AI Meets Genomics: Predictive Oncology Breakthrough Coincides with Regeneron's $256M 23andMe Acquisition
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.
Predictive Oncology (NASDAQ: POAI) ha annunciato importanti progressi nella scoperta di farmaci oncologici supportata dall'IA, sfruttando il suo ampio biobanca composta da oltre 150.000 campioni di cellule tumorali vive provenienti da 137 tipi di tumore. L'azienda ha sviluppato con successo modelli predittivi di risposta tumorale per 21 molecole mai testate prima, provenienti dall'Università del Michigan, mirate a tumori comuni come quelli al seno, al colon e all'ovaio.
Questo annuncio arriva in concomitanza con l'acquisizione da 256 milioni di dollari di 23andMe da parte di Regeneron, sottolineando il cambiamento dell'industria verso la scoperta di farmaci basata sui dati. La piattaforma proprietaria di machine learning attivo dell'azienda consente test di risposta ai farmaci in silico prima della conferma in vitro, accelerando notevolmente i tempi di sviluppo e migliorando la Probabilità di Successo Tecnico nello sviluppo dei farmaci.
Predictive Oncology (NASDAQ: POAI) ha anunciado avances significativos en el descubrimiento de fármacos contra el cáncer habilitados por IA, aprovechando su extensa biobanco con más de 150,000 muestras de células tumorales vivas en 137 indicaciones de cáncer. La compañía desarrolló con éxito modelos predictivos de respuesta tumoral para 21 moléculas nunca antes probadas de la Universidad de Michigan, dirigidas a cánceres comunes como de mama, colon y ovario.
Este anuncio coincide con la adquisición de 256 millones de dólares de 23andMe por parte de Regeneron, destacando el cambio de la industria hacia el descubrimiento de fármacos basado en datos. La plataforma propietaria de aprendizaje automático activo de la compañía permite pruebas de respuesta a fármacos in silico antes de la confirmación in vitro, acelerando significativamente los tiempos de desarrollo y mejorando la Probabilidad de Éxito Técnico en el desarrollo de medicamentos.
Predictive Oncology (나스닥: POAI)는 137가지 암 적응증에 걸쳐 15만 개 이상의 생체 세포 종양 샘플을 활용하여 AI 기반 암 치료제 개발에서 중요한 진전을 이루었다고 발표했습니다. 이 회사는 미시간 대학교에서 제공한 21개의 이전에 테스트되지 않은 분자에 대해 예측 종양 반응 모델을 성공적으로 개발했으며, 유방암, 대장암, 난소암 등 일반적인 암을 대상으로 합니다.
이 발표는 Regeneron의 23andMe 2억 5600만 달러 인수와 동시에 이루어져, 데이터 기반 신약 개발로의 산업 전환을 강조합니다. 회사의 독자적인 능동형 머신러닝 플랫폼은 시험관 내 확인 전에 인실리코 약물 반응 테스트를 가능하게 하여 개발 일정을 크게 단축하고 신약 개발에서 기술적 성공 확률을 향상시킵니다.
Predictive Oncology (NASDAQ : POAI) a annoncé des progrès significatifs dans la découverte de médicaments contre le cancer assistée par IA, en s'appuyant sur sa vaste biobanque de plus de 150 000 échantillons de cellules tumorales vivantes couvrant 137 indications cancéreuses. L'entreprise a développé avec succès des modèles prédictifs de réponse tumorale pour 21 molécules jamais testées auparavant de l'Université du Michigan, ciblant des cancers courants tels que le sein, le côlon et l'ovaire.
Cette annonce coïncide avec l'acquisition de 256 millions de dollars de 23andMe par Regeneron, mettant en lumière le virage de l'industrie vers la découverte de médicaments basée sur les données. La plateforme propriétaire d'apprentissage automatique actif de l'entreprise permet des tests de réponse aux médicaments in silico avant la confirmation in vitro, accélérant considérablement les délais de développement et améliorant la probabilité de succès technique dans le développement des médicaments.
Predictive Oncology (NASDAQ: POAI) hat bedeutende Fortschritte bei der KI-gestützten Krebsmedikamentenentwicklung bekannt gegeben und nutzt dabei seine umfangreiche Biobank mit über 150.000 lebenden Tumorzellproben aus 137 Krebsindikationen. Das Unternehmen entwickelte erfolgreich prädiktive Tumorantwortmodelle für 21 bisher ungetestete Moleküle der University of Michigan, die sich gegen häufige Krebsarten wie Brust-, Darm- und Eierstockkrebs richten.
Diese Ankündigung fällt mit Regenerons 256-Millionen-Dollar-Übernahme von 23andMe zusammen und unterstreicht den Wandel der Branche hin zur datengetriebenen Wirkstoffentwicklung. Die firmeneigene aktive Machine-Learning-Plattform ermöglicht In-silico-Tests der Arzneimittelreaktion vor der In-vitro-Bestätigung, was die Entwicklungszeiten erheblich verkürzt und die Wahrscheinlichkeit eines technischen Erfolgs in der Wirkstoffentwicklung verbessert.
- 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
- None.
Insights
POAI positions in AI-driven drug discovery using its tumor biobank data as Regeneron validates data-driven approach by acquiring 23andMe.
Predictive Oncology is strategically leveraging its proprietary biobank of over 150,000 heterogeneous live cell tumor samples and corresponding drug response data to accelerate oncology drug discovery through artificial intelligence. This announcement coincides with Regeneron's $256 million acquisition of 23andMe, which validates the increasing industry value placed on biological datasets for drug development.
The company has reached a significant technical milestone by successfully developing predictive tumor response models for 21 previously untested molecules from the University of Michigan's Natural Products Discovery Core. These models target common cancers including breast, colon, and ovarian cancers. What's particularly notable is that POAI's platform generated these predictions without prior response data for these compounds.
Their competitive advantage stems from a proprietary active machine learning platform that models tumor responses across 137 cancer indications. The platform enables in silico testing before confirming results in vitro in their CLIA-certified laboratory, potentially reducing development timelines and improving success probabilities in early-stage drug discovery.
This press release represents POAI's effort to position itself within the growing field of AI-driven drug discovery, highlighting similarities to the Regeneron-23andMe deal. While 23andMe focuses on genomic data, POAI's differentiation is in its tumor sample biobank and drug response models. The reference to 23andMe's previous $300 million GSK partnership further emphasizes the potential commercial value of such biological datasets when paired with AI capabilities.
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
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