Structural Dynamics of Agonist and Antagonist Binding to the Androgen Receptor

Illuminating ligand-induced dynamics in nuclear receptors through MD simulations

Nuclear receptors (NRs) regulate gene expression in critical physiological processes, with their functionality finely tuned by ligand-induced conformational changes. While NRs may sometimes undergo significant conformational motions in response to ligand-binding, these effects are more commonly subtle and challenging to study by traditional structural or biophysical methods. Molecular dynamics (MD) simulations are a powerful tool to bridge the gap between static protein-ligand structures and dynamical changes that govern NR function. Here, we summarize a handful of recent studies that apply MD simulations to study NRs. We present diverse methodologies for analyzing simulation data with a detailed examination of the information each method can yield. By delving into the strengths, limitations and unique contributions of these tools, this review provides guidance for extracting meaningful data from MD simulations to advance the goal of understanding the intricate mechanisms by which ligands orchestrate a range of functional outcomes in NRs.

Discovery of Novel Anti-Resistance AR Antagonists Guided by Funnel Metadynamics Simulation

Androgen receptor (AR) antagonists are widely used for the treatment of prostate cancer (PCa), but their therapeutic efficacy is usually compromised by the rapid emergence of drug resistance. However, the lack of the detailed interaction between AR and its antagonists poses a major obstacle to the design of novel AR antagonists. Here, funnel metadynamics is employed to elucidate the inherent regulation mechanisms of three AR antagonists (hydroxyflutamide, enzalutamide, and darolutamide) on AR. For the first time it is observed that the binding of antagonists significantly disturbed the C-terminus of AR helix-11, thereby disrupting the specific internal hydrophobic contacts of AR-LBD and correspondingly the communication between AR ligand binding pocket (AR-LBP), activation function 2 (AF2), and binding function 3 (BF3). The subsequent bioassays verified the necessity of the hydrophobic contacts for AR function. Furthermore, it is found that darolutamide, a newly approved AR antagonist capable of fighting almost all reported drug resistant AR mutants, can induce antagonistic binding structure. Subsequently, docking-based virtual screening toward the dominant binding conformation of AR for darolutamide is conducted, and three novel AR antagonists with favorable binding affinity and strong capability to combat drug resistance are identified by in vitro bioassays. This work provides a novel rational strategy for the development of anti-resistant AR antagonists.

Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list

Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.

Androgen receptor cofactors: A potential role in understanding prostate cancer

Prostate cancer (PCa) is witnessing a concerning rise in incidence annually, with the androgen receptor (AR) emerging as a pivotal contributor to its growth and progression. Mounting evidence underscores the AR's ability to recruit cofactors, influencing downstream gene transcription and thereby fueling the proliferation and metastasis of PCa cells. Although, clinical strategies involving AR antagonists provide some relief, managing castration resistant prostate cancer (CRPC) remains a formidable challenge. Thus, the need of the hour lies in unearthing new drugs or therapeutic targets to effectively combat PCa. This review encapsulates the pivotal roles played by coactivators and corepressors of AR, notably androgen receptor-associated protein (ARA) and steroid receptor Coactivators (SRC) in PCa. Our data unveils how these cofactors intricately modulate histone modifications, cell cycling, SUMOylation, and apoptosis through their interactions with AR. Among the array of cofactors scrutinised, such as ARA70β, ARA24, ARA160, ARA55, ARA54, PIAS1, PIAS3, SRC1, SRC2, SRC3, PCAF, p300/CBP, MED1, and CARM1, several exhibit upregulation in PCa. Conversely, other cofactors like ARA70α, PIASy, and NCoR/SMRT demonstrate downregulation. This duality underscores the complexity of AR cofactor dynamics in PCa. Based on our findings, we propose that manipulating cofactor regulation to modulate AR function holds promise as a novel therapeutic avenue against advanced PCa. This paradigm shift offers renewed hope in the quest for effective treatments in the face of CRPC's formidable challenges.

