Novel Corona Virus (COVID-19): Current Strategies and Future Aspects

Novel coronavirus (COVID-19) recently emerged as a new pandemic disease that affects millions of people worldwide. This disease considers as a potential threat to human society. Researchers are continuously working to identify virus structure, the pathophysiology of the disease, and possible treatment of the disease. Currently, to fight against the coronavirus, two major strategies have been adopted throughout the world;one is to target virus-cell machinery, and the second is to improve patient immunity. In this review, we have described detailed information about the structure and life cycle of the novel coronavirus, current therapy, and future strategies to fight against this pandemic disease. Computational methods are useful for understanding virus structure, disease pathology, and discovering novel anti-COVID agents. These methods can provide fast and efficient solutions to fight against this disease. We also highlighted the potential role of robotic technology and its importance in various clinical aspects. These robotic technologies may also play an important role in fighting COVID-19. © 2021 by the authors.

FDA approved five-membered ring fused pyrimidine-based derivatives and their biological properties

Pyrimidines-based drugs are one of the most important drugs for novel and recurring viruses, including the coronavirus. This chapter deals with 41 FDA-approved five-membered ring fused pyrimidine-based drugs, their synthetic strategies, and pharmacological activities. © 2023 Elsevier Ltd. All rights reserved.

Revealing Potential Binding Affinity of FDA Approved Therapeutics Targeting Main Protease (3CLpro) in Impairing Novel Coronavirus (SARS-CoV-2) Replication that Causes COVID-19

Background: Spread of COVID-19 attains a crucial transition in reveling its pandemic across the boundaries. In combating the infection caused by SARS-CoV-2, there is a spectrum of ideal strategies that have been adopted globally, of which repurposing of approved drugs considerably having high clinical relevance. 3-chymotrypsin-like protease (3CL pro) is considered to be the potential target for the researchers as it is highly essential for cleavage of polyprotein to get 16 nonstructural proteins (called nsp1-nsp16). These proteins are highly essential for viral replication and hence become a primary target for enzyme inhibitors. 3CL pro, having a structural projectile helical chain with biologically active site involved in processing viral polyproteins that are evolved from RNA genome translation. Objective(s): The major objective of the present investigation is to evaluate the enzyme inhibition potential of FDA approved therapeutic leads in targeting 3CLpro that medicates the viral replication. Method(s): Docking calculations were carried out for an array of FDA approved molecules which leads to a notable few molecules such as Emtricitabine, Oseltamivir, Ganciclovir, Chloroquine, Baricitinib, Favipiravir, Lopinavir, Ritonavir, Remdesivir, Ribavirin, Tenofovir, Umifenovir, Carbapenam, Ertap-enem and Imipenam which have both specificity and selectivity in terms of binding efficiency against 3CL proenzyme. Result(s): A combinatorial evaluation employing in-silico screening shows a major lead for remdesivir which possesses a substantial affinity to 3CL pro binding on core amino acid residues, such as Leu 27, His 41, Gly 143, Cys 145, His 164, Met 165, Glu 166, Pro 168 and His 172 which share the biological significance in mediating enzymatic action. Results of docking simulation by Autodock over a host of FDA approved molecules show high degree of selectivity and specificity in the increasing order of binding capacity;Remdesivir> Ertapenem> Imipenam> Tenofovir> Umifenovir> Chloroquine> Lopinavir> Ritonavir> Emtricitabine> Ganciclovir> Baricitinib> Ribavirin>Oseltamivir>Favipiravir> Carbapenam. Conclusion(s): Till date, there is no known cure attained for treating COVID-19 infection. In conclusion, lead molecules from already approved sources provoke promising potential which grabs the attention of the clinicians in availing potential therapeutic candidate as a drug of choice in the clinical management of COVID-19 time-dependently.Copyright © 2020 Bentham Science Publishers.

Suramin, penciclovir, and anidulafungin exhibit potential in the treatment of COVID-19 via binding to nsp12 of SARS-CoV-2.

