SGLT2-Inhibition reverts urinary peptide changes associated with severe COVID-19: an <i>in-silico</i> proof-of-principle of proteomics-based drug repurposing
Bibliographic record
Abstract
Abstract Severe COVID-19 is reflected by significant changes in multiple urine peptides. Based on this observation, a clinical test based on urinary peptides predicting COVID-19 severity, CoV50, was developed and registered as IVD in Germany. We have hypothesized that molecular changes displayed by CoV50, to a large degree likely reflective of endothelial damage, can be significantly reversed by specific drugs. To test this hypothesis, we have collected urinary peptide data from patients without COVID-19 prior and after drug treatment. The drugs chosen were selected based on availability of sufficient number of participants in the dataset (n>20) and potential value of drug therapies in the treatment of COVID-19 based on reports in the literature. In these participants without COVID-19, while spironolactone did not demonstrate a significant impact on CoV50 scoring, empagliflozin treatment resulted in a significant change in CoV50 scoring, indicative of a potential therapeutic benefit. The results serve as a proof-of-principle for a drug repurposing approach based on human urinary peptide signatures and support the initiation of a randomised control trial testing a potential positive effect of empagliflozin in the treatment of severe COVID-19, possibly via endothelial protective mechanisms. Significance of the study COVID-19 pandemic has imposed a heavy burden on society, health care and economics. Although multiple drugs have been tested in the context of COVID-19, effective treatments for patients experiencing severe disease are still missing, with some drugs demonstrating benefit only at earlier disease stage. Computational drug repurposing emerged as a promising approach to boost drug development, allowing to predict drug efficacy based on the molecular signature of drug impact, mainly using transcriptomics data from cell lines. Recently we demonstrated that urinary proteomics profiles significantly differ between patients with severe COVID-19 course and those with mild/ moderate disease. This resulted in the development of a molecular signature associated with COVID-19 severity (CoV50), allowing to predict COVID-19 course, and enabling guiding intervention. Here we report on the first study demonstrating the application of clinical proteomics data (from clinical trial participants) in a drug repurposing approach. We used the CoV50 signature to examine if the molecular changes associated with COVID-19 severity in patients without COVID-19 might be altered by existing drugs. In a study population without COVID-19, empagliflozin demonstrated a partial, yet significant reversion of the CoV50 signature, indicating a potential benefit in the context of severe COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".