Predictive Oncology Expands the Application of its Live-Cell Tumor Platform to De-Risk Drug Discovery and Accelerate Pipeline Development
Predictive Oncology Inc. (NASDAQ: POAI) has expanded applications for its AI-driven drug discovery platform to address patient heterogeneity and improve drug development success rates. The company's approach integrates artificial intelligence, machine learning, and a biobank of cryopreserved patient-derived tumor samples to introduce patient heterogeneity earlier in the drug discovery process. This strategy aims to increase the Probability of Technical Success (PTS) for drug candidates, potentially reducing failures in late-stage clinical trials.
The platform's capabilities include comprehensive patient sample response analysis, biomarker discovery, clinical trial optimization, and target validation. By leveraging these tools, Predictive Oncology seeks to help pharmaceutical companies make more informed decisions about drug development, potentially accelerating timelines and improving resource allocation in oncology R&D.
Predictive Oncology Inc. (NASDAQ: POAI) ha ampliato le applicazioni della sua piattaforma di scoperta di farmaci basata sull'IA per affrontare l'eterogeneit脿 dei pazienti e migliorare i tassi di successo nello sviluppo dei farmaci. L'approccio dell'azienda integra intelligenza artificiale, apprendimento automatico e una biobanca di campioni tumorali derivati da pazienti crioconservati per introdurre l'eterogeneit脿 dei pazienti in anticipo nel processo di scoperta dei farmaci. Questa strategia mira ad incrementare la Probabilit脿 di Successo Tecnico (PTS) per i candidati farmaceutici, potenzialmente riducendo i fallimenti nelle fasi avanzate degli studi clinici.
Le capacit脿 della piattaforma includono analisi completa delle risposte dei campioni dei pazienti, scoperta di biomarcatori, ottimizzazione degli studi clinici e validazione degli obiettivi. Sfruttando questi strumenti, Predictive Oncology cerca di aiutare le aziende farmaceutiche a prendere decisioni pi霉 informate riguardo lo sviluppo dei farmaci, potenzialmente accelerando i tempi e migliorando l'allocazione delle risorse nella R&S oncologica.
Predictive Oncology Inc. (NASDAQ: POAI) ha ampliado las aplicaciones de su plataforma de descubrimiento de f谩rmacos impulsada por IA para abordar la heterogeneidad del paciente y mejorar las tasas de 茅xito en el desarrollo de medicamentos. El enfoque de la compa帽铆a integra inteligencia artificial, aprendizaje autom谩tico y una biobanco de muestras tumorales de pacientes crioconservadas para introducir la heterogeneidad del paciente m谩s temprano en el proceso de descubrimiento de f谩rmacos. Esta estrategia tiene como objetivo aumentar la Probabilidad de 脡xito T茅cnico (PTS) para los candidatos a f谩rmacos, reduciendo potencialmente los fracasos en ensayos cl铆nicos en etapas avanzadas.
Las capacidades de la plataforma incluyen an谩lisis integral de la respuesta de las muestras de pacientes, descubrimiento de biomarcadores, optimizaci贸n de ensayos cl铆nicos y validaci贸n de objetivos. Al aprovechar estas herramientas, Predictive Oncology busca ayudar a las empresas farmac茅uticas a tomar decisiones m谩s informadas sobre el desarrollo de medicamentos, acelerando potencialmente los cronogramas y mejorando la asignaci贸n de recursos en I+D oncol贸gica.
Predictive Oncology Inc. (NASDAQ: POAI)電� AI 旮半皹 鞎诫 氚滉铂 頂岆灚韽检潣 鞚戩毄鞚� 頇曥灔頄堨姷雼堧嫟電� 頇橃瀽鞚� 鞚挫靹膘潉 雼る（瓿� 鞎诫 臧滊皽 靹标车毳犾潉 頄レ儊鞁滍偆旮� 鞙勴暔鞛呺媹雼�. 須岇偓鞚� 鞝戧芳 氚╈嫕鞚� 鞚戈车歆�電�, 旮瓣硠 頃欖姷 氚� 雰夒彊 氤挫〈霅� 頇橃瀽 鞙犽灅 膦呾枒 靸橅攲鞚� 氚旍澊鞓る眳韥�毳� 韱淀暕頃橃棳 鞎诫 氚滉铂 瓿检爼鞐愳劀 頇橃瀽鞚� 鞚挫靹膘潉 雿� 鞚检皪 霃勳瀰頃橂姅 瓴冹瀰雼堧嫟. 鞚� 鞝勲灥鞚� 鞎诫 頉勲炒鞛愳潣 旮办垹鞝� 靹标车 頇曤(PTS)鞚� 雴掛澊電� 瓴冹潉 氇╉憸搿� 頃橂┌, 鞚措姅 鞛犾灛鞝侅溂搿� 頉勱赴 雼硠鞚� 鞛勳儊 鞁滍棙鞐愳劀 鞁ろ尐毳� 欷勳澕 靾� 鞛堨姷雼堧嫟.
