Executable network of SARS-CoV-2-host interaction predicts drug combination treatments
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Abstract
The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.
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SciScore for 10.1101/2021.07.27.453973: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Cells were then incubated with 1μg/ml CR3009 SARS-CoV-2 cross-reactive antibody (a kind gift from Dr. Laura McCoy) in permeabilisation buffer for 30 min at room temperature, washed once and incubated with secondary Alexa Fluor 488-Donkey-anti-Human IgG (Jackson Labs). 488-Donkey-anti-Human IgGsuggested: NoneExperimental Models: Cell Lines Sentences Resources Caco-2 cells were trypsinised, stained with fixable Zombie UV Live/Dead dye (Biolegend) and fixed with 4% PFA before intracellular staining for nucleocapsid protein. Caco-2suggested: CLS Cat# 300137/p1665_CaCo-2, RRID:CVCL_0025)Results from OddPub: We …
SciScore for 10.1101/2021.07.27.453973: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Cells were then incubated with 1μg/ml CR3009 SARS-CoV-2 cross-reactive antibody (a kind gift from Dr. Laura McCoy) in permeabilisation buffer for 30 min at room temperature, washed once and incubated with secondary Alexa Fluor 488-Donkey-anti-Human IgG (Jackson Labs). 488-Donkey-anti-Human IgGsuggested: NoneExperimental Models: Cell Lines Sentences Resources Caco-2 cells were trypsinised, stained with fixable Zombie UV Live/Dead dye (Biolegend) and fixed with 4% PFA before intracellular staining for nucleocapsid protein. Caco-2suggested: CLS Cat# 300137/p1665_CaCo-2, RRID:CVCL_0025)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several computational approaches that have been applied to drug screening that are being applied to COVID-19, each with their own strengths and weaknesses. For example, as the structures of SARS-CoV-2 proteins have been determined, existing databases of small molecules have been leveraged to survey thousands of candidates to bind to these proteins73. Such studies helped identify 3CLpro as a common target, as well as use of drugs such as Lopinavir and Remdesivir25. Another approach is the use of protein-protein interaction networks, which are particularly suited to identifying host-directed therapies74,75. These networks can be analysed for their structure alone and can cover a broad range of potential targets. These have been used to identify, for example, the potential for cancer drug repurposing45,46. However, static-network analyses rely on the assumption that certain features of the network structure can identify the best targets, e.g., proximity to disease-associated nodes, but they cannot predict specific effects. Moreover, they rely on pre-existing network databases, or require additional curation76,77 or machine learning78 for new network types. Mathematical models such as Ordinary Differential Equations can interrogate the dynamics of the disease, but require precise data to fit model parameters, and so can only handle smaller set of variables79,80. By contrast, our approach combines scalable, executable modelling with transparent, biologically plausible ex...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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