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

用于药物开发的传统动物模型往往无法准确模拟人体的复杂性，这对创造有效的药物构成了巨大的挑战。此外，这些动物实验引起了伦理上的担忧，并需要减少它们的使用。为了解决这些问题，推进药物开发和个性化治疗，NSF EPSCoR RII Track-4研究人员项目专注于创建与人体动力学非常相似的健康和疾病组织和器官的计算模型。该项目将尖端的单芯片器官(OOC)实验与先进的计算工具和基于物理化学的多尺度模型相结合，以预测疾病如何发展以及药物如何与人体相互作用。这种创新的方法还通过减少获取有用数据所需的实验数量来改进OOC实验。它使临床前过程更有效率，并有助于开发更有效的药物，剂量正确，副作用更少。这项研究对社会有重大好处：它加快了有效药物的发现，潜在地为个别患者量身定做治疗方案，减少副作用和治疗失败，最终导致更好、更快、更负担得起的医疗保健，同时减少对动物试验的需求。OOC技术在加速药物发现和降低相关成本方面的巨大潜力需要开发一个最先进的框架来实现和评估它。研究的重点是开发一个基于学习的多尺度建模框架，以增强对使用OOC数据的药物递送动力学的理解。这一根本性和极具挑战性的问题将通过一种结合机器学习和第一原理多尺度模型的混合建模方法来解决。提出的混合模型比标准的基于ML的模型具有更好的性能。它可以对OOC数据进行精确的内插和外推。它更容易分析、解释，并且需要的训练样本要少得多。由于利用了理论和数据驱动的建模方法的优势，这些优势是合理的。此外，我们的综合方法通过最大限度地减少收集信息数据所需的实验，提高临床前过程效率，并指导开发更有效的药物，具有最佳剂量和更少的副作用，从而优化了OOC实验。为了实现这一目标，EPSCoR Track-4研究人员计划支持堪萨斯州立大学的一名助理教授和一名研究生访问领先的生物工程研究机构之一--Terasaki生物医学创新研究所(TIBI)并与其合作。建议的方法将以芯片上的肝脏系统为基准，这是TIBI用于模拟非酒精性脂肪性肝病的成熟的OOC技术。此外，研究将提供一个跨学科的学生培训、指导和与社区互动的平台。PI旨在培养具有高级数学、计算和数据科学专业知识的化学工程毕业生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.