Discovery of Potential Flavonoid Inhibitors Against COVID-19 3CL Proteinase Based on Virtual Screening Strategy

Primary Antioxidant Power and Mpro SARS-CoV-2 Non-Covalent Inhibition Capabilities of Miquelianin

The antioxidant power of quercetin-3-O-glucuronide (miquelianin) has been studied, at the density functional level of theory, in both lipid-like and aqueous environments. In the aqueous phase, the computed pKa equilibria allowed the identification of the neutral and charged species present in solution that can react with the ⋅OOH radical. The Hydrogen Atom Transfer (HAT), Single Electron Transfer (SET) and Radical Adduct Formation (RAF) mechanisms were considered, and the individual, total and fraction corrected rate constants were obtained. Potential non-covalent inhibition of Mpro from SARS-CoV-2 by miquelianin has been also evaluated.

In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling

The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.

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.

Further quantitative in silico analysis of SARS-CoV-2 S-RBD Omicron BA.4, BA.5, BA.2.75, BQ.1, and BQ.1.1 transmissibility

The Covid-19 variants' transmissibility was further quantitatively analyzed in silico to study the binding strength with ACE-2 and find the binding inhibitors. The molecular interaction energy values of their optimized complex structures (MIFS) demonstrated that Omicron BA.4 and 5's MIFS value (344.6 kcal mol^-1) was equivalent to wild-type MIFS (346.1 kcal mol^-1), that of Omicron BQ.1 and BQ. 1.1's MIFS value (309.9 and 364.6 kcal mol^-1). Furthermore, the MIFS value of Omicron BA.2.75 (515.1 kcal mol^-1) was about Delta-plus (511.3 kcal mol^-1). The binding strength of Omicron BA.4, BA. 5, and BQ.1.1 may be neglectable, but that of Omicron BA.2.75 was urging. Furthermore, the 79 medicine candidates were analyzed as the binding inhibitors from binding strength with ACE-2. Only carboxy compounds were repulsed from the ACE-2 binding site indicating that further modification of medical treatment candidates may produce an effective binding inhibitor.

Computational investigation on the antioxidant activities and on the Mpro SARS-CoV-2 non-covalent inhibition of isorhamnetin

In the present work, we report a computational study on some important chemical properties of the flavonoid isorhamnetin, used in traditional medicine in many countries. In the course of the study we determined the acid-base equilibria in aqueous solution, the possible reaction pathways with the •OOH radical and the corresponding kinetic constants, the complexing capacity of copper ions, and the reduction of these complexes by reducing agents such as superoxide and ascorbic anion by using density functional level of theory Density Functional Theory. Finally, the non-covalent inhibition ability of the SARS-CoV-2 main protease enzyme by isorhamnetin was examined by molecular dynamics (MD) and docking investigation.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

Discovery of the key active compounds in Citri Reticulatae Pericarpium (Citrus reticulata "Chachi") and their therapeutic potential for the treatment of COVID-19 based on comparative metabolomics and network pharmacology

Edible herbal medicines contain macro- and micronutrients and active metabolites that can take part in biochemical processes to help achieve or maintain a state of well-being. Citri Reticulatae Pericarpium (CRP) is an edible and medicinal herb used as a component of the traditional Chinese medicine (TCM) approach to treating COVID-19 in China. However, the material basis and related mechanistic research regarding this herb for the treatment of COVID-19 are still unclear. First, a wide-targeted UPLC-ESI-MS/MS-based comparative metabolomics analysis was conducted to screen for the active metabolites of CRP. Second, network pharmacology was used to uncover the initial linkages among these metabolites, their possible targets, and COVID-19. Each metabolite was then further studied via molecular docking with the identified potential SARS-CoV-2 targets 3CL hydrolase, host cell target angiotensin-converting enzyme II, spike protein, and RNA-dependent RNA polymerase. Finally, the most potential small molecule compound was verified by in vitro and in vivo experiments, and the mechanism of its treatment of COVID-19 was further explored. In total, 399 metabolites were identified and nine upregulated differential metabolites were screened out as potential key active metabolites, among which isorhamnetin have anti-inflammatory activity in vitro validation assays. In addition, the molecular docking results also showed that isorhamnetin had a good binding ability with the key targets of COVID-19. Furthermore, in vivo results showed that isorhamnetin could significantly reduced the lung pathological injury and inflammatory injury by regulating ATK1, EGFR, MAPK8, and MAPK14 to involve in TNF signaling pathway, PI3K-Akt signalling pathway, and T cell receptor signaling pathway. Our results indicated that isorhamnetin, as screened from CRP, may have great potential for use in the treatment of patients with COVID-19. This study has also demonstrated that comparative metabolomics combined with network pharmacology strategy could be used as an effective approach for discovering potential compounds in herbal medicines that are effective against COVID-19.

