Sci Transl Med. 2021 Sep 22;13(612):eabd5524. doi: 10.1126/scitranslmed.abd5524. Epub 2021 Sep 22.

ABSTRACT

[Figure: see text].

PMID:34550729 | DOI:10.1126/scitranslmed.abd5524

Int J Pept Res Ther. 2021 Sep 17:1-21. doi: 10.1007/s10989-021-10287-9. Online ahead of print.

ABSTRACT

Staphylococcus aureus infection is a leading cause of mortality and morbidity in community, hospital and live-stock sectors, especially with the widespread emergence of methicillin-resistant S. aureus (MRSA) strains. To identify new drug molecules to treat MRSA patients, we have undertaken to search essential proteins that are indispensable for their survival but non-homologous to human host proteins. The current study utilizes a subtractive genome and proteome approach to screen the possible therapeutic targets against S. aureus USA300. Bacterial essential genes are obtained from the DEG database and are compared to avoid cross-reactivity with human host genes. In silico analysis shows 198 proteins that may be considered as therapeutic candidates. Depending on their sub-cellular localization, proteins are grouped as either vaccine or drug targets or both. Extracellular proteins such as cell division proteins (Q2FZ91, Q2FZ95), penicillin-binding proteins (Q2FZ94, Q2FYI0) of the bacterial cell wall, phosphoglucomutase (Q2FE11) and lipoteichoic acid synthase (Q2FIS2) are considered as vaccine targets, and their epitopes have been mapped. Altogether, 53 drug targets are identified, which have shown similarity with the drug targets available in the DrugBank database. Predicted drug targets belong to the common metabolic pathways of MRSA, such as fatty acid biosynthesis, folate biosynthesis, peptidoglycan biosynthesis, ribosome, etc. Protein-protein interaction analysis emphasizing peptidoglycan biosynthesis reveals the connection between penicillin-binding proteins, mur-family proteins and FemXAB proteins. In this study, staphylococcal FemA protein (P0A0A5) is subjected to structure-based virtual screening for the drug repurposing approach. There are 20 residues missing in the crystal structure of FemA, and 12 of these residues are located at the catalytic site. The missing residues are modelled, and stereochemistry is checked. FDA approved drugs available in the DrugBank database have been used in virtual screening with FemA in search of potential repurposed molecules. This approach provides us with 10 drugs that may be used in the treatment of methicillin-resistant staphylococcal mediated diseases. AutoDock 4.2 is used for in silico screening and shows a comparable inhibition constant (Ki) for all 10 FDA-approved drugs towards FemA. Most of these drugs are used in the treatment of various cancers, migraines and leukaemia. Protein-drug interaction analysis shows that the drugs mostly interact with hydrophobic residues of FemA. Moreover, Tyr328 and Lys383 contribute largely to hydrogen bondings during interactions. All interacting amino acids that bind to the drugs are part of the active site cavity of FemA.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10989-021-10287-9.

PMID:34548853 | PMC:PMC8446483 | DOI:10.1007/s10989-021-10287-9

Recent Pat Anticancer Drug Discov. 2021;16(2):120-121. doi: 10.2174/157489281602210806102833.

NO ABSTRACT

PMID:34547996 | DOI:10.2174/157489281602210806102833

IEEE Trans Neural Netw Learn Syst. 2021 Sep 21;PP. doi: 10.1109/TNNLS.2021.3111745. Online ahead of print.

ABSTRACT

The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies have been implemented to find potential anti-COVID-19 drugs in a short span of time. Motivated by this critical global challenge, in this review, we detail approaches that have been used for drug repurposing for COVID-19 and suggest improvements to the existing deep learning (DL) approach to identify and repurpose drugs to treat this complex disease. By optimizing hyperparameter settings, deploying suitable activation functions, and designing optimization algorithms, the improved DL approach will be able to perform feature extraction from quality big data, turning the traditional DL approach, referred to as a ``black box,'' which generalizes and learns the transmitted data, into a ``glass box'' that will have the interpretability of its rationale while maintaining a high level of prediction accuracy. When adopted for drug repurposing for COVID-19, this improved approach will create a new generation of DL approaches that can establish a cause and effect relationship as to why the repurposed drugs are suitable for treating COVID-19. Its ability can also be extended to repurpose drugs for other complex diseases, develop appropriate treatment strategies for new diseases, and provide precision medical treatment to patients, thus paving the way to discover new drugs that can potentially be effective for treating COVID-19.