頂岆灚韽检潣 旮半姤鞐愲姅 頇橃瀽 靸橅攲 氚橃潙 攵勳劃, 氚旍澊鞓る旎� 氚滉铂, 鞛勳儊 鞁滍棙 斓滌爜頇� 氚� 韮�瓴� 瓴�歃�鞚� 韽暔霅╇媹雼�. Predictive Oncology電� 鞚措煬頃� 霃勱惮毳� 頇滌毄頃橃棳 鞝滌暯 須岇偓霌れ澊 鞎诫 臧滊皽鞐� 雽�頃� 雿� 鞝曤炒 旮半皹鞚� 瓴办爼鞚� 雮措Υ 靾� 鞛堧弰搿� 霃勳檧欷岇溂搿滌崹, 鞛犾灛鞝侅溂搿� 鞚检爼 雼稌 氚� 膦呾枒 鞐瓣惮 臧滊皽鞐愳劀 鞛愳洂 頃犽嫻鞚� 臧滌劆頃橁碃鞛� 頃╇媹雼�.
Predictive Oncology Inc. (NASDAQ: POAI) a 茅tendu les applications de sa plateforme de d茅couverte de m茅dicaments aliment茅e par l'IA pour traiter l'h茅t茅rog茅n茅it茅 des patients et am茅liorer les taux de r茅ussite dans le d茅veloppement de m茅dicaments. L'approche de l'entreprise int猫gre intelligence artificielle, apprentissage automatique, et une biobanque d'茅chantillons tumoraux d茅riv茅s de patients cryoconserv茅s pour introduire l'h茅t茅rog茅n茅it茅 des patients plus t么t dans le processus de d茅couverte de m茅dicaments. Cette strat茅gie vise 脿 augmenter la Probabilit茅 de Succ猫s Technique (PTS) pour les candidats m茅dicaments, r茅duisant potentiellement les 茅checs lors des essais cliniques en phase avanc茅e.
Les capacit茅s de la plateforme incluent l'analyse compl猫te des r茅ponses des 茅chantillons de patients, la d茅couverte de biomarqueurs, l'optimisation des essais cliniques et la validation des cibles. En exploitant ces outils, Predictive Oncology cherche 脿 aider les entreprises pharmaceutiques 脿 prendre des d茅cisions plus 茅clair茅es concernant le d茅veloppement de m茅dicaments, en acc茅l茅rant potentiellement les d茅lais et en am茅liorant l'allocation des ressources dans la R&D oncologique.
Predictive Oncology Inc. (NASDAQ: POAI) hat die Anwendungen seiner KI-gesteuerten Plattform zur Wirkstoffentdeckung erweitert, um die Heterogenit盲t der Patienten zu adressieren und die Erfolgsraten bei der Arzneimittelentwicklung zu verbessern. Der Ansatz des Unternehmens integriert k眉nstliche Intelligenz, maschinelles Lernen und eine Biobank mit kryokonservierten tumoralen Proben von Patienten, um die Heterogenit盲t der Patienten fr眉her im Wirkstoffentdeckungsprozess einzuf眉hren. Diese Strategie zielt darauf ab, die Wahrscheinlichkeit des technischen Erfolgs (PTS) f眉r Arzneimittelkandidaten zu erh枚hen, was potenziell die Misserfolge in sp盲ten klinischen Studien reduzieren k枚nnte.
Die F盲higkeiten der Plattform umfassen umfassende Analysen der Patientenprobenreaktionen, Entdeckung von Biomarkern, Optimierung klinischer Studien und Validierung von Zielstrukturen. Durch die Nutzung dieser Werkzeuge m枚chte Predictive Oncology den Pharmaunternehmen helfen, fundiertere Entscheidungen 眉ber die Arzneimittelentwicklung zu treffen, wodurch Zeitpl盲ne beschleunigt und die Ressourcenverteilung in der onkologischen F&E verbessert werden k枚nnten.
- Expanded AI platform applications to address patient heterogeneity in early drug discovery stages
- Potential to increase Probability of Technical Success (PTS) for drug candidates
- Capability to facilitate biomarker discovery and clinical trial optimization
- Extensive biobank of cryopreserved patient-derived tumor samples across 137 tumor types
- Platform's potential to accelerate drug development timelines and improve resource allocation
- None.
Insights
Predictive Oncology's expanded platform application represents a significant leap in oncology drug discovery. By introducing patient heterogeneity early in the process, they're addressing a critical gap in traditional drug development. The use of 137 different tumor types from cryopreserved samples is particularly noteworthy, as it provides a robust foundation for AI-driven analysis.
The platform's ability to increase the Probability of Technical Success (PTS) is a game-changer. This metric is important for pharmaceutical companies in making informed decisions about target selection and clinical trial design. By potentially reducing late-stage failures, this approach could lead to substantial cost savings and faster drug development timelines.
However, it's important to note that while this technology is promising, its real-world impact on drug approval rates remains to be seen. The oncology field will be watching closely to see if this translates into higher success rates in Phase II and III trials.