Repositioning of anti-dengue compounds against SARS-CoV-2 as viral polyprotein processing inhibitor

A therapy for COVID-19 (Coronavirus Disease 19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) remains elusive due to the lack of an effective antiviral therapeutic molecule. The SARS-CoV-2 main protease (Mpro), which plays a vital role in the viral life cycle, is one of the most studied and validated drug targets. In Several prior studies, numerous possible chemical entities were proposed as potential Mpro inhibitors; however, most failed at various stages of drug discovery. Repositioning of existing antiviral compounds accelerates the discovery and development of potent therapeutic molecules. Hence, this study examines the applicability of anti-dengue compounds against the substrate binding site of Mpro for disrupting its polyprotein processing mechanism. An in-silico structure-based virtual screening approach is applied to screen 330 experimentally validated anti-dengue compounds to determine their affinity to the substrate binding site of Mpro. This study identified the top five compounds (CHEMBL1940602, CHEMBL2036486, CHEMBL3628485, CHEMBL200972, CHEMBL2036488) that showed a high affinity to Mpro with a docking score > -10.0 kcal/mol. The best-docked pose of these compounds with Mpro was subjected to 100 ns molecular dynamic (MD) simulation followed by MM/GBSA binding energy. This showed the maximum stability and comparable ΔG binding energy against the reference compound (X77 inhibitor). Overall, we repurposed the reported anti-dengue compounds against SARS-CoV-2-Mpro to impede its polyprotein processing for inhibiting SARS-CoV-2 infection.

Exploring the inhibitory potential of Saussurea costus and Saussurea involucrata phytoconstituents against the Spike glycoprotein receptor binding domain of SARS-CoV-2 Delta (B.1.617.2) variant and the main protease (Mpro) as therapeutic candidates, using Molecular docking, DFT, and ADME/Tox studies

The B.1.617.2 Delta variant is considered to be the most infectious of all SARS-CoV2 variants. Here, an attempt has been made through in-silico screening of 55 bioactive compounds from two selected plants, Saussurea costus and Saussurea involucrata as potential inhibitors of two viral proteases, main protease Mpro (PDB ID:6LU7) and the RBD of SGP of Sars-CoV-2 B1.617.2 Delta variant (PDB ID:7ORB) where the binding energy, molecular interactions, ADMET/Tox, chemical descriptors and Quantum-Chemical Calculations were explored. Molecular docking results demonstrated that the three top docked compounds formed relatively stable complexes within the active site and displayed remarkable binding energy in the order of Tangshenoside III, Rutin and Hesperidin (-9.35, -9.14 and -8.57 kcal/mol, respectively) with Mpro and Rutin, Tangshenoside III and Hesperidin (-9.07, -7.71 and -7.57 kcal/mol) with RBD of SGP. These compounds are non-Mutagen and non-carcinogen. Therefore, according to the Lipinski's Rule of Five they exhibited three violations concerning hydrogen acceptor, donor and molecular weight. However, based on the Quantum-Chemical Calculations results the selected ligands have effective reactivity, as they showed lower band gaps. The difference of the E_LUMO and E_HOMO was low, ranging from 0.0639 to 0.0978 a.u, implying the strong affinity of these inhibitors towards the target proteins. Among the three inhibitors, Rutin exhibited higher reactivity against two viral proteases, main protease (Mpro) and the Sars-CoV-2 B1.617.2, as the band energy gap was lowest among all the three phytochemicals, 0.0639 a.u This could indicate that Rutincan be potential anti-viral drug candidates against the existing SARS-CoV-2, the B.1.617.2 Delta variant.

Dithymoquinone Analogues as Potential Candidate(s) for Neurological Manifestation Associated with COVID-19: A Therapeutic Strategy for Neuro-COVID

The COVID-19 era has prompted several researchers to search for a linkage between COVID-19 and its associated neurological manifestation. Toll-like receptor 4 (TLR-4) acts as one such connecting link. spike protein of SARS-CoV-2 can bind either to ACE-2 receptors or to TLR-4 receptors, leading to aggregation of α-synuclein and neurodegeneration via the activation of various cascades in neurons. Recently, dithymoquinone has been reported as a potent multi-targeting candidate against SARS-CoV-2. Thus, in the present study, dithymoquinone and its six analogues were explored to target 3CL^pro (main protease of SARS-CoV-2), TLR4 and PREP (Prolyl Oligopeptidases) by using the molecular docking and dynamics approach. Dithymoquinone (DTQ) analogues were designed in order to investigate the effect of different chemical groups on its bioactivity. It is noteworthy to mention that attention was given to the feasibility of synthesizing these analogues by a simple photo-dimerisation reaction. The DTQ analogue containing the 4-fluoroaniline moiety [Compound (4)] was selected for further analysis by molecular dynamics after screening via docking-interaction analyses. A YASARA structure tool built on the AMBER14 force field was used to analyze the 100 ns trajectory by taking 400 snapshots after every 250 ps. Moreover, RMSD, RoG, potential energy plots were successfully obtained for each interaction. Molecular docking results indicated strong interaction of compound (4) with 3CL^pro, TLR4 and PREP with a binding energy of -8.5 kcal/mol, -10.8 kcal/mol and -9.5 kcal/mol, respectively, which is better than other DTQ-analogues and control compounds. In addition, compound (4) did not violate Lipinski's rule and showed no toxicity. Moreover, molecular dynamic analyses revealed that the complex of compound (4) with target proteins was stable during the 100 ns trajectory. Overall, the results predicted that compound (4) could be developed into a potent anti-COVID agent with the ability to mitigate neurological manifestations associated with COVID-19.

