In silico identification of novel benzophenone-coumarin derivatives as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) inhibitors.

In this study, we propose our novel benzophenone-coumarin derivatives (BCDs) as potent inhibitors of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 virus, one of the key targets that are involved in the viral genome replication. We aim to evaluate the in silico antiviral potential of BCDs against this protein target, which involves molecular docking simulations, druglikeliness and pharmacokinetic evaluations, PASS analysis, molecular dynamics simulations, and computing binding free energy. Out of all the BCDs screened through these parameters, BCD-8 was found to be the most efficient and potent inhibitor of SARS-CoV-2 RdRp. During molecular docking simulation, BCD-8 showed an extensive molecular interaction in comparison with that of the standard control used, remdesivir. The druglikeliness and pharmacokinetic analyses also proved the efficiency of BCD-8 as an effective drug without adverse effects. Further, pharmacological potential analysis through PASS depicted the antiviral property of BCD-8. With these findings, we performed molecular dynamics simulations, where BCD-8 edged out remdesivir with its exemplary stable interaction with SARS-CoV-2 RdRp. Furthermore, binding free energy of both BCD-8 and remdesivir was calculated, where BCD-8 showed a lower binding energy and standard deviations in comparison with that of remdesivir. Moreover, being a non-nucleoside analogue, BCD-8 can be used effectively against SARS-CoV-2, whereas nucleoside analogues like remdesivir may become non-functional or less functional due to exonuclease activity of nsp14 of the virus. Therefore, we propose BCD-8 as a SARS-CoV-2 RdRp inhibitor, showing higher predicted efficiency than remdesivir in all the in silico experiments conducted.Communicated by Ramaswamy H. Sarma.

Computer-Aided Drug Discovery Approaches in the Identification of Natural Products against SARS-CoV-2: A Review.

BACKGROUND: The COVID-19 pandemic is raising a worldwide search for compounds that could act against the disease, mainly due to its mortality. With this objective, many researchers invested in the discovery and development of drugs of natural origin. To assist in this search, the potential of computational tools to reduce the time and cost of the entire process is known. OBJECTIVE: Thus, this review aimed to identify how these tools have helped in the identification of natural products against SARS-CoV-2. METHODS AND RESULTS: For this purpose, a literature review was carried out with scientific articles with this proposal where it was possible to observe that different classes of primary and, mainly, secondary metabolites were evaluated against different molecular targets, mostly being enzymes and spike, using computational techniques, with emphasis on the use of molecular docking. CONCLUSION: However, it is noted that in silico evaluations still have much to contribute to the identification of an anti-SARS-CoV-2 substance, due to the vast chemical diversity of natural products, identification and use of different molecular targets and computational advancement.

Development of Effective Therapeutic Molecule from Natural Sources against Coronavirus Protease.

The SARS-CoV-2 main protease (Mpro) is one of the molecular targets for drug design. Effective vaccines have been identified as a long-term solution but the rate at which they are being administered is slow in several countries, and mutations of SARS-CoV-2 could render them less effective. Moreover, remdesivir seems to work only with some types of COVID-19 patients. Hence, the continuous investigation of new treatments for this disease is pivotal. This study investigated the inhibitory role of natural products against SARS-CoV-2 Mpro as repurposable agents in the treatment of coronavirus disease 2019 (COVID-19). Through in silico approach, selected flavonoids were docked into the active site of Mpro. The free energies of the ligands complexed with Mpro were computationally estimated using the molecular mechanics-generalized Born surface area (MM/GBSA) method. In addition, the inhibition process of SARS-CoV-2 Mpro with these ligands was simulated at 100 ns in order to uncover the dynamic behavior and complex stability. The docking results showed that the selected flavonoids exhibited good poses in the binding domain of Mpro. The amino acid residues involved in the binding of the selected ligands correlated well with the residues involved with the mechanism-based inhibitor (N3) and the docking score of Quercetin-3-O-Neohesperidoside (-16.8 Kcal/mol) ranked efficiently with this inhibitor (-16.5 Kcal/mol). In addition, single-structure MM/GBSA rescoring method showed that Quercetin-3-O-Neohesperidoside (-87.60 Kcal/mol) is more energetically favored than N3 (-80.88 Kcal/mol) and other ligands (Myricetin 3-Rutinoside (-87.50 Kcal/mol), Quercetin 3-Rhamnoside (-80.17 Kcal/mol), Rutin (-58.98 Kcal/mol), and Myricitrin (-49.22 Kcal/mol). The molecular dynamics simulation (MDs) pinpointed the stability of these complexes over the course of 100 ns with reduced RMSD and RMSF. Based on the docking results and energy calculation, together with the RMSD of 1.98 ± 0.19 Å and RMSF of 1.00 ± 0.51 Å, Quercetin-3-O-Neohesperidoside is a better inhibitor of Mpro compared to N3 and other selected ligands and can be repurposed as a drug candidate for the treatment of COVID-19. In addition, this study demonstrated that in silico docking, free energy calculations, and MDs, respectively, are applicable to estimating the interaction, energetics, and dynamic behavior of molecular targets by natural products and can be used to direct the development of novel target function modulators.

