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. Unfortunately, there are no concrete treatment options available which has severely hampered the pharmacotherapy of this devastating infection. This calls for an urgent need to consider alternative strategies which can be employed quickly, as 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. Accordingly, we investigated the drug repurposing potential of a library of dipeptidyl peptidase 4 (DPP4) inhibitors which are currently marketed for type-2 diabetes, to treat SARS-CoV-2 infections. Computational studies were conducted in the crystal structure of the substrate binding site of viral protease, the SARS-CoV-2 Mpro dimer, which led to the identification of three marketed DPP4 inhibitors; gemigliptin, linagliptin and evogliptin exhibiting favorable binding, in the SARS-CoV-2 Mpro dimer, viral protease. These studies supports further investigation of repurposing DPP4 class of inhibitors and their potential in treating SARS-CoV-2 infections, especially in elderly patients with type-2 diabetes, who are at a greater risk of suffering from increased disease severity and mortality.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".