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.

癌症是世界范围内导致死亡的主要原因之一，估计每年有近 1000 万人死亡。尽管在开发有效的癌症诊断和治疗方法方面做出了巨大努力，但预测患者对抗癌治疗药物反应的能力仍然难以捉摸。这是一个重要的里程碑，因为尽早选择正确的治疗方法可能意味着卓越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、耐药性的产生、没有预期益处的副作用以及治疗成本的增加。一个网络物理系统，可以准确预测患者肿瘤对抗癌治疗的反应；也就是说，实现实时精准医疗不仅可以对健康结果产生变革性影响，还可以对治疗成本产生变革性影响。因此，该项目的目标是开发一种工程网络物理系统，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、自动筛选抗癌药物并优化其剂量。这将推动科学实现长期以来渴望的精准医学范式，从而产生重大的社会影响。该项目还具有其他社会影响，包括通过更多地采用工程人类癌症和心脏组织模型系统，最大限度地减少过去几年围绕使用动物而呈指数级增长的道德问题。该项目将为 K-12 学生提供促进 STEM 教育、培训学生（尤其是来自弱势群体的学生）以及向公众传播科学和工程知识的机会。研究人员将利用他们在生物制造、组织工程、微流体、生物分析和人工智能开发通用的、自我剂量优化的“多传感器集成多器官芯片”平台，可用于准确预测抗癌疗效和安全性该项目中的治疗方案。第一个创新是采用三维生物打印来生成血管化导管癌模型和血管化心脏组织模型，从而构建真正的仿生人体心肌来评估药物毒性。两种生物打印模型对微流体系统的适应也是一项重大创新。此外，对关键生物理化参数的实时非侵入性监测将生成大规模多维数据，以实现准确的数据驱动的预测建模。此外，该平台将通过新颖的联合贝叶斯建模实现芯片上的自我剂量优化，该建模由两个能够分别解决多实例学习和多维数据序列依赖性的深度学习模型实现。该项目将使用一系列商用单元来构建模型并进行初始平台开发和优化。预计在未来的迭代和其他癌症治疗、药物组合和抗癌方案中的剂量优化中，将扩展用于人体样本，作为快速、安全的测试平台。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。