Mixed‐Model QSAR at the Glucocorticoid Receptor: Predicting the Binding Mode and Affinity of Psychotropic Drugs

M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy

Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing's syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner.

Dynamic Profiling and Binding Affinity Prediction of NBTI Antibacterials against DNA Gyrase Enzyme by Multidimensional Machine Learning and Molecular Dynamics Simulations

Bacterial type II topoisomerases are well-characterized and clinically important targets for antibacterial chemotherapy. Novel bacterial topoisomerase inhibitors (NBTIs) are a newly disclosed class of antibacterials. Prediction of their binding affinity to these enzymes would be beneficial for de novo design/optimization of new NBTIs. Utilizing in vitro NBTI experimental data, we constructed two comprehensive multidimensional DNA gyrase surrogate models for Staphylococcus aureus (q2 = 0.791) and Escherichia coli (q2 = 0.806). Both models accurately predicted the IC50s of 26 NBTIs from our recent studies. To investigate the NBTI's dynamic profile and binding to both targets, 10 selected NBTIs underwent molecular dynamics (MD) simulations. The analysis of MD production trajectories confirmed key hydrogen-bonding and hydrophobic contacts that NBTIs establish in both enzymes. Moreover, the binding free energies of selected NBTIs were computed by the linear interaction energy (LIE) method employing an in-house derived set of fitting parameters (α = 0.16, β = 0.029, γ = 0.0, and intercept = -1.72), which are successfully applicable to DNA gyrase of Gram-positive/Gram-negative pathogens. Both methods offer accurate predictions of the binding free energies of NBTIs against S. aureus and E. coli DNA gyrase. We are confident that this integrated modeling approach could be valuable in the de novo design and optimization of efficient NBTIs for combating resistant bacterial pathogens.

A battery of in silico models application for pesticides exerting reproductive health effects: Assessment of performance and prioritization of mechanistic studies

Given the high attention to endocrine disrupting chemicals (EDC), there is an urgent need for the development of rapid and reliable approaches for the screening of large numbers of chemicals with respect to their endocrine disruption potential. This study aimed at the assessment of the correlation between the predicted results of a battery of in silico tools and the reported observed adverse effects from in vivo reproductive toxicity studies. We used VirtualToxLab (VTL) software and the EndocrineDisruptome (ED) online tool to evaluate the binding affinities to nuclear receptors of 17 pesticides, 7 of which were classified as reprotoxic substances under Regulation (EC) No 1272/2008 on the classification, labelling and packaging of substances and mixtures (CLP). Then, we aligned the results of the in silico modelling with data from ToxCast assays and in vivo reproductive toxicity studies. We combined results from different in silico tools in two different ways to improve the characteristics of their predictive performance. Reproductive toxicity can be caused by various mechanisms; however, in this study, we demonstrated that the use of a battery of in silico tools for assessing the binding to nuclear receptors can be useful for identifying hazardous compounds and for prioritizing further studies.

Triclocarban, Triclosan, Bromochlorophene, Chlorophene, and Climbazole Effects on Nuclear Receptors: An in Silico and in Vitro Study

BACKGROUND

Endocrine-disrupting chemicals can interfere with hormonal homeostasis and have adverse effects for both humans and the environment. Their identification is increasingly difficult due to lack of adequate toxicological tests. This difficulty is particularly problematic for cosmetic ingredients, because in vivo testing is now banned completely in the European Union.

OBJECTIVES

The aim was to identify candidate preservatives as endocrine disruptors by in silico methods and to confirm endocrine receptors' activities through nuclear receptors in vitro.

METHODS

We screened preservatives listed in Annex V in the European Union Regulation on cosmetic products to predict their binding to nuclear receptors using the Endocrine Disruptome and VirtualToxLab™ version 5.8 in silico tools. Five candidate preservatives were further evaluated for androgen receptor (AR), estrogen receptor (ER α ), glucocorticoid receptor (GR), and thyroid receptor (TR) agonist and antagonist activities in cell-based luciferase reporter assays in vitro in AR-EcoScreen, hER α -HeLa- 9903 , MDA-kb2, and GH3.TRE-Luc cell lines. Additionally, assays to test for false positives were used (nonspecific luciferase gene induction and luciferase inhibition).

