Investigating the binding affinity, interaction, and structure-activity-relationship of 76 prescription antiviral drugs targeting RdRp and Mpro of SARS-CoV-2

New combined Inverse-QSAR and molecular docking method for scaffold-based drug discovery

Computer-aided drug discovery plays a vital role in developing novel medications for various diseases. The COVID-19 pandemic has heightened the need for innovative approaches to design lead compounds with the potential to become effective drugs. Specifically, designing promising inhibitors of the SARS-CoV-2 main protease (Mpro) is crucial, as it plays a key role in viral replication. Phytochemicals, primarily flavonoids and flavonols from medicinal plants, were screened. Fifty small molecules were selected for molecular docking analysis against SARS-CoV-2 Mpro (PDB ID: 6LU7). Binding energies and interactions were analyzed and compared to those of the anti-SARS-CoV-2 inhibitor Nirmatrelvir. Using these 50 structures as a training set, a QSAR model was built employing simple, reversible topological descriptors. An inverse-QSAR analysis was then performed on 2⁹ = 512 hydroxyl combinations at nine possible positions on the flavone and flavonol scaffold. The model predicted three novel, promising compounds exhibiting the most favorable binding energies (-8.5 kcal/mol) among the 512 possible hydroxyl combinations: 3,6,7,2',4'-pentahydroxyflavone (PF9), 6,7,2',4'-tetrahydroxyflavone (PF11), and 3,6,7,4'-tetrahydroxyflavone (PF15). Molecular dynamics (MD) simulations demonstrated the stability of the PF9/Mpro complex over 300 ns of simulation. These predicted structures, reported here for the first time, warrant synthesis and further evaluation of their biological activity through in vitro and in vivo studies.

Structure and dynamics of whole-sequence homology model of ORF3a protein of SARS-CoV-2: An insight from microsecond molecular dynamics simulations

The ORF3a is a large accessory protein in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which plays an important role in virulence and viral replication; especially in inflammasome activation and apoptosis. However,, the existing cryo-EM structure of SARS-CoV-2 ORF3a is incomplete, . making it challenging to understand its structural and functional features. The aim of this study is to investigate the dynamic behaviors of the full-sequence homology model of ORF3a and compare it with the cryo-EM structure using microsecond molecular dynamics simulations. The previous studies indicated that the unresolved residues of the cryo-EM structure are not only involved in the pathogenesis of the SARS-CoV-2 but also exhibit a significant antigenicity. The dynamics scenario of homology model revealed higher RMSD, Rg, and SASA values with stable pattern when compared to the cryo-EM structure. Moreover, the RMSF analysis demonstrated higher fluctuations at specific positions (1-43, 97-110, 172-180, 219-243) in the model structure, whereas the cryo-EM structure displayed lower overall drift (except 1-43) in comparison to the model structure.Secondary structural features indicated that a significant unfolding in the transmembrane domains and β-strand at positions 166 to 172, affecting the stability and compactness of the cryo-EM structure , whereas the model exhibited noticeable unfolding in transmembrane domains and small-coiled regions in the N-terminal. , The results from molecular docking and steered molecular dynamics investigations showed the model structure had a greater number of non-bonding interactions, leading to enhanced stability when compared to the cryo-EM structure. Consequently, higher forces were necessary for unbinding of the baricitinib and ruxolitinib inhibitors from the model structure.. Our findings can help better understanding of the significance of unresolved residues at the molecular level. Additionally, this information can guide researchers for experimental endeavors aimed at completing the full-sequence structure of the ORF3a.Communicated by Ramaswamy H. Sarma.

Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSA

This study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (in silico). NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations. A total of 170,906 compounds were analyzed. Molecular docking screening revealed the top four NBCs with a high affinity with the spike (affinity energy <-7 kcal/mol) to be ZINC000045789238, ZINC000004098448, ZINC000008662732, and ZINC000003995616. In the MD analysis, the four ligands formed a complex with the highest dynamic equilibrium S1 (mean RMSD <0.3 nm), lowest fluctuation of the complex amino acid residues (RMSF <1.3), and solvent accessibility stability. However, the ZINC000045789238-spike complex (naringenin-4'-O glucuronide) was the only one that simultaneously had minus signal (-) MM/PBSA and MM/GBSA binding free energy values (-3.74 kcal/mol and -15.65 kcal/mol, respectively), indicating favorable binding. This ligand (naringenin-4'-O glucuronide) was also the one that produced the highest number of hydrogen bonds in the entire dynamic period (average = 4601 bonds per nanosecond). Six mutant amino acid residues formed these hydrogen bonds from the RBD region of S1 in the Omicron variant: Asn417, Ser494, Ser496, Arg403, Arg408, and His505. Naringenin-4'-O-glucuronide showed promising results as a potential drug candidate against COVID-19. In vitro and preclinical studies are needed to confirm these findings.Communicated by Ramaswamy H. Sarma.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Threat of respiratory syncytial virus infection knocking the door: a proposed potential drug candidate through molecular dynamics simulations, a future alternative

The discovery of antiviral approaches to prevent or cure respiratory syncytial virus (RSV) infections is critical, particularly because RSV is one of the most common causes of infant respiratory problems. There is currently no approved vaccination available to treat RSV infections. FDA has approved the drug ribavirin, but it is not sufficient to treat RSV. This work aimed to find and study in silico anti-RSV drugs that target matrix protein and nucleoprotein. In this study, we have identified five drug candidates that had better binding energies than ribavirin. Garenoxacin appeared as top lead compounds between them. AutoDock Vina was used to execute molecular docking of a library of chosen chemicals. The high-score compound was then confirmed using the Maestro 12.3 module's molecular dynamics simulation and the binding energies derived using Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA). Comparative molecular dynamics simulations revealed that garenoxacin has better stability and high residue contacts with high binding affinity than ribavirin. This study showed garenoxacin could prevent RSV infection better than ribavirin. In pursuing a more effective RSV control drug, additional research into these chemicals in vitro and in vivo is essential.

A review on structural, non-structural, and accessory proteins of SARS-CoV-2: Highlighting drug target sites

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is a highly transmittable and pathogenic human coronavirus that first emerged in China in December 2019. The unprecedented outbreak of SARS-CoV-2 devastated human health within a short time leading to a global public health emergency. A detailed understanding of the viral proteins including their structural characteristics and virulence mechanism on human health is very crucial for developing vaccines and therapeutics. To date, over 1800 structures of non-structural, structural, and accessory proteins of SARS-CoV-2 are determined by cryo-electron microscopy, X-ray crystallography, and NMR spectroscopy. Designing therapeutics to target the viral proteins has several benefits since they could be highly specific against the virus while maintaining minimal detrimental effects on humans. However, for ongoing and future research on SARS-CoV-2, summarizing all the viral proteins and their detailed structural information is crucial. In this review, we compile comprehensive information on viral structural, non-structural, and accessory proteins structures with their binding and catalytic sites, different domain and motifs, and potential drug target sites to assist chemists, biologists, and clinicians finding necessary details for fundamental and therapeutic research.

Potential antiviral peptides against the nucleoprotein of SARS-CoV-2

Nucleoprotein is a conserved structural protein of SARS-CoV-2, which is involved in several functions, including replication, packaging, and transcription. In this research, 21 antiviral peptides that are known to have inhibitory function against nucleoprotein in several other viruses, were screened computationally against the nucleoprotein of SARS-CoV-2. The complexes of five best performing peptides (AVP1142, AVP1145, AVP1148, AVP1150, AVP1155) with nucleoprotein were selected for subsequent screening via 5 ns molecular dynamics (MD) simulation. Two peptides, namely AVP1145 and AVP1155, came out as promising candidates and hence were selected for 200 ns MD simulation for further validation, incorporating a DMPC-based membrane environment. In the long MD simulation, both AVP1155 and AVP1145 utilized multiple residues-mainly aromatic, acidic, and nonpolar residues-as interacting points to remain in contact with the nucleoprotein and formed predominantly hydrogen bonds along with hydrophobic and electrostatic interactions. However, AVP1155 proved to be superior to AVP1145 when its complex with nucleoprotein was analyzed in terms of root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessible surface area and free energy landscape. In a nutshell, the findings of this research may guide future studies in the development of selective peptide inhibitors of SARS-CoV-2 nucleoprotein.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-022-02514-4.

