Evaluating Repurposed Drugs in Advanced Cancer: Evidence, Mechanisms, and AI-Informed Insights (2026)

Introduction

Repurposing existing drugs for oncology offers a promising pathway to identify affordable, globally accessible therapies for advanced cancer. These agents may modulate metabolism, inflammation, or cellular stress pathways relevant to tumor growth.

However, mainstream oncology guidelines (ASCO, NCCN, ESMO) recommend interventions only when supported by robust clinical evidence, typically from randomized controlled trials (RCTs) or well-designed prospective studies. Mechanistic plausibility, in vitro studies, or anecdotal cases alone do not constitute sufficient evidence to guide treatment.

This article reviews 19 repurposed drugs highlighted in the OneDayMD AI ranking, situating each within an evidence-aware framework, and clarifies the distinction between AI-generated prioritization and clinical validation.

Diverse cancer hallmarks targeted by repurposed non-oncology drugs. This figure was created with Biorender.com. Source: Nature 2024

Evidence Tier Framework

To organize discussion, agents are grouped by current evidence:

• Tier A – Clinical or Translational Evidence: Supported by RCTs or significant human data.

• Tier B – Observational or Translational Signals: Supported by epidemiology, small clinical series, or mechanistic rationale.

• Tier C – Experimental / Anecdotal Evidence: Supported primarily by preclinical models, case reports, or AI inference.

This framework emphasizes research context, not clinical endorsement.

Tier B: Observational / Translational Signals

Metformin

• Observational studies associate metformin with reduced cancer incidence and improved outcomes in diabetic populations, particularly for colorectal, pancreatic, and liver cancers.

• Mechanistically, metformin activates AMPK and modulates tumor metabolism.

• High-quality RCTs are ongoing; definitive survival benefit in non-diabetic populations remains unproven.
  (Evidence tier: B)

Green Tea Extract / EGCG

• Catechins exhibit antioxidant and metabolic modulatory effects.

• Observational studies suggest potential cancer risk reduction, particularly in prostate and gastrointestinal cancers.
  (Evidence tier: B)

Curcumin

• Anti-inflammatory and anti-proliferative effects are well-documented in preclinical models.

• Clinical translation is limited by bioavailability; small trials suggest adjunctive benefits but no conclusive survival effect.
  (Evidence tier: B)

Cimetidine / Propranolol

• Cimetidine may reduce metastatic spread perioperatively; propranolol may inhibit stress-driven tumor progression.

• Evidence is preliminary, derived from observational studies or small pilot trials.
  (Evidence tier: B)

Doxycycline

• Exhibits mitochondrial and anti-matrix activity in vitro; clinical signals in advanced cancer are limited.
  (Evidence tier: C)

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Tier C: Experimental or Anecdotal Evidence

Ivermectin

• Preclinical anti-proliferative activity reported in vitro and in xenografts.

• Anecdotal human reports exist, but RCT data are lacking.
  (Evidence tier: C)

Fenbendazole / Albendazole / Mebendazole

• Mechanistic rationale based on microtubule inhibition and metabolic effects.

• Evidence in humans is anecdotal; clinical trials are absent.
  (Evidence tier: C)

Chloroquine / Hydroxychloroquine

• Investigated for autophagy modulation; clinical studies show mixed results and no definitive survival benefit.
  (Evidence tier: C)

Resveratrol

• Preclinical anti-oxidative and metabolic effects; human survival outcomes unproven.
  (Evidence tier: C)

CBD Oil / Tocotrienols

• Experimental metabolic or immunomodulatory effects; robust clinical evidence in terminal cancer is lacking.
  (Evidence tier: C)

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AI-Generated Ranking: Interpretation and Limitations

The original AI ranking prioritizes agents based on:

• Pathway coverage inferred from molecular or metabolic data

• Aggregated anecdotal and case report signals

Important considerations:

• AI rankings do not replace clinical trial evidence.

• Mechanistic activity or pathway overlap does not equate to proven survival benefit.

• Some agents ranked highly (e.g., Ivermectin, Fenbendazole) are experimental and should be considered hypothesis-generating, not guideline-compliant.

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Integration with Mainstream Oncology

• Mainstream guidelines endorse repurposed drugs only when clinical outcomes are validated (e.g., aspirin for colorectal cancer prevention).

• Most agents in the AI ranking are off-label, experimental, or supported solely by preclinical data.

• Such agents may be candidates for well-designed Phase II or Phase III trials with survival or quality-of-life endpoints.

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Next Steps for Research and Responsible Exploration

While repurposed drugs and metabolic interventions show mechanistic promise, their clinical translation requires rigorous evaluation. Future research should focus on:

1. Adaptive and N-of-1 Trial Designs

   • Personalized or adaptive trials allow preliminary testing of repurposed agents in small cohorts.

   • Outcomes such as tumor biomarkers, metabolic modulation, and patient-reported quality of life can guide further studies before large-scale trials.

2. Translational Biomarker Development

   • Identifying predictive biomarkers for response can help select patients most likely to benefit from specific metabolic interventions.

   • Examples include AMPK activation for metformin, autophagy markers for chloroquine, or immune modulation indicators for propranolol or cimetidine.

3. Combination and Sequencing Studies

   • Research should explore how repurposed drugs interact with standard therapies (chemotherapy, immunotherapy, targeted therapy).

   • Mechanistic synergy or antagonism must be evaluated in controlled laboratory and clinical settings.

4. Robust Preclinical Validation

   • Agents should undergo reproducible in vitro and in vivo studies, including xenograft and organoid models, to understand pharmacodynamics, toxicity, and metabolic effects.

5. Ethical and Regulatory Considerations

   • All studies must adhere to ethical guidelines, including informed consent and transparent reporting of adverse events.

   • Investigational agents should be administered in formal trial settings, avoiding unsupervised use in patients.

6. Systematic Data Collection and Sharing

   • Case reports and observational findings should be aggregated in registries to generate hypothesis-driven datasets.

   • Sharing data accelerates evidence synthesis, reduces duplication, and improves reproducibility.

7. Integration with Evidence-Based Guidelines

   • Research should remain aligned with standard-of-care frameworks. Repurposed drugs should supplement, not replace, guideline-supported therapies.

   • Outcomes from trials can inform updates to guideline recommendations if sufficient evidence accumulates.

Summary Statement:

Repurposed drugs for advanced cancer are a promising research frontier. Responsible exploration requires combining mechanistic insights, rigorous trial designs, biomarker-driven patient selection, and integration with standard-of-care therapies. AI-generated hypotheses can guide this process, but clinical validation is essential before these interventions can be recommended as therapeutic options.