT reuptake inhibitor (5-HT-RI) was commonly detected in biological tissues and result in significant adverse health effects. Homology modeling was used to characterize the functionalities (efficacy and resistance), and the adverse outcome pathway was used to characterize its human health interferences (olfactory toxicity, neurotoxicity, and gut microbial interference). The convolutional neural network coupled with the gated recurrent unit (CNN-GRU) deep learning method was used to construct a comprehensive model of 5-HT-RI functionality and human health interference effects selectivity with small sample data. The architecture with 2 SE, 320 neuronal nodes and 6-folds cross-validation showed the best applicability. The results showed that the confidence interval of the constructed model reached 90 % indicating that the model had reliable prediction ability and generalization ability. Based on the CNN-GRU deep learning model, seven high-priority chemicals with a weak comprehensive effect, including D-VEN, (1R,4S)-SER, S-FLX, CTP, S-CTP, NEF, and VEN, were screened. Based on the molecular three-dimensional structure information, a comprehensive-effect three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed to confirm the reliability of the constructed control list of 5-HT-RI high-priority chemicals. Analysis with the ranking of calculated values based on the molecular dynamics method and predicted values based on the CNN-GRU deep learning model, we found that the consistency of the three methods was above 85 %. Additionally, by analyzing the sensitivity, molecular electrostatic potential, polar surface area of the comprehensive-effect CNN-GRU deep learning model, and the electrostatic field of the 3D-QSAR models, we found that the significant effects of five key characteristics (DM, Qyy, Qxz, I, and BP), molecular electronegativity, and polarity significantly affected the high-priority degree of 5-HT-RI. In this study, we provided reasonable and reliable prediction tools and discussed theoretical methods for the risk assessment of functionality and human health interference of emerging pollutants such as 5-HT-RI.

Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation

Over the past few decades, extensive research has indicated that exposure to bisphenol A (BPA) increases the health risks in humans. Toxicological studies have demonstrated that BPA can bind to the androgen receptor (AR), resulting in endocrine-disrupting effects. In recent investigations, many alternatives to BPA have been detected in various environmental media as major pollutants. However, related experimental evaluations of BPA alternatives have not been systematically implemented for the assessment of chemical safety and the effects of structural characteristics on the antagonistic activity of the AR. To promote the green development of BPA alternatives, high-throughput toxicological screening is fundamental for prioritizing chemical tests. Therefore, we proposed a hybrid deep learning architecture that combines molecular descriptors and molecular graphs to predict AR antagonistic activity. Compared to previous models, this hybrid architecture can extract substantial chemical information from various molecular representations to improve the model's generalization ability for BPA alternatives. Our predictions suggest that lignin-derivable bisguaiacols, as alternatives to BPA, are likely to be nonantagonist for AR compared to bisphenol analogues. Additionally, molecular dynamics (MD) simulations identified the dihydrotestosterone-bound pocket, rather than the surface, as the major binding site of bisphenol analogues. The conformational changes of key helix H12 from an agonistic to an antagonistic conformation can be evaluated qualitatively by accelerated MD simulations to explain the underlying mechanism. Overall, our computational study is helpful for toxicological screening of BPA alternatives and the design of environmentally friendly BPA alternatives.

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

Zebrafish as model organisms for toxicological evaluations in the field of food science

Food safety has long been an area of concern. The selection of stable and efficient model organisms is particularly important for food toxicology studies. Zebrafish (Danio rerio) are small model vertebrates, and 70% of human genes have at least one zebrafish ortholog. Zebrafish have advantages as model organisms due to their short life cycle, strong reproductive ability, easy rearing, and low cost. Zebrafish embryos have the advantage of being sensitive to the breeding environment and thus have been used as biosensors. Zebrafish and their embryos have been widely used for food toxicology assessments. This review provides a systematic and comprehensive summary of food toxicology studies using zebrafish as model organisms. First, we briefly introduce the multidimensional mechanisms and structure-activity relationship studies of food toxicological assessment. Second, we categorize these studies according to eight types of hazards in foods, including mycotoxins, pesticides, antibiotics, heavy metals, endocrine disruptors, food additives, nanoparticles, and other food-related ingredients. Finally, we list the applications of zebrafish in food toxicology studies in line with future research prospects, aiming to provide a valuable reference for researchers in the field of food science.