Discovering new potential inhibitors to SARS-CoV-2 RNA dependent RNA polymerase (RdRp) using high throughput virtual screening and molecular dynamics simulations

RNA dependent RNA polymerase (RdRp), is an essential in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to SARS-CoV-2 RdRp over the active form of the popular drug remdesivir (RTP) and adenosine triphosphate (ATP) by utilizing high-throughput virtual screening (HTVS) against the vast ZINC compound database coupled with extensive molecular dynamics (MD) simulations. After post-trajectory analysis, we found that the simulations of complexes containing both ATP and RTP remained stable for the duration of their trajectories. Additionally, it was revealed that the phosphate tail of RTP was stabilized by both the positive amino acid pocket and magnesium ions near the entry channel of RdRp which includes residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~ 17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.

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.

SARS-CoV-2 VOCs, Mutational diversity and clinical outcome: Are they modulating drug efficacy by altered binding strength?

The global COVID-19 pandemic continues due to emerging Severe Acute Respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC). Here, we performed comprehensive analysis of in-house sequenced SARS-CoV-2 genome mutations dynamics in the patients infected with the VOCs - Delta and Omicron, within Recovered and Mortality patients. Statistical analysis highlighted significant mutations - T4685A, N4992N, and G5063S in RdRp; T19R in NTD spike; K444N and N532H in RBD spike, associated with Delta mortality. Mutations, T19I in NTD spike, Q493R and N440K in the RBD spike were significantly associated with Omicron mortality. We performed molecular docking for possible effect of significant mutations on the binding of Remdesivir. We found that Remdesivir showed less binding efficacy with the mutant Spike protein of both Delta and Omicron mortality compared to recovered patients. This indicates that mortality associated mutations could have a modulatory effect on drug binding which could be associated with disease outcome.

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.

Structure and dynamics of membrane protein in SARS-CoV-2

SARS-CoV-2 membrane (M) protein performs a variety of critical functions in virus infection cycle. However, the expression and purification of membrane protein structure is difficult despite tremendous progress. In this study, the 3 D structure is modeled followed by intensive validation and molecular dynamics simulation. The lack of suitable homologous templates (>30% sequence identities) leads us to construct the membrane protein models using template-free modeling (de novo or ab initio) approach with Robetta and trRosetta servers. Comparing with other model structures, it is evident that trRosetta (TM-score: 0.64; TM region RMSD: 2 Å) can provide the best model than Robetta (TM-score: 0.61; TM region RMSD: 3.3 Å) and I-TASSER (TM-score: 0.45; TM region RMSD: 6.5 Å). 100 ns molecular dynamics simulations are performed on the model structures by incorporating membrane environment. Moreover, secondary structure elements and principal component analysis (PCA) have also been performed on MD simulation data. Finally, trRosetta model is utilized for interpretation and visualization of interacting residues during protein-protein interactions. The common interacting residues including Phe103, Arg107, Met109, Trp110, Arg131, and Glu135 in the C-terminal domain of M protein are identified in membrane-spike and membrane-nucleocapsid protein complexes. The active site residues are also predicted for potential drug and peptide binding. Overall, this study might be helpful to design drugs and peptides against the modeled membrane protein of SARS-CoV-2 to accelerate further investigation. Communicated by Ramaswamy H. Sarma.

The FDA-Approved Drug Cobicistat Synergizes with Remdesivir To Inhibit SARS-CoV-2 Replication In Vitro and Decreases Viral Titers and Disease Progression in Syrian Hamsters