In Vitro, In Vivo, and In Silico Analysis of Pyraclostrobin and Cyprodinil and Their Mixture Reveal New Targets and Signaling Mechanisms

Pyraclostrobin and cyprodinil are broad-spectrum fungicides that are used in crops to control diseases. However, they are excessively used and, as a result, end up in the environment and threaten human health and ecosystems. Hence, knowledge of their mechanisms of action is critical to revealing their environmental fate and negative effects and regulating their use. In the present study, we conducted a comprehensive study to show the adverse effects of pyraclostrobin, cyprodinil, and their mixture using zebrafish larvae and different cell lines. Several end points were investigated, including mortality, development, gene expression, reporter assays, and molecular docking simulations. We found that both compounds and their mixture caused developmental delays and mortality in zebrafish, with a higher effect displayed by pyraclostrobin. Both compounds altered the expression of genes involved in several signaling pathways, including oxidative stress and mitochondrial function, lipid and drug metabolisms, the cell cycle, DNA damage, apoptosis, and inflammation. A noteworthy result of this study is that cyprodinil and the mixture group acted as NFκB activators, while pyraclostrobin demonstrated antagonist activity. The AHR activity was also upregulated by cyprodinil and the mixture group; however, pyraclostrobin did not show any effect. For the first time, we also demonstrated that pyraclostrobin had androgen receptor antagonist activity.

A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases

In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.

Androgen Receptor and Cardiovascular Disease: A Potential Risk for the Abuse of Supplements Containing Selective Androgen Receptor Modulators

The androgen receptor (AR) is a member of the family of ligand-activated transcription factors. Selective androgen receptor modulators (SARMs) exert their biological function through complex interactions with the AR. It has been speculated that overexertion of AR signaling cascades as a result of SARM abuse can be a risk factor for the development of various cardiovascular diseases. The present literature review explores the implications of the interaction between SARMs and the AR on cardiovascular health by focusing on the AR structure, function, and mechanisms of action, as well as the current clinical literature on various SARMs. It is shown that SARMs may increase the risk of cardiovascular diseases through implications on the renin-angiotensin system, smooth muscle cells, sympathetic nervous system, lipid profile, inflammation, platelet activity, and various other factors. More research on this topic is necessary as SARM abuse is becoming increasingly common. There is a noticeable lack of clinical trials and literature on the relationship between SARMs, cardiovascular diseases, and the AR. Future in vivo and in vitro studies within this field are vital to understand the mechanisms that underpin these complex interactions and risk factors.

Norethindrone suppress the germ cell development via androgen receptor resulting in male bias

Progestins are widely used and detected in surface waters, and can affect gonad development and sexual differentiation in fish. However, the toxicological mechanisms of sexual differentiation induced by progestins are not well understood. Here, we investigated the effects of norethindrone (NET) and androgen receptor (AR) antagonist flutamide (FLU) on gonadal differentiation in zebrafish from 21 dpf (days post-fertilization) to 49 dpf. The results showed that NET caused male bias, while FLU resulted in female bias at 49 dpf. The NET and FLU mixtures significantly decreased the percentage of males compared to the NET single exposure. Molecular docking analysis showed that FLU and NET had similar docking pocket and docking posture with AR resulting in competitively forming the hydrogen bond with Thr334 of AR. These results suggested that binding to AR was the molecular initiating event of sex differentiation induced by NET. Moreover, NET strongly decreased transcription of biomarker genes (dnd1, ddx4, dazl, piwil1 and nanos1) involved in germ cell development, while FLU significantly increased transcription of these target genes. There was an increase in the number of juvenile oocytes, which was consistent with the female bias in the combined groups. The bliss independence model analysis further showed that NET and FLU had antagonistic effect on transcription and histology during gonadal differentiation. Thus, NET suppressed the germ cell development via AR, resulting in male bias. Understanding the molecular initiation of sex differentiation in progestins is essential to provide a comprehensive biological basis for ecological risk assessment.