COVID-19, for which no confirmed therapeutic agents are available, has claimed over 48,14,000 lives globally. A feasible and quicker method to resolve this problem may be 'drug repositioning'. We investigated selected FDA and WHO-EML approved drugs based on their previously promising potential as antivirals, antibacterials or antifungals. These drugs were docked onto the nsp12 protein, which reigns the RNA-dependent RNA polymerase activity of SARS-CoV-2, a key therapeutic target for coronaviruses. Docked complexes were reevaluated using MM-GBSA analysis and the top three inhibitor-protein complexes were subjected to 100 ns long molecular dynamics simulation followed by another round of MM-GBSA analysis. The RMSF plots, binding energies and the mode of physicochemical interaction of the active site of the protein with the drugs were evaluated. Suramin, Penciclovir, and Anidulafungin were found to bind to nsp12 with similar binding energies as that of Remdesivir, which has been used as a therapy for COVID-19. In addition, recent experimental evidences indicate that these drugs exhibit antiviral efficacy against SARS-CoV-2. Such evidence, along with the significant and varied physical interactions of these drugs with the key viral enzyme outlined in this investigation, indicates that they might have a prospective therapeutic potential in the treatment of COVID-19 as monotherapy or combination therapy with Remdesivir.

G4-binding drugs, chlorpromazine and prochlorperazine, repurposed against COVID-19 infection in hamsters.

The COVID-19 pandemic caused by SARS-CoV-2 has caused millions of infections and deaths worldwide. Limited treatment options and the threat from emerging variants underline the need for novel and widely accessible therapeutics. G-quadruplexes (G4s) are nucleic acid secondary structures known to affect many cellular processes including viral replication and transcription. We identified heretofore not reported G4s with remarkably low mutation frequency across >5 million SARS-CoV-2 genomes. The G4 structure was targeted using FDA-approved drugs that can bind G4s - Chlorpromazine (CPZ) and Prochlorperazine (PCZ). We found significant inhibition in lung pathology and lung viral load of SARS-CoV-2 challenged hamsters when treated with CPZ or PCZ that was comparable to the widely used antiviral drug Remdesivir. In support, in vitro G4 binding, inhibition of reverse transcription from RNA isolated from COVID-infected humans, and attenuated viral replication and infectivity in Vero cell cultures were clear in case of both CPZ and PCZ. Apart from the wide accessibility of CPZ/PCZ, targeting relatively invariant nucleic acid structures poses an attractive strategy against viruses like SARS-CoV-2, which spread fast and accumulate mutations quickly.

Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning.

Severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) induced cytokine storm is the major cause of COVID­19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor­Kappa B (NF­κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS­CoV­2 induced cytokine storm pathway. We developed machine learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID­19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.

Structure-Based Virtual Screening and Functional Validation of Potential Hit Molecules Targeting the SARS-CoV-2 Main Protease.

The recent global health emergency caused by the coronavirus disease 2019 (COVID-19) pandemic has taken a heavy toll, both in terms of lives and economies. Vaccines against the disease have been developed, but the efficiency of vaccination campaigns worldwide has been variable due to challenges regarding production, logistics, distribution and vaccine hesitancy. Furthermore, vaccines are less effective against new variants of the SARS-CoV-2 virus and vaccination-induced immunity fades over time. These challenges and the vaccines' ineffectiveness for the infected population necessitate improved treatment options, including the inhibition of the SARS-CoV-2 main protease (Mpro). Drug repurposing to achieve inhibition could provide an immediate solution for disease management. Here, we used structure-based virtual screening (SBVS) to identify natural products (from NP-lib) and FDA-approved drugs (from e-Drug3D-lib and Drugs-lib) which bind to the Mpro active site with high-affinity and therefore could be designated as potential inhibitors. We prioritized nine candidate inhibitors (e-Drug3D-lib: Ciclesonide, Losartan and Telmisartan; Drugs-lib: Flezelastine, Hesperidin and Niceverine; NP-lib: three natural products) and predicted their half maximum inhibitory concentration using DeepPurpose, a deep learning tool for drug-target interactions. Finally, we experimentally validated Losartan and two of the natural products as in vitro Mpro inhibitors, using a bioluminescence resonance energy transfer (BRET)-based Mpro sensor. Our study suggests that existing drugs and natural products could be explored for the treatment of COVID-19.