RESULTS

Triclocarban had agonist activity on AR and ER α at 1 μ M and antagonist activity on GR at 5 μ M and TR at 1 μ M . Triclosan showed antagonist effects on AR, ER α , GR at 10 μ M and TR at 5 μ M , and bromochlorophene at 1 μ M (AR and TR) and at 10 μ M (ER α and GR). AR antagonist activity of chlorophene was observed [inhibitory concentration at 50% (IC50) IC 50 = 2.4 μ M ], as for its substantial ER α agonist at > 5 μ M and TR antagonist activity at 10 μ M . Climbazole showed AR antagonist (IC 50 = 13.6 μ M ), ER α agonist at > 10 μ M , and TR antagonist activity at 10 μ M .

DISCUSSION

These data support the concerns of regulatory authorities about the endocrine-disrupting potential of preservatives. These data also define the need to further determine their effects on the endocrine system and the need to reassess the risks they pose to human health and the environment. https://doi.org/10.1289/EHP6596.

Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives

The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, as well as aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects. The simulations are conducted at the atomic level and explicitly allow for a mechanistic interpretation of the results (in real-time 3D/4D), thereby complying with the Setubal principles put forward in 2002 for computational approaches to toxicology. Moreover, the underlying "ab-initio" protocol is independent from any training data and makes the approach universal with respect to the applicability domain. The VirtualToxLab runs in client-server mode and is freely available to academic and non-profit organizations. As the underlying technology yields a thermodynamic estimate of the binding affinity, the associated ligand-protein complexes have been challenged by molecular-dynamics simulations to probe their kinetic stability. Human African trypanosomiasis is a neglected tropical disease caused by two subspecies of Trypanosoma brucei. The control of this parasitic infection relies on a few chemotherapeutic agents, most of which were discovered decades ago and pose many challenges including adverse side effects, poor efficacy, and the occurrence of drug resistances. Natural products, on the other hand, offer a high potential for the discovery of new drug leads due to their chemical diversity. In this in silico study, we analyze a series of 89 natural products and derivatives displaying anti-trypanosomal activity for their potential to trigger adverse effects. Our results indicate a moderate potential for a number of those compounds to bind to nuclear receptors and thereby ease the development of endocrine disregulation. A few others would seem to inhibit enzymes of the cytochrome P450 family and, hence, sustain drug-drug interactions.

Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls

The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q(2) of 0.632 and r(2) of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand-receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR.

Structural classification of steroid-binding sites on proteins by coarse-grained atomic environment and its correlation with their biological function

Steroid hormone is extensively used for transmitting variety of biological signals in organisms. Natural steroid hormone is synthesized from cholesterol in adrenal cortex and in sexual gland in vertebrates. Appropriately dosed synthetic steroid hormones can be used for medication. Despite their positive effects as medicine, they sometimes cause significant side effects due to their wide range of actions, and the studies for discovering the mechanisms of side effects were carried out aiming to reduce the side effects. The fundamental cause of the side effects seems to be interactions between the steroid and a non-target protein. To understand the possible range of interaction of steroid molecule, we gathered all the three-dimensional structures of protein-steroid complex determined by X-ray crystallography, compared the atomic environments of the steroid-binding sites in proteins and classified the pattern of steroid binding. Protein Data Bank contained 871 structures of steroid-protein complexes in 382 entries. For this study, we selected 832 steroid binding proteins. Using a newly developed method to describe the atomic environments of these steroid molecules and their function, we were able to separate the environments into six patterns. This classification had a potential to predict the function of function-unknown proteins with a co-crystallized steroid molecule. We speculated that the proteins grouped into the same pattern of nuclear receptors were the candidates of non-targeted proteins causing a side effect by a therapeutic prescription of steroid hormone.

