In a breakthrough for cancer research, Predictive Oncology Inc. has announced significant progress in using artificial intelligence (AI) to identify potential new treatments from existing drugs.

The company’s AI-driven platform has successfully analyzed nearly 1,000 drug-tumor interactions, identifying multiple candidates for treating ovarian, colon, and breast cancers—including two drugs that outperformed a standard colon cancer treatment in early testing.

The results mark a major advancement in drug repurposing, a strategy that seeks to find new uses for existing drugs rather than developing treatments from scratch. By using AI, the company has significantly reduced the time needed for early-stage drug discovery—accelerating the process by at least 18 months, according to their latest report.

Their AI platform examined 92 drug-tumor combinations over an eight-week period, gathering enough data to confidently predict the results of 964 additional experiments—covering nearly 80% of all possible drug-cancer pairings in their dataset.

Among the most promising discoveries, two drugs showed superior performance compared to a widely used colon cancer treatment. While the company has not yet disclosed the names of these drugs, the findings suggest they may be viable alternatives or enhancements to existing therapies.

Traditional drug development is a slow and expensive process, often taking over a decade to bring a new treatment from discovery to market. By contrast, AI-driven drug repurposing dramatically speeds up the process by prioritizing the most promising candidates for further study.

The company’s approach also reduces the financial burden of cancer drug development. Unlike traditional research, which requires massive investments in new drug compounds, repurposing existing drugs can cut costs while still delivering effective new treatments.

Predictive Oncology is now looking to partner with pharmaceutical companies to advance the most promising drug candidates into clinical trials. If successful, these AI-identified treatments could move forward at a fraction of the time and cost compared to conventional methods.

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