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Latest developments expand Predictive’s AI-driven drug discovery platform to include biomarker discovery and drug repurposing
With the global biomarker discovery market valued at $14.5 billion in 2024 and projected to grow at a 19.4% CAGR through 2030, Predictive Oncology is poised to lead in precision oncology innovation
PITTSBURGH, May 20, 2025 (GLOBE NEWSWIRE) -- Predictive Oncology Inc. (NASDAQ: POAI), a leader in AI-driven drug discovery, today issued the following shareholder update recapping recent progress:
To Our Shareholders,
Thank you for the opportunity to share an update on the progress we're making at Predictive Oncology as we work to position ourselves as a leader—and partner of choice—in the critical and fast-evolving field of AI-driven biomarker and drug discovery.
Today, I am more confident than ever that our unique combination of assets and capabilities—most notably our vast biobank of diverse live-cell tumor specimens—sets us apart in the industry. These advantages provide us with a strong foundation to drive long-term value and play a significant role in shaping the future of cancer treatment, drug development, and clinical decision-making tools.
Advancing Survival Prediction Models in Ovarian Cancer
One of our most important achievements this past year was our collaboration with UPMC Magee-Womens Hospital, where we successfully developed AI-powered multi-omic machine learning models that predict short- and long-term survival outcomes in ovarian cancer patients—outperforming models based solely on clinical data.
These breakthrough results were presented at the prestigious American Society of Clinical Oncology (ASCO) Annual Meeting in June 2024.
The implications are far-reaching. Beyond improving early drug discovery, these models may also enhance clinical decision-making by helping providers tailor treatments to individual patients more effectively—potentially improving patient monitoring, management, and outcomes.
This is particularly important for high-grade serous ovarian cancer, a difficult cancer to treat, where relapse rates after frontline treatment remain high. We are actively refining these models with the goal of integrating them into clinical practice at leading cancer centers worldwide.
Pioneering Biomarker Discovery
Building on the success of the Magee study, we identified novel ovarian cancer biomarkers linked to patient survival and drug response using advanced deep learning methods. These insights were achieved using our existing datasets and tools.
We believe biomarker discovery represents a transformative opportunity for our AI platform—not only in ovarian cancer, but across a wide range of tumor types. According to Grand View Research, the biomarker discovery market reached $14.5 billion in 2024, with expected growth of 19.4% CAGR through 2030. We are actively exploring strategic partnerships with biopharmaceutical companies and healthcare networks to further capitalize on this momentum.
Enhancing Drug Discovery Success
Our efforts in early-stage drug discovery remain core to our mission. With clinical trial failure rates in oncology remaining high, particularly in Phases II and III, we address a critical industry challenge: the late introduction of patient heterogeneity.
By integrating real-world diversity from our biobank of 150,000 tumor samples across 137 cancer types, we validate AI drug response predictions with wet-lab testing in the earliest stages—boosting the Probability of Technical Success (PTS) and improving decision-making for target selection, clinical trial design, and pipeline development.
This capability allows our partners to accelerate timelines, reduce risk, and optimize R&D investments—making it a central component of ongoing business development discussions.
Unlocking Value in Drug Repurposing
A unique and often overlooked advantage of our platform is its ability to repurpose previously abandoned oncology drugs. Recently, we screened a curated set of such compounds using active machine learning and identified three candidates worth re-evaluating in ovarian and colon cancer.
This capability provides enormous value to drug developers by unlocking the potential of shelved assets and efficiently transitioning them back into clinical readiness. We are now applying this approach to a broader range of publicly available compounds.