COVID-19 Treatment-Current Status, Advances, and Gap.

COVID-19, which emerged in December 2019, was declared a global pandemic by the World Health Organization (WHO) in March 2020. The disease was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has caused millions of deaths worldwide and caused social and economic disruption. While clinical trials on therapeutic drugs are going on in an Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) public-private partnership collaboration, current therapeutic approaches and options to counter COVID-19 remain few. Therapeutic drugs include the FDA-approved antiviral drugs, Remdesivir, and an immune modulator, Baricitinib. Hence, therapeutic approaches and alternatives for COVID-19 treatment need to be broadened. This paper discusses efforts in approaches to find treatment for COVID-19, such as inhibiting viral entry and disrupting the virus life cycle, and highlights the gap that needs to be filled in these approaches.

Chapter 5 - FDA approved five-membered ring fused pyrimidine-based derivatives and their biological properties

Pyrimidines-based drugs are one of the most important drugs for novel and recurring viruses, including the coronavirus. This chapter deals with 41 FDA-approved five-membered ring fused pyrimidine-based drugs, their synthetic strategies, and pharmacological activities.

FDA Coronavirus Treatment Acceleration Program: approved drugs and those in clinical trials

Coronavirus disease is a highly infectious disease caused by the 2019 novel coronavirus, now recognized as severe acute respiratory syndrome coronavirus 2. This pandemic caused great stress in the general public and led to the collapse of healthcare system in some places. Till date, there has not been an effective vaccine or antiviral drug for its treatment. As at the time of write-up, scientists, researchers, and industries in different countries are working collaboratively to develop an effective therapy or alternative to combat this deadly and highly infectious virus. To combat or overcome these difficulties, the United States Food and Drug Administration launched an FDA Coronavirus Treatment Acceleration Program (CTAP) to accelerate the development of new investigational drugs, biological therapies, vaccines, and medical devices for the treatment of COVID-19 patients. This chapter provides details about this, the role of FDA CTAP and the drugs they are evaluating for the treatment of this disease. © 2022 Elsevier Inc. All rights reserved.

Repurposing of FDA-approved drugs as potential inhibitors of the SARS-CoV-2 main protease: Molecular insights into improved therapeutic discovery.

With numerous infections and fatalities, COVID-19 has wreaked havoc around the globe. The main protease (Mpro), which cleaves the polyprotein to form non-structural proteins, thereby helping in the replication of SARS-CoV-2, appears as an attractive target for antiviral therapeutics. As FDA-approved drugs have shown effectiveness in targeting Mpro in previous SARS-CoV(s), molecular docking and virtual screening of existing antiviral, antimalarial, and protease inhibitor drugs were carried out against SARS-CoV-2 Mpro. Among 53 shortlisted drugs with binding energies lower than that of the crystal-bound inhibitor α-ketoamide 13 b (-6.7 kcal/mol), velpatasvir, glecaprevir, grazoprevir, baloxavir marboxil, danoprevir, nelfinavir, and indinavir (-9.1 to -7.5 kcal/mol) were the most significant on the list (hereafter referred to as the 53-list). Molecular dynamics (MD) simulations confirmed the stability of their Mpro complexes, with the MMPBSA binding free energy (ΔGbind) ranging between -124 kJ/mol (glecaprevir) and -28.2 kJ/mol (velpatasvir). Despite having the lowest initial binding energy, velpatasvir exhibited the highest ΔGbind value for escaping the catalytic site during the MD simulations, indicating its reduced efficacy, as observed experimentally. Available inhibition assay data adequately substantiated the computational forecast. Glecaprevir and nelfinavir (ΔGbind = -95.4 kJ/mol) appear to be the most effective antiviral drugs against Mpro. Furthermore, the remaining FDA drugs on the 53-list can be worth considering, since some have already demonstrated antiviral activity against SARS-CoV-2. Hence, theoretical pKi (Ki = inhibitor constant) values for all 53 drugs were provided. Notably, ΔGbind directly correlates with the average distance of the drugs from the His41-Cys145 catalytic dyad of Mpro, providing a roadmap for rapid screening and improving the inhibitor design against SARS-CoV-2 Mpro.

