Predictive Oncology Enters Biomarker Discovery Market After Successful Retrospective Ovarian Cancer Study Yields Compelling Results
Predictive Oncology Enters Biomarker Discovery Market After Successful Retrospective Ovarian Cancer Study Yields Compelling Results
Expands AI/ML driven offering to include novel oncology biomarker discovery to predict patient outcomes and drug response in oncology
扩展人工智能/机器学习驱动的方案,包括新型肿瘤学生物标志物的发现,以预测患者的结果和肿瘤药物反应
Biomarker discovery market estimated by third party research to be $51.5 billion in 2024
第三方研究预计生物标志物发现市场在2024年将达到515亿美元。
PITTSBURGH, July 25, 2024 (GLOBE NEWSWIRE) -- Predictive Oncology Inc. (NASDAQ: POAI), a leader in AI-driven drug discovery and biologics, today announced that it is expanding its AI/ML driven drug discovery platform to pursue discovery of novel biomarkers that can be used to predict patient outcomes and drug response in oncology.
2024年7月25日,医疗公司Predictive Oncology Inc.(纳斯达克股票代码:POAI)宣布,其扩大了其基于人工智能的药物发现和生物制品领域的AI/ML驱动的药物发现平台,以追求发现可用于预测肿瘤患者预后和药物反应的新型生物标志物。
Predictive Oncology's biomarker discovery initiative stems, in part, from results obtained in the retrospective ovarian cancer study with UPMC Magee-Womens Hospital, which were presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting. In that study, Predictive Oncology successfully developed muti-omic machine learning models that identified key features that could more accurately predict both short-term (two-year) and long-term (five-year) survival outcomes among ovarian cancer patients as compared to clinical data alone. Through this process, Predictive Oncology obtained and analyzed data that supports novel ovarian cancer biomarker discovery and development that will be further explored both independently and in partnership with biopharma companies.
Predictive Oncology的生物标志物发现计划部分源于UPMC Magee-Womens医院对卵巢癌的回顾性研究的结果,在2024年美国临床肿瘤学会(ASCO)年会上进行了介绍。在该研究中,Predictive Oncology成功地开发了多组学机器学习模型,识别出可以比单纯的临床数据更准确地预测卵巢癌患者近期(两年)和远期(五年)生存结果的关键特征。通过这个过程,Predictive Oncology获得并分析了支持新型卵巢癌生物标志物的发现和开发的数据,这将在独立和与生物制药公司的合作伙伴共同探索下进一步探讨。
"We have already demonstrated the capabilities of our active machine learning platform to selectively utilize our diverse patient samples preserved in our biobank to predict responses to drugs with a very high degree of accuracy," said Arlette H. Uihlein, MD, SVP, Translational Medicine and Drug Discovery and Medical Director at Predictive Oncology. "We are now taking this one step further by applying state-of-the-art deep learning approaches for biomarker discovery related to both patient overall survival (OS) and drug response, which can be done with existing resources. Our platform enables us to apply deep learning to the correct patient cohorts and accelerate the initial stages of biomarker discovery."
"我们已经展示了我们的主动机器学习平台利用我们的生物库的多样化患者样本选择性地预测药物反应的能力,其准确性非常高," Predictive Oncology的翻译医生和医药领域和药物发现和医学主任Arlette H.Uihlein,MD表示:"我们现在正在进一步应用最先进的深度学习方法进行生物标志物发现,这涉及到患者的整体生存率和药物反应,这可以通过现有的资源实现。我们的平台使我们能够将深度学习应用于正确的患者队列,并加速生物标志物发现的初期阶段。"
"We believe the identification of novel cancer biomarkers represents the next significant opportunity for the application of our platform, which leverages the substantial value inherent in the diversified patient samples and data that we possess, as well as additional potential revenue streams for our company. Our technology has broad applicability, including the development of a clinical decision support tool to screen for clinical trial enrollment, and to inform subsequent drug discovery and development," stated Raymond Vennare, Chief Executive Officer of Predictive Oncology. "These capabilities extend well beyond ovarian cancer and can be used in the discovery of biomarkers for other cancer types as well, and we look forward to further validating these capabilities through development collaborations with leading biopharmaceutical partners and healthcare networks."
"我们认为新型癌症生物标志物的识别代表着我们平台应用的下一个重大机会,其利用我们拥有的多元化患者样本和数据的巨大价值,以及我们公司的其他潜在收入流。我们的技术具有广泛的适用性,包括开发临床决策支持工具以筛选临床试验入组,以及指导后续的药物发现和开发。" Predictive Oncology的首席执行官 Raymond Vennare表示:"这些能力远远超出了卵巢癌,也可以用于其他癌症类型的生物标志物发现,我们期待通过与领先的生物制药合作伙伴和医疗网络开发合作来进一步验证这些能力。"
The total biomarker discovery market is estimated by third party research to be $51.5 billion in 2024.1
第三方研究预计在2024年,生物标志物发现市场将达到515亿美元。1
Predictive Oncology also announced today the release of a new white paper that discusses its biomarker discovery capabilities in greater detail. The white paper can be accessed at: .
Predictive Oncology今天还发布了一份新的白皮书,更详细地介绍了其生物标志物发现能力。白皮书可以通过以下方式获得访问:。
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 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.
医学预测正在积极推进利用人工智能和机器学习来加速早期药物发现,并为全球癌症患者获得药物开发提供支持。该公司经过科学验证的AI平台PADAL可以预测肿瘤样本对某种药物化合物的反应,准确率为92%,从而更加科学地选择药物/肿瘤类型组合,进行随后的体外测试。 还有,该公司拥有超过15万个可用于实验的异质性人类肿瘤样本的生物库,为学术界和行业伙伴提供了业界最广泛的基于人工智能的药物发现解决方案,进一步得到了完全拥有的CLIA实验室和GMP设施的支持。医学预测总部位于宾夕法尼亚州匹兹堡市。
Predictive Oncology处于利用人工智能和机器学习迅速增长的最前沿,以加快早期生物标志物和药物发现,并为全球癌症患者提供药物开发。公司的科学验证AI平台PEDAL能够以92%的准确率预测肿瘤样本对某种药物化合物的反应,从而允许更明智地选择药物/肿瘤类型组合进行后续体外测试。结合公司拥有的超过150,000个异质性人类肿瘤样本的生物库,Predictive Oncology为其学术和工业合作伙伴提供了行业内最广泛的AI基于药物发现的解决方案。同时还拥有完全拥有CLIA实验室和GMP设施。Predictive Oncology总部位于宾夕法尼亚州匹兹堡市。
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LifeSci Advisors,LLC
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