Kidney-On-Chip and Organoid Models: Harnessing Mechanical Forces for Translational Kidney Biology
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Updated: Sep 10, 2026

Isogenic Kidney Glomerulus Chip Engineered from Human Induced Pluripotent Stem Cells
Published on: November 4, 2022
Abigail Daily1, Nanditha Anandakrishnan1, Jonathan Haydak1
1Barbara T. Murphy Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York.
Abstract:
The US Food and Drug Administration Modernization Act and the federal policy changes in 2025 have signaled a shift toward the use of nonanimal, human-centered models for preclinical drug development and toxicity screening, emphasizing 3D cell-based, organ-on-chip, and organoid platforms as alternatives to animal models. While animal models have advanced our understanding of disease mechanisms, they do not allow decoupling of biomechanical and biochemical effects during pathogenesis. In vitro systems offer improved accessibility but lack physiologically relevant microenvironments. In this review, we discuss how kidney-on-chip and induced pluripotent stem cell-derived kidney organoid models serve as physiologically relevant platforms for modeling nephrotoxicity, kidney development, and pathophysiology. Kidney-on-chip models recapitulate in vivo mechanical forces, such as fluid shear stress and mechanical strain, experienced by cells, enabling real-time functional readouts of glomerular filtration, tubular reabsorption, and nephrotoxic response. Integrating on-chip platforms with patient-derived induced pluripotent stem cells and differentiated kidney cell types allows human-relevant responses unavailable in static culture. Induced pluripotent stem cell-derived kidney organoids recapitulate the 3D architecture of nephron segments and ureteric bud branching patterns, demonstrating selective transport, toxicity responses, and structural stability over months in culture. We detail how bioengineering approaches, including organoid-on-chip models, bioprinting, and multi-organ-on-chip integration, could address the limitations of organoids, such as maturity, scalability, and vascularization. We discuss how integrating publicly available clinical databases and machine learning approaches with on-chip validation can improve translational relevance. We conclude that interdisciplinary collaboration between engineers, biologists, and physician scientists remains essential to translating bioengineered kidney models into clinical and therapeutic applications.

