Targeting Cellular Senescence for Healthy Aging: Advances in Senolytics and Senomorphics
Bibliographic record
Abstract
Esther Ugo Alum,1 Sylvester Chibueze Izah,2 Daniel Ejim Uti,1 Okechukwu Paul-Chima Ugwu,1 Peter A Betiang,3 Mariam Basajja,4 Regina Idu Ejemot-Nwadiaro5 1Department of Research Publications, Kampala International University, Kampala, Uganda; 2Department of Community Medicine, Faculty of Clinical Sciences, Bayelsa Medical University, Yenagoa, Bayelsa, Nigeria; 3Department of Access Early Childhood and Special Needs Education, Kampala International University, Kampala, Uganda; 4Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, Netherlands; 5Directorate of Research, Innovation, Consultancy and Extension (RICE), Kampala International University, Kampala, UgandaCorrespondence: Esther Ugo Alum, Email esther.alum@kiu.ac.ug; alumesther79@gmail.comBackground: Cellular senescence is a fundamental characteristic of aging, marked by permanent cell cycle cessation and the release of pro-inflammatory mediators. Although senescence plays advantageous roles in tissue regeneration and tumor suppression, its accumulation leads to aging-related illnesses and functional deterioration.Objective: This review examines the processes of cellular senescence, its effects on aging and age-related disorders, and emerging therapeutic strategies to modulate senescence for promoting healthy aging.Methods: A thorough literature review was performed using peer-reviewed studies on cellular senescence, its molecular pathways, and therapeutic interventions. Emphasis was placed on senolytics, senomorphics, and lifestyle interventions that modulate senescence-associated pathways. Studies published in Scopus, Web of Science and PubMed between 2014– 2025 were selected.Results: Recent discoveries underscore the dual function of cellular senescence in aging and pathology. The senescence-associated secretory phenotype (SASP) fosters chronic inflammation and tissue dysfunction, connecting senescence to age-related diseases including cardiovascular conditions, dementia, and metabolic disorders. Therapeutic strategies, including senolytics (drugs that specifically eradicate senescent cells) and senomorphics (compounds that suppress SASP without killing cells), show promise in preclinical and clinical studies. Notably, dosing interals (intermittent vs continuous) influence both therapeutic efficacy and adverse events such as thrombocytopenia. Additionally, the state and limitations of clinical validation of aging biomarkers (eg, p16^INK4a, β-galactosidase) remain major hurdles for translation. Lifestyle interventions such as calorie restriction and exercise have also been identified as natural modulators of senescence pathways.Conclusion: Targeting cellular senescence offers a promising avenue for promoting healthy aging and mitigating age-linked diseases. Continued research into senescence-modulating interventions may lead to novel therapeutics designed to prolong healthspan and lifespan.Plain Language Summary: As we age, some of our cells stop dividing in a process called cellular senescence. These cells do not die, but instead release harmful substances that can cause inflammation and damage nearby healthy cells. While this process can be helpful early in life (like preventing cancer), too many of these cells in old age contribute to diseases such as heart problems, diabetes, and dementia.This study reviews recent research into ways to remove or control these “senescent” cells to support healthier aging. Two promising strategies are:Senolytics: drugs that kill senescent cells.Senomorphics: drugs that make senescent cells less harmful without killing them.Scientists are testing both types of drugs in animals and humans. Some natural compounds, like quercetin (found in apples and onions) and fisetin (found in strawberries), show potential benefits. Others like metformin and rapamycin, which are already used for diabetes or immune issues, might also help slow aging by targeting senescent cells.The study also emphasizes that healthy habits, like exercise and calorie restriction, naturally reduce the harmful effects of senescence.Despite promising results, challenges remain. We need more human studies to understand:Which treatments are safest and most effectiveHow to deliver them to the right parts of the bodyHow often they should be usedThe authors believe that combining senolytics, senomorphics, and lifestyle changes could significantly improve our health as we age. Keywords: cellular senescence, aging, senolytics, senomorphics, healthy aging, SASP, age-related diseases
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".