Abstract:The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug–disease associa… Show more
“…To discover novel repurposable drugs and evaluate the magnitude to which a given drug can be repositioned for COVID-19, we exploited the recently developed SAveRUNNER algorithm [40] . The rationale behind SAveRUNNER builds on the hypothesis that for a drug to be effective against a specific disease, its associated targets (drug module) and the disease-specific associated genes (disease module) should be located nearby in the human interactome [20] ( Fig.…”
Section: Resultsmentioning
confidence: 99%
“…1 SAveRUNNER. A) The network-based algorithm used to identify off-label drug indications against COVID-19 [40] . B) The SAveRUNNER outcome network showing the high-confidence predicted drug-disease associations (p-value ≤ 0.05) connecting COVID-19 with 399 FDA-approved non-COVID-19 drugs.…”
Section: Resultsmentioning
confidence: 99%
“…To predict and prioritize off-label drug indications for COVID-19, we used a novel network-based algorithm for drug repurposing called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk) [40] , [41] . Specifically, SAveRUNNER predicts drug-disease associations by quantifying the interplay between the drug targets and disease-associated proteins in the human interactome via a novel network-based similarity measure that rewards associations between drugs and diseases located in the same network neighborhoods.…”
Section: Methodsmentioning
confidence: 99%
“…As disease genes, we used the genes falling in the COVID-19 module; as drug targets, we assembled target information of the 1,873 FDA-approved drugs obtained from DrugBank [35] ; and as a reference interactome we used the version provided by Cheng et al [20] . A comprehensive description of SAveRUNNER algorithm can be found in [40] , [41] .…”
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.
“…To discover novel repurposable drugs and evaluate the magnitude to which a given drug can be repositioned for COVID-19, we exploited the recently developed SAveRUNNER algorithm [40] . The rationale behind SAveRUNNER builds on the hypothesis that for a drug to be effective against a specific disease, its associated targets (drug module) and the disease-specific associated genes (disease module) should be located nearby in the human interactome [20] ( Fig.…”
Section: Resultsmentioning
confidence: 99%
“…1 SAveRUNNER. A) The network-based algorithm used to identify off-label drug indications against COVID-19 [40] . B) The SAveRUNNER outcome network showing the high-confidence predicted drug-disease associations (p-value ≤ 0.05) connecting COVID-19 with 399 FDA-approved non-COVID-19 drugs.…”
Section: Resultsmentioning
confidence: 99%
“…To predict and prioritize off-label drug indications for COVID-19, we used a novel network-based algorithm for drug repurposing called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk) [40] , [41] . Specifically, SAveRUNNER predicts drug-disease associations by quantifying the interplay between the drug targets and disease-associated proteins in the human interactome via a novel network-based similarity measure that rewards associations between drugs and diseases located in the same network neighborhoods.…”
Section: Methodsmentioning
confidence: 99%
“…As disease genes, we used the genes falling in the COVID-19 module; as drug targets, we assembled target information of the 1,873 FDA-approved drugs obtained from DrugBank [35] ; and as a reference interactome we used the version provided by Cheng et al [20] . A comprehensive description of SAveRUNNER algorithm can be found in [40] , [41] .…”
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.
“…As infections with the SARS-CoV-2 virus have become more aggressive, there is an urgent need for evaluating different drugs that may contribute to a better and effective treatment of this infection. The majority of drugs used for SARS-CoV-2 treatment are drugs currently in use for treatment of other diseases [243][244][245], and these have been evaluated for their efficacy using computational drug discovery analysis [246][247][248]. Although NGS has been used primarily for genome identification of SARS-CoV-2 [249][250][251][252], as well as for evaluation of mutations developed during viral spread in different countries [253][254][255], there are some studies wherein RNA sequencing is used for identifying new drug treatments.…”
Section: Ngs In Sars-cov-2 Drug Discoverymentioning
Novel technologies and state of the art platforms developed and launched over the last two decades such as microarrays, next-generation sequencing, and droplet PCR have provided the medical field many opportunities to generate and analyze big data from the human genome, particularly of genomes altered by different diseases like cancer, cardiovascular, diabetes and obesity. This knowledge further serves for either new drug discovery or drug repositioning. Designing drugs for specific mutations and genotypes will dramatically modify a patient’s response to treatment. Among other altered mechanisms, drug resistance is of concern, particularly when there is no response to cancer therapy. Once these new platforms for omics data are in place, available information will be used to pursue precision medicine and to establish new therapeutic guidelines. Target identification for new drugs is necessary, and it is of great benefit for critical cases where no alternatives are available. While mutational status is of highest importance as some mutations can be pathogenic, screening of known compounds in different preclinical models offer new and quick strategies to find alternative frameworks for treating more diseases with limited therapeutic options.
From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug–disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of “disease module”, as well as the introduction of more articulated models, like multi‐scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug–disease systems highlighting some open issues.
This article is categorized under:
Neurological Diseases > Computational Models
Infectious Diseases > Computational Models
Cardiovascular Diseases > Computational Models
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