ACE2 and SARS-CoV-2-Main Protease Capillary Columns for Affinity Chromatography: Testimony of the Binding of Dexamethasone and its Carbon Nanotube Nanovector

In this paper, each of the two following proteins, the angiotensin-converting enzyme 2 (ACE2) and the Main protease (Main pro) of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) were grafted for the first time on homemade neutravidin poly(GMA-co-EDMA) capillary columns for the research of their ligands. The effect of the column diameter on the quantity of immobilized biotinylated protein was studied. For a capillary length of 40 mm, when its internal diameter varied from 75 to 25 μm, the grafted quantity of ACE2 decreased by 85% (from 1.50 to 0.24 μg). Among all the studied ligands, a particular vigilance has been given for dexamethasone, a widely used molecule today for adult patients hospitalized with SARS-CoV-2. Competition experiments were performed with SARS-CoV-2 Receptor Binding Domain used as reference molecule with the ACE2 affinity column to assess the orthosteric binding site of dexamethasone (Dex) on ACE2. This ligand was then immobilized on Multiwall Carbon Nanotubes (Dex/MWCNT). By comparison of the normalized breakthrough curves measured for Dex and Dex/MWCNT on both the ACE2 and Main pro affinity columns, it was showed for the first time that nanovectorisation of Dex with MWCNT enhanced and stabilized its binding to both ACE2 and Main pro. This last result reinforced the use of Dex and the interest of MWCNT for boosting immune health against COVID 19.

Exploration of chalcones as 3-chymotrypsin-like protease (3CLpro) inhibitors of SARS-CoV-2 using computational approaches

The main protease 3CL^pro is one of the potential targets against coronavirus. Inhibiting this enzyme leads to the interruption of viral replication. Chalcone and its derivatives were reported to possess the ability to bind to 3CL^pro protease in the binding pocket. This study explored an in-house database of 269 chalcones as 3CL^pro inhibitors using in silico screening models, including molecular docking, molecular dynamics simulation, binding free energy calculation, and ADME prediction. C264 and C235 stand out as the two most potential structures. The top hit compound C264 was with the Jamda score of −2.8329 and the MM/GBSA binding energy mean value of −28.23 ± 3.53 kcal/mol, which was lower than the reference ligand. Despite the lower mean binding energy (−22.07 ± 3.39 kcal/mol), in-depth analysis of binding interaction suggested C235 could be another potential candidate. Further, in vitro and in vivo experiments are required to confirm the inhibitory ability.

Flavonols and dihydroflavonols inhibit the main protease activity of SARS-CoV-2 and the replication of human coronavirus 229E

Since December 2019, the deadly novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the current COVID-19 pandemic. To date, vaccines are available in the developed countries to prevent the infection of this virus; however, medicines are necessary to help control COVID-19. Human coronavirus 229E (HCoV-229E) causes the common cold. The main protease (M^pro) is an essential enzyme required for the multiplication of these two viruses in the host cells, and thus is an appropriate candidate to screen potential medicinal compounds. Flavonols and dihydroflavonols are two groups of plant flavonoids. In this study, we report docking simulation with two M^pro enzymes and five flavonols and three dihydroflavonols, in vitro inhibition of the SARS-CoV-2 M^pro, and in vitro inhibition of the HCoV 229E replication. The docking simulation results predicted that (+)-dihydrokaempferol, (+)- dihydroquercetin, (+)-dihydromyricetin, kaempferol, quercetin, myricentin, isoquercitrin, and rutin could bind to at least two subsites (S1, S1’, S2, and S4) in the binding pocket and inhibit the activity of SARS-CoV-2 M^pro. Their affinity scores ranged from −8.8 to −7.4 (kcal/mol). Likewise, these compounds were predicted to bind and inhibit the HCoV-229E M^pro activity with affinity scores ranging from −7.1 to −7.8 (kcal/mol). In vitro inhibition assays showed that seven available compounds effectively inhibited the SARS-CoV-2 M^pro activity and their IC₅₀ values ranged from 0.125 to 12.9 μM. Five compounds inhibited the replication of HCoV-229E in Huh-7 cells. These findings indicate that these antioxidative flavonols and dihydroflavonols are promising candidates for curbing the two viruses.

