Multi-Tissue MAFLD Chip for Mechanism-Based Drug Testing

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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.