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Predictive Oncology Successfully Develops Predictive Models Derived from Never-Before-Seen Compounds for Prevalent Cancer Indications Including Breast, Colon and Ovary

1. POAI developed models from 21 compounds at University of Michigan. 2. Three compounds outperformed Doxorubicin in tumor response testing. 3. ML models reduced lab testing time by nearly two years. 4. Natural products are vital to drug discovery and commercialization. 5. Future collaborations are planned to explore more compounds.

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Why Very Bullish?

The success in developing predictive models using strong tumor response compounds is pivotal. Historical examples show similar advancements led to significant stock increases for biotech firms.

How important is it?

The advancements in drug discovery and AI applications strongly position POAI for market leadership, increasing investor confidence.

Why Long Term?

Due to the time needed for drug approvals and market entry, benefits will manifest over time. Historical biotech successes, like Gilead’s Hepatitis C breakthroughs, show impactful long-term value.

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March 25, 2025 07:00 ET  | Source: Predictive Oncology Inc. 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 MoyerLifeSci Advisors, LLCmmoyer@lifesciadvisors.com Forward-Looking Statements: Certain matters discussed in this release contain forward-looking statements. These forward- looking statements reflect our current expectations and projections about future events and are subject to substantial risks, uncertainties and assumptions about our operations and the investments we make. All statements, other than statements of historical facts, included in this press release regarding our strategy, future operations, future financial position, future revenue and financial performance, projected costs, prospects, changes in management, plans and objectives of management are forward-looking statements. The words “anticipate,” “believe,” “estimate,” “expect,” “intend,” “may,” “plan,” “would,” “target” and similar expressions are intended to identify forward-looking statements, although not all forward-looking statements contain these identifying words. Our actual future performance may materially differ from that contemplated by the forward-looking statements as a result of a variety of factors including, among other things, factors discussed under the heading “Risk Factors” in our filings with the SEC. Except as expressly required by law, the company disclaims any intent or obligation to update these forward-looking statements.

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