Theoretical evaluation of the malathion and its chemical derivatives interaction with cytosolic phospholipase A2 from zebrafish

Cytosolic phospholipase A2 (cPLA2) belongs to a large family of proteins and plays a crucial role in the regulation of arachidonic acid metabolism and inflammation cascade in zebrafish (Danio rerio). This enzyme with a molecular weight of 85 kDa, has two distinct domains. One is the regulatory and calcium-dependent (Ca²⁺) domain called C2, the other is the catalytic α/β hydrolase Ca²⁺-independent domain, where serine and aspartic acid catalytic dyad residues are present. We investigated the interaction of malathion and their organophosphate metabolites in the cPLA2 using in silico tools. Molecular docking results showed hydrophobic interactions with the paraoxon and catalytic site residue (Ser 223). Malathion increases intracellular Ca²⁺ due to endoplasmic reticulum influx which in turn activities phospholipase A2 and arachidonic acid release. Molecular docking and homology modelling of proteins and ligands could be a complementary tool for ecotoxicology and environment pollution assessment.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Critical features identification for chemical chronic toxicity based on mechanistic forecast models

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Critical target identification and human health risk ranking of metal ions based on mechanism-driven modeling

Huge amounts of metals have been released into environment due to various anthropogenic activities, such as smelting and processing of metals and subsequent application in construction, automobiles, batteries, optoelectronic devices, and so on, resulting in widespread detection in environmental media. However, some metal ions are considered as "Environmental health hazards", leading to serious human health concerns through affecting critical targets. Hence, it is necessary to quickly and effectively recognize the key target of metal ions in living organisms. Fortunately, the development of high-throughput analysis and in silico approaches offer a promising tool for target identification. In this study, the key oncogenic target (tumor suppressor protein, p53) was screened by network analysis based on the comparative toxicogenomics database (CTD). Some metal ions could bind to p53 core domain, impair its function and induce the development of cancer risk, but its mechanisms were still unclear. Therefore, a quantitative structure-activity relationship (QSAR) model was constructed to characterize the binding constants (K_a) between DNA binding domain of p53 (p53 DBD) and nine metal ions (Mg²⁺, Ca²⁺, Cu²⁺, Zn²⁺, Co²⁺, Ni²⁺, Mn²⁺, Fe³⁺ and Ba²⁺). It had good robustness and predictive ability, which could be used to predict the K_a values of other six metal ions (Li⁺, Ag⁺, Cs⁺, Cd²⁺, Hg²⁺ and Pb²⁺) within application domain. The results showed strong binding affinity between Cd²⁺/Hg²⁺/Pb²⁺ and p53 DBD. Subsequent mechanism analyses revealed that first hydrolysis constant (|logK_OH|) and polarization force (Z²/r) were key metal ion-characteristic parameters. The metal ions with weak hydrolysis constants and strong polarization forces could readily interact with N-containing histidine and S-containing cysteine of p53 DBD, which resulted in high K_a values. This study identified p53 as potential target for metal ions, revealed the key characteristics affecting the actions and provide a basic understanding of metal ions-p53 DBD interaction.

Insight into the defluorination ability of per- and polyfluoroalkyl substances based on machine learning and quantum chemical computations

UV-generated hydrated electrons play a critical role in the defluorination reaction of poly- and perfluoroalkyl substances (PFAS). However, limited experimental data hinder insight into the effects of the structural characteristics of emerging PFAS on their defluorination abilities. Therefore, in this study, we adopted quantity structure-activity relationship models based on machine learning algorithms to develop the predictive models of the relative defluorination ability of PFAS. Five-fold cross-validations were used to perform the hyperparameter tuning of the models, which suggested that the gradient boosting algorithms with PaDEL descriptors as the best model possessed superior predictive performance (R²_test = 0.944 and RMSE_test = 0.114). The importance of the descriptor indicated that the electrostatic properties and topological structure of the compounds significantly affected the defluorination ability of the PFAS. For the emerging PFAS the best model showed that most compounds, such as potential alternatives of perfluorooctane sulfonic acid, were recalcitrant to reductive defluorination, whereas perfluoroalkyl ether carboxylic acids had relatively stronger defluorination abilities than perfluorooctanoic acid. The theoretical calculations implied that additional electrons on PFAS could cause molecular deconstruction, such as changes in the dihedral angle involved in the carbon chain, as well as C-F bond and ether C-O bond cleavages. In general, the current computational models could be useful for screening emerging PFAS to assess their defluorination ability for the molecular design of fluorochemical structures.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Adverse effects of bisphenol B exposure on the thyroid and nervous system in early life stages of zebrafish

Bisphenol B (BPB), a widely used alternative of bisphenol A (BPA), has been detected in various environmental media and foodstuffs. However, the knowledge of the health risks about BPB is still limited. In this study, the effects of BPB on thyroid hormone homeostasis and neuronal development were evaluated by exposure of embryos 2 h post-fertilization (hpf) to BPB (0, 1, 10, 100 and 1000 μg/L) until 144 hpf. The results showed that 100 and 1000 μg/L BPB exposed larvae exhibited abnormal morphologies in phenotype and brain histological patterns. Significant decline of thyroid hormone thyroxine (T4) content and elevation of 3,5,3'-triiodothyronine (T3) content, along with the up-regulated expression of tg, trhr1, dio1, dio2, thrα, thrβ genes and down-regulated expression of tsh, ttr and trh genes in BPB exposed zebrafish larvae were observed. Moreover, locomotor activity of larvae was decreased, and the transcription of genes (e.g., elavl3, gap43, zn5, α-tubulin, syn2a and mbp) related to neuronal development were inhibited after exposure to BPB. The mechanism of neurotoxicity and thyroid disruption in zebrafish larvae induced by BPB were discussed.

QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations Studies of Lysine-Specific Demethylase 1 (LSD1) Inhibitors as Anticancer Agents

BACKGROUND

Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.

OBJECTIVE

In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors.

METHODS

The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q²), noncross- validated correlation coefficient (r²_ncv) and predicted correlation coefficient (r²_pred) of the training and test set molecules, respectively.

RESULTS

The CoMFA (q², 0.670; r²_ncv, 0.930; r²_pred, 0.968), CoMFA-RF (q², 0.694; r²_ncr, 0.926; r²_pred, 0.927), CoMSIA (q², 0.834; r²_ncv, 0.956; r²_pred, 0.958) and HQSAR models (q², 0.854; r²_ncv, 0.900; r²_pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings.

CONCLUSION

These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.

Bisphenols emerging in Norwegian and Czech aquatic environments show transthyretin binding potency and other less-studied endocrine-disrupting activities

Bisphenols are increasingly recognized as environmental pollutants with endocrine-disrupting potential. Nonetheless, the study of environmental occurrence and some endocrine-disrupting activities of some bisphenols came widely into focus of research only recently. The aims of the present study were to: 1) determine the predominant bisphenols in Norwegian sewage sludge and sediment and in Czech surface waters, and 2) characterize the binding of bisphenols to a transport protein transthyretin (TTR) and their (anti-)thyroid, (anti-)progestagenic, and (anti-)androgenic activities. High-performance liquid chromatography with atmospheric pressure chemical ionization or photoionization coupled with high resolution mass spectrometry (HPLC-APCI/APPI-HRMS) and Chemically Activated LUciferase gene eXpression (CALUX) in vitro reporter gene bioassays were used to detect the target compounds and to determine endocrine-disrupting activities, respectively. Bisphenol A (BPA), 4,4'-bisphenol F (BPF), bisphenol S (BPS), and bisphenol E (BPE) were the most frequently found compounds in municipal sewage sludge. Furthermore, bisphenol TMC (BPTMC) and bisphenol AF (BPAF) frequently occurred in sediment and surface waters, respectively. BPA was the major contributor to Ʃ of bisphenols in Norwegian sewage sludge with exception of one sample where BPF predominated. We also monitored a few bisphenols in sediment but only BPTMC was found. BPA, BPAF and BPF were the dominant bisphenols in Czech surface waters. Some bisphenols have shown TTR binding potency (BPAF = BPF > BPA = BPE) and some have displayed the following endocrine-disrupting activities: anti-thyroid (BPAF), anti-progestagenic (BPTMC > BPA = BPAF), and anti-androgenic (BPAF > BPE > BPA > BPTMC > BPF > BPS). It is noteworthy that BPAF exhibited stronger or similarly potent endocrine-disrupting activities compared to BPA. Our results provide new insights into these less-studied endocrine-disrupting activities of environmentally relevant bisphenols and may be useful in prioritizing those compounds that deserve further attention in environmental monitoring and eco-toxicological research.

Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms

Biodegradation is a significant process for removing organic chemicals from water, soil and sediment environments, and therefore biodegradability is critical to evaluate the environmental persistence of organic chemicals. In this study, based on a dataset with 171 compounds, four quantitative structure-activity relationship (QSAR) models were developed for predicting primary and ultimate biodegradation rate rating with multiple linear regression (MLR) and support vector machine (SVM) algorithms. Two MLR models were built with a dataset with carbon atom number ≤9, and two SVM models were built with a dataset with carbon atom number >9. In the MLR models, n_ArX (number of X on aromatic ring) is the most important descriptor governing primary and ultimate biodegradation of organic chemicals. For the SVM models, determination coefficient (R²) values, cross-validated coefficients (Q²_LOO) and external validation coefficient (Q²_ext) values are over 0.9, indicating the SVM models have satisfactory goodness-of-fit, robustness and external predictive abilities. The applicability domains of these models were visualized by the Williams plot. The developed models can be used as effective tools to predict biodegradability of organic chemicals.

