Prototype System for AML Digital Twins

AML 数字孪生原型系统

• 批准号: 10588076
• 负责人: ILYA SHMULEVICH
• 金额: $ 73.39万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588076
• 关键词: Acute Myelocytic Leukemia; Address; Adult; Advocate; Age Years; Allogenic; Azacitidine; Blood specimen; Bone Marrow; Bone Marrow Aspiration; Caring; Clinical; Clinical Data; Clinical Trials; Communities; Computer Simulation; Data; Development; Disease; Disease Progression; Engineering; Event; Feedback; Finland; Foundations; Generations; Goals; Graph; Hematopoiesis; Hematopoietic Stem Cell Transplantation; Heterogeneity; Immune response; Knowledge; Learning; Light; Malignant Neoplasms; Medicine; Methods; Modeling; Molecular; Multi-Institutional Clinical Trial; National Center for Advancing Translational Sciences; Outcome; Outcome Measure; Output; Patient Selection; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Physiological; Property; Recommendation; Recording of previous events; Refractory; Regimen; Relapse; Research; Research Personnel; Residual Neoplasm; Resistance; Scientist; Selection for Treatments; Somatic Mutation; Source; Survival Rate; System; Systems Biology; The Cancer Genome Atlas; Therapeutic; Treatment outcome; Treatment-related toxicity; Universities; Update; Visualization; Work; chemotherapy; clinical decision-making; clinically relevant; cytokine; dashboard; data resource; design; digital twin; drug response prediction; drug sensitivity; experience; feature selection; improved; innovation; knowledge graph; learning progression; leukemia; leukemia treatment; machine learning method; manufacture; models and simulation; novel therapeutics; older patient; outcome prediction; patient oriented; peripheral blood; personalized decision; predicting response; prototype; response; simulation; statistical and machine learning; therapy outcome; traditional therapy; treatment response; virtual model

Project Abstract Conventional chemotherapy has reached its limits in treating Acute Myeloid Leukemia (AML), the most common leukemia in adults. Many patients who receive intensive chemotherapy followed by allogeneic hematopoietic stem-cell transplant eventually relapse. Therapeutic results remain particularly dismal for relapsed or refractory and older patients, who are often unfit for intensive therapies. Several new therapy options with the potential to improve treatment outcomes have emerged, but they suffer from heterogeneity in responses and a lack of methods for identifying patients who might benefit from such therapies. New detailed simulations called Digital Twins are starting to be implemented in many sectors including engineering, manufacturing, and medicine. Digital Twins are a simulation of a real-world system or object that allows for experimentation without real-world consequences. A patient oriented Digital Twin would simulate physiological response or disease and allow for outcome predictions following treatment. We propose to build a prototype of the ‘AML Digital Twin’ system (AML-DT), an interactive system to be used by doctors with their patients in which the patient’s clinical and molecular data, collected from bone marrow aspirates and peripheral blood, will be used to instantiate a digital twin model of the patient’s disease. The digital twin will enable the doctor and patient to explore personalized model-based predictions of drug response, using measurable outcomes in light of background knowledge generated from publicly available molecular data from AML patients. Importantly, the AML-DT system will continuously improve by learning from the experiences of patients and their digital twin models. The work, conducted jointly with clinician-scientists and computational researchers at the Institute for Systems Biology, the University of Helsinki, and Tampere University will center on patient-specific drug sensitivity prediction of venetoclax in combination with azacitidine by integrating with an ongoing clinical trial, VenEx, that includes ex-vivo patient selection and molecular data generation from bone marrow aspirates from AML patients in Finland. A prototype of the AML-DT system will be deployed at the midpoint of the 5-year project period, coinciding with the completion of the clinical trial. Following assessment of the prototype, further developments to the AML-DT will be carried out to accommodate other AML therapies and recommendations made by patient advocates and clinicians.

项目摘要 常规化学疗法在治疗急性髓样白血病（AML）方面已达到极限 成人最常见的白血病。许多接受强化化疗的患者 其次是同种异性造血干细胞移植最终中继。治疗性 对于接力或难治性和老年患者，结果仍然特别令人沮丧，他们经常 不适合密集型疗法。几种新的疗法选择，有可能改善治疗 结果已经出现，但它们的反应中遭受了异质性和缺乏方法的困扰 用于确定可能从这种疗法中受益的患者。 新的详细模拟称为Digital Twins，在许多领域都开始实现 包括工程，制造和医学。数字双胞胎是对 现实世界中的系统或对象允许实验而不会产生实际后果。一个 以患者为导向的数字双胞胎将模拟身体反应或疾病，并允许 治疗后的结果预测。 我们建议构建“ AML数字双胞胎”系统（AML-DT）的原型，这是一种交互式 患者的临床和分子的患者使用的医生使用的系统 从骨髓抽吸物和外周血收集的数据将用于实例化 患者病的数字双胞胎模型。数字双胞胎将使医生和患者能够 使用可测量的结果探索基于个性化模型的药物反应预测 由AML公开可用的分子数据产生的背景知识的光 患者。重要的是，AML-DT系统将通过向 患者及其数字双胞胎模型的经验。 这项工作与临床科学家和计算研究人员共同进行 系统生物学研究所，赫尔辛基大学和坦佩雷大学将集中在中心 特定于患者的药物敏感性预测Venetoclax与Azacitidine结合使用 与正在进行的临床试验综合，包括病人选择和 骨髓的分子数据产生从芬兰的AML患者吸出。一个 AML-DT系统的原型将在5年期间的中点部署， 与完成临床试验的完成。评估原型后，进一步 将进行AML-DT的发展，以适应其他AML疗法和 患者拥护者和临床医生提出的建议。