Hypothesis: The age-associated accumulation of T-helper cells that naturally clear senescent cells can be therapeutically primed to enhance the efficacy of low-dose senolytics, reducing off-target toxicity while promoting tissue rejuvenation.

Background: Senescent cells drive aging and disease via SASP, but current senolytics like dasatinib+quercetin have imperfect specificity Senolytics as Modulators of Critical Signaling Pathways. A subset of T-helper cells accumulates with age and appears to clear senescent cells endogenously Neuroscientists find immune cells that may slow aging, suggesting an untapped synergy. Recent machine learning has identified novel senolytics, such as panaxatriol, with targeted mechanisms Development and Application of a Senolytic Predictor, yet optimal dosing remains elusive due to off-target effects.

Mechanistic Rationale: Brief SASP exposure can stimulate beneficial immunity and cellular reprogramming The interplay of cellular senescence and reprogramming, so controlled SASP modulation might attract T-helper cells to senescent niches. For instance, transient IL-6 or IL-8 release could upregulate chemokines like CXCL9, guiding T-helper cell infiltration. Simultaneously, low-dose senolytics—particularly those targeting BCL-2 family proteins or modulating autophagy via mTOR inhibition Senolytics as Modulators of Critical Signaling Pathways—might sensitize senescent cells to immune clearance by increasing surface markers (e.g., MHC class I) that enhance T-helper cell recognition. This creates a positive feedback loop: senolytics weaken senescent cell defenses, while primed T-helper cells amplify apoptosis through cytokine release (e.g., IFN-γ).

Testable Prediction: In aged mouse models (e.g., 24-month-old C57BL/6), administering IL-2 or IL-15 to expand T-helper cell populations alongside sub-therapeutic doses of a targeted senolytic (e.g., panaxatriol at 50 μM, below its cytotoxic threshold Development and Application of a Senolytic Predictor) will reduce senescent cell burden in key tissues (liver, adipose, brain) more effectively than either intervention alone. Senescent cells can be quantified using multi-marker panels (p16^INK4a, p21, SA-β-gal, and HMGB1 loss) to minimize false positives. Outcome measures should include inflammatory cytokine levels (SASP profile), tissue function (e.g., grip strength, cognitive tests), and survival. If synergy is present, we expect a ≥30% greater clearance rate compared to monotherapy, with no increase in metastasis risk in cancer-prone models.

Falsification Criteria: The hypothesis is invalid if: (1) T-helper cell priming fails to augment senolytic clearance, indicated by no significant difference in senescent cell counts between combination and monotherapy groups; (2) the combination exacerbates SASP-driven inflammation (e.g., elevated IL-6 without compensatory anti-inflammatory signals); or (3) senolytics do not enhance T-helper cell recognition—for example, if MHC class I expression remains unchanged post-treatment, the proposed mechanism is unsupported. Additionally, if off-target toxicity persists despite low dosing, the approach may not address specificity concerns.

Implications: This strategy could redefine senolytic regimens by integrating endogenous immunity, potentially resolving key issues in translation: optimal dosing, durability, and cancer metastasis risk Cellular Models of Aging and Senescence. Machine learning could further identify senolytics with immunomodulatory properties, accelerating discovery. In clinical settings, co-administration of senolytics with checkpoint modulators (e.g., anti-PD-1 to sustain T-helper cell activity) might offer a personalized rejuvenation therapy, especially in age-related diseases like breast cancer where senescent cells reawaken dormant tumors State-of-the-Art Session to focus on role of senescence.