Drug repurposing for SARS-CoV-2 (COVID-19) treatment

Mtewa, Andrew G.; Amanjot, Annu; Yadesa, Tadele Mekuriya; Ngwira, Kennedy J.

doi:10.1016/b978-0-323-85156-5.00027-4

Cited by 3 publications

(4 citation statements)

References 89 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The models can be categorized into three categories (Sarker, 2021): (Matta et al, 2020) DNN for supervised or discriminative learning (Zhou P. et al, 2020); DNN for unsupervised or generative learning; and (Mtewa et al, 2022) DNN for hybrid learning which is the integration of discriminative and generative learning. Discriminative learning provides a discriminative function in classification applications, and this category generally includes Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN or ConvNet), and Recurrent Neural Networks (RNN or cyclic).…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…Due to the high rates of morbidity and mortality associated with the virus, it was unrealistic nor practical to design a new drug, also known as the de novo drug development (Zhou P. et al, 2020;Matta et al, 2020). This decision was made considering numerous obstacles, including the limited data available about the virus pathophysiology at the time of the outbreak, and the lengthy process of the aforementioned strategy, which ranges between 10 and 17 years (Mtewa et al, 2022). Furthermore, the costs estimated for de-novo drug development are at around 1.5-2.5 billion euros (Nosengo, 2016;Correia et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

et al. 2023

View full text Add to dashboard Cite

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

show abstract

Section: Deep Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Coronaviruses can infect and circulate among different animal species; the virus may spread from animals [1] (such as cats, dogs, or bats) to humans; the latest scientific research suggests that the potential scale of novel coronavirus generation in both wild and domestic animals is greater than previously thought [2,3]. New coronaviruses can emerge when two different strains co-infect an individual, causing the viral genetic material to recombine.…”

Section: Introductionmentioning

confidence: 99%

Study Models of COVID-19 in Discrete-Time and Fractional-Order

et al. 2023

View full text Add to dashboard Cite

The novel coronavirus disease (SARS-CoV-2) has caused many infections and deaths throughout the world; the spread of the coronavirus pandemic is still ongoing and continues to affect healthcare systems and economies of countries worldwide. Mathematical models are used in many applications for infectious diseases, including forecasting outbreaks and designing containment strategies. In this paper, we study two types of SIR and SEIR models for the coronavirus. This study focuses on the discrete-time and fractional-order of these models; we study the stability of the fixed points and orbits using the Jacobian matrix and the eigenvalues and eigenvectors of each case; moreover, we estimate the parameters of the two systems in fractional order. We present a statistical study of the coronavirus model in two countries: Saudi Arabia, which has successfully recovered from the SARS-CoV-2 pandemic, and China, where the number of infections remains significantly high.

show abstract

“…In addition to the development of novel therapies targeting viral infection, in recent years, the drug-repositioning phenomenon, also known as drug repurposing, has been widely considered a viable low-cost and time-saving alternative. It consists of finding effective molecules for viral disease treatment using existing drugs approved for other clinical indications [ 12 ]. Facing such challenging research, natural molecules have been proven to be an excellent alternative therapeutic approach and an interesting source for antiviral drug development or repositioning [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Repositioned Natural Compounds and Nanoformulations: A Promising Combination to Counteract Cell Damage and Inflammation in Respiratory Viral Infections

et al. 2023

View full text Add to dashboard Cite

Respiratory viral diseases are among the most important causes of disability, morbidity, and death worldwide. Due to the limited efficacy or side effects of many current therapies and the increase in antiviral-resistant viral strains, the need to find new compounds to counteract these infections is growing. Since the development of new drugs is a time-consuming and expensive process, numerous studies have focused on the reuse of commercially available compounds, such as natural molecules with therapeutic properties. This phenomenon is generally called drug repurposing or repositioning and represents a valid emerging strategy in the drug discovery field. Unfortunately, the use of natural compounds in therapy has some limitations, due to their poor kinetic performance and consequently reduced therapeutic effect. The advent of nanotechnology in biomedicine has allowed this limitation to be overcome, showing that natural compounds in nanoform may represent a promising strategy against respiratory viral infections. In this narrative review, the beneficial effects of some promising natural molecules, curcumin, resveratrol, quercetin, and vitamin C, which have been already studied both in native form and in nanoform, against respiratory viral infections are presented and discussed. The review focuses on the ability of these natural compounds, analyzed in in vitro and in vivo studies, to counteract inflammation and cellular damage induced by viral infection and provide scientific evidence of the benefits of nanoformulations in increasing the therapeutic potential of these molecules.

show abstract

Drug repurposing for SARS-CoV-2 (COVID-19) treatment

Cited by 3 publications

References 89 publications

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

Study Models of COVID-19 in Discrete-Time and Fractional-Order

Repositioned Natural Compounds and Nanoformulations: A Promising Combination to Counteract Cell Damage and Inflammation in Respiratory Viral Infections

Contact Info

Product

Resources

About