Comparative Proteomic and Transcriptomic Analysis of the Impact of Androgen Stimulation and Darolutamide Inhibition

Several inhibitors of androgen receptor (AR) function are approved for prostate cancer treatment, and their impact on gene transcription has been described. However, the ensuing effects at the protein level are far less well understood. We focused on the AR signaling inhibitor darolutamide and confirmed its strong AR binding and antagonistic activity using the high throughput cellular thermal shift assay (CETSA HT). Then, we generated comprehensive, quantitative proteomic data from the androgen-sensitive prostate cancer cell line VCaP and compared them to transcriptomic data. Following treatment with the synthetic androgen R1881 and darolutamide, global mass spectrometry-based proteomics and label-free quantification were performed. We found a generally good agreement between proteomic and transcriptomic data upon androgen stimulation and darolutamide inhibition. Similar effects were found both for the detected expressed genes and their protein products as well as for the corresponding biological programs. However, in a few instances there was a discrepancy in the magnitude of changes induced on gene expression levels compared to the corresponding protein levels, indicating post-transcriptional regulation of protein abundance. Chromatin immunoprecipitation DNA sequencing (ChIP-seq) and Hi-C chromatin immunoprecipitation (HiChIP) revealed the presence of androgen-activated AR-binding regions and long-distance AR-mediated loops at these genes.

Effects of endocrine active contaminating pesticides on RACK1 expression and immunological consequences in THP-1 cells

We have previously demonstrated that RACK1, which expression is under steroid hormone control, plays an important role in the activation of immune cells and its expression can be useful to evaluate the immunotoxic profile of endocrine disrupting chemicals (EDCs). Hence, we investigated the effects of three contaminating and persistent pesticides: the fungicide vinclozolin (VIN), the herbicide atrazine (ATR) and the insecticide cypermethrin (CYP) on RACK1 expression and on innate immune response. VIN resulted in modest alteration of RACK1 while ATR and CYP reduced in a dose dependent manner RACK1 expression, ultimately leading to the decrease in lipopolysaccharide-induced IL-8 and TNF-α release and CD86 and CD54 surface marker expression. Moreover, our data indicate that, after exposure to EDCs, alterations of RACK1 expression can also occur with mechanisms not directly mediated by an interaction with a nuclear or membrane steroid receptors. Therefore, RACK1 could represent a useful EDCs screening tool to evaluate their immunotoxic potential and to dissect their mechanisms of action.

Structure-Based Study to Overcome Cross-Reactivity of Novel Androgen Receptor Inhibitors

The mutation-driven transformation of clinical anti-androgen drugs into agonists of the human androgen receptor (AR) represents a major challenge for the treatment of prostate cancer patients. To address this challenge, we have developed a novel class of inhibitors targeting the DNA-binding domain (DBD) of the receptor, which is distanced from the androgen binding site (ABS) targeted by all conventional anti-AR drugs and prone to resistant mutations. While many members of the developed 4-(4-phenylthiazol-2-yl)morpholine series of AR-DBD inhibitors demonstrated the effective suppression of wild-type AR, a few represented by 4-(4-(3-fluoro-2-methoxyphenyl)thiazol-2-yl)morpholine (VPC14368) exhibited a partial agonistic effect toward the mutated T878A form of the receptor, implying their cross-interaction with the AR ABS. To study the molecular basis of the observed cross-reactivity, we co-crystallized the T878A mutated form of the AR ligand binding domain (LBD) with a bound VPC14368 molecule. Computational modelling revealed that helix 12 of AR undergoes a characteristic shift upon VPC14368 binding causing the agonistic behaviour. Based on the obtained structural data we then designed derivatives of VPC14368 to successfully eliminate the cross-reactivity towards the AR ABS, while maintaining significant anti-AR DBD potency.

In Vitro characterization of the endocrine disrupting effects of per- and poly-fluoroalkyl substances (PFASs) on the human androgen receptor

