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3 days ago
Predictive Oncology Successfully Develops Predictive Models Derived from Never-Before-Seen Compounds for Prevalent Cancer Indications Including Breast, Colon and Ovary
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.”
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8 months ago
Predictive Oncology Enters Biomarker Discovery Market After Successful Retrospective Ovarian Cancer Study Yields Compelling Results
Expands AI/ML driven offering to include novel oncology biomarker discovery to predict patient outcomes and drug response in oncology
Biomarker discovery market estimated by third party research to be $51.5 billion in 2024
PITTSBURGH, July 25, 2024 (GLOBE NEWSWIRE) -- Predictive Oncology Inc. (NASDAQ: POAI), a leader in AI-driven drug discovery and biologics, today announced that it is expanding its AI/ML driven drug discovery platform to pursue discovery of novel biomarkers that can be used to predict patient outcomes and drug response in oncology.
Predictive Oncology’s biomarker discovery initiative stems, in part, from results obtained in the retrospective ovarian cancer study with UPMC Magee-Womens Hospital, which were presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting. In that study, Predictive Oncology successfully developed muti-omic machine learning models that identified key features that could more accurately predict both short-term (two-year) and long-term (five-year) survival outcomes among ovarian cancer patients as compared to clinical data alone. Through this process, Predictive Oncology obtained and analyzed data that supports novel ovarian cancer biomarker discovery and development that will be further explored both independently and in partnership with biopharma companies.
“We have already demonstrated the capabilities of our active machine learning platform to selectively utilize our diverse patient samples preserved in our biobank to predict responses to drugs with a very high degree of accuracy,” said Arlette H. Uihlein, MD, SVP, Translational Medicine and Drug Discovery and Medical Director at Predictive Oncology. “We are now taking this one step further by applying state-of-the-art deep learning approaches for biomarker discovery related to both patient overall survival (OS) and drug response, which can be done with existing resources. Our platform enables us to apply deep learning to the correct patient cohorts and accelerate the initial stages of biomarker discovery.”
“We believe the identification of novel cancer biomarkers represents the next significant opportunity for the application of our platform, which leverages the substantial value inherent in the diversified patient samples and data that we possess, as well as additional potential revenue streams for our company. Our technology has broad applicability, including the development of a clinical decision support tool to screen for clinical trial enrollment, and to inform subsequent drug discovery and development,” stated Raymond Vennare, Chief Executive Officer of Predictive Oncology. “These capabilities extend well beyond ovarian cancer and can be used in the discovery of biomarkers for other cancer types as well, and we look forward to further validating these capabilities through development collaborations with leading biopharmaceutical partners and healthcare networks.”
The total biomarker discovery market is estimated by third party research to be $51.5 billion in 2024.1
Predictive Oncology also announced today the release of a new white paper that discusses its biomarker discovery capabilities in greater detail. The white paper can be accessed at: https://predictive-oncology.com/blog/BiomarkerDiscovery.
About Predictive Oncology
Predictive Oncology is on the cutting edge of the rapidly growing use of artificial intelligence and machine learning to expedite early biomarker and 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 lab and GMP facilities. Predictive Oncology is headquartered in Pittsburgh, PA.
Investor Relations Contact
Tim McCarthy
LifeSci Advisors, LLC
tim@lifesciadvisors.com
Forward-Looking Statements:
Certain matters discussed in this release contain forward-looking statements. These forward- looking statements refl