AI Meets Genomics: Predictive Oncology Breakthrough Coincides with Regeneron's $256M 23andMe Acquisition
Rhea-AI Summary
Predictive Oncology (NASDAQ: POAI) has announced significant progress in AI-enabled cancer drug discovery, leveraging its extensive biobank of over 150,000 live cell tumor samples across 137 cancer indications. The company successfully developed predictive tumor response models for 21 previously untested molecules from the University of Michigan, targeting common cancers including breast, colon, and ovarian cancers.
This announcement coincides with Regeneron's $256 million acquisition of 23andMe, highlighting the industry's shift toward data-driven drug discovery. The company's proprietary active machine learning platform enables in silico drug response testing before in vitro confirmation, significantly accelerating development timelines and improving the Probability of Technical Success in drug development.
Positive
- Successfully developed predictive tumor response models for 21 new molecules using AI
- Possesses extensive biobank of 150,000+ tumor samples across 137 cancer indications
- Proprietary AI/ML platform enables faster and lower-risk drug development
- CLIA laboratory capabilities for in vitro validation of AI predictions
Negative
- None.
News Market Reaction – POAI
On the day this news was published, POAI declined 10.09%, reflecting a significant negative market reaction.
Data tracked by StockTitan Argus on the day of publication.
Company Leverages More Than Twenty Years of Drug Response Data Derived from Massive Biobank of Live Cell Tumor Samples
PITTSBURGH, May 22, 2025 (GLOBE NEWSWIRE) -- Predictive Oncology Inc. (NASDAQ: POAI) moves to leverage its vast biobank of more than 150,000 heterogenous live cell tumor samples and drug response data to aggressively pursue novel drug discovery, biomarker discovery and drug repurposing using AI and machine learning.
Earlier this week, Regeneron Pharmaceuticals announced its acquisition of 23andMe for
23andMe houses one of the world’s largest and most comprehensive longitudinal genomic datasets, with many customers having consented to ongoing health tracking. This unique trove of real-world health data offers powerful insights into disease progression, treatment efficacy, and patient stratification—making it a highly valuable resource for precision drug development.
A testament to this value is 23andMe’s previous
With this acquisition, Regeneron is expected to integrate 23andMe’s consumer genomic and health data into its own R&D pipeline. The company aims to strengthen its capabilities in areas such as target identification, biomarker discovery, and clinical trial optimization, aligning with a broader trend across the biopharma landscape: the convergence of artificial intelligence, real-world data, and predictive analytics to improve therapeutic outcomes.
At the forefront of this transformation stands Predictive Oncology.
“We recently achieved a major milestone in AI-enabled cancer drug discovery,” said Raymond Vennare, Chairman and Chief Executive Officer of Predictive Oncology. “Using compounds sourced from the Natural Products Discovery Core at the University of Michigan, we successfully developed predictive tumor response models for 21 previously untested molecules. These models are targeted at some of the most common cancer types, including breast, colon, and ovarian cancers.
“What makes this advancement particularly significant is that these compounds had no prior response data—making this a clear demonstration of AI’s ability not just to enhance but to lead in early-stage drug discovery. Predictive Oncology’s proprietary active machine learning platform was able to model tumor response across diverse cancer types using insights derived from its biobank of over 150,000 tumor samples spanning 137 cancer indications.”
The combination of artificial intelligence, machine learning and empirical validation allows the company to test drug response in silico before confirming them in vitro in their CLIA laboratory, which has been proven to dramatically accelerate timelines and improve the Probability of Technical Success (PTS) in drug development.
“Our ability to combine artificial intelligence and machine learning with live cell tumor samples and real-world drug response data allows us to expedite early-stage drug discovery and de-risk downstream drug development. This strategic first-mover advantage enables our partners to accelerate timelines, reduce R&D risk, and maximize ROI. This proprietary AI/ML platform and robust scientific methodology is the cornerstone of our business development efforts in oncology drug discovery and repurposing,” Mr. Vennare concluded.
Not unlike Regeneron’s acquisition of 23andMe, Predictive Oncology’s AI-driven breakthroughs reflect a broader transformation in life sciences. The integration of genomics, machine learning, and real-world biological data is no longer an emerging trend—it’s now a foundational force driving the future of precision medicine.
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
Investor Relations Contact:
Mike Moyer
LifeSci Advisors, LLC
mmoyer@lifesciadvisors.com
Forward-Looking Statements
Certain statements made in this press release are “forward-looking statements” within the meaning of the “safe harbor” provisions of the Private Securities Litigation Reform Act of 1995. These forward- looking statements reflect Predictive Oncology’s current expectations and projections about future events and are subject to substantial risks, uncertainties and assumptions about Predictive Oncology’s operations and the investments Predictive Oncology makes. 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. Predictive Oncology’s 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 Predictive Oncology’s filings with the SEC. Except as expressly required by law, Predictive Oncology disclaims any intent or obligation to update these forward-looking statements. Predictive Oncology does not give any assurance that Predictive Oncology will achieve its expectations described in this press release.
FAQ
What breakthrough did Predictive Oncology (POAI) achieve in AI-driven cancer drug discovery?
How many tumor samples does Predictive Oncology's (POAI) biobank contain?
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What is the significance of Regeneron's acquisition of 23andMe mentioned in POAI's announcement?
