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

癌症是全世界的主要死因之一，估计每年死亡人数接近1 000万。尽管为开发有效的癌症诊断和治疗做出了重大努力，但预测患者对抗癌治疗剂的反应的能力仍然难以捉摸。这是一个重要的里程碑，因为早期获得正确的治疗选择可能意味着上级抗肿瘤结果和生存率的提高，而错误的选择意味着肿瘤复发，耐药性的发展，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，而且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个工程化的网络物理系统，将先进的生物模型与最先进的人工智能方法相结合，用于预测、自动筛选抗癌药物并优化其剂量。这将推动科学朝着实现长期渴望的精准医学范式的方向发展，从而产生重大的社会影响。该项目具有额外的社会影响，包括通过增加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中围绕使用动物而呈指数增长的伦理问题。该项目将为K-12学生提供促进STEM教育的机会，培训学生，特别是来自代表性不足群体的学生，并向公众传播科学和工程知识。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方面的专业知识，开发一个通用的，自我剂量优化的“多传感器集成多器官芯片”平台，可用于准确预测本项目抗癌方案的疗效和安全性。第一个创新是采用三维生物打印技术生成血管化导管癌模型和血管化心脏组织模型，从而构建真正仿生的人类心肌，用于评估药物毒性。这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，对关键生物化学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预测建模。此外，该平台将通过两个深度学习模型实现的新型联合贝叶斯建模来实现芯片上的自剂量优化，这两个深度学习模型分别能够解决多实例学习和多维数据序列中的依赖性。该项目将使用一系列商用细胞来构建模型，并进行初始平台开发和优化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.