Collaborative Research: CPS: Medium: AI-Boosted Precision Medicine through Continual in situ Monitoring of Microtissue Behaviors on Organs-on-Chips

合作研究：CPS：中：通过持续原位监测器官芯片上的微组织行为，人工智能推动精准医疗

• 批准号: 2225698
• 负责人: Y Shrike Zhang
• 金额: $ 60.66万
• 依托单位: Brigham & Women's Hospital Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225698&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; AI

Cancers are among the leading causes of death around the world, with an estimated annual mortality of close to 10 million. Despite significant efforts to develop effective cancer diagnosis and therapeutics, the ability to predict patient responses to anti-cancer therapeutic agents remains elusive. This is a critical milestone as getting the right choice of therapy early can mean superior anti-tumor outcomes and increased survival, while the wrong choice means tumor relapse, development of resistance, side effects without the desired benefit, and increased cost of treatment. An cyber-physical system that allows an accurate prediction of patient tumor responses to anti-cancer therapies; that is, enable real-time precision medicine, can have a transformative effect not only on health outcomes, but also on the costs of treatment. The goal of this project is therefore to develop an engineered cyber-physical system that combines advanced biological models with state-of-the-art artificial intelligence methods for predictive, automated screening of anti-cancer drugs and optimizations of their dosing. This will move science towards realizing the long-desired precision medicine paradigm leading to significant social impacts. The project has additional social impacts, including minimizing the exponentially growing ethical issues surrounding the use of animals in the past years through increased adoption of the engineered human cancer and heart tissue model systems. The project will provide opportunities to promote STEM education for K-12 students, train students, especially those from under-represented groups, and disseminate science and engineering knowledge to the public.The investigators will leverage their expertise in biofabrication, tissue engineering, microfluidics, bioanalysis, and artificial intelligence to develop a generalized, self-dose-optimizing "multi-sensor-integrated multi-organ-on-a-chip" platform, which can be used to accurately predict both efficacy and safety of anti-cancer regimens in this project. The first innovation is the adoption of three-dimensional bioprinting for generating the vascularized ductal carcinoma model and vascularized cardiac tissue model, leading to the construction of a truly biomimetic human myocardium for evaluating drug toxicity. The adaptation of both of the bioprinted models to microfluidic systems is also a major innovation. Additionally, the real-time yet non-invasive monitoring of key biophysicochemical parameters will generate large-scale multi-dimensional data to enable accurate data-driven predictive modeling. Moreover, the platform will enable self-dose-optimization on the chips through a novel joint Bayes modeling implemented by two deep learning models capable of addressing multiple-instance learning, and dependency in sequences of multi-dimensional data, respectively. The project will use a range of commercially available cells to construct models and pursue the initial platform development and optimizations. Extensions are anticipated for human specimens in future iterations and other cancer treatment, drug combination, and dose optimization in anti-cancer regimens as a rapid and safe testing-bed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

癌症是世界各地死亡的主要原因之一，估计年死亡率接近1000万。尽管为开发有效的癌症诊断和治疗剂而做出了重大努力，但预测患者对抗癌治疗剂反应的能力仍然难以捉摸。这是一个关键的里程碑，因为尽早获得正确的治疗方法可能意味着较高的抗肿瘤结果和增加的生存率，而错误的选择意味着肿瘤复发，耐药性的发展，没有所需的益处以及增加的治疗成本。一个网络物理系统，可以准确预测患者肿瘤对抗癌疗法的反应；也就是说，实现实时精确医学，不仅可以对健康结果产生变革性影响，而且对治疗费用也会产生变革性的影响。因此，该项目的目的是开发一种工程设计的网络物理系统，该系统将先进的生物模型与最先进的人工智能方法相结合，以预测，自动筛选抗癌药物和剂量的优化。这将使科学能够实现长期以来的精密医学范式，从而产生重大的社会影响。该项目具有其他社会影响，包括通过增加对工程人类癌症和心脏组织模型系统的采用，将围绕动物使用的指数增长的道德问题降至最低。该项目将提供机会，为K-12学生，培训学生，尤其是来自代表性不足的群体的学生，并向公众传播科学和工程知识的学生。平台可用于准确预测该项目中抗癌方案的功效和安全性。第一个创新是采用三维生物打印，用于产生血管化导管癌模型和血管化心脏组织模型，从而构建了真正仿生的人体心肌来评估药物毒性。两个生物打印模型对微流体系统的适应也是一项主要创新。此外，对关键生物物理化学参数的实时但无创的监测将生成大规模的多维数据，以实现准确的数据驱动的预测建模。此外，该平台将通过两种能够解决多个实体学习的深度学习模型实现的新型联合贝叶斯建模在芯片上进行自剂量优化，并分别依赖多维数据的序列。该项目将使用一系列可商用的单元格来构建模型并追求初始平台开发和优化。预计在未来的迭代术和其他癌症治疗，药物组合和抗癌治疗方案中的其他癌症治疗，作为快速安全的测试床的剂量优化的人类标本将扩展。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查审查的审查标准来通过评估来获得支持的。