Targeting SARS-CoV-2 Main Protease: A Computational Drug Repurposing Study

Ligand-based cheminformatics and free energy-inspired molecular simulations for prioritizing and optimizing G-protein coupled receptor kinase-6 (GRK6) inhibitors in multiple myeloma treatment

Multiple myeloma (MM) is the second most frequently diagnosed hematological malignancy, presenting limited treatment options with no curative potential and significant drug resistance. Recent studies involving genetic knockdown established the crucial role of GRK6 in upholding the viability of MM cells, emphasizing the need to identify potential inhibitors. Computational exploration of GRK6 inhibitors has not been attempted previously. Herein, the present study reports a multilayered lead prioritization and optimization framework using chemometrics and molecular simulations. 2D QSAR studies revealed that hydrogen bonding and polar interactions enhanced GRK6 inhibitory activity, while increased electron accessibility posed a risk of off-target effects. The pharmacophore hypothesis (DDHRRR_1) featured two hydrogen bond donors, one hydrophobic region, and three aromatic rings, laying the foundation for the 3D QSAR models. Hydrophobic groups, such as pyridine and pyrazole, were shown to enhance inhibition, while smaller groups, like ethyl and hydroxyl, reduced activity. 12,557 DrugBank compounds were screened using the developed chemometric models and molecular docking in tandem, which led to the identification of 7 potential parent leads for subsequent QSAR-guided structural optimizations. 350 lead analogs were generated and the top 4 were further analyzed using molecular docking, ADMET, molecular dynamics, and metadynamics analysis based on Principal Component Analysis (PCA), Probability Density Function (PDF), and Free Energy Landscapes (FEL). Upon cumulative retrospection, we propose a novel analog of DB07168 (DB07168-A13) (docking score: -11.2 kcal/mol, MM-GBSA binding energy: -55.2 kcal/mol) as the most promising GRK6 inhibitor, warranting further in vitro validation, for addressing prospective therapeutic intervention in MM.

Investigating the multitargeted anti-diabetic potential of cucurbitane-type triterpenoid from Momordica charantia: an LC-MS, docking-based MM\GBSA and MD simulation study

Type 2 diabetes accounts for the largest percentage of all diabetic cases worldwide. Cucurbitane-type triterpenes are mainly found in Momordica charantia and possess excellent pharmacological activities. This study was designed to identify cucurbitane-type triterpene from Momordica charantia using Liquid Chromatography-Mass Spectrometry (LC-MS) analysis, examine its anti-diabetic property with molecular docking against diabetes enzymes (alpha-amylase, alpha-glucosidase, dipeptidyl dipeptidase IV and peroxisome proliferator-activated receptor gamma). The stability and interactions of the docked complexes were investigated using molecular dynamics simulation, while the pharmacokinetic and toxicity profile of the ligand was examined using an ADMET server. (23E)-Cucurbita-5,23,25-triene-3,7-dione (CUB) was identified from the LC-MS profiling of the methanolic extract of M. charantia. The molecular docking studies showed that the identified phytochemical elicited good binding energy against all the target receptors. The RMSD and RMSF plots obtained from the 100 ns molecular dynamics simulation showed that the ligand was stable and established substantial interactions with the amino acid residues of the diabetes enzymes which were confirmed by the MM\GBSA computations. The pharmacokinetic and toxicity properties of the ligand showed it was safer as an anti-diabetic drug candidate. Extensive isolation, in vitro and in vivo studies of the ligand against the diabetic enzymes is recommended.Communicated by Ramaswamy H. Sarma.

Essential oil constituents of regional ethnomedicinal plants as potential inhibitors of SARS-CoV-2 Mpro: an integrated molecular docking, molecular dynamics and QM/MM study

The scientific community has achieved a remarkable milestone by creating efficacious vaccines against the SARS-CoV-2 virus. The treatment alternatives are still restricted, though. The bioactive ingredients present in natural plants are known to exhibit diverse pharmacological effects against many diseases. Using computational techniques such as molecular docking, drug-likeness, ADMET study, MD simulation, and our own N-layered Integrated molecular Orbital and Molecular mechanics (ONIOM) calculations, this study aimed to investigate essential oil constituents of Lindera neesiana, Litsea cubeba and Zanthoxylum armatum DC plants as a potential natural inhibitor of SARS-CoV-2 main protease (Mpro). To determine their binding affinity, 107 phytochemical substances in total were docked inside the binding pocket of Mpro. Copaene showed the highest binding affinity among the 107 compounds, with an energy of -7.90 kcal/mol. Furthermore, physiochemical and ADMET properties were evaluated for the top five phytocompounds. The studied phytocompounds showed good physiochemical and pharmacokinetic behaviour with no associated toxicity. MD simulation further provided evidence for stable interaction of phytocompounds within the binding pocket of Mpro. Subsequently, ONIOM calculation was done on the best-hit complex, wherein the hydrogen bonding interactions were retained with appreciable negative energy. These in silico results indicate that the specific phytocompounds present in essential oils of L. neesiana, L. cubeba, and Z. armatum DC have significant inhibitor ability against SARS-CoV-2 main protease and could be explored for future therapeutic investigations.

Network pharmacology and in silico approaches to uncover multitargeted mechanism of action of Zingiber zerumbet rhizomes for the treatment of idiopathic pulmonary fibrosis

Background

Idiopathic pulmonary fibrosis (IPF) is a disease with high mortality, and there are only two specific drugs available for therapeutic management with limitations. The study aims to identify comprehensive therapeutic mechanisms of Zingiber zerumbet rhizomes (ZZR) to treat IPF by using network pharmacology followed battery of in silico studies.