Per- and poly-fluoroalkyl substances (PFASs) are used extensively in a broad range of industrial applications and consumer products. While a few legacy PFASs have been voluntarily phased out, over 5000 PFASs have been produced as replacements for their predecessors. The potential endocrine disrupting hazards of most emerging PFASs have not been comprehensively investigated. In silico molecular docking to the human androgen receptor (hAR) combined with machine learning techniques were previously applied to 5206 PFASs and predicted 23 PFASs bind the hAR. Herein, the in silico results were validated in vitro for the five candidate AR ligands that were commercially available. Three manufactured PFASs namely (9-(nonafluorobutyl)- 2,3,6,7-tetrahydro-1 H,5 H,11 H-pyrano[2,3-f]pyrido[3,2,1-ij]quinolin-11-one (NON), 2-(heptafluoropropyl)- 3-phenylquinoxaline (HEP), and 2,2,3,3,4,4,5,5,5-nonafluoro-N-(4-nitrophenyl)pentanamide (NNN) elicited significant antiandrogenic effects at relatively low concentrations. We further investigated the mechanism of AR inhibition and found that all three PFASs inhibited AR transactivation induced by testosterone through a competitive binding mechanism. We then examined the antiandrogenic effects of these PFASs on AR expression and its responsive genes. Consistently, these PFASs significantly decreased the expression of PSA and FKBP5 and increased the expression of AR, similar to the effects elicited by a known competitive AR inhibitor, hydroxyflutamide. This suggests they are competitive antagonists of AR activity and western blot analysis revealed these PFASs decreased intracellular AR protein in androgen sensitive human prostate cancer cells. Hence, the findings presented here corroborate our published in silico approach and indicate these emerging PFASs may adversely affect the human endocrine system.

Predicting the binding of small molecules to nuclear receptors using machine learning

Nuclear receptors (NRs) are important biological targets of endocrine-disrupting chemicals (EDCs). Identifying chemicals that can act as EDCs and modulate the function of NRs is difficult because of the time and cost of in vitro and in vivo screening to determine the potential hazards of the 100 000s of chemicals that humans are exposed to. Hence, there is a need for computational approaches to prioritize chemicals for biological testing. Machine learning (ML) techniques are alternative methods that can quickly screen millions of chemicals and identify those that may be an EDC. Computational models of chemical binding to multiple NRs have begun to emerge. Recently, a Nuclear Receptor Activity (NuRA) dataset, describing experimentally derived small-molecule activity against various NRs has been created. We have used the NuRA dataset to develop an ensemble of ML-based models to predict the agonism, antagonism, binding and effector binding of small molecules to nine different human NRs. We defined the applicability domain of the ML models as a measure of Tanimoto similarity to the molecules in the training set, which enhanced the performance of the developed classifiers. We further developed a user-friendly web server named 'NR-ToxPred' to predict the binding of chemicals to the nine NRs using the best-performing models for each receptor. This web server is freely accessible at http://nr-toxpred.cchem.berkeley.edu. Users can upload individual chemicals using Simplified Molecular-Input Line-Entry System, CAS numbers or sketch the molecule in the provided space to predict the compound's activity against the different NRs and predict the binding mode for each.

An insight about the mechanism of action (MoA) of R-bicalutamide on the androgen receptor homodimer using molecular dynamic

R-bicalutamide is a first-line therapy used to treat prostate cancer (PCa) inhibiting the androgen receptor (AR) which plays an important role in the development and the progression of PCa. However, after a protracted drug administration, many patients develop a form of androgen insensitivity since R-bicalutamide starts to exhibit some agonistic properties lead by the W741L AR mutation in the ligand-binding pocket even if the mechanism of the antagonist-agonist switch is still not clear. To study the drug-resistant mechanism, we explored the structural effects of the antagonist R-bicalutamide on the homodimer stability considering both the AR wild-type and W741L employing molecular dynamic (MD) simulations. The results obtained indicate that the binding of R-bicalutamide in the two AR monomers induces a great instability in the homodimer, which may determine the monomer's dissociation preventing AR migration into the nucleus and avoiding the transcriptional activity. If the W741L mutation occurs, the homodimer tends to have a behaviour close to the agonistic system where the two monomers are tightly bound, which may explain the effect of the W741L in drug insensitivity from a structural point of view.