Comparative Quantitative Toxicology and QSAR Modeling of the Haloacetonitriles: Forcing Agents of Water Disinfection Byproduct Toxicity

The haloacetonitriles (HANs) is an emerging class of nitrogenous-disinfection byproducts (N-DBPs) present in disinfected drinking, recycled, processed wastewaters, and reuse waters. HANs were identified as primary forcing agents that accounted for DBP-associated toxicity. We evaluated the toxic characteristics of iodoacetonitrile (IAN), bromoacetonitrile (BAN), dibromoacetonitrile (DBAN), bromochloroacetonitrile (BCAN), tribromoacetonitrile (TBAN), chloroacetonitrile (CAN), dichloroacetonitrile (DCAN), trichloroacetonitrile (TCAN), bromodichloroacetonitrile (BDCAN), and chlorodibromoacetonitrile (CDBAN). This research generated the first quantitative, comparative analyses on the mammalian cell cytotoxicity, genotoxicity and thiol reactivity of these HANs. The descending rank order for HAN cytotoxicity was TBAN ≈ DBAN > BAN ≈ IAN > BCAN ≈ CDBAN > BDCAN > DCAN ≈ CAN ≈ TCAN. The rank order for genotoxicity was IAN ≈ TBAN ≈ DBAN > BAN > CDBAN ≈ BDCAN ≈ BCAN ≈ CAN ≈ TCAN ≈ DCAN. The rank order for thiol reactivity was TBAN > BDCAN ≈ CDBAN > DBAN > BCAN > BAN ≈ IAN > TCAN. These toxicity metrics were associated with membrane permeability and chemical reactivity. Based on their physiochemical parameters and toxicity metrics, we developed optimized, robust quantitative structure activity relationship (QSAR) models for cytotoxicity and for genotoxicity. These models can predict cytotoxicity and genotoxicity of novel HANs prior to analytical biological evaluation.

Assessment of endocrine-disrupting effects of emerging polyhalogenated carbazoles (PHCZs): In vitro, in silico, and in vivo evidence

Polyhalogenated carbazoles (PHCZs) are an emerging class of persistent, bioaccumulative compounds that are structurally and chemically related to dioxins. They have been detected widely in sediment, river, and soil samples, but their environmental risks are largely unknown. Therefore, seven common PHCZs were tested for their endocrine disrupting potential in silico, in vitro, and in vivo. A dual-luciferase reporter gene assay was used to detect receptor-mediated (agonist or antagonistic) activity (concentration range: 10^-9-10^-5 M) against the estrogen receptor α (ERα), glucocorticoid receptor α (GRα), and mineralocorticoid receptor (MR). The alterations in the steroidogenesis pathway were investigated in H295R cells. Antagonistic effects against GRα were observed with five PHCZs, along with an increase in the cortisol levels of H295R cells. The most common effect observed was that of the agonistic activity of ERα, with the molecular docking analysis further indicating that hydrogen bonding and hydrophobic interactions may stabilize the interaction between PHCZs and the estrogen receptor binding pocket. In addition, a seven-day exposure of young female rats to three PHCZs (27-BCZ, 3-BCZ, and 36-BCZ) resulted in changes in serum E2 levels, uterine epithelium cell heights, and relative uterus weights. In conclusion, endocrine-disrupting effects, especially the estrogenic effects, were observed for the tested PHCZs. Such adverse effects of PHCZs on humans and wildlife warrant further thorough investigation.

QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds

The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (K_PA-w). The major variables influencing K_PA-w in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R²) and cross-validation coefficient (Q²) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log K_PA-w values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.

Bisphenols as a Legacy Pollutant, and Their Effects on Organ Vulnerability

Bisphenols are widely used in the synthesis of polycarbonate plastics, epoxy resins, and thermal paper, which are used in manufacturing items of daily use. Packaged foods and drinks are the main sources of exposure to bisphenols. These chemicals affect humans and animals by disrupting the estrogen, androgen, progesterone, thyroid, and aryl hydrocarbon receptor functions. Bisphenols exert numerous harmful effects because of their interaction with receptors, reactive oxygen species (ROS) formation, lipid peroxidation, mitochondrial dysfunction, and cell signal alterations. Both cohort and case-control studies have determined an association between bisphenol exposure and increased risk of cardiovascular diseases, neurological disorders, reproductive abnormalities, obesity, and diabetes. Prenatal exposure to bisphenols results in developmental disorders in animals. These chemicals also affect the immune cells and play a significant role in initiating the inflammatory response. Exposure to bisphenols exhibit age, gender, and dose-dependent effects. Even at low concentrations, bisphenols exert toxicity, and hence deserve a critical assessment of their uses. Since bisphenols have a global influence on human health, the need to discover the underlying pathways involved in all disease conditions is essential. Furthermore, it is important to promote the use of alternatives for bisphenols, thereby restricting their uses.

