Drug repurposing strategies for COVID-19
Ontology highlight
ABSTRACT:
SUBMITTER: Senanayake S
PROVIDER: S-EPMC7117595 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
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Project description:The ongoing pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates strategies to identify prophylactic and therapeutic drug candidates for rapid clinical deployment. Here we describe a screening pipeline for the discovery of efficacious SARS-CoV-2 inhibitors. Two high-throughput, high-content imaging infection assays (one using HeLa cells expressing SARS-CoV-2 receptor ACE2 and the other using lung epithelial Calu-3 cells) were developed and used to screen ReFRAME, a best-in-class drug repurposing library. Among the promising hits, the antivirals nelfinavir and the parent of prodrug MK-4482 had most favorable in vitro activity, pharmacokinetic and human safety profiles, and both reduced SARS-CoV-2 replication in an orthogonal human differentiated primary cell model. However, only MK-4482 effectively blocked SARS-CoV-2 infection in a hamster model, likely due to inadequate plasma exposure of nelfinavir.
2021-04-02 | GSE168095 | GEO
Project description:Currently, there are no treatment options available for the deadly contagious disease, coronavirus disease 2019 (COVID-19). Drug repurposing is a process of identifying new uses for approved or investigational drugs and it is considered as a very effective strategy for drug discovery as it involves less time and cost to find a therapeutic agent in comparison to the de novo drug discovery process. The present review will focus on the repurposing efficacy of the currently used drugs against COVID-19 and their mechanisms of action, pharmacokinetics, dosing, safety, and their future perspective. Relevant articles with experimental studies conducted in-silico, in-vitro, in-vivo, clinical trials in humans, case reports, and news archives were selected for the review. Number of drugs such as remdesivir, favipiravir, ribavirin, lopinavir, ritonavir, darunavir, arbidol, chloroquine, hydroxychloroquine, tocilizumab and interferons have shown inhibitory effects against the SARS-CoV2 in-vitro as well as in clinical conditions. These drugs either act through virus-related targets such as RNA genome, polypeptide packing and uptake pathways or target host-related pathways involving angiotensin-converting enzyme-2 (ACE2) receptors and inflammatory pathways. Using the basic knowledge of viral pathogenesis and pharmacodynamics of drugs as well as using computational tools, many drugs are currently in pipeline to be repurposed. In the current scenario, repositioning of the drugs could be considered the new avenue for the treatment of COVID-19.
| S-EPMC7474498 | biostudies-literature
Project description:We have utilised the transcriptional response of lung epithelial cells following infection by the original Severe Acute Respiratory Syndrome coronavirus (SARS) to identify repurposable drugs for COVID-19. Drugs best able to recapitulate the infection profile are highly enriched for antiviral activity. Nine of these have been tested against SARS-2 and found to potently antagonise SARS-2 infection/replication, with a number now being considered for clinical trials. It is hoped that this approach may serve to broaden the spectrum of approved drugs that should be further assessed as potential anti-COVID-19 agents and may help elucidate how this seemingly disparate collection of drugs are able to inhibit SARS-2 infection/replication.
| S-EPMC7518800 | biostudies-literature
Project description:Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.
| S-EPMC7500917 | biostudies-literature
Project description:This study aimed to systematically investigate if any of the available drugs in the electronic health record (EHR) can be repurposed as potential treatment for coronavirus disease 2019 (COVID-19). Based on a retrospective cohort analysis of EHR data, drug-wide association studies (DrugWAS) were performed on 9,748 patients with COVID-19 at Vanderbilt University Medical Center (VUMC). For each drug study, multivariable logistic regression with overlap weighting using propensity score was applied to estimate the effect of drug exposure on COVID-19 disease outcomes. Patient exposure to a drug between 3-months prior to the pandemic and the COVID-19 diagnosis was chosen as the exposure of interest. All-cause of death was selected as the primary outcome. Hospitalization, admission to the intensive care unit, and need for mechanical ventilation were identified as secondary outcomes. Overall, 17 drugs were significantly associated with decreased COVID-19 severity. Previous exposure to two types of 13-valent pneumococcal conjugate vaccines, PCV13 (odds ratio (OR), 0.31, 95% confidence interval (CI), 0.12-0.81 and OR, 0.33, 95% CI, 0.15-0.73), diphtheria toxoid and tetanus toxoid vaccine (OR, 0.38, 95% CI, 0.15-0.93) were significantly associated with a decreased risk of death (primary outcome). Secondary analyses identified several other significant associations showing lower risk for COVID-19 outcomes: acellular pertussis vaccine, 23-valent pneumococcal polysaccharide vaccine (PPSV23), flaxseed extract, ethinyl estradiol, estradiol, turmeric extract, ubidecarenone, azelastine, pseudoephedrine, dextromethorphan, omega-3 fatty acids, fluticasone, and ibuprofen. In conclusion, this cohort study leveraged EHR data to identify a list of drugs that could be repurposed to improve COVID-19 outcomes. Further randomized clinical trials are needed to investigate the efficacy of the proposed drugs.
| S-EPMC8426999 | biostudies-literature
Project description:To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.
| S-EPMC8690228 | biostudies-literature
Project description:BACKGROUND:The very limited time allowed to face the COVID-19 pandemic poses a pressing challenge to find proper therapeutic approaches. However, synthesis and full investigation from preclinical studies to phase III trials of new medications is a time-consuming procedure, and not viable in a global emergency, such as the one we are facing. MAIN BODY:Drug repurposing/repositioning, a strategy effectively employed in cancer treatment, can represent a valid alternative. Most drugs considered for repurposing/repositioning in the therapy of the COVID-19 outbreak are commercially available and their dosage and toxicity in humans is well known, due to years (or even decades) of clinical use. This can allow their fast-track evaluation in phase II-III clinical trials, or even within straightforward compassionate use. Several drugs being re-considered for COVID-19 therapy are or have been used in cancer therapy. Indeed, virus-infected cells are pushed to enhance the synthesis of nucleic acids, protein and lipid synthesis and boost their energy metabolism, in order to comply to the "viral program". Indeed, the same features are seen in cancer cells, making it likely that drugs interfering with specific cancer cell pathways may be effective as well in defeating viral replication. SHORT CONCLUSION:To our knowledge, cancer drugs potentially suitable for facing SARS-CoV-2 infection have not been carefully reviewed. We present here a comprehensive analysis of available information on potential candidate cancer drugs that can be repurposed for the treatment of COIVD-19.
| S-EPMC7214852 | biostudies-literature
Project description:In the COVID-19 pandemic, drug repositioning has presented itself as an alternative to the time-consuming process of generating new drugs. This review describes a drug repurposing process that is based on a new data-driven approach: we put forward five information paths that associate COVID-19-related genes and COVID-19 symptoms with drugs that directly target these gene products, that target the symptoms or that treat diseases that are symptomatically or genetically similar to COVID-19. The intersection of the five information paths results in a list of 13 drugs that we suggest as potential candidates against COVID-19. In addition, we have found information in published studies and in clinical trials that support the therapeutic potential of the drugs in our final list.
| S-EPMC8520166 | biostudies-literature
Project description:ObjectiveTo discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.MethodsWe propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach.ResultsAccuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in identifying accurate semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (i.e., paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed.ConclusionWe showed that a LBD approach can be feasible not only for discovering drug candidates for COVID-19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.
| S-EPMC7869625 | biostudies-literature
Project description:Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.
| S-EPMC7665348 | biostudies-literature