Methods

The protein-protein interaction network was developed using Cytoscape to obtain core disease targets involved in IPF and their interactive molecules of ZZR. Based on the pharmacophore properties of phytomolecules from ZZR, the drug targets in IPF were explored. Protein-protein interaction network was built in Cytoscape to screen potential targets and components of ZZR. Molecular docking and dynamics were conducted as an empirical study to investigate the mechanism explored through network pharmacology in relation to the hub targets.

Results

The network analysis conferred kaempferol derivatives that had demonstrated a promising therapeutic effect on the perturbed, robust network hubs of TGF-β1, EGFR, TNF-α, MMP2 & MMP9 reported to alter the biological process of mesenchymal transition, myofibroblast proliferation, and cellular matrix deposition in pulmonary fibrosis. The phytomolecules of ZZR act on two major significant pathways, namely the TGF-β-signaling pathway and the FOXO-signaling pathway, to inhibit IPF. Confirmational molecular docking and dynamics simulation studies possessed good stability and interactions of the protein-ligand complexes by RMSD, RMSF, rGyr, SASA, and principal component analysis (PCA). Validated molecular docking and dynamics simulations provided new insight into exploring the mechanism and multi-target effect of ZZR to treat pulmonary fibrosis by restoring the alveolar phenotype through cellular networking.

Conclusions

Network pharmacology and in silico studies confirm the multitargeted activity of ZZR in the treatment of IPF. Further in vitro and in vivo studies are to be conducted to validate these findings.

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

BACKGROUND

The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses.

METHODS

In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα.

RESULTS

Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity.

CONCLUSION

Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Human ACE2 orthologous peptide sequences show better binding affinity to SARS-CoV-2 RBD domain: Implications for drug design

Computational methods coupled with experimental validation play a critical role in the identification of novel inhibitory peptides that interact with viral antigenic determinants. The interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and the helical peptide of human angiotensin-converting enzyme-2 (ACE2) is a necessity for the initiation of viral infection. Herein, natural orthologs of human ACE2 helical peptide were evaluated for competitive inhibitory binding to the viral RBD by use of a computational approach, which was experimentally validated. A total of 624 natural ACE2 orthologous 32-amino acid long peptides were identified through a similarity search. Molecular docking was used to virtually screen and rank the peptides based on binding affinity metrics, benchmarked against human ACE2 peptide docked to the RBD. Molecular dynamics (MD) simulations were done for the human reference and the Nipponia nippon peptide as it exhibited the highest binding affinity (Gibbs free energy; -14 kcal/mol) predicted from the docking results. The MD simulation confirmed the stability of the assessed peptide in the complex (-12.3 kcal/mol). The top three docked-peptides (from Chitinophaga sancti, Nipponia nippon, and Mus musculus) and the human reference were experimentally validated by use of surface plasmon resonance technology. The human reference exhibited the weakest binding affinity (Kd of 318-441 pM) among the peptides tested, in agreement with the docking prediction, while the peptide from Nipponia nippon was the best, with 267-538-fold higher affinity than the reference. The validated peptides merit further investigation. This work showcases that the approach herein can aid in the identification of inhibitory biosimilar peptides for other viruses.

Computational drug repurposing of Akt-1 allosteric inhibitors for non-small cell lung cancer

Non-small cell lung carcinomas (NSCLC) are the predominant form of lung malignancy and the reason for the highest number of cancer-related deaths. Widespread deregulation of Akt, a serine/threonine kinase, has been reported in NSCLC. Allosteric Akt inhibitors bind in the space separating the Pleckstrin homology (PH) and catalytic domains, typically with tryptophan residue (Trp-80). This could decrease the regulatory site phosphorylation by stabilizing the PH-in conformation. Hence, in this study, a computational investigation was undertaken to identify allosteric Akt-1 inhibitors from FDA-approved drugs. The molecules were docked at standard precision (SP) and extra-precision (XP), followed by Prime molecular mechanics-generalized Born surface area (MM-GBSA), and molecular dynamics (MD) simulations on selected hits. Post XP-docking, fourteen best hits were identified from a library of 2115 optimized FDA-approved compounds, demonstrating several beneficial interactions such as pi-pi stacking, pi-cation, direct, and water-bridged hydrogen bonds with the crucial residues (Trp-80 and Tyr-272) and several amino acid residues in the allosteric ligand-binding pocket of Akt-1. Subsequent MD simulations to verify the stability of chosen drugs to the Akt-1 allosteric site showed valganciclovir, dasatinib, indacaterol, and novobiocin to have high stability. Further, predictions for possible biological interactions were performed using computational tools such as ProTox-II, CLC-Pred, and PASSOnline. The shortlisted drugs open a new class of allosteric Akt-1 inhibitors for the therapy of NSCLC.

