Drug Repurposing of Approved Drugs Elbasvir, Ledipasvir, Paritaprevir, Velpatasvir, Antrafenine and Ergotamine for Combating COVID19

Mevada, Vishal; Dudhagara, Pravin; Gandhi, Himani; Vaghamshi, Nilam; Beladiya, Urvisha; Patel, Rajesh

doi:10.26434/chemrxiv.12115251

Cited by 10 publications

(4 citation statements)

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“…Drugs from all three classes showed to lower the virus titer and to tune down the cytokine storm syndrome in the most severe cases of the disease [ 49 ]. As expected, our approach identified antiviral drugs (against hepatitis C), which were already predicted as a COVID-19 treatment, or are currently in clinical trials against SARS-CoV-2 infection ( https://clinicaltrials.gov/ct2/show/NCT04498936 ) [ 50 , 51 ]. Among the three identified antibiotics, delafloxacin, a fluoroquinolone antimicrobial agent, was also studied for its antiviral activity at the early stages of the COVID-19 pandemic [ 52 ].…”

Section: Resultsmentioning

confidence: 98%

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

Serra

Fratello

Federico

et al. 2021

Briefings in Bioinformatics

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The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design. Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.

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Section: Resultsmentioning

confidence: 98%

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

Serra

Fratello

Federico

et al. 2021

Briefings in Bioinformatics

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“…This experimental result indirectly corroborated the applicability of the proposed workow for accelerating the repurposing of FDA-approved drugs for COVID-19 treatment, thus pushing for the future experimental evaluation of the other drugs here identied. In this context, velpatasvir is interestingly used in the treatment of Hepatitis C, 57 another single-stranded, positive-sense RNA virus, and its use in the treatment of SARS-CoV-2 was already suggested, [58][59][60] though focusing the attention on the main protease. This represents a further indication of the robustness of the methodology.…”

Section: Resultsmentioning

confidence: 99%

Accelerating the repurposing of FDA-approved drugs against coronavirus disease-19 (COVID-19)

et al. 2020

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“…As shown in Table 3 , among the 15 drugs we identified, 9 drugs are antiviral drugs and 7 of them are used in treating Hepatitis C as NS5A inhibitor. Especially, two of Hepatitis C treatment, Elbasvir and Velpatasvir, have been shown as potential candidates for COVID-19 treatment by using other approaches 37 – 39 . Moreover, two drugs shows anti-inflammatory or immune regulating function, and have the potential in regulate immune response under COVID-19 infection.…”

Section: Resultsmentioning

confidence: 99%

A deep learning framework for high-throughput mechanism-driven phenotype compound screening

Pham

Qiu

Zeng

et al. 2020

Preprint

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Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

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Drug Repurposing of Approved Drugs Elbasvir, Ledipasvir, Paritaprevir, Velpatasvir, Antrafenine and Ergotamine for Combating COVID19

Cited by 10 publications

References 0 publications

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

Accelerating the repurposing of FDA-approved drugs against coronavirus disease-19 (COVID-19)

A deep learning framework for high-throughput mechanism-driven phenotype compound screening

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