Combinations of direct-acting antivirals are needed to minimize drug resistance mutations and stably suppress replication of RNA viruses. Currently, there are limited therapeutic options against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and testing of a number of drug regimens has led to conflicting results. Here, we show that cobicistat, which is an FDA-approved drug booster that blocks the activity of the drug-metabolizing proteins cytochrome P450-3As (CYP3As) and P-glycoprotein (P-gp), inhibits SARS-CoV-2 replication. Two independent cell-to-cell membrane fusion assays showed that the antiviral effect of cobicistat is exerted through inhibition of spike protein-mediated membrane fusion. In line with this, incubation with low-micromolar concentrations of cobicistat decreased viral replication in three different cell lines including cells of lung and gut origin. When cobicistat was used in combination with remdesivir, a synergistic effect on the inhibition of viral replication was observed in cell lines and in a primary human colon organoid. This was consistent with the effects of cobicistat on two of its known targets, CYP3A4 and P-gp, the silencing of which boosted the in vitro antiviral activity of remdesivir in a cobicistat-like manner. When administered in vivo to Syrian hamsters at a high dose, cobicistat decreased viral load and mitigated clinical progression. These data highlight cobicistat as a therapeutic candidate for treating SARS-CoV-2 infection and as a potential building block of combination therapies for COVID-19.

Insights into the leaves of Ceriscoides campanulata: Natural proanthocyanidins alleviate diabetes, inflammation, and esophageal squamous cell cancer via in vitro and in silico models

Fourteen flavones (1-14) including twelve polymethoxylated flavones, two A-type proanthocyanidins (oligomeric flavonoids) (15, 16), one benzoyl glucoside (17), one triterpenoid (18), and one phenylpropanoid (19) were isolated from the leaves of the South Asian medicinal plant Ceriscoides campanulata (Roxb.) Tirveng (Rubiaceae). The structures of the compounds were identified based on their spectroscopic and spectrometric data and in comparison with literature data. Isolated compounds were tested in vitro against inflammatory enzymes (COX-2, iNOS), pro-inflammatory cytokines (IL-1β, IL-6, TNF-α), esophageal squamous carcinoma cell line (TE13), and carbohydrate digestion enzymes (α-amylase, α-glucosidase). Proanthocyanidins 15 and 16 significantly attenuated the LPS-induced inflammatory response of COX-2, iNOS, IL-1β, IL-6, TNF-α in RAW 264.7 cells. Proanthocyanidins also satisfactorily inhibited the regrowth (64%), migration (51%), and formation of tumor-sphere (48%) in ESCC cell line TE13 at 50% toxic concentration. Compounds 15 and 16 showed the most potent effect against mammalian α-amylase (IC₅₀ 8.4 ± 0.3 μM and 3.5 ± 0.02 μM, respectively) compared to reference standard acarbose (IC₅₀ 5.9 ± 0.1 μM). As yeast α-glucosidase inhibitors, compounds 15 and 16 also displayed significant activities (IC₅₀ 6.2 ± 0.3 and 4.7 ± 0.1 μM, respectively), while compounds 1-6 displayed weaker α-glucosidase inhibitory activities, ranging from 49 to 142 μM, compared to acarbose (IC₅₀ 665 ± 42 μM). In an anticholinesterase assay, compounds 1, 2, 6 (IC₅₀ 51 ± 2, 53 ± 7, 64 ± 5 μM, respectively), and 4 (IC₅₀ 44 ± 1 μM) showed moderate inhibitory activities against acetylcholinesterase and butyrylcholinesterase, respectively. Furthermore, molecular docking and molecular dynamic simulation analyses of compounds 15 and 16 were performed against human pancreatic α-amylase and human lysosomal acid α-glucosidase to elucidate the interactions of these compounds in the respective enzymes' active sites.

Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies

Repurposing of existing drugs and drug candidates is an ideal approach to identify new potential therapies for SARS-CoV-2 that can be tested without delay in human trials of infected patients. Here we applied a virtual screening approach using Autodock Vina and molecular dynamics simulation in tandem to calculate binding energies for repurposed drugs against the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). We thereby identified 80 promising compounds with potential activity against SARS-Cov2, consisting of a mixture of antiviral drugs, natural products and drugs with diverse modes of action. A substantial proportion of the top 80 compounds identified in this study had been shown by others to have SARS-CoV-2 antiviral effects in vitro or in vivo, thereby validating our approach. Amongst our top hits not previously reported to have SARS-CoV-2 activity, were eribulin, a macrocyclic ketone analogue of the marine compound halichondrin B and an anticancer drug, the AXL receptor tyrosine kinase inhibitor bemcentinib. Our top hits from our RdRp drug screen may not only have utility in treating COVID-19 but may provide a useful starting point for therapeutics against other coronaviruses. Hence, our modelling approach successfully identified multiple drugs with potential activity against SARS-CoV-2 RdRp.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43556-021-00050-3.