Phytochemical rich Himalayan Rhododendron arboreum petals inhibit SARS-CoV-2 infection in vitro

Phytochemicals with potential to competitively bind to the host receptors or inhibit SARS-CoV-2 replication, may prove to be useful as adjunct therapeutics for COVID-19. We profiled and investigated the phytochemicals of Rhododendron arboreum petals sourced from Himalayan flora, undertook in vitro studies and found it as a promising candidate against SARS-CoV-2. The phytochemicals were reported in various scientific investigations to act against a range of virus in vitro and in vivo, which prompted us to test against SARS-CoV-2. In vitro assays of R. arboreum petals hot aqueous extract confirmed dose dependent reduction in SARS-CoV-2 viral load in infected Vero E6 cells (80% inhibition at 1 mg/ml; IC50 = 173 µg/ml) and phytochemicals profiled were subjected to molecular docking studies against SARS CoV-2 target proteins. The molecules 5-O-Feruloyl-quinic acid, 3-Caffeoyl-quinic acid, 5-O-Coumaroyl-D-quinic acid, Epicatechin and Catechin showed promising binding affinity with SARS-CoV-2 Main protease (M^Pro; PDB ID: 6LU7; responsible for viral replication) and Human Angiotensin Converting Enzyme-2 (ACE2; PDB ID: 1R4L; mediate viral entry in the host). Molecular dynamics (MD) simulation of 5-O-Feruloyl-quinic acid, an abundant molecule in the extract complexed with the target proteins showed stable interactions. Taken together, the phytochemical profiling, in silico analysis and in vitro anti-viral assay revealed that the petals extract act upon M^Pro and may be inhibiting SARS-CoV-2 replication. This is the first report highlighting R. arboreum petals as a reservoir of antiviral phytochemicals with potential anti-SARS-CoV-2 activity using an in vitro system.

Phytoestrogen β-Sitosterol Exhibits Potent In Vitro Antiviral Activity against Influenza A Viruses

Influenza is a contagious infection in humans that is caused frequently by low pathogenic seasonal influenza viruses and occasionally by pathogenic avian influenza viruses (AIV) of H5, H7, and H9 subtypes. Recently, the clinical sector in poultry and humans has been confronted with many challenges, including the limited number of antiviral drugs and the rapid evolution of drug-resistant variants. Herein, the anti-influenza activities of various plant-derived phytochemicals were investigated against highly pathogenic avian influenza A/H5N1 virus (HPAIV H5N1) and seasonal low pathogenic human influenza A/H1N1 virus (LPHIV H1N1). Out of the 22 tested phytochemicals, the steroid compounds β-sitosterol and β-sitosterol-O-glucoside have very potent activity against the predefined influenza A viruses (IAV). Both steroids could induce such activity by affecting multiple stages during IAV replication cycles, including viral adsorption and replication with a major and significant impact on the virus directly in a cell-free status "viricidal effect". On a molecular level, several molecular docking studies suggested that β-sitosterol and β-sitosterol-O-glucoside exhibited viricidal effects through blocking active binding sites of the hemagglutinin surface protein, as well as showing inhibitory effects against replication through the binding with influenza neuraminidase activity and blocking the active sites of the M2 proton channel activity. The phytoestrogen β-sitosterol has structural similarity with the active form of the female sex hormone estradiol, and this similarity is likely one of the molecular determinants that enables the phytoestrogen β-sitosterol and its derivative to control IAV infection in vitro. This promising anti-influenza activity of β-sitosterol and its O-glycoside derivative, according to both in vitro and cheminformatics studies, recommend both phytochemicals for further studies going through preclinical and clinical phases as efficient anti-influenza drug candidates.

In silico Screening of Potential SARS-CoV-2 Main Protease Inhibitors from Thymus schimperi

Background

COVID-19 is still instigating significant social and economic chaos worldwide; however, there is no approved antiviral drug yet. Here, we used in silico analysis to screen potential SARS-CoV-2 main protease (Mpro) inhibitors extracted from the essential oil of Thymus schimperi which could contribute to the discovery of potent anti-SARS-CoV-2 phytochemicals.

Methods

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of compounds were determined through SwissADME and ProToxII servers. AutoDock tools were used for molecular docking analysis studies, while Chimera, DS studio, and LigPlot were used for post-docking studies. Molecular dynamic simulations were performed for 200 ns under constant pressure.

Results

All compounds exhibited a bioavailability score of ≥0.55 entailing that at least 55% of the drugs can be absorbed unchanged. Only five (9%), nine (16%) and two (3.6%) of the compounds showed active hepatotoxicity, carcinogenicity, and immunotoxicity, respectively. Except for flourazophore P, which showed a little mutagenicity, all other compounds did not show mutagenic properties. On the other hand, only pinene beta was found to have a little cytotoxicity. Five compounds demonstrated effective binding to the catalytic dyad of the SARS-CoV-2 Mpro substrate binding pocket, while two of them (geranylisobutanoate and 3-octane) are found to be the best hits that formed hydrogen bonds with Glu166 and Ser144 of SARS-CoV-2 Mpro.

Conclusion

Based on our in silico analysis, top hits from Thymus schimperi may serve as potential anti-SARS-CoV-2 compounds. Further in vitro and in vivo studies are recommended to characterize these compounds for clinical applications.

