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'eterogeneità 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'eterogeneità dei pazienti in anticipo nel processo di scoperta dei farmaci. Questa strategia mira ad incrementare la Probabilità di Successo Tecnico (PTS) per i candidati farmaceutici, potenzialmente riducendo i fallimenti nelle fasi avanzate degli studi clinici.
Le capacità 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 più 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 fármacos impulsada por IA para abordar la heterogeneidad del paciente y mejorar las tasas de éxito en el desarrollo de medicamentos. El enfoque de la compañía integra inteligencia artificial, aprendizaje automático y una biobanco de muestras tumorales de pacientes crioconservadas para introducir la heterogeneidad del paciente más temprano en el proceso de descubrimiento de fármacos. Esta estrategia tiene como objetivo aumentar la Probabilidad de Éxito Técnico (PTS) para los candidatos a fármacos, reduciendo potencialmente los fracasos en ensayos clínicos en etapas avanzadas.
Las capacidades de la plataforma incluyen análisis integral de la respuesta de las muestras de pacientes, descubrimiento de biomarcadores, optimización de ensayos clínicos y validación de objetivos. Al aprovechar estas herramientas, Predictive Oncology busca ayudar a las empresas farmacéuticas a tomar decisiones más informadas sobre el desarrollo de medicamentos, acelerando potencialmente los cronogramas y mejorando la asignación de recursos en I+D oncológica.
Predictive Oncology Inc. (NASDAQ: POAI)는 AI 기반 약물 발견 플랫폼의 응용을 확장했습니다는 환자의 이질성을 다루고 약물 개발 성공률을 향상시키기 위함입니다. 회사의 접근 방식은 인공지능, 기계 학습 및 냉동 보존된 환자 유래 종양 샘플의 바이오뱅크를 통합하여 약물 발견 과정에서 환자의 이질성을 더 일찍 도입하는 것입니다. 이 전략은 약물 후보자의 기술적 성공 확률(PTS)을 높이는 것을 목표로 하며, 이는 잠재적으로 후기 단계의 임상 시험에서 실패를 줄일 수 있습니다.
플랫폼의 기능에는 환자 샘플 반응 분석, 바이오마커 발견, 임상 시험 최적화 및 타겟 검증이 포함됩니다. Predictive Oncology는 이러한 도구를 활용하여 제약 회사들이 약물 개발에 대해 더 정보 기반의 결정을 내릴 수 있도록 도와줌으로써, 잠재적으로 일정 단축 및 종양 연구 개발에서 자원 할당을 개선하고자 합니다.
Predictive Oncology Inc. (NASDAQ: POAI) a étendu les applications de sa plateforme de découverte de médicaments alimentée par l'IA pour traiter l'hétérogénéité des patients et améliorer les taux de réussite dans le développement de médicaments. L'approche de l'entreprise intègre intelligence artificielle, apprentissage automatique, et une biobanque d'échantillons tumoraux dérivés de patients cryoconservés pour introduire l'hétérogénéité des patients plus tôt dans le processus de découverte de médicaments. Cette stratégie vise à augmenter la Probabilité de Succès Technique (PTS) pour les candidats médicaments, réduisant potentiellement les échecs lors des essais cliniques en phase avancée.
Les capacités de la plateforme incluent l'analyse complète des réponses des échantillons de patients, la découverte de biomarqueurs, l'optimisation des essais cliniques et la validation des cibles. En exploitant ces outils, Predictive Oncology cherche à aider les entreprises pharmaceutiques à prendre des décisions plus éclairées concernant le développement de médicaments, en accélérant potentiellement les délais et en améliorant 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 Heterogenität der Patienten zu adressieren und die Erfolgsraten bei der Arzneimittelentwicklung zu verbessern. Der Ansatz des Unternehmens integriert künstliche Intelligenz, maschinelles Lernen und eine Biobank mit kryokonservierten tumoralen Proben von Patienten, um die Heterogenität der Patienten früher im Wirkstoffentdeckungsprozess einzuführen. Diese Strategie zielt darauf ab, die Wahrscheinlichkeit des technischen Erfolgs (PTS) für Arzneimittelkandidaten zu erhöhen, was potenziell die Misserfolge in späten klinischen Studien reduzieren könnte.
Die Fähigkeiten der Plattform umfassen umfassende Analysen der Patientenprobenreaktionen, Entdeckung von Biomarkern, Optimierung klinischer Studien und Validierung von Zielstrukturen. Durch die Nutzung dieser Werkzeuge möchte Predictive Oncology den Pharmaunternehmen helfen, fundiertere Entscheidungen über die Arzneimittelentwicklung zu treffen, wodurch Zeitpläne beschleunigt und die Ressourcenverteilung in der onkologischen F&E verbessert werden könnten.
- 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.
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’s AI platform to support biomarker discovery, clinical trial optimization and target validation
Proprietary tool accounts for patient heterogeneity and increases Probability of Technical Success
PITTSBURGH, Aug. 13, 2024 (GLOBE NEWSWIRE) -- Predictive Oncology Inc. (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’s 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.
“The 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.” said Dr. Arlette Uihlein, SVP of Translational Medicine and Drug Discovery at Predictive Oncology. “These analyses utilize genomics, digitized pathology data, and phenotype profiling of heterogenous responses across drug treatments and across heterogenous patient cell populations.”
“Oncology 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,” stated Raymond Vennare, Chief Executive Officer of Predictive Oncology. “Our 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.
“Beyond 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.
“The 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,” 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: https://predictive-oncology.com/blog/heterogeneity
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 proprietary AI/ML platform has been scientifically validated to predict with
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