Experimental liver models: From cell culture techniques to microfluidic organs-on-chip

Liver research has long relied on in vitro models to study drug metabolism and toxicity. Traditional two-dimensional (2D) cell cultures have been widely used for toxicity screening but lack the structural and functional complexity of the native liver. These models fail to fully represent the three-dimensional (3D) architecture and cellular interactions found in vivo. As a result, researchers have sought more advanced models that better reflect liver physiology. Improved 3D culture systems have been developed to address this gap by incorporating multiple cell types and mimicking liver architecture. These models aim to capture the interactions between parenchymal and nonparenchymal cells, which are essential for liver function. Despite these advances, limitations remain in replicating dynamic physiological conditions such as fluid flow. This has led to the development of Liver-on-Chip (LoC) platforms that integrate microfluidic systems to simulate in vivo environments. These newer models offer promising alternatives for drug screening and disease modeling.

The purpose of this review is to examine the progression of liver models from basic 2D cultures to advanced 3D and LoC systems. The study aims to evaluate how each model contributes to liver research and drug development. A key problem addressed is the inability of 2D models to accurately represent liver function. The motivation stems from the need for more physiologically relevant models that can support high-throughput and reproducible studies. The authors focus on comparing the strengths and limitations of each model type. Their goal is to highlight how 3D and LoC systems better mimic the liver's native environment. This work is driven by the desire to improve the accuracy of liver toxicity and disease modeling. The review also seeks to identify how these models can be applied in tissue engineering and drug discovery.

The authors conducted a literature review to trace the evolution of liver models. They analyzed the transition from 2D hepatocyte cultures to 3D systems and LoC platforms. The review approach included examining how each model type addresses liver function and toxicity. They assessed the structural and functional fidelity of each model in replicating liver architecture. The authors evaluated the role of parenchymal and nonparenchymal cell interactions in each system. They also considered how LoC models integrate microfluidic channels to simulate physiological fluid flow. The synthesis of findings focused on the advantages and limitations of each model type. The review highlights how LoC models offer a more dynamic and physiologically relevant environment.

The review highlights that 2D models have been widely used for toxicity screening but lack 3D architecture. Improved 3D models incorporate multiple cell types and better mimic liver structure. These models represent both parenchymal and nonparenchymal cells, enhancing functional accuracy. Liver-on-Chip (LoC) models integrate microfluidic systems to simulate physiological flow. LoC models have been adopted for pathophysiological studies and drug screening. These models offer unprecedented fidelity in replicating liver environments. The review shows that LoC systems are promising for tissue engineering applications. The results suggest that LoC models provide a more accurate platform for studying liver diseases.

The authors conclude that traditional 2D models have limitations in capturing liver physiology. They propose that 3D models offer a better representation of liver architecture and function. The review suggests that LoC models provide a more dynamic and realistic environment for liver studies. These models are seen as a promising tool for drug screening and disease modeling. The authors highlight the importance of incorporating fluid flow in LoC systems. They emphasize that LoC models can better replicate the native liver microenvironment. The findings suggest that LoC systems are valuable for tissue engineering applications. The authors propose that these models will continue to evolve for more accurate liver research.