Hepatitis C Virus Protease Inhibitors Show Differential Efficacy and Interactions with Remdesivir for Treatment of SARS-CoV-2 In Vitro

Antivirals targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could improve treatment of COVID-19. We evaluated the efficacy of clinically relevant hepatitis C virus (HCV) NS3 protease inhibitors (PIs) against SARS-CoV-2 and their interactions with remdesivir, the only direct-acting antiviral approved for COVID-19 treatment. HCV PIs showed differential potency in short-term treatment assays based on the detection of SARS-CoV-2 spike protein in Vero E6 cells. Linear PIs boceprevir, telaprevir, and narlaprevir had 50% effective concentrations (EC₅₀) of ∼40 μM. Among the macrocyclic PIs, simeprevir had the highest (EC₅₀, 15 μM) and glecaprevir the lowest (EC₅₀, >178 μM) potency, with paritaprevir, grazoprevir, voxilaprevir, vaniprevir, danoprevir, and deldeprevir in between. Acyclic PIs asunaprevir and faldaprevir had EC₅₀s of 72 and 23 μM, respectively. ACH-806, inhibiting the HCV NS4A protease cofactor, had an EC₅₀ of 46 μM. Similar and slightly increased PI potencies were found in human hepatoma Huh7.5 cells and human lung carcinoma A549-hACE2 cells, respectively. Selectivity indexes based on antiviral and cell viability assays were highest for linear PIs. In short-term treatments, combination of macrocyclic but not linear PIs with remdesivir showed synergism in Vero E6 and A549-hACE2 cells. Longer-term treatment of infected Vero E6 and A549-hACE2 cells with 1-fold EC₅₀ PI revealed minor differences in the barrier to SARS-CoV-2 escape. Viral suppression was achieved with 3- to 8-fold EC₅₀ boceprevir or 1-fold EC₅₀ simeprevir or grazoprevir, but not boceprevir, in combination with 0.4- to 0.8-fold EC₅₀ remdesivir; these concentrations did not lead to viral suppression in single treatments. This study could inform the development and application of protease inhibitors for optimized antiviral treatments of COVID-19.

COVID-19 Therapeutics for Low- and Middle-Income Countries: A Review of Candidate Agents with Potential for Near-Term Use and Impact

Low- and middle-income countries (LMICs) face significant challenges in the control of COVID-19, given limited resources, especially for inpatient care. In a parallel effort to that for vaccines, the identification of therapeutics that have near-term potential to be available and easily administered is critical. Using the United States (US), European Union (EU), and World Health Organization (WHO) clinical trial registries, we reviewed COVID-19 therapeutic agents currently under investigation. The search was limited to oral or potentially oral agents, with at least a putative anti-SARS-CoV-2 virus mechanism and with at least five registered trials. The search yielded 1,001, 203, and 1,128 trials, in the US, EU, and WHO trial registers, respectively. These trials covered 13 oral or potentially oral repurposed agents that are currently used as antimicrobials and immunomodulatory therapeutics with established safety profiles. The available evidence regarding proposed mechanisms of action, potential limitations, and trial status is summarized. The results of the search demonstrate few published studies of high quality, a low proportion of trials completed, and the vast majority with negative results. These findings reflect limited investment in COVID-19 therapeutics development compared with vaccines. We also identified the need for better coordination of trials of accessible agents and their combinations in LMICs. To prevent COVID-19 from becoming a neglected tropical disease, there is a critical need for rapid and coordinated efforts in the evaluation and deployment of those agents found to be efficacious.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Druggability of cavity pockets within SARS-CoV-2 spike glycoprotein and pharmacophore-based drug discovery

Aim: Virus spike glycoprotein of SARS-CoV-2 is a good target for drug discovery. Objective: To examine the potential for druggability of spike protein for pharmacophore-based drug discovery and to investigate the binding affinity of natural products with SARS-CoV-2 spike protein. Methods: Druggable cavities were searched though CavityPlus. A pharmacophore was built and used for hit identification. Autodock Vina was used to evaluate the hits' affinities. 10 chemical derivatives were also made from the chemical backbone to optimize the lead compound. Results: 10 druggable cavities were found within the glycoprotein spike. Only one cavity with the highest score at the binding site was selected for pharmacophore extraction. Hit identification resulted in the identification of 410 hits. Discussion: This study provides a druggable region within viral glycoprotein and a candidate compound to block viral entry.