Multi-Targeting Approach in Selection of Potential Molecule for COVID-19 Treatment

The coronavirus disease (COVID-19) is a pandemic that started in the City of Wuhan, Hubei Province, China, caused by the spread of coronavirus (SARS-CoV-2). Drug discovery teams around the globe are in a race to develop a medicine for its management. It takes time for a novel molecule to enter the market, and the ideal way is to exploit the already approved drugs and repurpose them therapeutically. We have attempted to screen selected molecules with an affinity towards multiple protein targets in COVID-19 using the Schrödinger suit for in silico predictions. The proteins selected were angiotensin-converting enzyme-2 (ACE2), main protease (MPro), and spike protein. The molecular docking, prime MM-GBSA, induced-fit docking (IFD), and molecular dynamics (MD) simulations were used to identify the most suitable molecule that forms a stable interaction with the selected viral proteins. The ligand-binding stability for the proteins PDB-IDs 1ZV8 (spike protein), 5R82 (Mpro), and 6M1D (ACE2), was in the order of nintedanib > quercetin, nintedanib > darunavir, nintedanib > baricitinib, respectively. The MM-GBSA, IFD, and MD simulation studies imply that the drug nintedanib has the highest binding stability among the shortlisted. Nintedanib, primarily used for idiopathic pulmonary fibrosis, can be considered for repurposing for us against COVID-19.

Innovative, rapid, high-throughput method for drug repurposing in a pandemic-A case study of SARS-CoV-2 and COVID-19

Several efforts to repurpose drugs for COVID-19 treatment have largely either failed to identify a suitable agent or agents identified did not translate to clinical use. Reasons that have been suggested to explain the failures include use of inappropriate doses, that are not clinically achievable, in the screening experiments, and the use of inappropriate pre-clinical laboratory surrogates to predict efficacy. In this study, we used an innovative algorithm, that incorporates dissemination and implementation considerations, to identify potential drugs for COVID-19 using iterative computational and wet laboratory methods. The drugs were screened at doses that are known to be achievable in humans. Furthermore, inhibition of viral induced cytopathic effect (CPE) was used as the laboratory surrogate to predict efficacy. Erythromycin, pyridoxine, folic acid and retapamulin were found to inhibit SARS-CoV-2 induced CPE in Vero cells at concentrations that are clinically achievable. Additional studies may be required to further characterize the inhibitions of CPE and the possible mechanisms.

Virtual structure-based docking and molecular dynamics of FDA-approved drugs for the identification of potential IKKB inhibitors possessing dopaminergic activity in Alzheimer’s disease

In Alzheimer's disease (AD), neuroinflammation is detrimental in causing neurodegeneration. In the central nervous system, inhibitor of nuclear factor kappa B kinase subunit beta (IKK2/IKKβ/IKKB/IKBKB) signaling is linked to neuroinflammation-mediated learning and memory deficits through canonical pathway, while dopamine agonists have been known to reverse such effects. Our in silico analysis predicted if dopaminergic agonists could have IKKB inhibitory actions, to ameliorate neuroinflammation-associated learning and memory deficits. Here, the FDA-approved Zinc 15 database was screened with IKKB (PDB ID 4KIK). Potential molecules with IKKB inhibition were identified through docking, which also possessed dopaminergic activity. Molecular mechanics—generalized Born and surface area (MMGBSA), induced fit docking (IFD) and molecular dynamic (MD) studies of 100 ns simulation time were done. Apomorphine and rotigotine showed greater non-bonding and bonding interactions with amino acids of IKKB as compared to Aripiprazole in docking studies. The IFD studies predicted improved interactions with IKKB. MMGBSA scores indicated that the complex binding free energies were favorable, and MD studies showed an acceptable root mean square deviation between protein and ligands. The protein–ligand interactions showed hydrogen bonds, water and salt bridges necessary for IKKB inhibition, as well as solvent system stability. On the protein–ligand contact map, the varying color band intensities represented the ligand’s ability to bind with amino acids. Dopamine agonists apomorphine, rotigotine, and aripiprazole were predicted to bind and inhibit IKKB in in silico system.

Graphical Abstract

A multi-reference poly-conformational method for in silico design, optimization, and repositioning of pharmaceutical compounds illustrated for selected SARS-CoV-2 ligands

Background

This work presents a novel computational multi-reference poly-conformational algorithm for design, optimization, and repositioning of pharmaceutical compounds.

Methods

The algorithm searches for candidates by comparing similarities between conformers of the same compound and identifies target compounds, whose conformers are collectively close to the conformers of each compound in the reference set. Reference compounds may possess highly variable MoAs, which directly, and simultaneously, shape the properties of target candidate compounds.

Results

The algorithm functionality has been case study validated in silico, by scoring ChEMBL drugs against FDA-approved reference compounds that either have the highest predicted binding affinity to our chosen SARS-CoV-2 targets or are confirmed to be inhibiting such targets in-vivo. All our top scoring ChEMBL compounds also turned out to be either high-affinity ligands to the chosen targets (as confirmed in separate studies) or show significant efficacy, in-vivo, against those selected targets. In addition to method case study validation, in silico search for new compounds within two virtual libraries from the Enamine database is presented. The library's virtual compounds have been compared to the same set of reference drugs that we used for case study validation: Olaparib, Tadalafil, Ergotamine and Remdesivir. The large reference set of four potential SARS-CoV-2 compounds has been selected, since no drug has been identified to be 100% effective against the virus so far, possibly because each candidate drug was targeting only one, particular MoA. The goal here was to introduce a new methodology for identifying potential candidate(s) that cover multiple MoA-s presented within a set of reference compounds.

Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies

Background

Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19.

Methods

A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure.

Results

A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2.