The isolation and expression analysis of cinnamate 4-hydroxylase and chalcone synthase genes of Scrophularia striata under different abiotic elicitors

The phenylpropanoid pathway serves as a rich source of metabolites in plants, and it is considered as a starting point for the production of many other important compounds such as the flavonoids, flavonols, coumarins, and lignans. Scrophularia striata is a member of the Lamiaceae family with some biological activities similar to flavonoid compounds such as antioxidant, antibacterial, anti-inflammatory and analgesic activities. Cinnamate 4-hydroxylase (C4H) and Chalcone synthase (CHS) are key enzymes of the phenylpropanoid pathway, leading to the biosynthesis of several secondary metabolites. In this study, two S. striata CHS and C4H were isolated and then analyzed. The investigation of the expression of these genes was performed under the effects of three salicylic acid (SA), jasmonic acid (JA), and gibberellic acid (GA) at concentrations of 100 and 300 ppm with a completely randomized design at the transcript level using Real Time PCR method. These have different expression patterns at developmental stages. Moreover, these genes present different sensitivities to hormonal treatment. Considering the total results, it was found that the amount of expression of these genes during the reproductive phase is higher than that of the vegetative phase. Additionally, the treatment of 300 ppm SA in the reproductive phase is the most effective treatment on increasing the corresponding phenylpropanoid compounds. A correlation analysis was performed between the phenylpropanoid compounds content and both CHS and C4H expression values at different phenological development stages. The results indicate that the expression variations of both CHS and C4H are significantly related to the changes in total phenolic content. We believe that the isolation of CHS and C4H can be helpful in better understanding phenylpropanoid metabolis.

Semi-Synthesis of N-Aryl Amide Analogs of Piperine from Piper nigrum and Evaluation of Their Antitrypanosomal, Antimalarial, and Anti-SARS-CoV-2 Main Protease Activities

Piper nigrum, or black pepper, produces piperine, an alkaloid that has diverse pharmacological activities. In this study, N-aryl amide piperine analogs were prepared by semi-synthesis involving the saponification of piperine (1) to yield piperic acid (2) followed by esterification to obtain compounds 3, 4, and 5. The compounds were examined for their antitrypanosomal, antimalarial, and anti-SARS-CoV-2 main protease activities. The new 2,5-dimethoxy-substituted phenyl piperamide 5 exhibited the most robust biological activities with no cytotoxicity against mammalian cell lines, Vero and Vero E6, as compared to the other compounds in this series. Its half-maximal inhibitory concentration (IC₅₀) for antitrypanosomal activity against Trypanosoma brucei rhodesiense was 15.46 ± 3.09 μM, and its antimalarial activity against the 3D7 strain of Plasmodium falciparum was 24.55 ± 1.91 μM, which were fourfold and fivefold more potent, respectively, than the activities of piperine. Interestingly, compound 5 inhibited the activity of 3C-like main protease (3CL^Pro) toward anti-SARS-CoV-2 activity at the IC₅₀ of 106.9 ± 1.2 μM, which was threefold more potent than the activity of rutin. Docking and molecular dynamic simulation indicated that the potential binding of 5 in the 3CL^pro active site had the improved binding interaction and stability. Therefore, new aryl amide analogs of piperine 5 should be investigated further as a promising anti-infective agent against human African trypanosomiasis, malaria, and COVID-19.

Halogenated Baicalein as a Promising Antiviral Agent toward SARS-CoV-2 Main Protease

The coronavirus disease pandemic is a constant reminder that global citizens are in imminent danger of exposure to emerging infectious diseases. Therefore, developing a technique for inhibitor discovery is essential for effective drug design. Herein, we proposed fragment molecular orbital (FMO)-based virtual screening to predict the molecular binding energy of potential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease inhibitors. The integration of quantum mechanical approaches and trajectory analysis from a microsecond molecular dynamics simulation was used to identify potential inhibitors. We identified brominated baicalein as a potent inhibitor of the SARS-CoV-2 main protease and confirmed its inhibitory activity in an in vitro assay. Brominated baicalein did not demonstrate significant toxicity in either in vitro or in vivo studies. The pair interaction energy from FMO-RIMP2/PCM and inhibitory constants based on the protease enzyme assay suggested that the brominated baicalein could be further developed into novel SARS-CoV-2 protease inhibitors.

Jusanin, a New Flavonoid from Artemisia commutata with an In Silico Inhibitory Potential against the SARS-CoV-2 Main Protease

