Predictive Oncology Develops Novel Approach to Identifying Clinically Viable Abandoned Drugs
Predictive Oncology (NASDAQ: POAI) has developed a novel approach to repurpose abandoned oncology drugs using active machine learning and their biobank of patient-derived tumor cells. The company successfully identified three promising compounds in less than 12 weeks:
1. Afuresertib (Akt inhibitor): Showed strong results in ovarian and colon tumors
2. Alisertib (Aurora A Inhibitor): Outperformed standard care drugs in colon and breast cancer
3. Entinostat (HDAC1/3 inhibitor): Demonstrated strong response in colon samples
The screening method efficiently evaluated these compounds against tumor indications that were previously unexplored. Notably, Alisertib and Entinostat outperformed Oxaliplatin in colon tumor treatment, while Alisertib also showed superior results compared to Ribociclib in breast cancer treatment.
Predictive Oncology (NASDAQ: POAI) ha sviluppato un approccio innovativo per riutilizzare farmaci oncologici abbandonati, sfruttando l'apprendimento automatico attivo e la loro biobanca di cellule tumorali derivate da pazienti. L'azienda ha identificato con successo tre composti promettenti in meno di 12 settimane:
1. Afuresertib (inibitore di Akt): ha mostrato risultati significativi nei tumori ovarici e del colon
2. Alisertib (inibitore di Aurora A): ha superato i farmaci standard per il trattamento del cancro del colon e del seno
3. Entinostat (inibitore HDAC1/3): ha evidenziato una forte risposta nei campioni di colon
Il metodo di screening ha valutato efficacemente questi composti contro indicazioni tumorali precedentemente inesplorate. In particolare, Alisertib ed Entinostat hanno mostrato risultati migliori rispetto a Oxaliplatino nel trattamento dei tumori del colon, mentre Alisertib ha ottenuto risultati superiori rispetto a Ribociclib nel trattamento del cancro al seno.
Predictive Oncology (NASDAQ: POAI) ha desarrollado un enfoque innovador para reutilizar medicamentos oncológicos abandonados utilizando aprendizaje automático activo y su biobanco de células tumorales derivadas de pacientes. La compañía identificó con éxito tres compuestos prometedores en menos de 12 semanas:
1. Afuresertib (inhibidor de Akt): mostró resultados sólidos en tumores de ovario y colon
2. Alisertib (inhibidor de Aurora A): superó a los medicamentos estándar en cáncer de colon y mama
3. Entinostat (inhibidor de HDAC1/3): demostró una fuerte respuesta en muestras de colon
El método de cribado evaluó eficientemente estos compuestos contra indicaciones tumorales previamente inexploradas. Notablemente, Alisertib y Entinostat superaron a Oxaliplatino en el tratamiento del tumor de colon, mientras que Alisertib también mostró mejores resultados en comparación con Ribociclib en el tratamiento del cáncer de mama.
Predictive Oncology (NASDAQ: POAI)는 활성 머신러닝과 환자 유래 종양 세포의 바이오뱅크를 활용하여 버려진 종양학 약물을 재활용하는 새로운 접근법을 개발했습니다. 회사는 12주도 채 되지 않아 세 가지 유망한 화합물을 성공적으로 식별했습니다:
1. Afuresertib (Akt 억제제): 난소 및 대장 종양에서 강력한 효과를 보임
2. Alisertib (Aurora A 억제제): 대장암과 유방암에서 표준 치료제보다 우수한 성과
3. Entinostat (HDAC1/3 억제제): 대장 샘플에서 강한 반응을 나타냄
스크리닝 방법은 이전에 탐색되지 않은 종양 적응증에 대해 이 화합물들을 효율적으로 평가했습니다. 특히 Alisertib와 Entinostat는 대장 종양 치료에서 Oxaliplatin보다 뛰어난 효과를 보였으며, Alisertib는 유방암 치료에서 Ribociclib보다 우수한 결과를 나타냈습니다.
