Dataset Information

Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations.

ABSTRACT:

Background

COVID-19 pandemic has claimed millions of lives and devastated the health service system, livelihood, and economy in many countries worldwide. Despite the vaccination programs in many countries, the spread of the pandemic continues, and effective treatment is still urgently needed. Although some antiviral drugs have been shown to be effective, they are not widely available. Repurposing of anti-parasitic drugs with in vitro anti-SARS-CoV-2 activity is a promising approach being tested in many clinical trials. Combination of these drugs is a plausible way to enhance their effectiveness.

Methods

The in vitro anti-SARS-CoV-2 activity of combinations of niclosamide, ivermectin and chloroquine were evaluated in Vero E6 and lung epithelial cells, Calu-3.

Results

All the two-drug combinations showed higher potency resulting in up to 4-fold reduction in the half maximal inhibitory concentration (IC₅₀) values compared to individual drugs. Among these combinations, niclosamide-ivermectin achieved the highest inhibitory level of over 99%. Combination synergy analysis showed niclosamide-ivermectin combination to have the best synergy score with a mean Loewe synergy score of 4.28 and a peak synergy score of 24.6 in Vero E6 cells and a mean Loewe synergy score of 3.82 and a peak synergy score of 10.86 in Calu-3 cells.

Conclusions

The present study demonstrated the benefit of drug combinations on anti-SARS-CoV-2 activity. Niclosamide and ivermectin showed the best synergistic profile and should be further tested in clinical trials.

SUBMITTER: Jitobaom K

PROVIDER: S-EPMC9206137 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations.

Jitobaom Kunlakanya K Boonarkart Chompunuch C Manopwisedjaroen Suwimon S Punyadee Nuntaya N Borwornpinyo Suparerk S Thitithanyanont Arunee A Avirutnan Panisadee P Auewarakul Prasert P

BMC pharmacology & toxicology 20220618 1

<h4>Background</h4>COVID-19 pandemic has claimed millions of lives and devastated the health service system, livelihood, and economy in many countries worldwide. Despite the vaccination programs in many countries, the spread of the pandemic continues, and effective treatment is still urgently needed. Although some antiviral drugs have been shown to be effective, they are not widely available. Repurposing of anti-parasitic drugs with in vitro anti-SARS-CoV-2 activity is a promising approach being ...[more]

PMID: 35717393

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Project description:While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.

Dataset Information

Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations.

Background

Methods

Results

Conclusions

Publications

Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations.

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