Conclusion

The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worldwide spread of coronavirus disease 19 (COVID-19), and till now, it has caused death to more than 6.2 million people. Although various vaccines and drug candidates are being tested globally with limited to moderate success, a comprehensive therapeutic cure is yet to be achieved. In this study, we applied computational drug repurposing methods complemented with the analyses of the already existing gene expression data to find better therapeutics in treatment and recovery. Primarily, we identified the most crucial proteins of SARS-CoV-2 and host human cells responsible for viral infection and host response. An in-silico screening of the existing drugs was performed against the crucial proteins for SARS-CoV-2 infection, and a few existing drugs were shortlisted. Further, we analyzed the gene expression data of SARS-CoV-2 in human lung epithelial cells and investigated the molecules that can reverse the cellular mRNA expression profiles in the diseased state. LINCS L1000 and Comparative Toxicogenomics Database (CTD) were utilized to obtain two sets of compounds that can be used to counter SARS-CoV-2 infection from the gene expression perspective. Indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), and Vitamin-A were found in two sets of compounds, and in the in-silico screening of existing drugs to treat SARS-CoV-2. Our in-silico findings on Indomethacin were further successfully validated by in-vitro testing in Vero CCL-81 cells with an IC50 of 12 μM. Along with these findings, we briefly discuss the possible roles of Indomethacin and Vitamin-A to counter the SARS-CoV-2 infection in humans.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

In Silico Studies on Zinc Oxide Based Nanostructured Oil Carriers with Seed Extracts of Nigella sativa and Pimpinella anisum as Potential Inhibitors of 3CL Protease of SARS-CoV-2

Coming into the second year of the pandemic, the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants continue to be a serious health hazard globally. A surge in the omicron wave, despite the discovery of the vaccines, has shifted the attention of research towards the discovery and use of bioactive compounds, being potential inhibitors of the viral structural proteins. The present study aimed at the green synthesis of zinc oxide (ZnO) nanoparticles with seed extracts of Nigella sativa and Pimpinella anisum—loaded nanostructured oil carriers (NLC)—using a mixture of olive and black seed essential oils. The synthesized ZnO NLC were extensively characterized. In addition, the constituent compounds in ZnO NLC were investigated as a potential inhibitor for the SARS-CoV-2 main protease (3CLpro or Mpro) where 27 bioactive constituents, along with ZnO in the nanostructure, were subjected to molecular docking studies. The resultant high-score compounds were further validated by molecular dynamics simulation. The study optimized the compounds dithymoquinone, δ-hederin, oleuropein, and zinc oxide with high docking energy scores (ranging from −7.9 to −9.9 kcal/mol). The RMSD and RMSF data that ensued also mirrored these results for the stability of proteins and ligands. RMSD and RMSF data showed no conformational change in the protein during the MD simulation. Histograms of every simulation trajectory explained the ligand properties and ligand–protein contacts. Nevertheless, further experimental investigations and validation of the selected candidates are imperative to take forward the applicability of the nanostructure as a potent inhibitor of COVID-19 (Coronavirus Disease 2019) for clinical trials.

Molecular mechanism of inhibition of COVID-19 main protease by β-adrenoceptor agonists and adenosine deaminase inhibitors using in silico methods

Novel coronavirus (COVID-19) responsible for viral pneumonia which emerged in late 2019 has badly affected the world. No clinically proven drugs are available yet as the targeted therapeutic agents for the treatment of this disease. The viral main protease which helps in replication and transcription inside the host can be an effective drug target. In the present study, we aimed to discover the potential of β-adrenoceptor agonists and adenosine deaminase inhibitors which are used in asthma and cancer/inflammatory disorders, respectively, as repurposing drugs against protease inhibitor by ligand-based and structure-based virtual screening using COVID-19 protease-N3 complex. The AARRR pharmacophore model was used to screen a set of 22,621 molecules to obtain hits, which were subjected to high-throughput virtual screening. Extra precision docking identified four top-scored molecules such as +/--fenoterol, FR236913 and FR230513 with lower binding energy from both categories. Docking identified three major hydrogen bonds with Gly143, Glu166 and Gln189 residues. 100 ns MD simulation was performed for four top-scored molecules to analyze the stability, molecular mechanism and energy requirements. MM/PBSA energy calculation suggested that van der Waals and electrostatic energy components are the main reasons for the stability of complexes. Water-mediated hydrogen bonds between protein-ligand and flexibility of the ligand are found to be responsible for providing extra stability to the complexes. The insights gained from this combinatorial approach can be used to design more potent and bio-available protease inhibitors against novel coronavirus.Communicated by Ramaswamy H. Sarma.

Molecular Docking as a Potential Approach in Repurposing Drugs Against COVID-19: a Systematic Review and Novel Pharmacophore Models

Purpose of Review

This article provides a review of the recent literature related to the FDA-approved drugs that had been repurposed as potential drug candidates against COVID-19. Moreover, we performed a quality pharmacophore study for frequently studied targets, namely, the main protease, RNA-dependent RNA polymerase, and spike protein.

Recent Findings

Ever since the COVID-19 pandemic, the whole spectrum of scientific community is still unable to invent an absolute therapeutic agent for COVID-19. Considering such a fact, drug repurposing strategies seem a truly viable approach to develop novel therapeutic interventions.

Summery

Drug repurposing explores previously approved drugs of known safety and pharmacokinetics profile for possible new effects, reducing the cost, time, and predicting prospective side effects and drug interactions. COVID-19 virulent machinery appeared similar to other viruses, making antiviral agents widely repurposed in pursuit for curative candidates. Our main protease pharmacophoric study revealed multiple features and could be a probable starting point for upcoming research.