Molecular dynamics simulations reveal the plausible agonism/antagonism mechanism by steroids on androgen receptor mutations

Androgen receptors (AR) are the primary drug target in prostate cancer (PCa). There are several drugs developed against its activity for prostate cancer treatment, but cancer cells revive AR signaling against those drugs by using alternative steroids such as glucocorticoids. In addition, antagonists become agonists due to emergence of mutations in AR gene. The mechanism by which antagonists are converted into agonists and how AR signaling is recovered by other steroids has yet to be fully elucidated. In this study, we interrogated the role of bicalutamide conformation in its antagonist function and how glucocorticoids such as prednisolone and dexamethasone revive AR signaling at the molecular level by means of molecular dynamics. We found that the ''closed'' conformation of bicalutamide is essential for its antagonist function and W741 residue is forcing it into this conformation. Moreover, we show that prednisolone and dexamethasone behave like natural agonist DHT which confirm the experimental results that show their role in the reviving AR signaling in the case of AR^L701H mutation.

Prostate cancer treatment and the relationship of androgen deprivation therapy to cognitive function

Prostate cancer is the second most common form of cancer in men. For advanced, high risk prostate cancer, androgen deprivation therapy (ADT) is the preferred treatment and can induce remission, but resistance to ADT brings biochemical recurrence and progression of cancer. ADT brings adverse effects such as erectile dysfunction, decreased libido, and diminished physical strength. It is estimated that between 25 and 50% of men on ADT manifest some form of cognitive dysfunction that may be self-reported or reported by a family member. There is concern that impaired cognitive function with ADT is due to loss of testosterone support. Testosterone and its metabolites are known to possess neuroprotective properties. While a direct causal relationship between ADT and cognitive decline in prostate cancer patients has not been established, this review describes the controversy surrounding the possible connection between ADT and neurocognitive deterioration. The cellular and molecular mechanisms believed to underlie the protection of neuronal integrity by androgens are discussed. Results from animal models and human clinical studies are presented. Finally, we call attention to lifestyle modifications that may minimize cognitive issues in prostate cancer patients.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Initial Stages of Spontaneous Binding of Folate-Based Vectors to Folate Receptor-α Observed by Unbiased Molecular Dynamics

Active targeting is a prospective strategy for controlled drug delivery to malignant tumor tissues. One of the approaches relies on recognition of a bioactive ligand by a receptor expressed abundantly on the surface of cancer cell membranes. A promising ligand-receptor pair is folic acid (or its dianionic form, folate) combined with the folate receptor-α (FRα). A number of targeting drug delivery systems based on folate have been suggested, but the mechanism of binding of the ligand or its derivatives to the receptor is not fully known at the molecular level. The current study summarizes the results from unbiased all-atom molecular dynamics simulations at physiological conditions describing the binding of two forms of folate and four of its synthetically available derivatives to FRα. The models (ca. 185,000 atoms) contain one receptor molecule, embedded in the outer leaflet of a lipid bilayer, and one ligand, all immersed in saline. The bilayer represents a human cancer cell membrane and consists of 370 asymmetrically distributed lipid molecules from 35 types. The ability of the vector molecules to bind to the receptor, the position of binding, and the interactions between them are analyzed. Spontaneous binding on the nanosecond scale is observed for all molecules, but its time, position, and persistence depend strongly on the ligand. Only folate, 5-methyltetrahydrofolate, and raltitrexed bind selectively at the active site of the receptor. Two binding poses are observed, one of them (realized by raltitrexed) corresponding qualitatively to that reported for the crystallographic structure of the complex folate-FRα. Pemetrexed adsorbs nonspecifically on the protein surface, while methotrexate and pteroyl ornithine couple much less to the receptor. The molecular simulations reproduce qualitatively correctly the relative binding affinity measured experimentally for five of the ligands. Analysis of the interactions between the ligands and FRα shows that in order to accomplish specific binding to the active site, a combination of hydrogen bonding, π-stacking, and van der Waals and Coulomb attraction should be feasible simultaneously for the vector molecule. The reported results demonstrate that it is possible to observe receptor-ligand binding without applying bias by representing the local environment as close as possible and contain important molecular-level guidelines for the design of folate-based systems for targeted delivery of anticancer drugs.

