Current Advances and Future Perspectives of Liver-on-a-Chip Platforms Incorporating Dynamic Fluid Flow

TL;DR

This paper reviews how liver-on-a-chip systems with dynamic fluid flow improve liver modeling for drug testing and disease research.

Contribution

The paper provides a comprehensive review of recent advances in liver-on-a-chip platforms with dynamic fluid flow for improved physiological modeling.

Findings

Dynamic liver-on-a-chip systems better replicate in vivo conditions compared to static cultures.

Integration with multi-organ-on-chip platforms allows studying inter-organ interactions.

These systems enhance drug screening and toxicity testing accuracy.

Abstract

The liver is a vital organ responsible for a broad range of metabolic functions, including glucose and lipid metabolism, detoxification, and protein synthesis. Its structural complexity, characterized by hexagonal hepatic lobules composed of diverse parenchymal and non-parenchymal cell types, supports its broad spectrum of physiological activities. Traditional in vitro liver models have contributed significantly to our understanding of hepatic biology and the development of therapies for liver-related diseases. However, static culture systems fail to replicate the dynamic in vivo microenvironment, particularly the continuous blood flow and shear stress that are critical for maintaining hepatocyte function and metabolic zonation. Recent advances in microphysiological systems (MPS) incorporating dynamic fluid flow have addressed these limitations by providing more physiologically relevant platforms for modeling liver function. These systems offer improved fidelity for applications in drug screening, toxicity testing, and disease modeling. Furthermore, the integration of liver MPS with other organ models in multi-organ-on-chip platforms has enabled the investigation of inter-organ crosstalk, enhancing the translational potential of in vitro systems. This review summarizes recent progress in the development of dynamic liver MPS, highlights their biomedical applications, and discusses future directions for creating more comprehensive and predictive in vitro models.

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