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization.

The novel coronavirus pneumonia COVID-19 infected by SARS-CoV-2 has attracted worldwide attention. It is urgent to find effective therapeutic strategies for stopping COVID-19. In this study, a Bounded Nuclear Norm Regularization (BNNR) method is developed to predict anti-SARS-CoV-2 drug candidates. First, three virus-drug association datasets are compiled. Second, a heterogeneous virus-drug network is constructed. Third, complete genomic sequences and Gaussian association profiles are integrated to compute virus similarities; chemical structures and Gaussian association profiles are integrated to calculate drug similarities. Fourth, a BNNR model based on kernel similarity (VDA-GBNNR) is proposed to predict possible anti-SARS-CoV-2 drugs. VDA-GBNNR is compared with four existing advanced methods under fivefold cross-validation. The results show that VDA-GBNNR computes better AUCs of 0.8965, 0.8562, and 0.8803 on the three datasets, respectively. There are 6 anti-SARS-CoV-2 drugs overlapping in any two datasets, that is, remdesivir, favipiravir, ribavirin, mycophenolic acid, niclosamide, and mizoribine. Molecular dockings are conducted for the 6 small molecules and the junction of SARS-CoV-2 spike protein and human angiotensin-converting enzyme 2. In particular, niclosamide and mizoribine show higher binding energy of -8.06 and -7.06 kcal/mol with the junction, respectively. G496 and K353 may be potential key residues between anti-SARS-CoV-2 drugs and the interface junction. We hope that the predicted results can contribute to the treatment of COVID-19.

Co-crystallization and structure determination: An effective direction for anti-SARS-CoV-2 drug discovery.

Safer and more-effective drugs are urgently needed to counter infections with the highly pathogenic SARS-CoV-2, cause of the COVID-19 pandemic. Identification of efficient inhibitors to treat and prevent SARS-CoV-2 infection is a predominant focus. Encouragingly, using X-ray crystal structures of therapeutically relevant drug targets (PLpro, Mpro, RdRp, and S glycoprotein) offers a valuable direction for anti-SARS-CoV-2 drug discovery and lead optimization through direct visualization of interactions. Computational analyses based primarily on MMPBSA calculations have also been proposed for assessing the binding stability of biomolecular structures involving the ligand and receptor. In this study, we focused on state-of-the-art X-ray co-crystal structures of the abovementioned targets complexed with newly identified small-molecule inhibitors (natural products, FDA-approved drugs, candidate drugs, and their analogues) with the assistance of computational analyses to support the precision design and screening of anti-SARS-CoV-2 drugs.

Mutational analysis in international isolates and drug repurposing against SARS-CoV-2 spike protein: molecular docking and simulation approach.

The novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) is spreading, as the causative pathogen of coronavirus disease-19 (COVID-19). It has infected more than 1.65 billion people all over the world since it was discovered and reported 3.43 million deaths by mid of May 2021. SARS-CoV-2 enters the host cell by binding to viral surface glycoprotein (S protein) with human ACE2 (angiotensin-converting enzyme2). Spike protein (contains S1 and S2 sub-domains) molecular interaction with the host cells is considered as a major step in the viral entry and disease initiation and progression and this identifies spike protein as a promising therapeutic target against antiviral drugs. Currently, there are no efficient antiviral drugs for the prevention of COVID-19 infection. In this study, we have analyzed global 8719 spike protein sequences from patients infected with SAR-CoV-2. These SAR-CoV-2 genome sequences were downloaded from the GISAID database. By using an open reading frame (ORF) tool we have identified the spike protein sequence. With these, all spike protein amino acid sequences are subjected to multiple sequence alignment (MSA) with Wuhan strain spike protein sequence as a query sequence, and it shows all SAR-CoV strain spike proteins are 99.8% identical. In the mutational analysis, we found 639 mutations in the spike protein sequence of SARS-CoV-2 and identified/highlighted 20 common mutations L5F, T22I, T29I, H49Y, L54F, V90F, S98F, S221L, S254F, V367F, A520S, T572I, D614G, H655Y, P809S, A879S, D936Y, A1020S, A1078S, and H1101Y. Further, we have analyzed the crystal structure of the 2019-nCoV chimeric receptor-binding complex with ACE2 (PDB ID: 6VW1) as a major target protein. The spike receptor binding protein (RBD) used as target region for our studies with FDA-approved drugs for repurposing, and identified few anti-SARS-CoV2 potential drugs (Silmitasertib, AC-55541, Merimepodib, XL413, AZ3451) based on their docking score and binding mode calculations expected to strongly bind to motifs of ACE2 receptor and may show impart relief in COVID-19 patients.

A new high-content screening assay of the entire hepatitis B virus life cycle identifies novel antivirals.

BACKGROUND & AIMS: Chronic hepatitis B is an incurable disease. Addressing the unmet medical need for therapies has been hampered by a lack of suitable cell culture models to investigate the HBV life cycle in a single experimental setup. We sought to develop a platform suitable to investigate all aspects of the entire HBV life cycle. METHODS: HepG2-NTCPsec+ cells were inoculated with HBV. Supernatants of infected cells were transferred to naïve cells. Inhibition of infection was determined in primary and secondary infected cells by high-content imaging of viral and cellular factors. Novel antivirals were triaged in cells infected with cell culture- or patient-derived HBV and in stably virus replicating cells. HBV internalisation and target-based receptor binding assays were conducted. RESULTS: We developed an HBV platform, screened 2,102 drugs and bioactives, and identified 3 early and 38 late novel HBV life cycle inhibitors using infectious HBV genotype D. Two early inhibitors, pranlukast (EC50 4.3 µM; 50% cytotoxic concentration [CC50] >50 µM) and cytochalasin D (EC50 0.07 µM; CC50 >50 µM), and 2 late inhibitors, fludarabine (EC50 0.1 µM; CC50 13.4 µM) and dexmedetomidine (EC50 6.2 µM; CC50 >50 µM), were further investigated. Pranlukast inhibited HBV preS1 binding, whereas cytochalasin D prevented the internalisation of HBV. Fludarabine inhibited the secretion of HBV progeny DNA, whereas dexmedetomidine interfered with the infectivity of HBV progeny. Patient-derived HBV genotype C was efficiently inhibited by fludarabine (EC50 0.08 µM) and dexmedetomidine (EC50 8.7 µM). CONCLUSIONS: The newly developed high-content assay is suitable to screen large-scale drug libraries, enables monitoring of the entire HBV life cycle, and discriminates between inhibition of early and late viral life cycle events. LAY SUMMARY: HBV infection is an incurable, chronic disease with few available treatments. Addressing this unmet medical need has been hampered by a lack of suitable cell culture models to study the entire viral life cycle in a single experimental setup. We developed an image-based approach suitable to screen large numbers of drugs, using a cell line that can be infected by HBV and produces large amounts of virus particles. By transferring viral supernatants from these infected cells to uninfected target cells, we could monitor the entire viral life cycle. We used this system to screen drug libraries and identified novel anti-HBV inhibitors that potently inhibit HBV in various phases of its life cycle. This assay will be an important new tool to study the HBV life cycle and accelerate the development of novel therapeutic strategies.

Virtual screening of quinoline derived library for SARS-COV-2 targeting viral entry and replication.