In silico and in vitro assessment of androgen receptor antagonists

There is a growing concern for male reproductive health as studies suggest that there is a sharp increase in prostate cancer and other fertility related problems. Apart from lifestyle, pollutants are also known to negatively affect the reproductive system. In addition to many other compounds that have been shown to alter androgen signaling, several environmental pollutants are known to disrupt androgen signaling via binding to androgen receptor (AR) or indirectly affecting the androgen synthesis. We analyzed here the molecular mechanism of the interaction between the human AR Ligand Binding Domain (hAR-LBD) and two environmental pollutants, linuron (a herbicide) and procymidone (a pesticide), and compared with the steroid agonist dihydrotestosterone (DHT) and well-known hAR antagonists bicalutamide and enzalutamide. Using molecular docking and dynamics simulations, we showed that the co-activator interaction site of the hAR-LBD is disrupted in different ways by different ligands. Binding free energies of the ligands were also ordered in increasing order as follows: linuron, procymidone, DHT, bicalutamide, and enzalutamide. These data were confirmed by in vitro assays. Reporter assay with MDA-kb2 cells showed that linuron, procymidone, bicalutamide and enzalutamide can inhibit androgen mediated activation of luciferase activity. Gene expression analysis further showed that these compounds can inhibit the expression of prostate specific antigen (PSA) and microseminoprotein beta (MSMB) in prostate cell line LNCaP. Comparative analysis showed that procymidone is more potent than linuron in inhibiting AR activity. Furthermore, procymidone at 10 μM dose showed equivalent and higher activity to AR inhibitor enzalutamide and bicalutamide respectively.

Computational evaluation of endocrine activity of biocidal active substances

Exposure to endocrine disrupting chemicals is an important public health concern although only a few endocrine disruption chemicals have been identified so far. To speed up their identification, in silico toxicological models appear to be the most appropriate, since the potential endocrine disruption of a large number of compounds can be estimated in a short time. In this study three in silico models (Endocrine disruptome software, VirtualToxLab and COSMOS KNIME) have been used. In silico predictions of the endocrine disruption potential of biocidal active substances have been made and predictions then compared with the available in vitro experimental binding affinities to androgen, estrogen, glucocorticoid and thyroid receptors. The chosen models had similar accuracies (around 60%), while differences were shown between the models in specificity and sensitivity. VirtualToxLab was the most balanced model. Additionally, three combined models were prepared and evaluated. As expected, the majority rule approach model was more accurate and balanced. However, the positive consensus rule model, that improved the specificity of predictions (≥80% for all studied nuclear receptors) was more applicable. This reduction of false positive predictions is especially useful in the search for positive (active) compounds. On the other hand, the novel negative consensus rule model improved the specificity of prediction (≥80% for all studied nuclear receptors), giving good predictions of negative (inactive) compounds that can be excluded from further testing. The results obtained by these combined models have great added value, since they can significantly reduce further experimental testing.

Discovery of A031 as effective proteolysis targeting chimera (PROTAC) androgen receptor (AR) degrader for the treatment of prostate cancer

Androgen receptor (AR) is an effective therapeutic target for the treatment of prostate cancer. We report herein the design, synthesis, and biological evaluation of highly effective proteolysis targeting chimeras (PROTAC) androgen receptor (AR) degraders, such as compound A031. It could induce the degradation of AR protein in VCaP cell lines in a time-dependent manner, achieving the IC 50 value of less than 0.25 μM. The A031 is 5 times less toxic than EZLA and works with an appropriate half-life (t 1/2) or clearance rate (Cl). Also, it has a significant inhibitory effect on tumor growth in zebrafish transplanted with human prostate cancer (VCaP). Therefore, A031 provides a further idea of developing novel drugs for prostate cancer.

Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew's correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

Structure-based discovery of the endocrine disrupting effects of hydraulic fracturing chemicals as novel androgen receptor antagonists