PMID:34546931 | DOI:10.1109/TNNLS.2021.3111745

Med Biol Eng Comput. 2021 Sep 20. doi: 10.1007/s11517-021-02436-5. Online ahead of print.

ABSTRACT

Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localization is imperative for the functional characterization of protein sequences. Diverse techniques are used on protein sequences for feature extraction. However, many times a single feature extraction technique leads to poor prediction performance. In this paper, two feature augmentations are described through sequence induced, physicochemical, and evolutionary information of the amino acid residues. While augmented features preserve the sequence-order-information and protein-residue-properties. Two bacterial protein datasets Gram-Positive (G +) and Gram-Negative (G-) are utilized for the experimental work. After performing essential preprocessing on protein datasets, two sets of feature vectors are obtained. These feature vectors are used separately to train the different individual and ensembles such as decision tree (C 4.5), k-nearest neighbor (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, gradient boosting machine (GBM), and random forest (RF) with fivefold cross-validation. Prediction results of the model demonstrate that overall accuracy reported by C4.5 is highest 99.57% on G + and 97.47% on G- datasets with known protein sequences. Similarly, for the UPS overall accuracy of G + is 85.17% with SVM and 82.45% with G- dataset using MLP.

PMID:34545514 | DOI:10.1007/s11517-021-02436-5

ArXiv. 2021 Sep 14:arXiv:2109.06377v1. Preprint.

ABSTRACT

Intercellular heterogeneity is a major obstacle to successful personalized medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potentials for personalized medicine are yet to be reached. Towards this, we propose A Single-cell Guided pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD can repurpose single drugs for each cell cluster and for multiple cell clusters at individual patient levels; it can also predict personalized drug combinations to address the intercellular heterogeneity within each patient. We tested ASGARD on three independent datasets, including advanced metastatic breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC=0.95) compared to two other single-cell pipelines (AUC 0.69 and 0.57) and two other bulk-cell-based drug repurposing methods (AUC 0.80 and 0.75). The top-ranked drugs, such as fulvestrant and neratinib for breast cancer, tretinoin and vorinostat for leukemia, and chloroquine and enalapril for severe COVID19, are either approved by FDA or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising pipeline guided by single-cell RNA-seq data, for repurposing personalized drugs and drug combinations. ASGARD is free for academic use at https://github.com/lanagarmire/ASGARD.

PMID:34545335 | PMC:PMC8452105

J Cheminform. 2021 Sep 20;13(1):70. doi: 10.1186/s13321-021-00553-9.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

PMID:34544500 | DOI:10.1186/s13321-021-00553-9

J Proteins Proteom. 2021 Sep 15:1-14. doi: 10.1007/s42485-021-00074-x. Online ahead of print.

ABSTRACT

COVID-19 [coronavirus disease 2019] has resulted in over 204,644,849 confirmed cases and over 4,323,139 deaths throughout the world as of 12 August 2021, a total of 4,428,168,759 vaccine doses have been administered. The lack of potentially effective drugs against the virus is making the situation worse and dangerous. Numerous forces are working on finding an effective treatment against the virus but it is believed that a de novo drug would take several months even if huge financial support is provided. The only solution left with is drug repurposing that would not only provide effective therapy with the already used clinical drugs, but also save time and cost of the de novo drug discovery. The initiation of the COVID-19 infection starts with the attachment of spike glycoprotein of SARS-CoV-2 to the host receptor. Hence, the inhibition of the binding of the virus to the host membrane and the entry of the viral particle into the host cell are one of the main therapeutic targets. This paper not only summarizes the structure and the mechanism of spike protein, but the main focus is on the potential covalent spike protein inhibitors.

PMID:34539131 | PMC:PMC8440732 | DOI:10.1007/s42485-021-00074-x

J Invest Dermatol. 2021 Sep 15:S0022-202X(21)02160-6. doi: 10.1016/j.jid.2021.07.170. Online ahead of print.

