Company successfully developed predictive models derived from 21 unique compounds from the Natural Products Discovery Core at the University of Michigan

Tumor response models for novel compounds represent true drug discovery using Predictive's active machine learning platform

PITTSBURGH, March 25, 2025 (GLOBE NEWSWIRE) -- Predictive Oncology Inc. (NASDAQ: POAI), a leader in AI-driven drug discovery, announced today that it has successfully developed predictive models derived from 21 unique compounds from the Natural Products Discovery Core (NPDC) at the University of Michigan Life Sciences Institute.

Predictive Oncology, in partnership with the NPDC, recently evaluated 21 novel compounds using Predictive’s active machine learning platform. The platform is used to shorten the time necessary to select drug candidates, while increasing the probability of technical success using live-cell tumor samples from its extensive biobank of frozen specimens.

The U-M Natural Products Discovery Core is home to a best-in-class library, and among one of the largest pharmaceutically viable natural products libraries in the United States, with specimens collected from biodiverse hotspots around the globe including Asia-Pacific, the Middle East, South America, North America and the Antarctic.

Natural products are specialized molecules with diverse biological activities. At least half of the small-molecule drugs approved during the past three decades were derived from these products, underscoring their importance in drug discovery and the potential to patent and market these assets.

“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,” said Dr. Arlette Uihlein, SVP of Translational Medicine and Drug Discovery at Predictive Oncology. “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,” Dr. Uihlein concluded.

Three tumor types — breast, colon and ovary — were selected for testing with 21 NPDC compounds and a benchmark known anti-cancer drug. After only measuring 7% of the possible wet lab experiments, the predictive ML model was capable of making confident predictions to cover a total of 73% of all experiments, virtually eliminating up to two years of laboratory testing.

“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,” added NPDC Director Dr. Ashu Tripathi. “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.”

About Predictive Oncology
Predictive Oncology is on the cutting edge of the rapidly growing use of artificial intelligence and machine learning to expedite early drug discovery and enable drug development for the benefit of cancer patients worldwide. The company’s scientifically validated AI platform, PEDAL, is able to predict with 92% accuracy if a tumor sample will respond to a certain drug compound, allowing for a more informed selection of drug/tumor type combinations for subsequent in-vitro testing. Together with the company’s vast biobank of more than 150,000 assay-capable heterogenous human tumor samples, Predictive Oncology offers its academic and industry partners one of the industry’s broadest AI-based drug discovery solutions, further complimented by its wholly owned CLIA laboratory facility. Predictive Oncology is headquartered in Pittsburgh, PA.

Investor Relations Contact:
Michael Moyer
LifeSci Advisors, LLC
mmoyer@lifesciadvisors.com

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