Faculty Opinions recommendation of The Achilles' heel of senescent cells: from transcriptome to senolytic drugs.
Bibliographic record
Abstract
The healthspan of mice is enhanced by killing senescent cells using a transgenic suicide gene.Achieving the same using small molecules would have a tremendous impact on quality of life and the burden of age-related chronic diseases.Here, we describe the rationale for identification and validation of a new class of drugs termed senolytics, which selectively kill senescent cells.By transcript analysis, we discovered increased expression of prosurvival networks in senescent cells, consistent with their established resistance to apoptosis.Using siRNA to silence expression of key nodes of this network, including ephrins (EFNB1 or 3), PI3Kd, p21, BCL-xL, or plasminogen-activated inhibitor-2, killed senescent cells, but not proliferating or quiescent, differentiated cells.Drugs targeting these same factors selectively killed senescent cells.Dasatinib eliminated senescent human fat cell progenitors, while quercetin was more effective against senescent human endothelial cells and mouse BM-MSCs.The combination of dasatinib and quercetin was effective in eliminating senescent MEFs.In vivo, this combination reduced senescent cell burden in chronologically aged, radiation-exposed, and progeroid Ercc1 À/D mice.In old mice, cardiac function and carotid vascular reactivity were improved 5 days after a single dose.Following irradiation of one limb in mice, a single dose led to improved exercise capacity for at least 7 months following drug treatment.Periodic drug administration extended healthspan in Ercc1 À/Δ mice, delaying age-related symptoms and pathology, osteoporosis, and loss of intervertebral disk proteoglycans.These results demonstrate the feasibility of selectively ablating senescent cells and the efficacy of senolytics for alleviating symptoms of frailty and extending healthspan.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.194 | 0.095 |
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".