Chronic thiamethoxam exposure impairs the HPG and HPT axes in adult Chinese rare minnow (Gobiocypris rarus): Docking study, hormone levels, histology, and transcriptional responses

Thiamethoxam has emerged as an environmental contaminant detected in aqueous environments, and its endocrine-disrupting effect at chronic exposure in teleosts remains unknown. In the present study, a docking experiment and an in vivo test were integrated to systematically explore the toxic mechanisms of thiamethoxam in fish. Histological analysis, plasma VTG and hormone level (E2, 11-KT, T3 and T4) determinations, and HPG and HPT gene expression quantification were performed after Chinese rare minnow (Gobiocypris rarus) was exposed to thiamethoxam (0, 0.5, 5, and 50 μg/L) for 90 days. According to the docking study, thiamethoxam had different interactions with ERα, AR and TRα via hydrogen bonding. A decrease in body length and plasma T4 was observed in both genders. The histological damage in liver and delayed gonadal development were observed in both genders at 50 μg/L thiamethoxam treatment. In males, the following HPG axis genes were upregulated: gnrh and cyp19b in the brain; vtg and cyp19a in the liver; and cyp17 and cyp19a in the gonad. In females, erɑ in the liver was significantly upregulated with 0.5 μg/L thiamethoxam treatment, and cyp17 in the gonad was upregulated with all treatment. The suppression of cyp19a, gnrh, cyp11a, and ttr was observed at the concentration of 5 μg/L in the female liver. Taken together, the endocrine system of Chinese rare minnow might be disrupted after chronic exposure to thiamethoxam.

Stronger estrogenic and antiandrogenic effects on zebrafish larvae displayed by 6:2 polyfluoroalkyl phosphate diester than the 8:2 congener at environmentally relevant concentrations

Polyfluoroalkyl phosphate esters (PAPs) are one kind of emerging polyfluoroalkyl substances in the environment. However, their in vivo toxicities are largely unknown, especially at environmental relevant concentrations. To fill this gap, zebrafish embryos were exposed to 6:2 or 8:2 diPAP at environmentally relevant concentrations (0.5, 5, 50 ng/L) for 7 d. 6:2 and 8:2 diPAPs upregulated the mRNA and protein levels of aromatase in the exposed larvae, and elevated estradiol (E2) and vitellogenin (VTG) levels, but reduced testosterone (T) and 11-ketotestosterone (11-KT) levels, demonstrating estrogenic and antiandrogenic effects. Among the three ER subtypes, ERβ2 displayed the highest in vivo mRNA expression and the lowest in silico binding energies, suggesting that it was the main target ER subtype responsible for the estrogenic effect. Molecular simulation results indicated that diPAPs and E2 could bind to one common residue, arginine (Arg) 87, in the binding pocket of ERβ2, inducing similar estrogenic disruption mechanisms as E2. Both compounds could form hydrophobic interaction with glutamic acid (Glu) 12 and tryptophan (Trp) 80 and two hydrogen bonds with Arg81 of androgen receptor (AR) ligand-binding domains (LBDs) in antagonistic mode, resulting in a reduced level of AR upon exposure. The in silico binding energies of 6:2 diPAP with both ER and AR were lower than 8:2 diPAP, explaining the observed greater in vivo estrogenic and antiandrogenic activities of 6:2 diPAP. This study provided the first line of evidences that diPAPs could display adverse effects on the endocrine functions of fish species.

Spectroscopic, chemical reactivity, molecular docking investigation and QSAR analyses of (2E)‑1‑(3‑bromo‑2‑thienyl)‑3‑(2,5‑dimethoxyphenyl)prop‑2‑en‑1‑one

Chalcone derivative of (2E)‑1‑(3‑bromo‑2‑thienyl)‑3‑(2,5‑dimethoxyphenyl) prop‑2‑en‑1‑one (BTD) molecule has been deliberated for spectroscopic properties experimentally and theoretically. The title compound was characterized by FT-IR, FT-Raman and UV-Vis analyses. The structural activity and vibrational wavenumbers were calculated by a DFT method. The Natural Bond Orbital (NBO) analysis which reveals the hyper conjugative interactions of the present molecule has been performed. Meanwhile, the Chemical reactivity of Condensed Fukui function, MEP and HOMO-LUMO energies of the molecule were also analyzed. Furthermore, Multiwfn 3.3.9 program has been utilized to study MEP and the electron excitation analysis. Docking studies which play a significant role in determining the endothelial nitric oxide synthase inhibition activity of the present compound have also been carried out to predict the binding energy and inhibition constant of the title compound. In addition, drug resemblance parameters have also considered by QSAR study in which the comparison of chemical parameters of chalcone drugs of title molecule has been done.