ABSTRACT

Expression-based systematic drug repositioning has been explored to predict novel treatments for a number of skin disorders. Here, we utilize this approach to identify, to our knowledge, previously unreported therapies for epidermolysis bullosa simplex (EBS). RNA sequencing analysis was performed on skin biopsies of acute blisters (<1 week) (n=9) and non-blistered epidermis (n=11) obtained from 11 EBS patients. Transcriptomic analysis of blistered epidermis in EBS patients revealed a set of 1276 genes dysregulated in EBS blisters. The IL-6, IL-8, and IL-10 pathways were upregulated in epidermis from EBS. Consistent with this, predicted upstream regulators included TNF-α, IL-1β, IL2, IL-6, PI3K, and mTOR. The 1276 gene EBS blister signature was integrated with molecular signatures from cell lines treated with 2423 drugs using the ConnectivityMap CLUE platform. mTOR inhibitors and PI3K inhibitors most opposed the EBS signature. To determine if mTOR inhibitors could be used clinically in EBS, we conducted an independent pilot study of 2 patients with EBS treated with topical sirolimus for painful plantar keratoderma due to chronic blistering. Both individuals experienced marked clinical improvement and notable reduction of keratoderma. In summary, a computational drug repositioning analysis successfully identified, to our knowledge, previously unreported targets in the treatment of EBS.

PMID:34536484 | DOI:10.1016/j.jid.2021.07.170

Naunyn Schmiedebergs Arch Pharmacol. 2021 Sep 18. doi: 10.1007/s00210-021-02144-9. Online ahead of print.

ABSTRACT

The term "antibiotics" is a broadly used misnomer to designate antibacterial drugs. In a recent article, we have proposed to replace, e.g., the term "antibiotics" by "antibacterial drugs", "antibiosis" by "antibacterial therapy", "antibiogram" by "antibacteriogram", and "antibiotic stewardship" by "antibacterial stewardship" (Seifert and Schirmer Trends Microbiol, 2021). In the present article, we show that many traditional terms related to antibiotics are used much more widely in the biomedical literature than the respective scientifically precise terms. This practice should be stopped. Moreover, we provide arguments to end the use of other broadly used terms in the biomedical literature such as "narrow-spectrum antibiotics" and "reserve antibiotics", "chemotherapeutics", and "tuberculostatics". Finally, we provide several examples showing that antibacterial drugs are used for non-antibacterial indications and that some non-antibacterial drugs are used for antibacterial indications now. Thus, the increasing importance of drug repurposing renders it important to drop short designations of drug classes such as "antibiotics". Rather, the term "drug" should be explicitly used, facilitating the inclusion of newly emerging indications such as antipsychotic and anti-inflammatory. This article is part of an effort to implement a new rational nomenclature of drug classes across the entire field of pharmacology.

PMID:34536087 | DOI:10.1007/s00210-021-02144-9

BMC Med Genomics. 2021 Sep 17;14(1):226. doi: 10.1186/s12920-021-01079-7.

ABSTRACT

BACKGROUND: Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development.

METHODS: Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease-gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis.

RESULTS: In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein-protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession.

CONCLUSION: Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.

PMID:34535131 | PMC:PMC8447809 | DOI:10.1186/s12920-021-01079-7

Eur J Med Chem. 2021 Sep 4;226:113814. doi: 10.1016/j.ejmech.2021.113814. Online ahead of print.

ABSTRACT

Indomethacin (INM), a well-known non-steroidal anti-inflammatory drug, has recently gained attention for its antiviral activity demonstrated in drug repurposing studies against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although the mechanism of action of INM is not yet fully understood, recent studies have indicated that it acts at an early stage of the coronaviruses (CoVs) replication cycle. In addition, a proteomic study reported that the anti-SARS-CoV-2 activity of INM could be also ascribed to its ability to inhibit human prostaglandin E synthase type 2 (PGES-2), a host protein which interacts with the SARS-CoV-2 NSP7 protein. Although INM does not potently inhibit SARS-CoV-2 replication in infected Vero E6 cells, here we have explored for the first time the application of the Proteolysis Targeting Chimeras (PROTACs) technology in order to develop more potent INM-derived PROTACs with anti-CoV activity. In this study, we report the design, synthesis, and biological evaluation of a series of INM-based PROTACs endowed with antiviral activity against a panel of human CoVs, including different SARS-CoV-2 strains. Two PROTACs showed a strong improvement in antiviral potency compared to INM. Molecular modelling studies support human PGES-2 as a potential target of INM-based antiviral PROTACs, thus paving the way toward the development of host-directed anti-CoVs strategies. To the best of our knowledge, these PROTACs represent the first-in-class INM-based PROTACs with antiviral activity and also the first example of the application of PROTACs to develop pan-coronavirus agents.

