Drug Repurposing: Dipeptidyl Peptidase IV (DPP4) Inhibitors as Potential Agents to Treat SARS-CoV-2 (2019-nCoV) Infection
Bibliographic record
Abstract
The current outbreak of severe acute respiratory distress syndrome (SARS) or nCOVID-19 pandemic, caused by the coronavirus-2 (CoV-2), continues to wreak havoc globally. As novel vaccines are being discovered and developed, small molecule drugs still constitute a viable treatment option for SARS-CoV-2 infections due to their advantages such as superior patient compliance for oral therapies, reduced manufacturing costs and ease of large scale distribution due to better stability and storage profiles. Discovering new drugs for SARS-CoV-2 infections is a time consuming and expensive proposition. In this regard, drug repurposing is an appealing approach which can provide rapid access to therapeutics with proven record of safety and efficacy. We investigated the drug repurposing potential of a library of dipeptidyl peptidase 4 (DPP4) inhibitors which are currently marketed for type-2 diabetes as treatment option for SARS-CoV-2 infections. These computational studies led to the identification of three marketed DPP4 inhibitors; gemigliptin, linagliptin and evogliptin as potential inhibitors of SARS-CoV-2 Mpro viral cysteine protease. In addition, our computational modeling shows that these drugs have the potential to inhibit other viral cysteine proteases from the beta coronavirus family, including the SAR-CoV Mpro and MERS-CoV CLpro suggesting their potential to be repurposed as broad-spectrum antiviral agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".