UCLA researchers develop maestro AI system to find prostate cancer drug combos
UCLA scientists introduced MAESTRO, an artificial‑intelligence platform that leverages large language models to scan biomedical literature and rank drug pairings for prostate cancer treatment resistance. The system processed 3,592 natural compounds, evaluated each more than 100 times, and produced 150 high‑confidence candidates for laboratory testing alongside the androgen‑receptor blocker…
Key points
- MAESTRO screened 3,592 natural products and identified 150 high‑confidence candidates for testing with enzalutamide.
- In vitro tests showed 23 compounds synergized with enzalutamide; berberine emerged as the most promising.
- The berberine‑enzalutamide combo activated AMPK and suppressed mTORC1, indicating a clear mechanistic rationale.
In vitro experiments on 3D prostate‑cancer spheroids found 23 compounds that significantly enhanced enzalutamide’s anti‑cancer effect. Among them, the plant‑derived alkaloid berberine stood out, showing consistent synergy across four distinct prostate‑cancer models with different resistance mechanisms. Follow‑up assays linked the combination to increased AMPK activity and suppressed mTORC1 signaling, providing a mechanistic explanation for the observed efficacy. The researchers plan to validate the pairing in patient‑derived tumor models, animal studies, and to broaden MAESTRO’s data inputs for other cancers and resistance pathways.
Scientists stunned as new AI unlocks game-changing cancer treatment
independent.co.uk · 22 October 2026
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