Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcription-based drug repurposing for COVID-19.

ABSTRACT: 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.

SUBMITTER: Killick R

PROVIDER: S-EPMC7518800 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Transcription-based drug repurposing for COVID-19.

Killick Richard R Ballard Clive C Doherty Patrick P Williams Gareth G

Virus research 20200925

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 m ...[more]

PMID: 32987033

Similar Datasets

Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19.

Project description:The COVID-19 pandemic has motivated researchers all over the world in trying to find effective drugs and therapeutics for treating this disease. To save time, much effort has focused on repurposing drugs known for treating other diseases than COVID-19. To support these drug repurposing efforts, we built the CAS Biomedical Knowledge Graph and identified 1350 small molecules as potentially repurposable drugs that target host proteins and disease processes involved in COVID-19. A computer algorithm-driven drug-ranking method was developed to prioritize those identified small molecules. The top 50 molecules were analyzed according to their molecular functions and included 11 drugs in clinical trials for treating COVID-19 and new candidates that may be of interest for clinical investigation. The CAS Biomedical Knowledge Graph provides researchers an opportunity to accelerate innovation and streamline the investigative process not just for COVID-19 but also in many other diseases.

| S-EPMC8340579 | biostudies-literature

Drug repurposing strategies for COVID-19

Project description:COVID-19 has now been declared a pandemic and new treatments are urgently needed as we enter a phase beyond containment. Developing new drugs from scratch is a lengthy process, thus impractical to face the immediate global challenge. Drug repurposing is an emerging strategy where existing medicines, having already been tested safe in humans, are redeployed to combat difficult-to-treat diseases. While using such repurposed drugs individually may ultimately not yield a significant clinical benefit, carefully combined cocktails could be very effective, as was for HIV in the 1990s; the urgent question now being which combination.

| S-EPMC7117595 | biostudies-literature

In silico drug repurposing in COVID-19: A network-based analysis.

Project description:The SARS-CoV-2 pandemic is a worldwide public health emergency. Despite the beginning of a vaccination campaign, the search for new drugs to appropriately treat COVID-19 patients remains a priority. Drug repurposing represents a faster and cheaper method than de novo drug discovery. In this study, we examined three different network-based approaches to identify potentially repurposable drugs to treat COVID-19. We analyzed transcriptomic data from whole blood cells of patients with COVID-19 and 21 other related conditions, as compared with those of healthy subjects. In addition to conventionally used drugs (e.g., anticoagulants, antihistaminics, anti-TNFα antibodies, corticosteroids), unconventional candidate compounds, such as SCN5A inhibitors and drugs active in the central nervous system, were identified. Clinical judgment and validation through clinical trials are always mandatory before use of the identified drugs in a clinical setting.

| S-EPMC8316014 | biostudies-literature

Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19.

Project description:The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle.

| S-EPMC7573309 | biostudies-literature

Artificial intelligence in COVID-19 drug repurposing.

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

Drug repurposing approach to fight COVID-19.

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

DrugWAS: Drug-wide Association Studies for COVID-19 Drug Repurposing.

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

Network analytics for drug repurposing in COVID-19.

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

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.

Project description:Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes' effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git.

| S-EPMC7525234 | biostudies-literature

Using informative features in machine learning based method for COVID-19 drug repurposing.

Project description: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.

| S-EPMC8451172 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data