The COVID-19 pandemic infection has claimed many lives and added to the social, economic, and psychological distress. The contagious disease has quickly spread to almost 218 countries and territories following the regional outbreak in China. As the number of infected populations increases exponentially, there is a pressing demand for anti-COVID drugs and vaccines. Virtual screening provides possible leads while extensively cutting down the time and resources required for ab-initio drug design. We report structure-based virtual screening of a hundred plus library of quinoline drugs with established antiviral, antimalarial, antibiotic or kinase inhibitor activity. In this study, targets having a role in viral entry, viral assembly, and viral replication have been selected. The targets include: 1) RBD of receptor-binding domain spike protein S 2) Mpro Chymotrypsin main protease 3) Ppro Papain protease 4) RNA binding domain of Nucleocapsid Protein, and 5) RNA Dependent RNA polymerase from SARS-COV-2. An in-depth analysis of the interactions and G-score compared to the controls like hydroxyquinoline and remdesivir has been presented. The salient results are (1) higher scoring of antivirals as potential drugs (2) potential of afatinib by scoring as better inhibitor, and (3) biological explanation of the potency of afatinib. Further MD simulations and MM-PBSA calculations showed that afatinib works best to interfere with the the activity of RNA dependent RNA polymerase of SARS-COV-2, thereby inhibiting replication process of single stranded RNA virus. Communicated by Ramaswamy H. Sarma.

Identification of FDA approved drugs against SARS-CoV-2 RNA dependent RNA polymerase (RdRp) and 3-chymotrypsin-like protease (3CLpro), drug repurposing approach.

The RNA-dependent RNA polymerase (RdRp) and 3C-like protease (3CLpro) from SARS-CoV-2 play crucial roles in the viral life cycle and are considered the most promising targets for drug discovery against SARS-CoV-2. In this study, FDA-approved drugs were screened to identify the probable anti-RdRp and 3CLpro inhibitors by molecular docking approach. The number of ligands selected from the PubChem database of NCBI for screening was 1760. Ligands were energy minimized using Open Babel. The RdRp and 3CLpro protein sequences were retrieved from the NCBI database. For Homology Modeling predictions, we used the Swiss model server. Their structure was then energetically minimized using SPDB viewer software and visualized in the CHIMERA UCSF software. Molecular dockings were performed using AutoDock Vina, and candidate drugs were selected based on binding affinity (∆G). Hydrogen bonding and hydrophobic interactions between ligands and proteins were visualized using Ligplot and the Discovery Studio Visualizer v3.0 software. Our results showed 58 drugs against RdRp, which had binding energy of - 8.5 or less, and 69 drugs to inhibit the 3CLpro enzyme with a binding energy of - 8.1 or less. Six drugs based on binding energy and number of hydrogen bonds were chosen for the next step of molecular dynamics (MD) simulations to investigate drug-protein interactions (including Nilotinib, Imatinib and dihydroergotamine for 3clpro and Lapatinib, Dexasone and Relategravir for RdRp). Except for Lapatinib, other drugs-complexes were stable during MD simulation. Raltegravir, an anti-HIV drug, was observed to be the best compound against RdRp based on docking binding energy (-9.5 kcal/mole) and MD results. According to the MD results and binding energy, dihydroergotamine is a suitable candidate for 3clpro inhibition (-9.6 kcal/mol). These drugs were classified into several categories, including antiviral, antibacterial, anti-inflammatory, anti-allergic, cardiovascular, anticoagulant, BPH and impotence, antipsychotic, antimigraine, anticancer, and so on. The common prescription-indications for some of these medication categories appeared somewhat in line with manifestations of COVID-19. We hope that they can be beneficial for patients with certain specific symptoms of SARS-CoV-2 infection, but they can also probably inhibit viral enzymes. We recommend further experimental evaluations in vitro and in vivo on these FDA-approved drugs to assess their potential antiviral effect on SARS-CoV-2.

Computational screening of FDA approved drugs of fungal origin that may interfere with SARS-CoV-2 spike protein activation, viral RNA replication, and post-translational modification: a multiple target approach.

