Advancing Geroscience Research – A Scoping Review of Regulatory Environments for Gerotherapeutics
Bibliographic record
Abstract
BACKGROUND: Globally, older adults are living longer but often in poorer health with multiple chronic conditions straining healthcare systems. Gerotherapeutics, which target the biological mechanisms of aging, could reduce this burden by extending healthspan. However, before therapies can be adopted, they must undergo rigorous study and regulatory approval; existing regulatory frameworks and the barriers to their development are unknown. OBJECTIVE: We conducted a scoping review of geroscience regulatory frameworks and identified barriers to their development. METHODS: A comprehensive government, academic, and grey literature search in the United States, Europe, Canada, and other regions including Medline and EMBASE (via OVID), Google Scholar, CINAHL, and CADTH Grey Matters was conducted. For inclusion of only recent literature, searches were limited to English-language publications from 2014 to 2024. RESULTS: In 3,780 publications screened for inclusion, no regulatory frameworks for gerotherapeutics were found. In the 34 included publications, 4 major barriers were identified: 1) lack of recognition of the biological processes of aging as targets for medical intervention; 2) absence of clear regulatory pathways to evaluate aging-focused therapies; 3) economic uncertainties, including high development costs and limited incentives due to unclear regulatory environments; and 4) insufficient public and policy engagement. CONCLUSION: We did not identify any geroscience specific regulatory frameworks but identified barriers to their development. For biological aging to advance as a therapeutic target, stakeholders must develop comprehensive regulatory guidelines, incentivize research and conduct public education. Global collaboration is crucial to harmonize regulatory efforts and ensure equitable adoption of therapies, ultimately enhancing healthspan worldwide.
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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 | 0.084 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".