New AI platform helps uncover promising cancer drug combinations
UCLA investigators have developed a new artificial intelligence-based platform that could help scientists identify promising drug combinations to overcome treatment resistance in cancer.
UCLA researchers have created an AI-based platform called MAESTRO that can help scientists discover promising drug combinations to combat treatment resistance in cancer. The platform uses large language models (LLMs) to analyze scientific knowledge about drug actions and cancer cell survival pathways.
MAESTRO was used to evaluate thousands of natural products for their potential synergy with enzalutamide, a drug commonly used to treat advanced prostate cancer. The researchers identified 150 high-confidence drug candidates for testing in prostate cancer 3D spheroids. Of these, 23 showed evidence of synergy with enzalutamide, with berberine emerging as a particularly promising candidate.
Berberine, found in several plants, showed consistent synergy across four prostate cancer models with different resistance mechanisms when combined with enzalutamide. This was surprising, as berberine affects several biological pathways, making its potential interaction with other drugs difficult to predict. The combination increased AMPK activity, suppressed mTORC1 signaling, reduced cellular DNA replication, and impaired cell-cycle progression, suggesting potential as a treatment for resistant prostate cancers.
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