Coronavirus spread is an emergency reported globally, and a specific treatment strategy for this significant health issue is not yet identified. COVID-19 is a highly contagious disease and needs to be controlled promptly as millions of deaths have been reported. Due to the absence of proficient restorative alternatives and preliminary clinical restrictions, FDA-approved medications can be a decent alternative to deal with the coronavirus malady (COVID-19). The present study aims to meet the imperative necessity of effective COVID-19 drug treatment with a computational multi-target drug repurposing approach. This study focused on screening the FDA-approved drugs derived from the fungal source and its derivatives against the SARS-CoV-2 targets. All the selected drugs showed good binding affinity towards these targets, and out of them, bromocriptine was found to be the best candidate after the screening on the COVID-19 targets. Further, bromocriptine is analyzed by molecular simulation and MM-PBSA study. These studies suggested that bromocriptine can be the best candidate for TMPRSS2, Main protease, and RdRp protein. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-021-00089-8.

Phylogenic analysis of coronavirus genome and molecular studies on potential anti-COVID-19 agents from selected FDA-approved drugs.

The emergence of 2019 novel Coronavirus (COVID-19 or 2019-nCoV) has caused significant global morbidity and mortality with no consensus specific treatment. We tested the hypothesis that FDA-approved antiretrovirals, antibiotics, and antimalarials will effectively inhibit COVID-19 two major drug targets, coronavirus nucleocapsid protein (NP) and hemagglutinin-esterase (HE). To test this hypothesis, we carried out a phylogenic analysis of coronavirus genome to understand the origins of NP and HE, and also modeled the proteins before molecular docking, druglikeness, toxicity assessment, molecular dynamics simulation (MDS) and ligand-based pharmacophore modeling of the selected FDA-approved drugs. Our models for NP and HE had over 95% identity with templates 5EPW and 3CL5 respectively in the PDB database, with majority of the amino acids occupying acceptable regions. The active sites of the proteins contained conserved residues that were involved in ligand binding. Lopinavir and ritonavir possessed greater binding affinities for NP and HE relative to remdesivir, while levofloxacin and hydroxychloroquine were the most notable among the other classes of drugs. The Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of gyration (Rg), and binding energy values obtained after 100 ns of MDS revealed good stability of these compounds in the binding sites of the proteins while important pharmacophore features were also identified. The study showed that COVID-19 likely originated from bat, owing to the over 90% genomic similarity observed, and that lopinavir, levofloxacin, and hydroxychloroquine might serve as potential anti-COVID-19 lead molecules for additional optimization and drug development for the treatment of COVID-19.Communicated by Ramaswamy H. Sarma.

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.

In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3CLpro in severe acute respiratory syndrome coronavirus (SARS-CoV) for unknown compounds during the virtual screening process. This paper proposes a novel deep learning-based method to implement virtual screening with convolutional neural network (CNN) architecture. The descriptors represent chemical molecules, and these descriptors are input into the CNN framework to train a model and predict active compounds. When compared to other machine learning methods, including random forest, naive Bayes, decision tree, and support vector machine, the proposed CNN model's evaluation of the test set showed an accuracy of 0.86, a sensitivity of 0.45, a specificity of 0.96, a precision of 0.73, a recall of 0.45, an F-measure of 0.55, and a ROC of 0.71. The CNN model screened 17 out of 918 phytochemical compounds; 60 out of 423 from the natural product NCI divset IV; 17,831 out of 112,267 from the ZINC natural product database; and 315 out of 1556 FDA-approved drugs as anti-SARS-CoV agents. Further, to prioritize drug-like compounds, Lipinski's rule of five was applied to screen anti-SARS-CoV compounds (excluding FDA-approved drugs), resulting in 10, 59, and 14,025 hit molecules. Out of 10 phytochemical compounds, 9 anti-SARS-CoV agents belonged to the flavonoid group. In conclusion, the proposed CNN model can prove useful for developing novel target-specific anti-SARS-CoV compounds.