In Silico Predictions of Endocrine Disruptors Properties

Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

The critical factors affecting typical organophosphate flame retardants to mimetic biomembrane: An integrated in vitro and in silico study

Organophosphate flame retardants (OPFRs) have been reported to induce cytotoxicity in a structure-dependent manner. The toxic effects may be due to the damage of biomembrane integrity and/or the interference of membrane signal pathway. In this study, the damages of fifteen typical OPFRs (chlorinated phosphates, alkyl phosphates, aryl phosphates, and alkoxy phosphates) to mimetic biomembrane were determined by the electrochemical impedance spectroscopy (EIS). The molecular structure descriptors that characterized the action mechanisms were screened by stepwise regression. The six molecular descriptors (MATS7e, DLS_05, Mor19m, Mor22v, Mor12v and MATS8m) were screened to study the actions between OPFRs and mimetic biomembrane. A quantitative structure-activity relationship (QSAR) model was developed by the partial least squares (PLS) method. Statistical results indicated that the QSAR model had good robustness and mechanism interpretability. The distribution of atomic electronegativities (MATS7e) and atomic masses in three dimensional spaces (Mor19m) were the key factors influencing the actions between OPFRs and simulated biofilms. The compounds with strong electron-withdrawing property could invade the inner layer of membrane and destroy its integrity. High levels of steric hindrance could impair the damage capacity caused by electronegativity. Moreover, drug-like index (DLS_05), spatial structures of particle (Mor22v, Mor12v) and atomic masses (MATS8m) also affected the actions. The results revealed the mechanism of the actions of OPFRs with simulated biofilms and elucidated the key structural characteristics affecting the actions of OPFRs, which could provide theoretical basis for ecological risk assessment of OPFRs.

Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity Human Transthyretin Disruptors: An in Vitro and in Silico Study

Phenolic disinfection byproducts (phenolic-DBPs) have been identified in recent years. However, the toxicity data for phenolic-DBPs are scarce, hampering their risk assessment and the development of regulations on the acceptable concentration of phenolic-DBPs in water. In this study, the binding potency and underlying interaction mechanism between human transthyretin (hTTR) and five groups of representative phenolic-DBPs (2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes, 3,5-dihalo-4-hydroxybenzoic acids, halo-salicylic acids) were determined and probed by competitive fluorescence displacement assay integrated with in silico methods. Experimental results implied that 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, and 3,5-dihalo-4-hydroxybenzaldehydes have a high binding affinity with hTTR. The hTTR binding potency of the chemicals with electron-withdrawing groups on their molecular structures were higher than that with electron-donor groups. Molecular modeling methods were used to decipher the binding mechanism between model compounds and hTTR. The results documented that ionic pair, hydrogen bonding and hydrophobic interactions were dominant interactions. Finally, a mechanism-based model for predicting the hTTR binding affinity was developed. The determination coefficient ( R²), leave-one-out cross validation Q² ( Q_LOO²), bootstrapping coefficient ( Q_BOOT²), external validation coefficient ( Q_EXT²) and concordance correlation coefficient ( CCC) of the developed model met the acceptable criteria ( Q² > 0.600, R² > 0.700, CCC > 0.850), implying that the model had good goodness-of-fit, robustness, and external prediction performances. All the results indicated that the phenolic-DBPs have the hTTR disrupting effects, and further studies are needed to investigate their other mechanism of endocrine disruption.

The occurrence, potential toxicity, and toxicity mechanism of bisphenol S, a substitute of bisphenol A: A critical review of recent progress

Bisphenol S (BPS) has been introduced into the industry as a safer alternative to bisphenol A (BPA). The distribution of BPS has recently become an important issue worldwide, but investigations on the toxicity and mechanisms of BPS remain limited. A review of the literature reveals that BPS has widespread presence in environmental media, such as indoor dust, surface water, sediments, and sewage sludge. It has been detected in plants, paper products, some food items, and even in the human body. In addition, compared to BPA, BPS has a lower acute toxicity, similar or less endocrine disruption, similar neurotoxicity and immunotoxicity, and lower reproductive and developmental toxicity. The mechanisms underlying BPS toxicity may be related to the chemical properties of BPS in the human body, including interactions with estrogen receptors, and binding to DNA and some proteins, subsequently including exerting oxidative stress. However, further investigation on the potential risks of BPS to humans and its mechanisms of toxicity should be conducted to better understand and control the risks of such novel chemicals.

