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 传统化疗在治疗急性髓系白血病（AML）方面已达到极限， 成人中最常见的白血病，许多接受强化化疗的患者。 随后进行同种异体造血干细胞移植，最终治疗复发。 对于复发或难治性和老年患者来说，结果仍然特别令人沮丧，他们经常 不适合强化治疗的几种新的治疗方案有可能改善治疗。 结果已经出现，但由于反应的异质性和缺乏方法而受到影响 以确定可能受益于此类疗法的患者。 称为数字孪生的新详细模拟已开始在许多领域实施 包括工程、制造和医学。 允许进行实验而不会产生现实世界后果的现实世界系统或对象。 面向患者的数字孪生将模拟生理反应或疾病，并允许 治疗后的结果预测。 我们建议建立“AML数字孪生”系统（AML-DT）的原型，这是一个交互式的 医生与患者一起使用的系统，其中患者的临床和分子信息 从骨髓抽吸物和外周血收集的数据将用于实例化 患者疾病的数字孪生模型数字孪生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 根据公开的 AML 分子数据生成的背景知识 重要的是，AML-DT 系统将通过向患者学习不断改进。 患者的经历及其数字孪生模型。 这项工作是与临床医生科学家和计算研究人员联合进行的 系统生物学研究所、赫尔辛基大学和坦佩雷大学将重点研究 维奈托克与阿扎胞苷联合用药的患者特异性药物敏感性预测 与正在进行的临床试验 VenEx 相结合，其中包括离体患者选择和 从芬兰 AML 患者的骨髓抽吸中生成分子数据 A。 AML-DT系统的原型将在5年项目期的中期部署， 与临床试验的完成同时进行，进一步评估原型。 将进行 AML-DT 以适应其他 AML 疗法 由患者倡导者和信徒提出的建议。