Challenges of rapamycin repurposing as a potential therapeutic candidate for COVID-19: implications for skeletal muscle metabolic health in older persons
Bibliographic record
Abstract
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic that has spread worldwide, resulting in over 6 million deaths as of March 2022. Older people have been disproportionately affected by the disease, as they have a greater risk of hospitalization, are more vulnerable to severe infection, and have higher mortality than younger patients. Although effective vaccines have been rapidly developed and administered globally, several clinical trials are ongoing to repurpose existing drugs to combat severe infection. One such drug, rapamycin, is currently under study for this purpose, given its immunosuppressant effects that are mediated by its inhibition of the mechanistic target of rapamycin (mTOR), a master regulator of cell growth. Consistent with this premise, acute rapamycin administration in young healthy humans blocks or attenuates mTOR and its downstream effectors, leading to the inhibition of muscle protein synthesis (MPS). Skeletal muscle mass declines when MPS is chronically lower than muscle protein breakdown. This is consequential for older people who are more susceptible to anabolic resistance (i.e., the blunting of MPS) due to reduced activity, sedentariness, or bed rest such as that associated with COVID-19 hospitalization, and who have also demonstrated a delayed or blunted ability to regain inactivity-induced muscle loss. The lack of studies investigating rapamycin administration on skeletal muscle in older people, and the emergence of effective antiviral medications against severe infection, may indicate the reduced relevance of drug repurposing for present or future pandemics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".