Combined quantum mechanics/molecular mechanics (QM/MM) methods to understand the charge density distribution of estrogens in the active site of estrogen receptors

Diethyl-hexyl-cyclohexane (Eco-DEHCH) is a safer phthalate alternative that does not elicit neuroendocrine disrupting effects

Alternative phthalates (APs) have been developed due to the reported adverse effects of conventional phthalates (CPs). However, whether APs are nontoxic and can replace CPs remains controversial due to their endocrine-disrupting (ED) effects. Herein, to investigate the ED potential of diethyl-hexyl-cyclohexane (DEHCH), a newly developed non-phthalate-structured AP, we employed in silico (molecular docking simulation), in vitro (cell-based assays for estrogen and androgen receptors), and in vivo (zebrafish embryo model) methods. We also compared the results with two CPs (di(2-ethylhexyl) phthalate [DEHP] and diisononyl phthalate [DINP]) and two previously proposed non-phthalate-structured APs (1,2-cyclohexane dicarboxylic acid diisononyl ester [DINCH] and di-2-ethylhexyl terephthalate [DEHTP]). DEHCH did not exhibit the highest binding affinity for any of the five receptors such as estrogen, androgen, glucocorticoid receptors, and thyroid receptor alpha and beta. None of the tested phthalates exhibited agonistic or antagonistic effects on estrogen and androgen receptors. In zebrafish larvae, DEHCH did not affect the expression of the nine endocrine-related genes and neurobehaviors, which correlates well with the lack of changes in the endogenous concentrations of the five neurosteroids. In contrast, DINCH, DEHP, and DEHTP induced hyperactivity, and except for DEHCH, four phthalates significantly upregulated at least one gene. In addition, DINCH significantly increased the expression of cortisol and DEHP increased progesterone, allopregnanolone, and cortisol. These findings demonstrate that DEHCH is safer than CPs and the previously proposed APs in terms of ED effects, including neuronal system dysregulation.

Species Differences in Response to Binding Interactions of Bisphenol A and its Analogs with the Modeled Estrogen Receptor 1 and In Vitro Reporter Gene Assay in Human and Zebrafish

Adverse impacts associated with the interactions of numerous endocrine-disruptor chemicals (EDCs) with estrogen receptor 1 play a pivotal role in reproductive dysfunction. The predictive studies on these interactions thus are crucial in the risk assessment of EDCs but rely heavily on the accuracy of specific protein structure in three dimensions. As the three-dimensional (3D) structure of zebrafish estrogen receptor 1 (zEsr1) is not available, the 3D structure of zEsr1 ligand-binding domain (zEsr1-LBD) was generated using MODELLER and its quality was assessed by the PROCHECK, ERRAT, ProSA, and Verify-3D tools. After the generated model was verified as reliable, bisphenol A and its analogs were docked on the zEsr1-LBD and human estrogen receptor 1 ligand-binding domain (hESR1-LBD) using the Discovery Studio and Autodock Vina programs. The molecular dynamics followed by molecular docking were simulated using the Nanoscale Molecular Dynamics program and compared to those of the in vitro reporter gene assays. Some chemicals were bound with an orientation similar to that of 17β-estradiol in both models and in silico binding energies showed moderate or high correlations with in vitro results (0.33 ≤ r²  ≤ 0.71). Notably, hydrogen bond occupancy during molecular dynamics simulations exhibited a high correlation with in vitro results (r²  ≥ 0.81) in both complexes. These results show that the combined in silico and in vitro approaches is a valuable tool for identifying EDCs in different species, facilitating the assessment of EDC-induced reproductive toxicity. Environ Toxicol Chem 2022;41:2431-2443. © 2022 SETAC.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Monohaloacetonitriles induce cytotoxicity and exhibit different mode of action in endocrine disruption

Haloacetonitriles are emerging disinfection by-products that can be detected in various aquatic environments. They are cytotoxic, genotoxic, mutagenic, and tumorigenic in vitro and in vivo, but their endocrine-disrupting potency remains unknown. In this study, we examined the agonistic and antagonistic estrogenic and androgenic activities of haloacetonitriles, as well as their cytotoxicity, using a yeast-based reporter assay. We also investigated the interactions of haloacetonitriles with human estrogen receptor alpha (hERα) through molecular docking. We observed that iodoacetonitrile (median lethal dose: 1.96 × 10^-5 M) and bromoacetonitrile (median lethal dose: 1.97 × 10^-5 M) had similar cytotoxicities, which are higher than that of chloroacetonitrile (median lethal dose: 7.16 × 10^-5 M). We observed bromoacetonitrile and chloroacetonitrile elicited estrogenic activity with 10% effective concentrations of 3.30 × 10^-9 M and 2.36 × 10^-9 M, respectively. This finding indicates that bromoacetonitrile and chloroacetonitrile may mimic estrogen signaling through interaction with hERα. Consistent with that result, we identified bromoacetonitrile and chloroacetonitrile interacted with residues in the original estrogen recognition sites of hERα. Our results show that bromoacetonitrile and chloroacetonitrile affect the endocrine-disrupting potency mediated via estrogen receptors by using in vitro assay and molecular docking.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.