Hydraulic fracturing (HF) technology is increasingly utilized for oil and gas extraction operations. The widespread use of HF has led to concerns of negative impacts on both the environment and human health. Indeed, the potential endocrine disrupting impacts of HF chemicals is one such knowledge gap. Herein, we used structure-based molecular docking to assess the binding affinities of 60 HF chemicals to the human androgen receptor (AR). Five HF chemicals had relatively high predicted AR binding affinity, suggesting the potential for endocrine disruption. We next assessed androgenic and antiandrogenic activities of these chemicals in vitro. Of the five candidate AR ligands, only Genapol®X-100 significantly modified AR transactivation. To better understand the structural effect of Genapol®X-100 on the potency of AR inhibition, we compared the antiandrogenic activity of Genapol®X-100 with that of its structurally similar chemical, Genapol®X-080. Interestingly, both Genapol®X-100 and Genapol®X-080 elicited an antagonistic effect at AR with 20% relative inhibitory concentrations of 0.43 and 0.89 μM, respectively. Furthermore, we investigated the mechanism of AR inhibition of these two chemicals in vitro, and found that both Genapol®X-100 and Genapol®X-080 inhibited AR through a noncompetitive mechanism. The effect of these two chemicals on the expression of AR responsive genes, e.g. PSA, KLK2, and AR, was also investigated. Genapol®X-100 and Genapol®X-080 altered the expression of these genes. Our findings heighten awareness of endocrine disruption by HF chemicals and provide evidence that noncompetitive antiandrogenic Genapol®X-100 could cause adverse endocrine health effects.

Toward a Computational Ecotoxicity Assay

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.

APAn, a Class of ABA Receptor Agonism/Antagonism Switching Probes

In plants, biosynthesized ABA undergoes two important physiological processes of signal transduction and metabolism simultaneously. In this study, we described a class of ABA receptor agonist/antagonist switching probes APAn, which can regulate the agonistic activity or antagonistic activity according to the length of a 6'-alkoxyl chain. From APA1 to APA6, with the extension of the alkoxyl chain, it showed a gradually increased receptor-binding potential and decreased HAB1 inhibition activity. Theoretical analysis based on molecular docking and molecular dynamics simulation revealed that some factors outside the ligand-binding pocket in receptors could also affect the binding of the ligand to the receptor, for example, the van der Waals interaction between the alkyl chain in APAn and the 3'-tunnel of ABA receptors made it bind more tightly than iso-PhABA. This enhanced binding made it an antagonist rather than a weakened agonist.

Selective androgen receptor modulators (SARMs) as pharmacological treatment for muscle wasting in ongoing clinical trials

INTRODUCTION

Skeletal muscle wasting is a frequent clinical problem encountered in patients with chronic diseases. Increased levels of inflammatory markers play a role in the imbalance between muscle protein synthesis and degradation. Although testosterone has long been proposed as a treatment for patients with muscle wasting, undesirable side effects have raised concerns about prostatic hypertrophy in men as well as virilization in women. Selective androgen receptor modulators (SARMs) have demonstrated similar results like testosterone at improving lean body mass (LBM) with less side effects on androgen-dependent tissue.

AREAS COVERED

This review outlines the ongoing clinical development in the field of SARMs and their effectiveness in improving body composition and physical function. The included articles were collected at pubmed.gov and analyzed integrally.

EXPERT OPINION

There is an unmet clinical need for safe and effective anabolic compounds such as SARMs. Despite the effect on LBM shown by SARMs in phase II clinical trials, results on improved physical function and muscle strength are still lacking and long-term outcomes have to be assessed in these patients. Moreover, there is a need to determine the effect of resistance exercise training and protein intake associated with SARMs in the treatment of patients with muscle wasting.

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.

Steroids and TRP Channels: A Close Relationship

Transient receptor potential (TRP) channels are remarkable transmembrane protein complexes that are essential for the physiology of the tissues in which they are expressed. They function as non-selective cation channels allowing for the signal transduction of several chemical, physical and thermal stimuli and modifying cell function. These channels play pivotal roles in the nervous and reproductive systems, kidney, pancreas, lung, bone, intestine, among others. TRP channels are finely modulated by different mechanisms: regulation of their function and/or by control of their expression or cellular/subcellular localization. These mechanisms are subject to being affected by several endogenously-produced compounds, some of which are of a lipidic nature such as steroids. Fascinatingly, steroids and TRP channels closely interplay to modulate several physiological events. Certain TRP channels are affected by the typical genomic long-term effects of steroids but others are also targets for non-genomic actions of some steroids that act as direct ligands of these receptors, as will be reviewed here.