PITTSBURGH, Nov. 08, 2023 (GLOBE NEWSWIRE) — Predictive Oncology Inc. (NASDAQ: POAI), a leader in AI-driven drug discovery and biologics, announced today that it has completed a multi-year study with UPMC Magee-Womens Hospital. The objective of the study was to evaluate the use of AI to build multi-omic machine learning (ML) models and test if these models can learn associations between the datasets and ovarian cancer patient short- and long-term survival.
“This study incorporated one of the largest sets of multi-omic data from 235 ovarian cancer patients, to identify the key features from these datasets driving overall survival endpoints. It was no small feat to bring together these many complex datasets to create an AI-driven predictor of survival in ovarian cancer,” said Robert P. Edwards, MD, the Milton Lawrence McCall Professor and Chair, Department of Obstetrics, Gynecology & Reproductive Sciences and Co-Director, Gynecologic Oncology Research at Magee-Womens Hospital of UPMC. “Our collaboration with Predictive Oncology brought cutting-edge AI capabilities, using multi-omic features to deliver strong predictive models with high levels of accuracy. These models have the potential to become invaluable clinical support tools.”
“The power of our technology is promising because our multi-omic machine learning models have the potential to overcome the gap defining the prognostic subgroups within ovarian cancer,” explained Arlette H. Uihlein, MD, Senior Vice President of Translational Medicine and Drug Discovery, Medical Director, Predictive Oncology. “High grade serious carcinomas of the ovary make up the majority of ovarian cancer cases and have the lowest survival rates, so this work has the potential to favorably impact thousands of lives.”
“Ultimately, these models could support the tailoring of therapies to individual patients with the goal of positively affecting the overall survival of ovarian cancer patients,” said Raymond F. Vennare, Chief Executive Officer of Predictive Oncology. “These findings were only made possible because of the substantial resources at our disposal, including a robust machine learning platform, the use of digital pathology, access to an extensive biobank of cryopreserved tumor samples and our considerable scientific expertise.”
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 lab and GMP facilities. Predictive Oncology is headquartered in Pittsburgh, PA.
Contact:
Predictive Oncology Inc.
Theresa Ferguson, Senior Director of Marketing
Phone: (630) 566-2003
tferguson@predictive-oncology.com
Predictive Oncology Investor Relations
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 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.
