A systematic review of kidney-on-a-chip-based models to study human renal (patho-)physiology

As kidney diseases affect ∼10% of the world population, understanding the underlying mechanisms and developing therapeutic interventions are of high importance. Although animal models have enhanced knowledge of disease mechanisms, human (patho-)physiology may not be adequately represented in animals...

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Veröffentlicht in:Disease models & mechanisms 2023-06, Vol.16 (6)
Hauptverfasser: Nguyen, Vivian V T, Gkouzioti, Vasiliki, Maass, Christian, Verhaar, Marianne C, Vernooij, Robin W M, van Balkom, Bas W M
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Sprache:eng
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Zusammenfassung:As kidney diseases affect ∼10% of the world population, understanding the underlying mechanisms and developing therapeutic interventions are of high importance. Although animal models have enhanced knowledge of disease mechanisms, human (patho-)physiology may not be adequately represented in animals. Developments in microfluidics and renal cell biology have enabled the development of dynamic models to study renal (patho-)physiology in vitro. Allowing inclusion of human cells and combining different organ models, such as kidney-on-a-chip (KoC) models, enable the refinement and reduction of animal experiments. We systematically reviewed the methodological quality, applicability and effectiveness of kidney-based (multi-)organ-on-a-chip models, and describe the state-of-the-art, strengths and limitations, and opportunities regarding basic research and implementation of these models. We conclude that KoC models have evolved to complex models capable of mimicking systemic (patho-)physiological processes. Commercial chips and human induced pluripotent stem cells and organoids are important for KoC models to study disease mechanisms and assess drug effects, even in a personalized manner. This contributes to the Reduction, Refinement and Replacement of animal models for kidney research. A lack of reporting of intra- and inter-laboratory reproducibility and translational capacity currently hampers implementation of these models.
ISSN:1754-8403
1754-8411
DOI:10.1242/dmm.050113