OpenVirtualToxLab--a platform for generating and exchanging in silico toxicity data

The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

VirtualToxLab - a platform for estimating the toxic potential of drugs, chemicals and natural products

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on http://www.virtualtoxlab.org. The free platform - the OpenVirtualToxLab - is accessible (in client-server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Integrating structure-based and ligand-based approaches for computational drug design

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

Rational ligand-based virtual screening and structure-activity relationship studies in the ligand-binding domain of the glucocorticoid receptor-α

The interest in developing synthetic glucocorticoids (GCs) arises from the utility of endogenous steroids as potent anti-inflammatory and immunosuppressant agents. The first GCs to be discovered, such as cortisol or dexamethasone, still represent the main treatment for conditions of the inflammatory process, despite the fact that they carry a significant risk of side effects. Hence, there is a continuing need to find drugs that preserve the immune effects of GCs without the side effects, such as those on metabolism (diabetes), bone tissue (osteoporosis), muscles (myopathy), eyes and skin. In this review, we focus on the recent use of ligand-based computational approaches in glucocorticoid receptor (GR) drug-design efforts for the determination of novel GR ligands. We examine a number of ligand-based (similarity searches, pharmacophore screens and quantitative structure-activity relationships) approaches that have been implemented in recent years. A recent virtual high-throughput screening similarity search was successful in developing a novel series of nonsteroidal GR antagonists. Additionally, there has been considerable success in ligand-based structure-analysis relationship generation and lead optimization studies for the GR. Future trends toward integrated GR ligand design incorporating ligand- and structure-based methodologies are inevitable.

Probing small-molecule binding to cytochrome P450 2D6 and 2C9: An in silico protocol for generating toxicity alerts

Drug metabolism, toxicity, and their interaction profiles are major issues in the drug-discovery and lead-optimization processes. The cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of marketed drugs. Therefore, the prediction of the binding affinity towards CYP2D6 and CYP2C9 would be beneficial for identifying cytochrome-mediated adverse effects triggered by drugs or chemicals (e.g., toxic reactions, drug-drug, and food-drug interactions). By identifying the binding mode by using pharmacophore prealignment, automated flexible docking, and by quantifying the binding affinity by multidimensional QSAR (mQSAR), we validated a model family of 56 compounds (46 training, 10 test) and 85 compounds (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross-validated r²=0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards CYP2D6 and CYP2C9. The models were challenged by Y-scrambling and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9). To assess the probability of false-positive predictions, datasets of nonbinders (64 compounds for CYP2D6 and 56 for CYP2C9) were tested by using the same protocol. The two validated mQSAR models were subsequently added to the VirtualToxLab (VTL, http://www.virtualtoxlab.org).

In Silico Design and Evaluation of Carboxylesterase Inhibitors

Carboxylesterases (CEs) are important enzymes that catalyze biological detoxification, hydrolysis of certain pesticides, and metabolism of many esterified drugs. The development of inhibitors for CE has many potential uses, including increasing drug lifetime and altering biodistrubution; reducing or abrogating toxicity of metabolized drugs; and reducing pest resistance to insecticides. In this review, we discuss the major classes of known mammalian CE inhibitors and describe our computational efforts to design new scaffolds for development of novel, selective inhibitors. We discuss several strategies for in silico inhibitor development, including structure docking, database searching, multidimensional quantitative structure activity analysis (QSAR), and a newly-used approach that uses QSAR combined with de novo drug design. While our research is focused on design of specific inhibitors for human intestinal carboxylesterase (hiCE), the methods described are generally applicable to inhibitors of other enzymes, including CE from other tissues and organisms.

In Silico Toxicology in Drug Discovery — Concepts Based on Three-dimensional Models

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted ( in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery — used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound's ADMET ( adsorption, distribution, metabolism, elimination, toxicity) properties — in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level.