Predictive Oncology (NASDAQ : POAI) a développé une nouvelle approche pour réutiliser des médicaments oncologiques abandonnés en utilisant l'apprentissage automatique actif et leur biobanque de cellules tumorales dérivées de patients. L'entreprise a identifié avec succès trois composés prometteurs en moins de 12 semaines :
1. Afuresertib (inhibiteur d'Akt) : a montré des résultats solides dans les tumeurs ovariennes et du côlon
2. Alisertib (inhibiteur d'Aurora A) : a surpassé les médicaments standards dans le cancer du côlon et du sein
3. Entinostat (inhibiteur HDAC1/3) : a démontré une forte réponse dans des échantillons du côlon
La méthode de criblage a évalué efficacement ces composés contre des indications tumorales auparavant inexplorées. Notamment, Alisertib et Entinostat ont surpassé l'Oxaliplatine dans le traitement des tumeurs du côlon, tandis qu'Alisertib a également montré des résultats supérieurs comparés au Ribociclib dans le traitement du cancer du sein.
Predictive Oncology (NASDAQ: POAI) hat einen neuartigen Ansatz entwickelt, um aufgegebene onkologische Medikamente mithilfe von aktivem maschinellem Lernen und ihrer Biobank mit patientenabgeleiteten Tumorzellen neu zu verwenden. Das Unternehmen identifizierte erfolgreich drei vielversprechende Verbindungen in weniger als 12 Wochen:
1. Afuresertib (Akt-Inhibitor): zeigte starke Ergebnisse bei Eierstock- und Darmtumoren
2. Alisertib (Aurora-A-Inhibitor): übertraf Standardmedikamente bei Darm- und Brustkrebs
3. Entinostat (HDAC1/3-Inhibitor): zeigte eine starke Wirkung bei Darmproben
Die Screening-Methode bewertete diese Verbindungen effizient gegen Tumorindikationen, die zuvor unerforscht waren. Besonders Alisertib und Entinostat übertrafen Oxaliplatin bei der Behandlung von Darmtumoren, während Alisertib auch bessere Ergebnisse als Ribociclib bei der Brustkrebsbehandlung erzielte.
- Successfully identified three promising abandoned drugs for new cancer indications
- Demonstrated superior performance compared to standard care drugs
- Rapid screening process completed in under 12 weeks
- Created commercially sustainable approach for drug repurposing
- Identified drugs still require further clinical trials and validation
- testing to only ovarian and colon tumor types
Insights
Predictive Oncology's announced methodology represents a significant advancement in drug repurposing technology. By combining AI-driven screening with their proprietary biobank of patient-derived tumor cells, they've demonstrated the ability to identify new indications for abandoned oncology drugs in under 12 weeks - a remarkably accelerated timeframe in drug development.
The concrete results are particularly compelling: three specific compounds (Afuresertib, Alisertib, and Entinostat) showed strong efficacy in both ovarian and colon tumors. More notably, two of these compounds outperformed current standard-of-care treatments in their testing models - Alisertib and Entinostat surpassed Oxaliplatin in colon cancer models, while Alisertib also exceeded Ribociclib's performance in breast cancer models.
This approach addresses a critical industry pain point: the high failure rate and massive expense of clinical trials. By using AI to identify specific patient cohorts most likely to respond to particular compounds, POAI is effectively creating a de-risking mechanism for trial design. For pharmaceutical companies sitting on shelved compounds that showed promise but failed in broader populations, this platform offers a potential pathway to resurrect these assets through more targeted development.
The demonstrated capability to repurpose abandoned drugs efficiently positions POAI for potential partnership opportunities with pharmaceutical companies looking to extract value from their dormant assets. This represents a substantial commercial opportunity in an industry where bringing even one abandoned drug back to viability can generate significant returns.
POAI's announcement delivers tangible validation of their AI-driven drug discovery platform through specific, measurable results rather than just theoretical potential. For a micro-cap company (
The identification of three specific compounds with demonstrated efficacy in ovarian and colon tumors provides concrete evidence of the platform's capabilities. Particularly noteworthy is that Alisertib and Entinostat outperformed standard-of-care treatments in their testing - a significant benchmark that pharmaceutical partners would find compelling when evaluating this technology.
