Multi-Tissue MAFLD Chip for Mechanism-Based Drug Testing

项目摘要 代谢性脂肪性肝病(MAFLD)是一种病理生理谱系疾病，影响20-20岁的老年人。 发达国家40%的成年人。MAFLD病因复杂，受多器官、环境、 以及遗传因素，这种疾病被称为“多重平行打击模式”。代谢紊乱 信号轴(例如，肠道-肝脏和肝脏-脂肪)和途径(例如，外周胰岛素抵抗(IR))是 推动MAFLD发展和进步的多重打击模式的要素包括 脂肪变性、脂肪性肝炎和纤维化。 到目前为止，还没有FDA批准的针对MAFLD/NAFLD的治疗方法。几十家公司 已经投资于MAFLD药物开发计划，以竞争成为第一个上市的治疗方法。然而， 最近备受瞩目的III期试验失败突显了获得单一疗法治疗批准的挑战 太棒了。鉴于MAFLD的病理复杂性和单一治疗方法的有限疗效， 制药业已经开始开发组合疗法的策略，这将涉及几个 靶点/途径同步进行，以改善临床结果。 识别治疗MAFLD的药物组合的主要挑战之一是限制 目前针对复杂疾病的临床前药物发现平台。虽然动物模型是常用的 在药物发现后期，由于成本和伦理的原因，它们不适合进行“探索性”组合研究。 担忧。另一方面，目前MAFLD的体外模型仅设计用于研究肝脏 病理生理学。 为了解决这一局限性，我们建议开发一种互联的肝脏-脂肪MAFLD组织芯片来 在肠道-肝脏-脂肪的背景下测试针对疾病机制驱动因素的药物和药物组合 (GLA)信号轴。该药物测试平台结合了肝脏-脂肪微生理系统(MPS)， 在GLA信号轴的上下文中重建MAFLD的多命中假说的关键方面，以及系统 基于生物学(SB)的工作流程，以评估单一疗法和联合疗法的疗效和作用机制。 药物和药物组合将在组织芯片上进行疗效和结果测试，两者都是表型 和多组学，将与我们基于系统生物学的计算工作流程-基因组规模的代谢-一起使用 网络模型(GEM)，以获得机械性的见解。GEM将使用以下工具进行构建和验证 转录组和代谢组学数据。 我们相信，我们的平台将使基于机制的药物疗效评价成为可能 在动物研究之前的临床前发现阶段的联合治疗。这将降低成本，并 确定MAFLD的联合疗法并改善长期临床结果的时间表。

Project Summary Metabolic-associated fatty liver disease (MAFLD) is a pathophysiological spectrum disorder affecting 20- 40% of adults in developed countries. MAFLD etiology is complex, influenced by multiple organs, environmental, and genetic factors in what is referred to as the “multiple parallel-hit model” of disease. Dysregulated metabolic signals axes (e.g., gut-liver and liver-adipose) and pathways (e.g., peripheral insulin resistance (IR)) are elements of the multi-hit model that drive the development and progression of MAFLD hallmarks including steatosis, steatohepatitis, and fibrosis. As of today, there are no FDA-approved therapies specific for MAFLD/NAFLD. Dozens of companies have invested in MAFLD drug development programs competing to be first-to-market with a treatment. However, recent high-profile failures of Phase III trials highlight the challenge to win approval for monotherapies to treat MAFLD. Given the pathological complexity of MAFLD and the limited efficacy of mono-therapeutic approaches, the pharmaceutical industry has begun to develop strategies for combinatorial therapies that will engage several targets/pathways simultaneously to improve the clinical outcomes. One of the major challenges in the identification of drug combinations to treat MAFLD is the limitation of the current preclinical drug discovery platforms for complex diseases. While animal models are commonly used in late drug discovery phase, they are not suitable for “exploratory” combinatorial studies due to cost and ethical concerns. On the other hand, current in vitro models of MAFLD are designed to investigate liver only pathophysiologies. To address this limitation, we propose developing an interconnected liver-adipose MAFLD tissue chip to test drugs and drug combinations targeting mechanistic drivers of disease in the context of the gut-liver-adipose (GLA) signaling axis. This drug testing platform combines a liver-adipose microphysiological system (MPS) that recreates key aspects of the multi-hit hypothesis of MAFLD in the context of GLA signaling axis, and systems biology (SB)-based workflows to evaluate mono- and combinatorial therapies’ efficacy and mechanism of action. The drugs and drug combination will be tested on tissue chips for their efficacy and the results, both phenotypic and multi-omics, will be used with our systems biology-based computational workflow - genome-scale metabolic network models (GEMs) to gain mechanistic insights. GEMs will be constructed and validated using transcriptomics and metabolomics data, respectively. We believe that our platform will enable mechanism-based drug efficacy evaluation for mono- and combination therapies at the preclinical discovery stage prior to animal studies. This will reduce the cost and timeline to identify combination therapies for MAFLD and improve long-term clinical outcomes.