Screening of Potential PFOS Alternatives To Decrease Liver Bioaccumulation: Experimental and Computational Approaches

Perfluorooctanesulfonate (PFOS) is a persistent organic pollutant with significant bioaccumulation potential in liver tissues. Exposure to PFOS could cause increase of liver weight, induce adenomas of the liver, and cause hepatomegaly. Alternatives of PFOS might be designed and synthesized that have significantly lower liver bioaccumulation. In this study, we conducted animal exposure experiments to investigate tissue accumulations of 14 per- and polyfluoroalkyl substances. Correlation analysis demonstrated that accumulation of the compounds in rat liver had strong correlations with their binding affinities of liver fatty acid binding protein (LFABP). Thus, we combined a quantitative structure-activity relationship model with molecular dynamics (MD) simulations to develop computational models to predict the LFABP binding affinities of two newly synthesized alternatives, perfluorodecalin-2-sulfonic acid and N-diperfluorobutanoic acid. The binding characteristics of the PFOS alternatives for LFABP were elaborated to explore how the different structural modifications of molecules influenced the underlying binding mechanisms. Subsequent animal experiments demonstrated that the binding free energy calculations based on the MD simulations provided a good indicator to reflect the relative degree of liver accumulation of the PFOS alternatives in the same exposure doses and durations. Our findings from the combination of experimental exposure and computational model can provide helpful information to design potential alternatives of PFOS with weak LFABP binding capability and low liver accumulation.

Development of chicken and fish muscle protein - Water partition coefficients predictive models for ionogenic and neutral organic chemicals

Muscle protein was one of critical accumulation protein for anthropogenic chemicals. However, few predictive models were constructed for muscle protein up to now. In addition, some ionizable chemicals classes e.g. sulfonates were not successfully modeled in previously models, indicating considerable work would be needed. The major objective of this study was to develop quantitative structure-activity relationship (QSAR) models for predicting the muscle protein-water partition coefficient (logK_MP/w) of chicken and fish. In the modeling, the n-octanol/water distribution coefficient (logD), functional groups, atom-centred fragments and chemical form adjusted descriptors were used to construct the models. The application domain of the derived models was defined by the Euclidean distance-based method and Williams plot. The modeling results indicated that the determination coefficient (R²), leave-one out cross validation Q² (Q²_LOO) and bootstrapping coefficient (Q²_BOOT) of the QSAR models for chicken and fish were 0.882 and 0.929, 0.844 and 0.906, 0.779 and 0.792, respectively, implying the models had good goodness-of-fit and robustness. The coefficient determination (R²_EXT) and external validation coefficient (Q²_EXT) of the validation set for the two models were 0.874 and 0.937, 0.869 and 0.915, respectively, indicating the models had good predictive ability. The predictor variables selected to construct the logK_MP/w models of chicken and fish included logD, the function groups, and the fraction of the ionized species (δ_I). Considering the molecular descriptors used here can be calculated from their molecular structures directly, the developed models could be easily used to fill the logK_MP/w data gap for other chemicals within the applicability domain.

Effects of exposure to BPF on development and sexual differentiation during early life stages of zebrafish (Danio rerio)

Bisphenol F (BPF) has become a predominant bisphenol contaminant in recent years. It has significant estrogenic properties in both in vivo and in vitro studies. We have previously studied the disrupting mechanisms of BPF on the hypothalamic-pituitary-gonadal axis of adult zebrafish. However, the effects of BPF exposure on development and sexual differentiation of zebrafish embryos/larvae remain unclear. To determine the effects of BPF on the critical stage of sex differentiation in zebrafish, zebrafish embryos/larvae were exposed to 1, 10, 100, and 1000 μg/L BPF from fertilization to 60 days post-fertilization (dpf). Developmental malformations were induced by exposure to BPF from 2 h post-fertilization (hpf), with a LC₅₀ of 10,030 μg/L at 96 hpf and 9391 μg/L at 120 hpf. Long-term exposure during sex differentiation tended to result in a female sex ratio bias. Histological analyses at 60 dpf indicated that the development of ovo-testes and immature ovaries was induced by 100 and 1000 μg/L BPF. Homogenate testosterone levels decreased and 17β-estradiol levels increased in zebrafish in a concentration-dependent manner. BPF exposure suppressed gene expression of double sex, Mab3-related transcription factor 1(dmrt1), fushi tarazu factor 1d (ff1d), sry-box containing gene 9a (sox9a) and anti-Mullerian hormone (amh); induced expression of the forkhead box L2 transcription factor (foxl2), leading to increased expression of aromatase (cyp19a1a), which promoted production of estrogens, and further caused phenotypic feminization of zebrafish. These results suggest that developmental exposure to BPF has adverse effects on sexual differentiation, and the results were useful for a BPF risk assessment.