The 12-week timeframe to complete this evaluation process is remarkably efficient in an industry where drug development typically spans years. This acceleration factor alone could attract pharmaceutical companies seeking to quickly determine if their abandoned compounds might have untapped potential in different indications.
What makes this approach commercially viable is its dual value proposition: pharmaceutical companies can potentially resurrect abandoned assets while simultaneously accessing technology that helps identify the specific patient populations most likely to respond. This patient stratification capability addresses a fundamental challenge in oncology drug development - tumor heterogeneity - which has historically derailed many promising compounds.
While no immediate revenue impact is mentioned, the demonstration of this capability positions POAI to pursue partnership opportunities that could transform their business model and create multiple potential revenue streams through drug repurposing collaborations.
New indications discovered by applying active machine learning and using samples from the company’s vast biobank of live-cell tumors specimens
Builds upon Predictive’s biomarker discovery platform to select targeted patient cohorts to significantly de-risk clinical trials
PITTSBURGH, April 15, 2025 (GLOBE NEWSWIRE) -- Predictive Oncology Inc. (NASDAQ: POAI), a leader in AI-driven drug discovery, announced today that it has made significant progress along the continuum of biomarker discovery, drug discovery and drug repurposing. These latest developments build upon Predictive’s ongoing initiative to combine its in-house biomarker identification platform with its AI screening capabilities.
Identifying new indications using active machine learning and a biobank of patient derived dissociated tumor cells (DTCs) represents a novel and commercially sustainable approach to repurposing abandoned oncology drugs.
“This efficient screening approach on a small, curated cohort of abandoned drugs identified three compounds that warrant further exploration in tumor indications that have never been examined in this way,” said Dr. Arlette Uihlein, SVP of Translational Medicine and Drug Discovery and Medical Director at Predictive Oncology. “The work that we have done successfully demonstrates our ability to utilize our active machine learning and biobank of tumor samples to capture patient response heterogeneity in less than 12 weeks.”
The results of Predictive’s novel approach to identifying clinically viable abandoned drugs are compelling.
Three drugs gave strong results for both ovarian and colon tumors. Specifically, Afuresertib, Alisertib and Entinosta demonstrated the highest proportion of hits within those two tumor types. Notably, Alisertib and Entinostat outperformed Oxaliplatin, a standard of care drug in the colon tumor type, while Alisertib also outperformed Ribociclib, a standard of care in breast cancer.
Afuresertib, is an Akt inhibitor that was previously studied in esophageal cancer, multiple myeloma, and more recently, attempted in combination with paclitaxel in platinum resistant ovarian cancer.
Alisertib, a selective Aurora A Inhibitor previously studied for use in both EGFR-mutated non-small cell lung cancer (NSCLC) and metastatic breast cancer, showed strong tumor drug response in ovarian and colon tumors based on both Predictive’s wet lab testing and AI models. This drug is currently being evaluated in clinical trials for recurrent/metastatic breast cancer and lung cancer.
Entinostat, an HDAC1/3 inhibitor which was previously studied in solid tumor types, including breast and pancreatic, had a strong tumor drug response in the company’s colon sample models. This drug is currently in clinical trials for use as a combination therapy in patients with NSCLC and for the purpose of biomarker development.
Of particular significance to Predictive Oncology, this drug class was previously shown to induce mitochondrial dysfunction in colorectal cancer, which is a possible target for further exploration combining the company’s high content imaging (HCI) assay and its proprietary and internally derived HCI analysis pipeline.
“Applying this approach to other sets of publicly available abandoned drugs is a next logical step,” said Raymond Vennare, CEO of Predictive Oncology. “But, more importantly, from the perspective of partnering opportunities with pharmaceutical companies to retain or repurpose their own abandoned drugs, our methodology would be advantageous in terms of efficiently transitioning their abandoned drugs back to clinical trial readiness.”
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 are the three abandoned drugs identified by Predictive Oncology (POAI) in their latest research?
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