A new flavonoid, Jusanin, (1) has been isolated from the aerial parts of Artemisia commutata. The chemical structure of Jusanin has been elucidated using 1D, 2D NMR, and HR-Ms spectroscopic methods to be 5,2',4'-trihydroxy-6,7,5'-trimethoxyflavone. Being new in nature, the inhibition potential of 1 has been estimated against SARS-CoV-2 using different in silico techniques. Firstly, molecular similarity and fingerprint studies have been conducted for Jusanin against co-crystallized ligands of eight different SARS-CoV-2 essential proteins. The studies indicated the similarity between 1 and X77, the co-crystallized ligand SARS-CoV-2 main protease (PDB ID: 6W63). To confirm the obtained results, a DFT study was carried out and indicated the similarity of (total energy, HOMO, LUMO, gap energy, and dipole moment) between 1 and X77. Accordingly, molecular docking studies of 1 against the target enzyme have been achieved and showed that 1 bonded correctly in the protein's active site with a binding energy of -19.54 Kcal/mol. Additionally, in silico ADMET in addition to the toxicity evaluation of Jusanin against seven models have been preceded and indicated the general safety and the likeness of Jusanin to be a drug. Finally, molecular dynamics simulation studies were applied to investigate the dynamic behavior of the Mpro-Jusanin complex and confirmed the correct binding at 100 ns. In addition to 1, three other metabolites have been isolated and identified to be сapillartemisin A (2), methyl-3-[S-hydroxyprenyl]-cumarate (3), and β-sitosterol (4).

Multi-Targeted Approaches and Drug Repurposing Reveal Possible SARS-CoV-2 Inhibitors

The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Discovery of Diverse Natural Products as Inhibitors of SARS-CoV-2 Mpro Protease through Virtual Screening

SARS-CoV-2 is a type of coronavirus responsible for the international outbreak of respiratory illness termed COVID-19 that forced the World Health Organization to declare a pandemic infectious disease situation of international concern at the beginning of 2020. The need for a swift response against COVID-19 prompted to consider different sources to identify bioactive compounds that can be used as therapeutic agents, including available drugs and natural products. Accordingly, this work reports the results of a virtual screening process aimed at identifying antiviral natural product inhibitors of the SARS-CoV-2 M^pro viral protease. For this purpose, ca. 2000 compounds of the Selleck database of Natural Compounds were the subject of an ensemble docking process targeting the M^pro protease. Molecules that showed binding to most of the protein conformations were retained for a further step that involved the computation of the binding free energy of the ligand-M^pro complex along a molecular dynamics trajectory. The compounds that showed a smooth binding free energy behavior were selected for in vitro testing. From the resulting set of compounds, five compounds exhibited an antiviral profile, and they are disclosed in the present work.

l-Arginine Improves Solubility and ANTI SARS-CoV-2 Mpro Activity of Rutin but Not the Antiviral Activity in Cells

The COVID-19 pandemic outbreak prompts an urgent need for efficient therapeutics, and repurposing of known drugs has been extensively used in an attempt to get to anti-SARS-CoV-2 agents in the shortest possible time. The glycoside rutin shows manifold pharmacological activities and, despite its use being limited by its poor solubility in water, it is the active principle of many pharmaceutical preparations. We herein report our in silico and experimental investigations of rutin as a SARS-CoV-2 Mpro inhibitor and of its water solubility improvement obtained by mixing it with l-arginine. Tests of the rutin/l-arginine mixture in a cellular model of SARS-CoV-2 infection highlighted that the mixture still suffers from unfavorable pharmacokinetic properties, but nonetheless, the results of this study suggest that rutin might be a good starting point for hit optimization.

Engineering of 2D nanomaterials to trap and kill SARS-CoV-2: a new insight from multi-microsecond atomistic simulations

In late 2019, coronavirus disease 2019 (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Spike protein is one of the surface proteins of SARS-CoV-2 that is essential for its infectious function. Therefore, it received lots of attention for the preparation of antiviral drugs, vaccines, and diagnostic tools. In the current study, we use computational methods of chemistry and biology to study the interaction between spike protein and its receptor in the body, angiotensin-I-converting enzyme-2 (ACE2). Additionally, the possible interaction of two-dimensional (2D) nanomaterials, including graphene, bismuthene, phosphorene, p-doped graphene, and functionalized p-doped graphene, with spike protein is investigated. The functionalized p-doped graphene nanomaterials were found to interfere with spike protein better than the other tested nanomaterials. In addition, the interaction of the proposed nanomaterials with the main protease (M^pro) of SARS-CoV-2 was studied. Functionalized p-doped graphene nanomaterials showed more capacity to prevent the activity of M^pro. These 2D nanomaterials efficiently reduce the transmissibility and infectivity of SARS-CoV-2 by both the deformation of the spike protein and inhibiting the M^pro. The results suggest the potential use of 2D nanomaterials in a variety of prophylactic approaches, such as masks or surface coatings, and would deserve further studies in the coming years.