PMID:34534839 | PMC:PMC8416298 | DOI:10.1016/j.ejmech.2021.113814

Chem Biol Interact. 2021 Sep 14:109641. doi: 10.1016/j.cbi.2021.109641. Online ahead of print.

ABSTRACT

Breast cancer (BC) is the most frequently diagnosed female cancer and second leading cause of death. Despite the discovery of many antineoplastic drugs for BC, the current therapy is not totally efficient. In this study, we investigated the potential of repurposing the well-known diabetes type II drug liraglutide to modulate epigenetic modifications in BC cells lines in vitro and in vivo via Ehrlich mice tumors models. The in vitro results revealed a significant reduction on cell viability, migration, DNMT activity and displayed lower levels of global DNA methylation in BC cell lines after liraglutide treatment. The interaction between liraglutide and the DNMT enzymes resulted in a decrease profile of DNA methylation for the CDH1, ESR1 and ADAM33 gene promoter regions and, consequently, increased their gene and protein expression levels. To elucidate the possible interaction between liraglutide and the DNMT1 protein, we performed an in silico study that indicates liraglutide binding in the catalytic cleft via hydrogen bonds and salt bridges with the interdomain contacts and disturbs the overall enzyme conformation. The in vivo study was also able to reveal that liraglutide and the combined treatment of liraglutide and paclitaxel or methotrexate were effective in reducing tumor growth. Moreover, the modulation of CDH1 and ADAM33 mouse gene expression by DNA demethylation suggests a role for liraglutide in DNMT activity in vivo. Altogether, these results indicate that liraglutide may be further analysed as a new adjuvant treatment for BC.

PMID:34534549 | DOI:10.1016/j.cbi.2021.109641

Neurol Genet. 2021 Sep 9;7(5):e622. doi: 10.1212/NXG.0000000000000622. eCollection 2021 Oct.

ABSTRACT

BACKGROUND AND OBJECTIVES: To integrate genome-wide association study data with tissue-specific gene expression information to identify coexpression networks, biological pathways, and drug repositioning candidates for Alzheimer disease.

METHODS: We integrated genome-wide association summary statistics for Alzheimer disease with tissue-specific gene coexpression networks from brain tissue samples in the Genotype-Tissue Expression study. We identified gene coexpression networks enriched with genetic signals for Alzheimer disease and characterized the associated networks using biological pathway analysis. The disease-implicated modules were subsequently used as a molecular substrate for a computational drug repositioning analysis, in which we (1) imputed genetically regulated gene expression within Alzheimer disease implicated modules; (2) integrated the imputed gene expression levels with drug-gene signatures from the connectivity map to identify compounds that normalize dysregulated gene expression underlying Alzheimer disease; and (3) prioritized drug compounds and mechanisms of action based on the extent to which they normalize dysregulated expression signatures.

RESULTS: Genetic factors for Alzheimer disease are enriched in brain gene coexpression networks involved in the immune response. Computational drug repositioning analyses of expression changes within the disease-associated networks retrieved known Alzheimer disease drugs (e.g., memantine) as well as biologically meaningful drug categories (e.g., glutamate receptor antagonists).

DISCUSSION: Our results improve the biological interpretation of genetic data for Alzheimer disease and provide a list of potential antidementia drug repositioning candidates for which the efficacy should be investigated in functional validation studies.

PMID:34532569 | PMC:PMC8441674 | DOI:10.1212/NXG.0000000000000622

Curr Stem Cell Res Ther. 2021 Sep 16. doi: 10.2174/1574888X16666201221151853. Online ahead of print.