Target-Based In Silico Screening for Phytoactive Compounds Targeting SARS-CoV-2

Coronavirus disease 2019 (COVID-19), resulting from infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can cause severe and fatal pneumonia along with other life-threatening complications. The COVID-19 pandemic has taken a heavy toll on the healthcare system globally and has hit the economy hard in all affected countries. As a result, there is an unmet medical need for both the prevention and treatment of COVID-19 infection. Several herbal remedies have claimed to show promising clinical results, but the mechanisms of action are not clear. We set out to identify the anti-viral natural products of these herbal remedies that presumably inhibit the life cycle of SARS-CoV-2. Particularly we chose four key SARS-CoV-2 viral enzymes as targets: Papain-like protease, Main protease, RNA dependent RNA polymerase, and 2'-O-ribose methyltransferase, which were subjected to an unbiased in silico screening against a small molecule library of 33,765 compounds originating from herbs and medicinal plants. The small molecules were then ranked based on their free energy of fitting into the "druggable" pockets on the surface of each target protein. We have analyzed the best "fit" molecules and annotated them according to their plant sources and pharmacokinetic properties. Here we present a list of potential anti-viral ingredients of herbal remedies targeting SARS-CoV-2 and explore the potential mechanisms of action of these compounds as a framework for further development of chemoprophylaxis agents against COVID-19.

Combined crystallographic and computational investigation of the solvent disorder present in a new tipiracil hydrochloride methanol solvate–hydrate

We report a computational and crystallographic analysis of tipiracil hydrochloride (methanol solvate–hydrate). It shows a complex network of H-bonds with static discrete disorder in two out of the three symmetry-independent solvent molecules.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

Polyphenols as alternative treatments of COVID-19

Although scientists around the world have put lots of effort into the development of new treatments for COVID-19 since the outbreak, no drugs except Veklury (remdesivir) have been approved by FDA. There is an urgent need to discover some alternative antiviral treatment for COVID-19. Because polyphenols have been shown to possess antiviral activities, here we conducted a large-scale virtual screening for more than 400 polyphenols. Several lead compounds such as Petunidin 3-O-(6″-p-coumaroyl-glucoside) were identified to have promising binding affinities and convincing binding mechanisms. Analyzing the docking results and ADME properties sheds light on the potential efficacy of the top-ranked drug candidates and pinpoints the key residues on the target proteins for the future of drug development.

An overview of potential inhibitors targeting non-structural proteins 3 (PLpro and Mac1) and 5 (3CLpro/Mpro) of SARS-CoV-2

There is an urgent need to develop effective treatments for coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid spread of SARS-CoV-2 has resulted in a global pandemic that has not only affected the daily lives of individuals but also had a significant impact on the global economy and public health. Although extensive research has been conducted to identify inhibitors targeting SARS-CoV-2, there are still no effective treatment strategies to combat COVID-19. SARS-CoV-2 comprises two important proteolytic enzymes, namely, the papain-like proteinase, located within non-structural protein 3 (nsp3), and nsp5, both of which cleave large replicase polypeptides into multiple fragments that are required for viral replication. Moreover, a domain within nsp3, known as the macrodomain (Mac1), also plays an important role in viral replication. Inhibition of their functions should be able to significantly interfere with the replication cycle of the virus, and therefore these key proteins may serve as potential therapeutic targets. The functions of the above viral targets and their corresponding inhibitors have been summarized in the current review. This review provides comprehensive updates of nsp3 and nsp5 inhibitor development and would help advance the discovery of novel anti-viral therapeutics against SARS-CoV-2.

Discovery of Novel Small-Molecule Inhibitors of SARS-CoV-2 Main Protease as Potential Leads for COVID-19 Treatment

The main protease of SARS-CoV-2 virus, M^pro, is an essential element for viral replication, and inhibitors targeting M^pro are currently being investigated in many drug development programs as a possible treatment for COVID-19. An in vitro pilot screen of a highly focused collection of compounds was initiated to identify new lead scaffolds for M^pro. These efforts identified a number of hits. The most effective of these was compound SIMR-2418 having an inhibitory IC₅₀ value of 20.7 μM. Molecular modeling studies were performed to understand the binding characteristics of the identified compounds. The presence of a cyclohexenone warhead group facilitated covalent binding with the Cys¹⁴⁵ residue of M^pro. Our results highlight the challenges of targeting M^pro protease and pave the way toward the discovery of potent lead molecules.

The impact of curcumin derived polyphenols on the structure and flexibility COVID-19 main protease binding pocket: a molecular dynamics simulation study

The newly occurred SARS-CoV-2 caused a leading pandemic of coronavirus disease (COVID-19). Up to now it has infected more than one hundred sixty million and killed more than three million people according to 14 May 2021 World Health Organization report. So far, different types of studies have been conducted to develop an anti-viral drug for COVID-19 with no success yet. As part of this, silico were studied to discover and introduce COVID-19 antiviral drugs and results showed that protease inhibitors could be very effective in controlling. This study aims to investigate the binding affinity of three curcumin derived polyphenols against COVID-19 the main protease (Mpro), binding pocket, and identification of important residues for interaction. In this study, molecular modeling, auto-dock coupled with molecular dynamics simulations were performed to analyze the conformational, and stability of COVID-19 binding pocket with diferuloylmethane, demethoxycurcumin, and bisdemethoxycurcumin. All three compounds have shown binding affinity −39, −89 and −169.7, respectively. Demethoxycurcumin and bisdemethoxycurcumin showed an optimum binding affinity with target molecule and these could be one of potential ligands for COVID-19 therapy. And also, COVID-19 main protease binding pocket binds with the interface region by one hydrogen bond. Moreover, the MD simulation parameters indicated that demethoxycurcumin and bisdemethoxycurcumin were stable during the simulation run. These findings can be used as a baseline to develop therapeutics with curcumin derived polyphenols against COVID-19.

In silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors

COVID-19, a new strain of coronavirus family, was identified at the end of 2019 in China. The COVID-19 virus spread rapidly all over the world. Scientists strive to find virus-specific antivirals for the treatment of COVID-19. The present study reports a molecular docking study of the stilbenolignans and SARS-CoV-2 main protease (SARS-CoV-2 Mpro) inhibitors. The detailed interactions between the stilbenolignan analogues and SARS-CoV-2 Mpro inhibitors were determined as hydrophobic bonds, hydrogen bonds and electronic bonds, inhibition activity, ligand efficiency, bonding type and distance and etc. The binding energies of the stilbenolignan analogues were obtained from the molecular docking of SARS-CoV-2 Mpro. Lehmbachol D, Maackolin, Gnetucleistol, Gnetifolin F, Gnetofuran A and Aiphanol were found to be -7.7, -8.2, -7.3, -8.5, -8.0 and -7.3 kcal/mol, respectively. Osirus, Molinspiration and SwissADME chemoinformatic tools were used to examine ADMET properties, pharmacokinetic parameters and toxicological characteristics of the stilbenolignan analogues. All analogues obey the Lipinski's rule of five. Furthermore, stilbenolignan analogues were studied to predict their binding affinities against SARS-CoV-2 Mpro using molecular modeling and simulation techniques, and the binding free energy calculations of all complexes were calculated using the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) method. With the data presented here it has been observed that these analogues may be a good candidate for SARS-CoV-2 Mpro in vivo studies, so more research can be done on stilbenolignan analogues.

Structural Basis of Potential Inhibitors Targeting SARS-CoV-2 Main Protease

The Coronavirus disease-19 (COVID-19) pandemic is still devastating the world causing significant social, economic, and political chaos. Corresponding to the absence of globally approved antiviral drugs for treatment and vaccines for controlling the pandemic, the number of cases and/or mortalities are still rising. Current patient management relies on supportive treatment and the use of repurposed drugs as an indispensable option. Of a crucial role in the viral life cycle, ongoing studies are looking for potential inhibitors to the main protease (Mpro) of severe acute respiratory syndrome Coronavirus -2 (SARS-CoV-2) to tackle the pandemic. Although promising results have been achieved in searching for drugs inhibiting the Mpro, work remains to be done on designing structure-based improved drugs. This review discusses the structural basis of potential inhibitors targeting SARS-CoV-2 Mpro, identifies gaps, and provides future directions. Further, compounds with potential Mpro based antiviral activity are highlighted.

SARS-CoV-2 entry inhibitors by dual targeting TMPRSS2 and ACE2: An in silico drug repurposing study

The coronavirus disease (COVID-19) is spreading between human populations mainly through nasal droplets. Currently, the vaccines have great hope, but it takes years for testing its efficacy in human. As there is no specific drug treatment available for COVID-19 pandemic, we explored in silico repurposing of drugs with dual inhibition properties by targeting transmembrane serine protease 2 (TMPRSS2) and human angiotensin-converting enzyme 2 (ACE2) from FDA-approved drugs. The TMPRSS2 and ACE2 dual inhibitors in COVID-19 would be a novel antiviral class of drugs called “entry inhibitors.” For this purpose, approximately 2800 US-FDA approved drugs were docked using a virtual docking tool with the targets TMPRSS2 and ACE2. The best-fit drugs were selected as per docking scores and visual outcomes. Later on, drugs were selected on the basis of molecular dynamics simulations. The drugs alvimopan, arbekacin, dequalinum, fleroxacin, lopinavir, and valrubicin were shortlisted by visual analysis and molecular dynamics simulations. Among these, lopinavir and valrubicin were found to be superior in terms of dual inhibition. Thus, lopinavir and valrubicin have the potential of dual-target inhibition whereby preventing SARS-CoV-2 entry to the host. For repurposing of these drugs, further screening in vitro and in vivo would help in exploring clinically.

Screening of potent drug inhibitors against SARS-CoV-2 RNA polymerase: an in silico approach

COVID-19 has emerged as a rapidly escalating serious global health issue, affecting every section of population in a detrimental way. Present situation invigorated researchers to look for potent targets, development as well as repurposing of conventional therapeutic drugs. NSP12, a RNA polymerase, is key player in viral RNA replication and, hence, viral multiplication. In our study, we have screened a battery of FDA-approved drugs against SARS-CoV-2 RNA polymerase using in silico molecular docking approach. Identification of potent inhibitors against SARS-CoV-2 NSP12 (RNA polymerase) were screeened from FDA approved drugs by virtual screening for therapeutic applications in treatment of COVID-19. In this study, virtual screening of 1749 antiviral drugs was executed using AutoDock Vina in PyRx software. Binding affinities between NSP12 and drug molecules were determined using Ligplot⁺ and PyMOL was used for visualization of docking between interacting residues. Screening of 1749 compounds resulted in 14 compounds that rendered high binding affinity for NSP12 target molecule. Out of 14 compounds, 5 compounds which include 3a (Paritaprevir), 3d (Glecaprevir), 3h (Velpatasvir), 3j (Remdesivir) and 3l (Ribavirin) had a binding affinity of − 10.2 kcal/mol, −9.6 kcal/mol, − 8.5 kcal/mol, − 8.0 kcal/mol and − 6.8 kcal/mol, respectively. Moreover, a number of hydrophobic interactions and hydrogen bonding between these 5 compounds and NSP12 active site were observed. Further, 3l (Ribavirin) was docked with 6M71 and molecular dynamic simulation of the complex was also performed to check the stability of the conformation. In silico analysis postulated the potential of conventional antiviral drugs in treatment of COVID-19. However, these finding may be further supported by experimental data for its possible clinical application in present scenario.