Predictive Oncology's platform showcases an innovative application of AI and machine learning in healthcare. The integration of deep machine learning with cellular analysis, genomics and digitized pathology data creates a comprehensive approach to understanding drug responses.
What's particularly interesting is the use of active machine learning in a CLIA wet lab environment. This combination of AI with real-world biological samples is not common and could provide more accurate predictions than purely computational methods.
The platform's ability to identify potential new biomarkers and targets is a significant advantage. It could lead to more personalized treatment approaches and potentially uncover novel drug candidates that traditional methods might miss.
While the technology seems promising, it's important to consider the challenges of integrating such complex AI systems into existing pharmaceutical R&D processes. The success of this platform will largely depend on its ability to consistently deliver actionable insights that translate into clinical benefits.
From a financial perspective, Predictive Oncology's expanded platform capabilities could significantly impact the company's market position. The ability to de-risk drug discovery and accelerate pipeline development addresses a critical need in the pharmaceutical industry, potentially making POAI an attractive partner for major drug companies.
The platform's focus on increasing the Probability of Technical Success (PTS) is particularly noteworthy. Higher PTS could lead to more efficient resource allocation in R&D, potentially improving ROI for pharmaceutical companies. This could translate into increased demand for POAI's services and, consequently, revenue growth.
However, investors should note that the company's success will depend on the platform's real-world performance and adoption by pharmaceutical partners. While the technology seems promising, it's operating in a competitive and rapidly evolving field. The company's ability to demonstrate clear advantages over existing methods will be important for long-term financial success.
As always, potential investors should carefully consider the company's financial health, competitive landscape and future growth prospects before making investment decisions.
Findings demonstrate real-world applications of Company鈥檚 AI platform to support biomarker discovery, clinical trial optimization and target validation
Proprietary tool accounts for patient heterogeneity and 颈苍肠谤别补蝉别蝉听Probability of Technical Success
PITTSBURGH, Aug. 13, 2024 (GLOBE NEWSWIRE) -- Predictive Oncology Inc.听(NASDAQ: POAI), a leader in AI-driven drug discovery, today announced that it has expanded the available applications for its platform to account for patient heterogeneity, de-risk drug discovery, and accelerate pipeline development.
Responding to the historically high failure rate in drug development in Phase II and Phase III clinical trials, the Company is utilizing its artificial intelligence, active machine learning capabilities, in a CLIA wet lab environment, to leverage its extensive biobank of cryogenically preserved patient-derived live-cell tumor samples, across 137 different tumor types accumulated over nearly two decades, to account for patient heterogeneity ahead of any clinical phase development work.
One of the primary reasons for these late-stage failures is that the heterogeneity of human subjects is not introduced until clinical trials are well underway. Predictive Oncology鈥檚 platform addresses this challenge by applying its unique assets and resources to introduce patient heterogeneity into the earliest phases of AI-driven drug discovery, thereby increasing the Probability of Technical Success (PTS), a key metric in target selection, clinical trial design and pipeline replenishment.
鈥淭he outputs of our platform are used to more comprehensively identify which patient samples responded and why, through the use of our deep machine learning and cellular analysis capabilities.鈥� said Dr. Arlette Uihlein, SVP of Translational Medicine and Drug Discovery at Predictive Oncology. 鈥淭hese analyses utilize genomics, digitized pathology data, and phenotype profiling of heterogenous responses across drug treatments and across heterogenous patient cell populations.鈥�
鈥淥ncology drug discovery and development are time and resource intensive processes that, unfortunately, do not yield high rates of success when considering the relatively small number of compounds that are ultimately approved and made available to cancer patients,鈥� stated Raymond Vennare, Chief Executive Officer of Predictive Oncology. 鈥淥ur platform has successfully demonstrated an ability to increase the Probability of Technical Success for these compounds. The ability to accelerate drug development timelines would allow pharmaceutical companies to make critical go/no go decisions, redirect resources or reprioritize R&D efforts to pursue parallel or contingent drug development initiatives.
鈥淏eyond addressing the responsiveness of patient tumor cohorts, these capabilities can identify which of the many tumor features that we have deployed in our modeling would be the most fruitful to exploit for potential new biomarkers, targets, or even drugs.
鈥淭he ability to facilitate biomarker discovery, refine clinical trial optimization and provide decision support, speaks to the broad versatility of our offering. Artificial intelligence is poised to play a rapidly increasing role in pharmaceutical R&D, and we are working to remain at the forefront of this exciting evolution,鈥� Mr. Vennare concluded.
Predictive Oncology also announced today the release of a new white paper that discusses these capabilities in greater detail. The white paper can be accessed at:
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鈥檚 proprietary AI/ML platform has been scientifically validated to predict with
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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 鈥渁nticipate,鈥� 鈥渂elieve,鈥� 鈥渆stimate,鈥� 鈥渆xpect,鈥� 鈥渋ntend,鈥� 鈥渕ay,鈥� 鈥減lan,鈥� 鈥渨ould,鈥� 鈥渢arget鈥� 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 鈥淩isk Factors鈥� 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.
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