A Novel Action of Endocrine-Disrupting Chemicals on Wildlife; DDT and Its Derivatives Have Remained in the Environment

Huge numbers of chemicals are released uncontrolled into the environment and some of these chemicals induce unwanted biological effects, both on wildlife and humans. One class of these chemicals are endocrine-disrupting chemicals (EDCs), which are released even though EDCs can affect not only the functions of steroid hormones but also of various signaling molecules, including any ligand-mediated signal transduction pathways. Dichlorodiphenyltrichloroethane (DDT), a pesticide that is already banned, is one of the best-publicized EDCs and its metabolites have been considered to cause adverse effects on wildlife, even though the exact molecular mechanisms of the abnormalities it causes still remain obscure. Recently, an industrial raw material, bisphenol A (BPA), has attracted worldwide attention as an EDC because it induces developmental abnormalities even at low-dose exposures. DDT and BPA derivatives have structural similarities in their chemical features. In this short review, unclear points on the molecular mechanisms of adverse effects of DDT found on alligators are summarized from data in the literature, and recent experimental and molecular research on BPA derivatives is investigated to introduce novel perspectives on BPA derivatives. Especially, a recently developed BPA derivative, bisphenol C (BPC), is structurally similar to a DDT derivative called dichlorodiphenyldichloroethylene (DDE).

A plurality of molecular targets: The receptor ecosystem for bisphenol-A (BPA)

Bisphenol-A (BPA) is a well-known endocrine disrupting compound (EDC), capable of affecting the normal function and development of the reproductive system, brain, adipose tissue, and more. In spite of these diverse and well characterized effects, there is often comparatively little known about the molecular mechanisms which bring them about. BPA has traditionally been regarded as a primarily estrogenic EDC, and this perspective is often what guides research into the effects of BPA. However, emerging data from in-vitro and in-silico models show that BPA binds with a significant number of hormone receptors, including a number of nuclear and membrane-bound estrogen receptors, androgen receptors, as well as the thyroid hormone receptor, glucocorticoid receptor, and PPARγ. With this increased diversity of receptor targets, it may be possible to explain some of the more puzzling aspects of BPA pharmacology, including its non-monotonic dose-response curve, as well as experimental results which disagree with estrogenic positive controls. This paper reviews the receptors for which BPA has a known interaction, and discusses the implications of taking these receptors into account when studying the disruptive effects of BPA on growth and development.

Development of bovine serum albumin-water partition coefficients predictive models for ionogenic organic chemicals based on chemical form adjusted descriptors

The partition coefficients between bovine serum albumin (BSA) and water (K_BSA/w) for ionogenic organic chemicals (IOCs) were different greatly from those of neutral organic chemicals (NOCs). For NOCs, several excellent models were developed to predict their logK_BSA/w. However, it was found that the conventional descriptors are inappropriate for modeling logK_BSA/w of IOCs. Thus, alternative approaches are urgently needed to develop predictive models for K_BSA/w of IOCs. In this study, molecular descriptors that can be used to characterize the ionization effects (e.g. chemical form adjusted descriptors) were calculated and used to develop predictive models for logK_BSA/w of IOCs. The models developed had high goodness-of-fit, robustness, and predictive ability. The predictor variables selected to construct the models included the chemical form adjusted averages of the negative potentials on the molecular surface (V_s-adj^-), the chemical form adjusted molecular dipole moment (dipolemoment_adj), the logarithm of the n-octanol/water distribution coefficient (logD). As these molecular descriptors can be calculated from their molecular structures directly, the developed model can be easily used to fill the logK_BSA/w data gap for other IOCs within the applicability domain. Furthermore, the chemical form adjusted descriptors calculated in this study also could be used to construct predictive models on other endpoints of IOCs.

Rational Selection of the 3D Structure of Biomacromolecules for Molecular Docking Studies on the Mechanism of Endocrine Disruptor Action

Molecular modeling has become an essential tool in predicting and simulating endocrine disrupting effects of chemicals. A key prerequisite for successful application of molecular modeling lies in the correctness of 3D structure for biomacromolecules to be simulated. To date, there are several databases that can provide the experimentally-determined 3D structures. However, commonly, there are many challenges or disadvantageous factors, e.g., (a) lots of 3D structures for a given biomacromolecular target in the protein database; (b) the quality variability for those structures; (c) belonging to different species; (d) mutant amino acid residue in key positions, and so on. Once an inappropriate 3D structure of a target biomacromolecule was selected in molecular modeling, the accuracy and scientific nature of the modeling results could be inevitably affected. In this article, based on literature survey and an analysis of the 3D structure characterization of biomacromolecular targets belonging to the endocrine system in protein databases, six principles were proposed to guide the selection of the appropriate 3D structure of biomacromolecules. The principles include considering the species diversity, the mechanism of action, whether there are mutant amino acid residues, whether the number of protein chains is correct, the degree of structural similarity between the ligand in 3D structure and the target compounds, and other factors, e.g., the experimental pH conditions of the structure determined process and resolution.