Should COVID-19 be branded to viral thrombotic fever?

Coronaviruses can cause a diverse array of clinical manifestations, from fever with symptoms of the common cold to highly lethal severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS). SARS-CoV-2, the coronavirus discovered in Hubei province, China, at the end of 2019, became known worldwide for causing coronavirus disease 2019 (COVID-19). Over one year's time period, the scientific community has produced a large bulk of knowledge about this disease and countless reports about its immune-pathological aspects. This knowledge, including data obtained in postmortem studies, points unequivocally to a hypercoagulability state. However, the name COVID-19 tells us very little about the true meaning of the disease. Our proposal is more comprehensive; it intends to frame COVID-19 in more clinical terminology, making an analogy to viral haemorrhagic fever (VHF). Thus, we found irrefutable evidence in the current literature that COVID-19 is the first viral disease that can be branded as a viral thrombotic fever. This manuscript points out that SARS-CoV-2 goes far beyond pneumonia or SARS. COVID-19 infections promote remarkable interactions among the endothelium, coagulation, and immune response, building up a background capable of promoting a "thrombotic storm," much more than a "cytokine storm." The importance of a viral protease called main protease (Mpro) is highlighted as a critical component for its replication in the host cell. A deeper analysis of this protease and its importance on the coagulation system is also discussed for the first time, mainly because of its similarity with the thrombin and factor Xa molecules, as recently pointed out by structural comparison crystallographic structures.

Molecular Docking and Molecular Dynamics Simulation Studies of Triterpenes from Vernonia patula with the Cannabinoid Type 1 Receptor

A molecular docking approach was employed to evaluate the binding affinity of six triterpenes, namely epifriedelanol, friedelin, α-amyrin, α-amyrin acetate, β-amyrin acetate, and bauerenyl acetate, towards the cannabinoid type 1 receptor (CB1). Molecular docking studies showed that friedelin, α-amyrin, and epifriedelanol had the strongest binding affinity towards CB1. Molecular dynamics simulation studies revealed that friedelin and α-amyrin engaged in stable non-bonding interactions by binding to a pocket close to the active site on the surface of the CB1 target protein. The studied triterpenes showed a good capacity to penetrate the blood–brain barrier. These results help to provide some evidence to justify, at least in part, the previously reported antinociceptive and sedative properties of Vernonia patula.

Structural analogues of existing anti-viral drugs inhibit SARS-CoV-2 RNA dependent RNA polymerase: A computational hierarchical investigation

The Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a pandemic, resulting in an exponentially increased mortality globally and scientists all over the world are struggling to find suitable solutions to combat it. Multiple repurposed drugs have already been in several clinical trials or recently completed. However, none of them shows any promising effect in combating COVID-19. Therefore, developing an effective drug is an unmet global need. RdRp (RNA dependent RNA polymerase) plays a pivotal role in viral replication. Therefore, it is considered as a prime target of drugs that may treat COVID-19. In this study, we have screened a library of compounds, containing approved RdRp inhibitor drugs that were or in use to treat other viruses (favipiravir, sofosbuvir, ribavirin, lopinavir, tenofovir, ritonavir, galidesivir and remdesivir) and their structural analogues, in order to identify potential inhibitors of SARS-CoV-2 RdRp. Extensive screening, molecular docking and molecular dynamics show that five structural analogues have notable inhibitory effects against RdRp of SARS-CoV-2. Importantly, comparative protein-antagonists interaction revealed that these compounds fit well in the pocket of RdRp. ADMET analysis of these compounds suggests their potency as drug candidates. Our identified compounds may serve as potential therapeutics for COVID-19.