Predictive Oncology developed machine learning models from 21 compounds, demonstrating strong anti-tumor activity for potential drug discovery.
Quiver AI Summary
Predictive Oncology Inc. announced the successful development of predictive models for 21 unique compounds sourced from the University of Michigan's Natural Products Discovery Core, marking a significant advancement in drug discovery using its active machine learning platform. The models evaluated the novel compounds against live-cell tumor samples, demonstrating potential anti-tumor activity that exceeded that of the standard drug Doxorubicin across multiple tumor types, including breast, colon, and ovary. This approach has the potential to significantly reduce testing time, completing predictions for 73% of experiments after conducting only 7% of the wet lab tests. The results indicate strong promise for further exploration of these natural compounds in cancer treatment, with plans for continued collaboration and testing of additional compounds in the pipeline.
Potential Positives
- Successful development of predictive models from 21 unique compounds suggests a significant advancement in AI-driven drug discovery.
- Three identified compounds showed strong tumor drug responses exceeding those of Doxorubicin, indicating potential for effective cancer treatment options.
- The predictive machine learning model could cover 73% of experiments using only 7% of wet lab tests, expediting the drug discovery process by potentially saving up to two years of laboratory testing.
- Collaboration with the University of Michigan's Natural Products Discovery Core enhances the company’s access to a large collection of pharmaceutically viable natural products, increasing the potential for future discoveries.
Potential Negatives
- The press release heavily relies on forward-looking statements regarding the potential success of drug discovery, which inherently carries risks and uncertainties that could mislead investors about the company's actual future performance.
- Only a small percentage (7%) of possible wet lab experiments were measured, raising questions about the reliability and comprehensiveness of the predictive models developed.
- The reliance on a limited number of compounds (21 out of a much larger library) may signify a constrained scope of research that could impact the overall credibility and significance of the findings presented.
FAQ
What did Predictive Oncology announce on March 25, 2025?
Predictive Oncology announced the successful development of predictive models from 21 unique compounds at the University of Michigan's Natural Products Discovery Core.
How does Predictive's machine learning platform enhance drug discovery?
The platform shortens the time for selecting drug candidates and increases the probability of success using live-cell tumor samples.
What tumor types were tested with the NPDC compounds?
The tested tumor types included breast, colon, and ovary, compared with a benchmark anti-cancer drug.
What is the significance of natural products in drug discovery?
Natural products have diverse biological activities and account for at least half of small-molecule drugs approved in the last thirty years.
What does Predictive Oncology's AI platform predict?
The AI platform, PEDAL, predicts with 92% accuracy if a tumor will respond to a specific drug compound.
Disclaimer: This is an AI-generated summary of a press release distributed by GlobeNewswire. The model used to summarize this release may make mistakes. See the full release here.
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Full Release
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
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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