Computational analysis of macrolides as SARS-CoV-2 main protease inhibitors: a pattern recognition study based on molecular topology and validated by molecular docking

Macrolides share the same chemo-mathematical pattern as SARS-CoV-2 protease inhibitors.

Repurposing drugs for the management of COVID-19

Introduction: The coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 represents a serious health issue worldwide, with more than 61 million cases and more than 1.4 million deaths since the beginning of the epidemic near the end of 2019. The scientific community strongly responded to this emergency situation with massive research efforts, mostly focused on diagnosis and clinical investigation of therapeutic solutions. In this scenario, drug repurposing played a crucial role in accelerating advanced clinical testing and shortening the time to access the regulatory review.Areas covered: This review covers the main and most successful drug repurposing approaches from a design, clinical, and regulatory standpoint. Available patents on repurposed drugs are also discussed.Expert opinion: Drug repurposing proved highly successful in response to the current pandemic, with remdesivir becoming the first specific antiviral drug approved for the treatment of COVID-19. In parallel, a number of drugs such as corticosteroids and low molecular weight heparin (LMWH) are used to treat hospitalized COVID-19 patients, while clinical testing of additional therapeutic options is ongoing. It is reasonably expected that these research efforts will deliver optimized and specific therapeutic tools that will increase the preparedness of health systems to possible future epidemics.

Ethnicity and other COVID-19 death risk factors in Mexico

Introduction

Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may develop coronavirus disease 2019 (COVID-19). Risk factors associated with death vary among countries with different ethnic backgrounds. We aimed to describe the factors associated with death in Mexicans with confirmed COVID-19.

Material and methods

We analysed the Mexican Ministry of Health's official database on people tested for SARS-CoV-2 infection by real-time reverse transcriptase-polymerase chain reaction (rtRT-PCR) of nasopharyngeal fluids. Bivariate analyses were performed to select characteristics potentially associated with death, to integrate a Cox-proportional hazards model.

Results

As of May 18, 2020, a total of 177,133 persons (90,586 men and 86,551 women) in Mexico received rtRT-PCR testing for SARS-CoV-2. There were 5332 deaths among the 51,633 rtRT-PCR-confirmed cases (10.33%, 95% CI: 10.07-10.59%). The median time (interquartile range, IQR) from symptoms onset to death was 9 days (5-13 days), and from hospital admission to death 4 days (2-8 days). The analysis by age groups revealed that the significant risk of death started gradually at the age of 40 years. Independent death risk factors were obesity, hypertension, male sex, indigenous ethnicity, diabetes, chronic kidney disease, immunosuppression, chronic obstructive pulmonary disease, age > 40 years, and the need for invasive mechanical ventilation (IMV). Only 1959 (3.8%) cases received IMV, of whom 1893 were admitted to the intensive care unit (96.6% of those who received IMV).

Conclusions

In Mexico, highly prevalent chronic diseases are risk factors for death among persons with COVID-19. Indigenous ethnicity is a poorly studied factor that needs more investigation.

Potential therapeutic use of corticosteroids as SARS CoV-2 main protease inhibitors: a computational study

The outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), represents a pandemic threat to global public health. To date, ∼530,000 people died of this disease worldwide. Presently, researchers/clinicians are adopting the drug repurposing strategy to combat this disease. It has also been observed that some repurposed anti-viral drugs may serve as potent inhibitors of SARS CoV-2 Mpro, a key component of viral replication. Apart from these anti-viral drugs, recently dexamethasone (an important corticosteroid) is effectively used to treat COVID-19 patients. However, the mechanism behind the mode of its action is not so clear. Additionally, the effect of other well-known corticosteroids to control this disease by inhibiting the proteolytic activity of Mpro is ambiguous. In this study, we have adopted computational approaches to understand these aspects. Six well-known corticosteroids (cortisone, hydrocortisone, prednisolone, methylprednisolone, betamethasone and dexamethasone) and two repurposed drugs (darunavir and lopinavir) against COVID-19 were subjected for molecular docking studies. Two of them (betamethasone and dexamethasone) were selected by comparing their binding affinities with selected repurposed drugs toward Mpro. Betamethasone and dexamethasone interacted with both the catalytic residues of Mpro (His41 and Cys145). Molecular dynamics studies further revealed that these two Mpro-corticosteroid complexes are more stable, experience less conformational fluctuations and more compact than Mpro-darunavir/lopinavir complexes. These findings were additionally validated by MM-GBSA analysis. This study provides corroboration for execution of anti-COVID-19 activity of dexamethasone. Our study also emphasizes on the use of another important corticosteroid (betamethasone) as potential therapeutic agent for COVID-19 treatment.