Sub-Micromolar Inhibition of SARS-CoV-2 3CLpro by Natural Compounds

Inhibiting the main protease 3CLpro is the most common strategy in the search for antiviral drugs to fight the infection from SARS-CoV-2. We report that the natural compound eugenol is able to hamper in vitro the enzymatic activity of 3CLpro, the SARS-CoV-2 main protease, with an inhibition constant in the sub-micromolar range (K_i = 0.81 μM). Two phenylpropene analogs were also tested: the same effect was observed for estragole with a lower potency (K_i = 4.1 μM), whereas anethole was less active. The binding efficiency index of these compounds is remarkably favorable due also to their small molecular mass (MW < 165 Da). We envision that nanomolar inhibition of 3CLpro is widely accessible within the chemical space of simple natural compounds.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Flavonoid compounds of buah merah (Pandanus conoideus Lamk) as a potent SARS-CoV-2 main protease inhibitor: in silico approach

Background

COVID19 is a global pandemic that threatens all nations. As there is no effective antiviral drug for COVID19, we examined the potency of natural ingredients against the SARS-CoV-2 main protease (PDB ID 6YNQ). Buah merah is a typical fruit from Papua, Indonesia, which is known to contain high levels of carotenoids and flavonoids. The contents have been proven to be effective as antiparasitic and anti-HIV. An in silico approach to 16 metabolites of buah merah (Pandanus conoideus Lamk) was carried out using AutoDock Vina. Furthermore, the study of the dynamics of ligand–protein interactions was carried out using CABS Flex 2.0 server to determine the test ligand and receptor complexes' stability. ADMET prediction was also carried out to study the pharmacokinetic profile of potential antiviral candidates.

Result

The docking results showed that 3 of the 16 buah merah metabolites were potent inhibitors against the SARS-CoV-2 main protease. The flavonoid compounds are quercetin 3′-glucoside, quercetin 3-O-glucose, and taxifolin 3-O-α-arabinopyranose with a binding affinity of − 9.7, − 9.3, and − 8.8, respectively, with stable ligand–protein complex. ADMET study shows that the three compounds are easily dissolved, easily absorbed orally and topically, have a high unbound fraction, low toxicity, and non-irritant.

Conclusion

We conclude that quercetin 3′-glucoside, quercetin 3-O-glucose, and taxifolin 3-O-α-arabinopyranose can be used and improved as potential anti-SARS-CoV-2 agents in further study.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Flavonoids as potential phytotherapeutics to combat cytokine storm in SARS-CoV-2

Emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, COVID-19, has become the global panic since December 2019, which urges the global healthcare professionals to identify novel therapeutics to counteract this pandemic. So far, there is no approved treatment available to control this public health issue; however, a few antiviral agents and repurposed drugs support the patients under medical supervision by compromising their adverse effects, especially in emergency conditions. Only a few vaccines have been approved to date. In this context, several plant natural products-based research studies are evidenced to play a crucial role in immunomodulation that can prevent the chances of infection as well as combat the cytokine release storm (CRS) generated during COVID-19 infection. In this present review, we have focused on flavonoids, especially epicatechin, epigallocatechin gallate, hesperidin, naringenin, quercetin, rutin, luteolin, baicalin, diosmin, ge nistein, biochanin A, and silymarin, which can counteract the virus-mediated elevated levels of inflammatory cytokines leading to multiple organ failure. In addition, a comprehensive discussion on available in silico, in vitro, and in vivo findings with critical analysis has also been evaluated, which might pave the way for further development of phytotherapeutics to identify the potential lead candidatetoward effective and safe management of the SARS-CoV-2 disease.

Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 MPRO

SARS-CoV-2 main protease is a common target for inhibition assays due to its high conservation among coronaviruses. Since flavonoids show antiviral activity, several in silico works have proposed them as potential SARS-CoV-2 main protease inhibitors. Nonetheless, there is reason to doubt certain results given the lack of consideration for flavonoid promiscuity or main protease plasticity, usage of short library sizes, absence of control molecules and/or the limitation of the methodology to a single target site. Here, we report a virtual screening study where dorsilurin E, euchrenone a11, sanggenol O and CHEMBL2171598 are proposed to inhibit main protease through different pathways. Remarkably, novel structural mechanisms were observed after sanggenol O and CHEMBL2171598 bound to experimentally proven allosteric sites. The former drastically affected the active site, while the latter triggered a hinge movement which has been previously reported for an inactive SARS-CoV main protease mutant. The use of a curated database of 4.8 k flavonoids, combining two well-known docking software (AutoDock Vina and AutoDock4.2), molecular dynamics and MMPBSA, guaranteed an adequate analysis and robust interpretation. These criteria can be considered for future screening campaigns against SARS-CoV-2 main protease.

Cheohen CF, Esteves MEA, Leal da Silva M, Gondim TS, Monteiro MES, Tucci AR, Fintelman-Rodrigues N, Siqueira MM, Miranda MD, Costa FN, Simas RC, Leitão GG. Flavonoids from Siparuna cristata as Potential Inhibitors of SARS-CoV-2 Replication

The novel coronavirus SARS-CoV-2 has been affecting the world, causing severe pneumonia and acute respiratory syndrome, leading people to death. Therefore, the search for anti-SARS-CoV-2 compounds is pivotal for public health. Natural products may present sources of bioactive compounds; among them, flavonoids are known in literature for their antiviral activity. Siparuna species are used in Brazilian folk medicine for the treatment of colds and flu. This work describes the isolation of 3,3',4'-tri-O-methyl-quercetin, 3,7,3',4'-tetra-O-methyl-quercetin (retusin), and 3,7-di-O-methyl-kaempferol (kumatakenin) from the dichloromethane extract of leaves of Siparuna cristata (Poepp. & Endl.) A.DC., Siparunaceae, using high-speed countercurrent chromatography in addition to the investigation of their inhibitory effect against SARS-CoV-2 viral replication. Retusin and kumatakenin inhibited SARS-CoV-2 replication in Vero E6 and Calu-3 cells, with a selective index greater than lopinavir/ritonavir and chloroquine, used as control. Flavonoids and their derivatives may stand for target compounds to be tested in future clinical trials to enrich the drug arsenal against coronavirus infections.

