RII Track-4:NSF: Physics-Informed Machine Learning with Organ-on-a-Chip Data for an In-Depth Understanding of Disease Progression and Drug Delivery Dynamics

RII Track-4：NSF：利用器官芯片数据进行物理信息机器学习，深入了解疾病进展和药物输送动力学

• 批准号: 2327473
• 负责人: Davood Babaei Pourkargar
• 金额: $ 24.54万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327473&HistoricalAwards=false
• 关键词: RII; Track; NSF; Physics; Informed

Conventional animal models used in drug development often fail to accurately mimic the human body's complexities, which poses significant challenges in creating effective medicines. Furthermore, these animal experiments raise ethical concerns and the need to reduce their use. To tackle these issues and advance drug development and personalized treatments, this NSF EPSCoR RII Track-4 Research Fellows project focuses on creating computational models of healthy and diseased tissues and organs closely resembling the human body dynamics. The project integrates cutting-edge organ-on-a-chip (OoC) experiments with the advanced computational tools and physiochemical-based multiscale models to predict how diseases progress and how drugs interact with the body. This innovative approach also improves the OoC experiments by reducing the number of experiments needed to get useful data. It makes the preclinical process more efficient and helps develop more effective drugs with the right doses and fewer side effects. The research has major benefits for society: it speeds up the discovery of effective drugs, potentially tailors treatments to individual patients, reduces side effects and treatment failures, and ultimately leads to better, quicker, and more affordable healthcare while reducing the need for animal testing.The remarkable potential of OoC technology to accelerate drug discovery and reduce the associated costs necessitates developing a state-of-the-art framework to achieve and assess it. The research focuses on developing a learning-based multiscale modeling framework to enhance the understanding of drug delivery dynamics using OoC data. This fundamental and highly challenging problem will be addressed by a hybrid modeling approach integrating machine learning (ML) with the first-principles multiscale models. The proposed hybrid model has better properties than the standard ML-based models. It can accurately interpolate and extrapolate the OoC data. It is easier to analyze, interpret, and requires significantly fewer training samples. Such advantages are rational due to leveraging the benefits of theoretical and data-driven modeling approaches. Furthermore, our integrated approach optimizes the OoC experiments by minimizing the required experiments to collect informative data, increasing preclinical process efficiency, and guiding toward developing more effective drugs with optimal dosages and fewer side effects. To accomplish this, the EPSCoR Track-4 Research Fellows program supports an Assistant Professor and a graduate student at Kansas State University to visit and collaborate with one of the leading bioengineering research institutions, the Terasaki Institute for Biomedical Innovation (TIBI). The proposed approach will be benchmarked on liver-on-a-chip systems, a well-established OoC technology at TIBI for modeling nonalcoholic fatty liver disease. In addition, the research will provide a platform for interdisciplinary student training, mentoring, and engagement with the community. The PI aims to produce chemical engineering graduates with high-level mathematical, computational, and data-science expertise.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.

药物开发中使用的常规动物模型通常无法准确模仿人体的复杂性，这在创建有效的药物方面构成了重大挑战。此外，这些动物实验引发了道德问题，并需要减少其使用。为了解决这些问题并提高药物开发和个性化治疗，该NSF EPSCOR RII TRACK-4研究Fellows项目着重于创建健康和患病组织和器官的计算模型，与人体动态非常相似。该项目将尖端的器官芯片（OOC）实验与先进的计算工具和基于理化的多尺度模型进行了预测，以预测疾病的进展以及药物如何与人体相互作用。这种创新方法还通过减少获取有用数据所需的实验数量来改善OOC实验。它使临床前过程效率更高，并有助于开发更有效的药物，并使用正确的副作用和更少的副作用。这项研究对社会有重大好处：它加快了有效药物的发现，可能为个别患者量身定制治疗，减少副作用和治疗失败，并最终导致更好，更快，更快的医疗保健，同时减少动物测试的需求。该研究重点是开发基于学习的多尺度建模框架，以增强使用OOC数据对药物输送动态的理解。这种基本且高度挑战性的问题将通过将机器学习（ML）与第一原理多尺度模型整合（ML）的混合建模方法来解决。提出的混合模型比基于标准ML的模型具有更好的性能。它可以准确地插入并推断OOC数据。分析，解释和需要更少的培训样本更容易。由于利用理论和数据驱动建模方法的好处，因此这种优势是合理的。此外，我们的综合方法通过最大程度地减少所需的实验来收集信息性数据，提高临床前过程效率，并指导使用最佳剂量和更少的副作用开发更有效的药物，从而优化了OOC实验。为此，EPSCOR TRACK-4研究研究员计划支持堪萨斯州立大学的助理教授和一名研究生，与Terasaki生物医学创新研究所（TIBI）的一家领先的生物工程研究机构之一访问和合作。所提出的方法将在肝脏中的肝脏系统上进行基准测试，这是一种在TIBI上建立非酒精性脂肪肝病的良好的OOC技术。此外，该研究将为跨学科的学生培训，指导和与社区的互动提供一个平台。 PI旨在以高级数学，计算和数据科学专业知识来生产化学工程毕业生。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和更广泛影响的评估评估标准来通过评估来支持的。