Scientists stunned as new AI unlocks game-changing cancer treatment

Researchers at UCLA have designed a novel artificial intelligence system that can discover promising drug combinations to combat treatment resistance in cancer, according to a university announcement today.The computational platform, named MAESTRO and detailed in the journal Advanced Science, uses large language models, or LLMs, to process established scientific knowledge regarding drug mechanisms and the biological pathways malignant cells exploit to survive treatment. The system evaluates potential therapeutic pairings, ranks the most promising candidates, and generates biological explanations that researchers can subsequently test in the laboratory.”Developing drug combinations currently relies on either brute-force screening or researchers hypothesizing potential candidates based on existing literature,” said Neil Lin, an associate professor of mechanical and aerospace engineering and bioengineering at the UCLA Samueli School of Engineering and the study's senior author.The computational platform, named MAESTRO and detailed in the journal Advanced Science, uses large language models, or LLMs, to process established scientific knowledge regarding drug mechanisms and the biological pathways malignant cells exploit to survive treatment (AP)”It is time-consuming, and it can still be difficult to capture the complex interactions among multiple biological pathways. MAESTRO could substantially accelerate this process. By integrating existing biomedical knowledge, it can identify drug combinations that have a clear biological rationale and are most appropriate for experimental testing.”To uncover therapeutic options that can overcome treatment resistance in prostate cancer, the researchers deployed MAESTRO—short for Mechanistic Augmentation and Ensemble Selection for Therapeutic Ranking Optimization—to evaluate thousands of naturally occurring compounds. The platform highlighted several compounds that could potentially enhance the effectiveness of enzalutamide, an advanced prostate cancer medication originally discovered and developed at UCLA.The research team focused its investigation on prostate cancer, a disease where resistance to therapy remains a formidable clinical challenge. Prostate cancer cells frequently depend on androgen receptor signaling to grow and proliferate. Although targeted drugs like enzalutamide block this critical signaling, malignant cells can eventually adapt by activating alternative pathways that allow them to survive, ultimately leading to treatment resistance.Combining therapeutic agents that target these alternative escape routes offers a potential strategy to overcome resistance. However, with thousands of potential compounds available and numerous interconnected biological pathways involved, identifying truly effective drug pairings remains an immense challenge.President Donald Trump speaks to a group of children before signing executive orders on AI and pediatric cancer research, in the Oval Office (AFP/Getty)While artificial intelligence has shown promise in predicting successful drug combinations, many existing computational approaches require vast amounts of experimental training data and offer little insight into why specific drugs might work together. The UCLA team engineered MAESTRO specifically to overcome these limitations.Researchers supplied the LLM with scientific data directly relevant to the project, including prostate cancer biology, mechanisms of drug resistance, and known drug actions. The computational model then analyzed how those cellular mechanisms might interact alongside androgen receptor blockade and ranked prospective compounds as potential therapeutic partners for enzalutamide.Rather than depending on a single prediction, the researchers instructed the model to evaluate each candidate compound more than 100 times. They then analyzed how highly each compound ranked and assessed the consistency of those rankings across repeated computational runs. They categorized compounds that consistently earned high rankings as the most promising candidates for physical testing.The team utilized the platform to screen 3,592 natural products for their potential to work synergistically with enzalutamide. The system successfully identified 150 high-confidence candidates for laboratory testing in 3D prostate cancer spheroids. Of the 150 compounds evaluated in vitro, 23 showed clear coupling with enzalutamide, meaning the combination produced a significantly greater anti-cancer effect than the individual treatments alone. Subsequent testing identified berberine, a naturally occurring plant compound, as an exceptionally promising candidate. The combination of berberine and enzalutamide demonstrated consistent coupling across four distinct prostate cancer models featuring different underlying mechanisms of treatment resistance.A cancer patients holds the tubes that funnel chemotherapy drugs into his body at Cape Fear Valley Medical Center in North Carolina (Getty)“Berberine was not an obvious candidate because it affects several biological pathways, making it difficult to predict how it might work with other drugs,” said James C.H. Lo, a postdoctoral student in the bioengineering department at UCLA when the work was conducted and the study's first author. “What surprised us was that the combination showed consistent synergy across all of the prostate cancer models, despite their different resistance mechanisms. This is where MAESTRO demonstrated its strength, helping us identify a promising combination that might have been overlooked by examining each pathway separately.”The researchers also tested whether the drug pairing's biological effects matched the precise cellular mechanism MAESTRO proposed. They found that adding berberine to enzalutamide increased AMPK activity, a pathway vital to regulating cellular energy and stress, while suppressing mTORC1 signaling, which plays a critical role in cell growth and survival. The drug combination was also linked to increased cellular stress, along with reduced DNA replication and cell-cycle progression.These findings indicate that berberine and enzalutamide represent a promising combination for further investigation, particularly in prostate tumors that have acquired resistance to standard care. The authors also noted that the specific AMPK-mTORC1 mechanism highlighted in the study could serve as a valuable framework to guide the development of future drug combinations intended to bypass resistance mechanisms.Moving forward, the research team plans to further assess the drug combination in patient-derived tumor models and subsequent animal studies. They also aim to expand MAESTRO's capabilities by integrating additional biological data streams and applying the platform to explore new drug combinations, novel resistance mechanisms, and other cancer types.