Deuterated Drugs and Biomarkers in the COVID-19 Pandemic
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Initially identified in Wuhan (China) in December 2019, COVID-19 rapidly spread globally, resulting in the COVID-19 pandemic. Carriers of the SARS-CoV-2 can experience symptoms ranging from mild to severe (or no symptoms whatsoever). Although vaccination provides extra immunity toward SARS-CoV-2, there has been an urgent need to develop treatments for COVID-19 to alleviate symptoms for carriers of the disease. In seeking a potential treatment, deuterated compounds have played a critical role either as therapeutic agents or as internal MS standards for studying the pharmacological properties of new drugs by quantifying the parent compounds and metabolites. We have identified >70 examples of deuterium-labeled compounds associated with treatment of COVID-19. Of these, we found 9 repurposed drugs and >20 novel drugs studied for potential therapeutic roles along with a total of 38 compounds (drugs, biomarkers, and lipids) explored as internal mass spectrometry standards. This review details the synthetic pathways and modes of action of these compounds (if known), and a brief analysis of each study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".