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Fluidic Programmable Gravi-maze Array for High Throughput Multiorgan Drug Testing

Wong, H. C.; Collins, C. J.; Jude Jose, J. A.; Bhakta, I. N.; Collins, A. J.; Katara, G.; Kohana, J.; Saluja, H. S.; Collins, J.

2025-06-24 bioengineering
10.1101/2025.06.18.660241 bioRxiv
Show abstract

The high attrition rate of drug candidates in clinical trials underscores the urgent need for more predictive preclinical models that accurately replicate human physiology. Traditional two-dimensional (2D) cell cultures and animal models often fail to predict human responses due to their limited physiological relevance. This highlights the need for modeling and measurements of multiorgan interactions at higher throughput prompting the development of multiorgan-on-a-plate (MOAP) platforms. Here, we present OrganRX, a modular, gravity-driven recirculation-based MOAP system designed to imitate human organ function, fluid dynamics, and inter-organ communication in vitro. The platform based on Fluidic Programmable Gravi-maze Array (FPGA) integrates multiple organ types--gut, liver, kidney, brain, and endothelium--within a microfluidic architecture that replicates physiological shear stress and unidirectional flow. Using computational fluid dynamics (CFD) simulations and impedance-based flow validation, we confirmed accurate shear control across organ compartments. Organ-specific and multiorgan models were constructed with 3D extracellular matrix hydrogels and assessed for metabolism and senescence. Liver-kidney co-cultures demonstrated metabolic interplay via differential albumin and urea production. Additionally, in drug response studies, phenylbutyrate (PB) alone reduced brain ROS in a gut-brain model, while subsequent treatment with curcumin (C) unexpectedly reversed this benefit, revealing context-specific drug interactions not observable in isolated organ models. Overall, the FPGA trademarked as OrganRX offers a physiologically relevant, scalable, and automation-compatible platform for preclinical drug evaluation and disease modeling. Its ability to capture complex, dynamic inter-organ effects positions it as a powerful tool for advancing translational research and precision medicine.

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