Open fmughal opened 4 years ago
rando2
commented
4 years ago @rdvelazquez @gitter While running the getInternalData.py script, I noticed that this issue is flagged as not having a DOI, but here it looks like the citation is formatted correctly. I thought you might want to know -- I'm not sure if we've tested on any other citations that use the arXiv format.
agitter
commented
4 years ago I didn't double-check the code, but I believe the script only captures DOI references specifically right now. It has a pattern that looks for a DOI reference, and if there is a match it will capture that DOI to populate the table. Other valid Manubot references could be added later.
I edited the above citation because Manubot expects arxiv in lower case. GitHub converted the case to arXiv because that's how the user @arXiv is capitalized.
rando2
commented
4 years ago @agitter I checked the manubot guide and didn't even notice the capitalization was different... thank you for fixing that!
Title: Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
Please paste a link to the paper or a citation here:
Link: https://arxiv.org/abs/2004.07229
What is the paper's Manubot-style citation?
Citation: arxiv:2004.07229v1
Please list some keywords (3-10) that help identify the relevance of this paper to COVID-19
Which areas of expertise are particularly relevant to the paper?
Questions to answer about each paper:
Please provide 1-2 sentences introducing the study and its main findings
The authors of this study leveraged network medicine methods to map viral targets the human interactome for drug repurposing and disease comorbidity. Three approaches were used to identify drug candidates: graph theoretical proximity, diffusion-based distances, and graph-based machine learning. The predictions from all three methods were combined and ranked to produce a list of 81 repurposing candidates. The SARS-CoV2 targets identified in this study do not coincide with those of major diseases. Therefore relying on existing approved therapies may not be a possibility.
Study question(s) being investigated:
How many/what drugs/combinations are being considered?
The final interactome analyzed in this study comprised 18,505 proteins with 327,924 interactions. Interactions involving 26 SARS-CoV2 and 332 human proteins were traced onto this interactome, complementing with drug-target information consisting of 7, 591 drugs, 4,187 targets, and 26,167 interactions. The result was ranked list of 81 drug repurposing candidates based on a consensus of the three approaches used.
What are the main hypotheses being tested?
Contrary to conventional methods to repurpose drugs that rely on drugs that focus on human proteins that are viral binding targets, the hypothesis of this study was to identify drug candidates that may or may not bind to viral targets have the capability to disrupt the network proximity of the virus disease module.
Study population:
What is the model system (e.g., human study, animal model, cell line study)?
What is the sample size? If multiple groups are considered, give sample size for each group (including controls).
For human studies:
What countries/regions are considered?
What is the age range, gender, other relevant characteristics?
What is the setting of the study (random sample of school children, inpatient, outpatient, etc)?
What other specific inclusion-exclusion criteria are considered?
For example, do the investigators exclude patients with diagnosed neoplasms or patients over/under a certain age?
Treatment assignment:
How are treatments assigned?
For example, is it an interventional or an observational study?
Is the study randomized?
A study can be interventional but not randomized (e.g., a phase I or II clinical trial is interventional but often not randomized).
Provide other relevant details about the design.
This includes possible treatment stratification (e.g., within litters for animal studies, within hospitals for human studies), possible confounding variables (e.g., having a large age range of individuals), possible risks of bias and how they are addressed (e.g., is there masking in a clinical trial? how are individuals chosen in an observational study?).
Outcome Assessment:
Describe the outcome that is assessed and whether it is appropriate.
For example: Is the outcome assessed by a clinician or is it self-reported? Is the outcome based on viral load or a functional measurement (e.g., respiratory function, discharge from hospital)? What method is used to measure the outcome? How long after a treatment is the outcome measured?
Are outcome measurements complete?
For example, are there individuals lost to follow up?
Are outcome measurements subject to various kinds of bias?
For example, a lack of masking in randomized clinical trials.
Statistical Methods Assessment:
What methods are used for inference?
For example, logistic regression, nonparametric methods.
Are the methods appropriate for the study?
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
Are adjustments made for possible confounders?
For example, adjustment for age, sex, or comorbidities.
Results Summary:
What is the estimated association?
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
What measures of confidence or statistical significance are provided?
For example, confidence intervals, p-values, and/or Bayes factors.
Interpretation of results for study population:
Can we make a causal interpretation for the individuals in the study of drug -> outcome, such as "taking drug A improves likelihood of survival twofold over taking drug B."
For example, with a well-performed animal study or randomized trial it is often possible to infer causality. If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
Are there identified side effects or interactions with other drugs?
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
Are there specific subgroups with different findings?
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
Extrapolation of conclusions to other groups of individuals not specifically included in the study:
If the study is an animal study, which animal and how relevant is that model?
Is the model system appropriate? Is there evidence from past use that it's highly-relevant to therapeutic design in this context?
If it is a human study, what characteristics of the study population may support/limit extrapolation?
Summary of reliability
The interactome being analyzed was limited to the accuracy and reliability of the underlying dataset e.g. it did not capture interactions involving the ACE2 protein. As is the case with most computational studies, validation through molecular experimental means is required in order to test the efficacy of the identified drug candidates.
Progress
Check off the components as they are completed. If the component is not applicable, check the box as well.