Predictive Oncology Breaks New Ground with AI-Driven Models for Cancer-Fighting Compounds

March 25, 2025

DENVER, Colo., Mar 25, 2025 (247marketnews.com)- Predictive Oncology (NASDAQ:POAI) unveiled predictive models built from 21 never-before-seen compounds, sourced from the University of Michigan’s Natural Products Discovery Core (NPDC). Targeting prevalent cancers—breast, colon, and ovary—these models mark a true breakthrough in drug discovery, slashing timelines and boosting success odds with Predictive’s active machine learning platform.

Partnering with the NPDC at Michigan’s Life Sciences Institute, Predictive tapped into one of the U.S.’s largest natural products libraries—a treasure trove of pharmaceutically viable molecules collected from biodiverse hotspots like Asia-Pacific, the Middle East, South America, North America, and even Antarctica. These 21 unique compounds, tested against a benchmark anti-cancer drug, were run through Predictive’s platform, which leverages live-cell tumor samples from its vast biobank of frozen specimens.

Dr. Arlette Uihlein, Predictive Oncology’s SVP of Translational Medicine and Drug Discovery, stated, “Three compounds consistently demonstrated strong tumor drug response across all tumor types tested and demonstrated a stronger response than Doxorubicin, a benchmark compound, across tumor types. A fourth drug showed a strong response in the ovary and colon models and three additional compounds demonstrated the most ‘hit responses’ across all three tumor types.

“The efforts of this program and Predictive Oncology’s platform along with these novel compounds is tangibly driving and supporting true drug discovery.”

After measuring just 7% of possible wet lab experiments, the AI confidently predicted outcomes for 73% of all tests, potentially shaving up to two years off traditional lab work.

Natural products, specialized molecules with diverse biological punch, aren’t new to medicine; over half of small-molecule drugs approved in the last 30 years stem from them, per industry data.

By pairing NPDC’s best-in-class library with Predictive’s ML muscle, the models pinpoint tumor responses for novel compounds, opening a $5 billion oncology drug discovery market lane with patentable assets.

NPDC’s Director, Dr. Ashu Tripathi, commented, “Demonstrating that these natural compounds have such strong anti-tumor activity against several human tumor types strongly supports further investigations into these compounds and additional compounds, especially when considering that these results were achieved by including only about 1% of the available NPDC library. As we review these first data sets, we look forward to future collaborations with Predictive Oncology to test more of the hundreds of compounds in our drug discovery pipeline, as well as publishing our results.”

The three cancer types chosen—breast, colon, ovary—represent some of the most common and deadly, with over 700,000 U.S. cases yearly (CDC, 2024). Predictive’s platform, honed on its biobank’s depth, accelerates candidate selection; a process that typically drags on for years, while upping technical success rates.

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