COVID-19 Candidate Genes and Pathways Potentially Share the Association with Lung Cancer.

COVID-19 is considered as the most challenging in the current situation but lung cancer is also the leading cause of death in the global population. These two malignancies are among the leading human diseases and are highly complex in terms of diagnostic and therapeutic approaches as well as the most frequent and highly complex and heterogeneous in nature. Based on the latest update, it is known that the patients suffering from lung cancer, are considered to be significantly at higher risk of COVID-19 infection in terms of survival and there are a number of evidences which support the hypothesis that these diseases may share the same functions and functional components. Multi-level unwanted alterations such as (epi-)genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major sources which promote a number of complex diseases and such heterogeneous level of complexities are considered as the major barrier in the development of therapeutics. With so many challenges, it is critical to understand the relationships and the common shared aberrations between them which is difficult to unravel and understand. A simple approach has been applied for this study where differential gene expression analysis, pathway enrichment, and network level understanding are carried out. Since, gene expression changes and genomic alterations are related to the COVID-19 and lung cancer but their pattern varies significantly. Based on the recent studies, it appears that the patients suffering from lung cancer and and simultaneously infected with COVID-19, then survival chance is lessened. So, we have designed our goal to understand the genes commonly overexpressed and commonly enriched pathways in case of COVID-19 and lung cancer. For this purpose, we have presented the summarized review of the previous works where the pathogenesis of lung cancer and COVID-19 infection have been focused and we have also presented the new finding of our analysis. So, this work not only presents the review work but also the research work. This review and research study leads to the conclusion that growth promoting pathways (EGFR, Ras, and PI3K), growth inhibitory pathways (p53 and STK11), apoptotic pathways (Bcl- 2/Bax/Fas), and DDR pathways and genes are commonly and dominantly altered in both the cases COVID-19 and lung cancer.

Identification of Highly Conserved SARS-CoV-2 Antigenic Epitopes with Wide Coverage Using Reverse Vaccinology Approach.

One of the most effective strategies for eliminating new and emerging infectious diseases is effective immunization. The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) warrants the need for a maximum coverage vaccine. Moreover, mutations that arise within the virus have a significant impact on the vaccination strategy. Here, we built a comprehensive in silico workflow pipeline to identify B-cell- and T-cell-stimulating antigens of SARS-CoV-2 viral proteins. Our in silico reverse vaccinology (RV) approach consisted of two parts: (1) analysis of the selected viral proteins based on annotated cellular location, antigenicity, allele coverage, epitope density, and mutation density and (2) analysis of the various aspects of the epitopes, including antigenicity, allele coverage, IFN-γ induction, toxicity, host homology, and site mutational density. After performing a mutation analysis based on the contemporary mutational amino acid substitutions observed in the viral variants, 13 potential epitopes were selected as subunit vaccine candidates. Despite mutational amino acid substitutions, most epitope sequences were predicted to retain immunogenicity without toxicity and host homology. Our RV approach using an in silico pipeline may potentially reduce the time required for effective vaccine development and can be applicable for vaccine development for other pathogenic diseases as well.

Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach.

Improvement of COVID-19 clinical condition was seen in studies where combination of antiretroviral drugs, lopinavir and ritonavir, as well as immunomodulant antimalaric, chloroquine/hydroxychloroquine together with the macrolide-type antibiotic, azithromycin, was used for patient's treatment. Although these drugs are "old", their pharmacological and toxicological profile in SARS-CoV-2 - infected patients are still unknown. Thus, by using in silico toxicogenomic data-mining approach, we aimed to assess both risks and benefits of the COVID-19 treatment with the most promising candidate drugs combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine + azithromycin. The Comparative Toxicogenomics Database (CTD; http://CTD.mdibl.org), Cytoscape software (https://cytoscape.org) and ToppGene Suite portal (https://toppgene.cchmc.org) served as a foundation in our research. Our results have demonstrated that lopinavir/ritonavir increased the expression of the genes involved in immune response and lipid metabolism (IL6, ICAM1, CCL2, TNF, APOA1, etc.). Chloroquine/hydroxychloroquine + azithromycin interacted with 6 genes (CCL2, CTSB, CXCL8, IL1B, IL6 and TNF), whereas chloroquine and azithromycin affected two additional genes (BCL2L1 and CYP3A4), which might be a reason behind a greater number of consequential diseases. In contrast to lopinavir/ritonavir, chloroquine/hydroxychloroquine + azithromycin downregulated the expression of TNF and IL6. As expected, inflammation, cardiotoxicity, and dyslipidaemias were revealed as the main risks of lopinavir/ritonavir treatment, while chloroquine/hydroxychloroquine + azithromycin therapy was additionally linked to gastrointestinal and skin diseases. According to our results, these drug combinations should be administrated with caution to patients suffering from cardiovascular problems, autoimmune diseases, or acquired and hereditary lipid disorders.