GRAPHICAL ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s43450-021-00162-5.

Interaction of 8-anilinonaphthalene-1-sulfonate with SARS-CoV-2 main protease and its application as a fluorescent probe for inhibitor identification

The 3C-like main protease of SARS-CoV-2 (3CLPro) is responsible for the cleavage of the viral polyprotein. This process is essential for the viral life cycle. Therefore, 3CLPro is a promising target to develop antiviral drugs for COVID-19 prevention and treatment. Traditional enzymatic assays for the identification of 3CLPro inhibitors rely on peptide-based colorimetric or fluorogenic substrates. However, the COVID-19 pandemic has limit or delay access to these substrates, especially for researchers in developing countries attempting to screen natural product libraries. We explored the use of the fluorescent probe 8-anilinonaphthalene-1-sulfonate (ANS) as an alternative assay for inhibitor identification. Fluorescence enhancement upon binding of ANS to 3CLPro was observed, and this interaction was competitive with a peptide substrate. The utility of ANS-based competitive binding assay to identify 3CLPro inhibitors was demonstrated with the flavonoid natural products baicalein and rutin. The molecular nature of ANS and rutin interaction with 3CLPro was explored with molecular modeling. Our results suggested that ANS could be employed in a competitive binding assay to facilitate the identification of novel SARS-CoV-2 antiviral compounds.

Flavonoids are promising safe therapy against COVID-19

Flavonoids are a class of phenolic natural products, well-identified in traditional and modern medicines in the treatment of several diseases including viral infection. Flavonoids showed potential inhibitory activity against coronaviruses including the current pandemic outbreak caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and designated as COVID-19. Here, we have collected all data related to the potential inhibitory mechanisms of flavonoids against SARS-CoV-2 infection and their significant immunomodulatory activities. The data were mapped and compared to elect major flavonoids with a promising role in the current pandemic. Further, we have linked the global existence of flavonoids in medicinal plants and their role in protection against COVID-19. Computational analysis predicted that flavonoids can exhibit potential inhibitory activity against SARS-CoV-2 by binding to essential viral targets required in virus entry and/ or replication. Flavonoids also showed excellent immunomodulatory and anti-inflammatory activities including the inhibition of various inflammatory cytokines. Further, flavonoids showed significant ability to reduce the exacerbation of COVID-19 in the case of obesity via promoting lipids metabolism. Moreover, flavonoids exhibit a high safety profile, suitable bioavailability, and no significant adverse effects. For instance, plants rich in flavonoids are globally distributed and can offer great protection from COVID-19. The data described in this study strongly highlighted that flavonoids particularly quercetin and luteolin can exhibit promising multi-target activity against SARS-CoV-2, which promote their use in the current and expected future outbreaks. Therefore, a regimen of flavonoid-rich plants can be recommended to supplement a sufficient amount of flavonoids for the protection and treatment from SARS-CoV-2 infection.

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.

New perspectives on natural flavonoids on COVID-19-induced lung injuries

The SARS-CoV-2 virus, responsible for COVID-19, spread rapidly worldwide and became a pandemic in 2020. In some patients, the virus remains in the respiratory tract, causing pneumonia, respiratory failure, acute respiratory distress syndrome (ARDS), and sepsis, leading to death. Natural flavonoids (aglycone and glycosides) possess broad biological activities encompassing antiinflammatory, antiviral, antitumoral, antiallergic, antiplatelet, and antioxidant effects. While many studies have focused on the effects of natural flavonoids in experimental models, reports based on clinical trials are still insufficient. In this review, we highlight the effects of flavonoids in controlling pulmonary diseases, particularly the acute respiratory distress syndrome, a consequence of COVID-19, and their potential use in coronavirus-related diseases. Furthermore, we also focus on establishing a relationship between biological potential and chemical aspects of related flavonoids and discuss several possible mechanisms of action, pointing out some possible effects on COVID-19.

Rutin: A Potential Antiviral for Repurposing as a SARS-CoV-2 Main Protease (Mpro) Inhibitor

Various computational studies, including in silico ones, have identified several existing compounds that could serve as effective inhibitors of the SARS-CoV-2 main protease (Mpro), and thus preventing replication of the virus. Among these, rutin has been identified as a potential hit, having prominent binding affinity to the virus. Moreover, its presence in several traditional antiviral medicines prescribed in China to infected patients with mild to moderate symptoms of COVID-19 justify its promise as a repurposed bioactive secondary metabolite against SARS-CoV-2.