ABSTRACT

It has been almost 18 months since the first outbreak of COVID-19 disease was reported in Wuhan, China. This unexpected devastating phenomenon, raised a great deal of concerns and anxiety among people around the world and imposed a huge economic burden on the nations' health care systems. Accordingly, clinical scientists, pharmacologists and physicians worldwide felt an urgent demand for a safe, effective therapeutic agent, treatment strategy or vaccine in order to prevent or cure the recently-emerged disease. Initially, due to lack of specific pharmacological agents and approved vaccines to combat the COVID-19, the disease control in the confirmed cases was limited to supportive care. Accordingly, repositioning or repurposing current drugs and examining their possible therapeutic efficacy received a great deal of attention. Despite revealing promising results in some clinical trials, the overall results are conflicting. For this reason, there is an urgent to seek and investigate other potential therapeutics. Mesenchymal stem cells (MSC) representing immunomodulatory and regenerative capacity to treat both curable and intractable diseases, have been investigated in COVID-19 clinical trials carried out in different parts of the world. Nevertheless, up to now, none of MSC-based approaches has been approved in controlling COVID-19 infection. Thanks to the fact that the final solution for defeating the pandemic is developing a safe, effective vaccine, enormous efforts and clinical research have been carried out. In this review, we will concisely discuss the safety and efficacy of the most relevant pharmacological agents, MSC-based approaches and candidate vaccines for treating and preventing COVID-19 infection.

PMID:34530719 | DOI:10.2174/1574888X16666201221151853

Gene. 2021 Sep 13:145963. doi: 10.1016/j.gene.2021.145963. Online ahead of print.

ABSTRACT

As of July 2021, the outbreak of coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has led to more than 200 million infections and more than 4.2 million deaths globally. Complications of severe COVID-19 include acute kidney injury, liver dysfunction, cardiomyopathy, and coagulation dysfunction. Thus, there is an urgent need to identify proteins and genetic factors associated with COVID-19 susceptibility and outcome. We comprehensively reviewed recent findings of host-SARS-CoV-2 interactome analyses. To identify genetic variants associated with COVID-19, we focused on the findings from genome and transcriptome wide association studies (GWAS and TWAS) and bioinformatics analysis. We described established human proteins including ACE2, TMPRSS2, 40S ribosomal subunit, ApoA1, TOM70, HLA-A, and PALS1 interacting with SARS-CoV-2 based on cryo-electron microscopy results. Furthermore, we described approximately 1,000 human proteins showing evidence of interaction with SARS-CoV-2 and highlighted host cellular processes such as innate immune pathways affected by infection. We summarized the evidence on more than 20 identified candidate genes in COVID-19 severity. Predicted deleterious and disruptive genetic variants with possible effects on COVID-19 infectivity have been also summarized. These findings provide novel insights into SARS-CoV-2 biology and infection as well as potential strategies for development of novel COVID therapeutic targets and drug repurposing.

PMID:34530086 | PMC:PMC8437745 | DOI:10.1016/j.gene.2021.145963

Mol Inform. 2021 Sep 16:e2100062. doi: 10.1002/minf.202100062. Online ahead of print.

ABSTRACT

In the current study, we used 7922 FDA approved small molecule drugs as well as compounds in clinical investigation from NIH's NPC database in our drug repurposing study. SARS-CoV-2 main protease as well as Spike protein/ACE2 targets were used in virtual screening and top-100 compounds from each docking simulations were considered initially in short molecular dynamics (MD) simulations and their average binding energies were calculated by MM/GBSA method. Promising hit compounds selected based on average MM/GBSA scores were then used in long MD simulations. Based on these numerical calculations following compounds were found as hit inhibitors for the SARS-CoV-2 main protease: Pinokalant, terlakiren, ritonavir, cefotiam, telinavir, rotigaptide, and cefpiramide. In addition, following 3 compounds were identified as inhibitors for Spike/ACE2: Denopamine, bometolol, and rotigaptide. In order to verify the predictions of in silico analyses, 4 compounds (ritonavir, rotigaptide, cefotiam, and cefpiramide) for the main protease and 2 compounds (rotigaptide and denopamine) for the Spike/ACE2 interactions were tested by in vitro experiments. While the concentration-dependent inhibition of the ritonavir, rotigaptide, and cefotiam was observed for the main protease; denopamine was effective at the inhibition of Spike/ACE2 binding.