Rutin Is a Low Micromolar Inhibitor of SARS-CoV-2 Main Protease 3CLpro: Implications for Drug Design of Quercetin Analogs

The pandemic, due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has stimulated the search for antivirals to tackle COVID-19 infection. Molecules with known pharmacokinetics and already approved for human use have been demonstrated or predicted to be suitable to be used either directly or as a base for a scaffold-based drug design. Among these substances, quercetin is known to be a potent in vitro inhibitor of 3CLpro, the SARS-CoV-2 main protease. However, its low in vivo bioavailability calls for modifications to its molecular structure. In this work, this issue is addressed by using rutin, a natural flavonoid that is the most common glycosylated conjugate of quercetin, as a model. Combining experimental (spectroscopy and calorimetry) and simulation techniques (docking and molecular dynamics simulations), we demonstrate that the sugar adduct does not hamper rutin binding to 3CLpro, and the conjugated compound preserves a high potency (inhibition constant in the low micromolar range, K_i = 11 μM). Although showing a disruption of the pseudo-symmetry in the chemical structure, a larger steric volume and molecular weight, and a higher solubility compared to quercetin, rutin is able to associate in the active site of 3CLpro, interacting with the catalytic dyad (His41/Cys145). The overall results have implications in the drug-design of quercetin analogs, and possibly other antivirals, to target the catalytic site of the SARS-CoV-2 3CLpro.

Drug Repurposing Strategy (DRS): Emerging Approach to Identify Potential Therapeutics for Treatment of Novel Coronavirus Infection

Drug repurposing is also termed as drug repositioning or therapeutic switching. This method is applied to identify the novel therapeutic agents from the existing FDA approved clinically used drug molecules. It is considered as an efficient approach to develop drug candidates with new pharmacological activities or therapeutic properties. As the drug discovery is a costly, time-consuming, laborious, and highly risk process, the novel approach of drug repositioning is employed to increases the success rate of drug development. This strategy is more advantageous over traditional drug discovery process in terms of reducing duration of drug development, low-cost, highly efficient and minimum risk of failure. In addition to this, World health organization declared Coronavirus disease (COVID-19) as pandemic globally on February 11, 2020. Currently, there is an urgent need to develop suitable therapeutic agents for the prevention of the outbreak of COVID-19. So, various investigations were carried out to design novel drug molecules by utilizing different approaches of drug repurposing to identify drug substances for treatment of COVID-19, which can act as significant inhibitors against viral proteins. It has been reported that COVID-19 can infect human respiratory system by entering into the alveoli of lung via respiratory tract. So, the infection occurs due to specific interaction or binding of spike protein with angiotensin converting enzyme-2 (ACE-2) receptor. Hence, drug repurposing strategy is utilized to identify suitable drugs by virtual screening of drug libraries. This approach helps to determine the binding interaction of drug candidates with target protein of coronavirus by using computational tools such as molecular similarity and homology modeling etc. For predicting the drug-receptor interactions and binding affinity, molecular docking study and binding free energy calculations are also performed. The methodologies involved in drug repurposing can be categorized into three groups such as drug-oriented, target-oriented and disease or therapy-oriented depending on the information available related to quality and quantity of the physico-chemical, biological, pharmacological, toxicological and pharmacokinetic property of drug molecules. This review focuses on drug repurposing strategy applied for existing drugs including Remdesivir, Favipiravir, Ribavirin, Baraticinib, Tocilizumab, Chloroquine, Hydroxychloroquine, Prulifloxacin, Carfilzomib, Bictegravir, Nelfinavir, Tegobuvir and Glucocorticoids etc to determine their effectiveness toward the treatment of COVID-19.

Therapeutic Intervention of COVID-19 by Natural Products: A Population-Specific Survey Directed Approach

To date very few promising leads from natural products (NP) secondary metabolites with antiviral and immunomodulatory properties have been identified for promising/potential intervention for COVID-19. Using in-silico docking studies and genome based various molecular targets, and their in vitro anti-SARS CoV-2 activities against whole cell and/or selected protein targets, we select a few compounds of interest, which can be used as potential leads to counteract effects of uncontrolled innate immune responses, in particular those related to the cytokine storm. A critical factor for prevention and treatment of SARS-CoV-2 infection relates to factors independent of viral infection or host response. They include population-related variables such as concurrent comorbidities and genetic factors critically relevant to COVID-19 health disparities. We discuss population risk factors related to SARS-CoV-2. In addition, we focus on virulence related to glucose-6-phosphate dehydrogenase deficiency (G6PDd), the most common human enzymopathy. Review of data on the response of individuals and communities with high prevalence of G6PDd to NP, prompts us to propose the rationale for a population-specific management approach to rationalize design of therapeutic interventions of SARS-CoV-2 infection, based on use of NP. This strategy may lead to personalized approaches and improve disease-related outcomes.