PMID:34529322 | DOI:10.1002/minf.202100062

mSystems. 2021 Sep 14:e0038821. doi: 10.1128/mSystems.00388-21. Online ahead of print.

ABSTRACT

Current epidemics, such as AIDS or flu, and the emergence of new threatening pathogens, such as the one causing the current coronavirus disease 2019 (COVID-19) pandemic, represent major global health challenges. While vaccination is an important part of the arsenal to counter the spread of viral diseases, it presents limitations and needs to be complemented by efficient therapeutic solutions. Intricate knowledge of host-pathogen interactions is a powerful tool to identify host-dependent vulnerabilities that can be exploited to dampen viral replication. Such host-directed antiviral therapies are promising and are less prone to the development of drug-resistant viral strains. Here, we first describe proteomics-based strategies that allow the rapid characterization of host-pathogen interactions. We then discuss how such data can be exploited to help prioritize compounds with potential host-directed antiviral activity that can be tested in preclinical models.

PMID:34519533 | DOI:10.1128/mSystems.00388-21

J Biomol Struct Dyn. 2021 Sep 14:1-15. doi: 10.1080/07391102.2021.1977698. Online ahead of print.

ABSTRACT

COVID-19 disease caused by the SARS-CoV-2 virus has shaken our health and wealth foundations. Although COVID-19 vaccines will become available allowing for attenuation of disease progression rates, distribution of vaccines can create other challenges and delays. Hence repurposed drugs against SARS-CoV-2 can be an attractive parallel strategy that can be integrated into routine clinical practice even in poorly-resourced countries. The present study was designed using knowledge of viral pathogenesis and pharmacodynamics of broad-spectrum antiviral agents (BSAAs). We carried out the virtual screening of BSAAs against the SARS-CoV-2 spike glycoprotein, RNA dependent RNA polymerase (RdRp), the main protease (Mpro) and the helicase enzyme of SARS-CoV-2. Imatinib (a tyrosine kinase inhibitor), Suramin (an anti-parasitic), Glycyrrhizin (an anti-inflammatory) and Bromocriptine (a dopamine agonist) showed higher binding affinity to multiple targets. Further through molecular dynamics simulation, critical conformational changes in the target protein molecules were revealed upon drug binding which illustrates the favorable binding conformations of antiviral drugs against SARS-CoV-2 target proteins. The resulting drugs from the present study in combination and in cocktails from the arsenal of existing drugs could reduce the translational distance and could offer substantial clinical benefit to decrease the burden of COVID-19 illness. This also creates a roadmap for subsequent viral diseases that emerge.Communicated by Ramaswamy H. Sarma.

PMID:34519259 | DOI:10.1080/07391102.2021.1977698

Heliyon. 2021 Sep;7(9):e07962. doi: 10.1016/j.heliyon.2021.e07962. Epub 2021 Sep 9.

ABSTRACT

Drug repurposing is an important approach to the assignment of already approved drugs for new indications. This technique bypasses some steps in the traditional drug approval system, which saves time and lives in the case of pandemics. Direct acting antivirals (DAAs) have repeatedly repurposed from treating one virus to another. In this study, 16 FDA-approved hepatitis C virus (HCV) DAA drugs were studied to explore their activities against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) human and viral targets. Among the 16 HCV DAA drugs, telaprevir has shown the best in silico evidence to work on both indirect human targets (cathepsin L [CTSL] and human angiotensin-converting enzyme 2 [hACE2] receptor) and direct viral targets (main protease [Mpro]). Moreover, the docked poses of telaprevir inside both hACE2 and Mpro were subjected to additional molecular dynamics simulations monitored by calculating the binding free energy using MM-GBSA. In vitro analysis of telaprevir showed inhibition of SARS-CoV-2 replication in cell culture (IC50 = 11.552 μM, CC50 = 60.865 μM, and selectivity index = 5.27). Accordingly, based on the in silico studies and supported by the presented in vitro analysis, we suggest that telaprevir may be considered for therapeutic development against SARS-CoV-2.

PMID:34518806 | PMC:PMC8426143 | DOI:10.1016/j.heliyon.2021.e07962