A Focused Review of Modelling and Technological Advances in Organ-on-a-Chip Systems

Abstract

Organ-on-a-Chip (OoC) technology offers a promising alternative to traditional in vitro and animal models by replicating key physiological features of human organs within microfluidic platforms. These systems are increasingly used in drug development, toxicity testing, and disease modelling. However, widespread adoption is limited by challenges such as complex design requirements, scalability issues, data interpretation difficulties, and the integration of diverse technologies. This review explores the role of advanced modelling approaches, such as computational fluid dynamics (CFD), finite element analysis (FEA), pharmacokinetic/pharmacodynamic (PK/PD) models, and artificial intelligence (AI), in addressing these barriers. These tools enable precise simulation, optimization, and data analysis of OoC systems, supporting their design and predictive capabilities. Key challenges identified include limited data quality, computational complexity, organ scaling, and system integration. Modelling solutions, including explainable AI and multiscale simulation, offer pathways to overcome these issues. The integration of emerging technologies like 3D printing, real-time sensing, and automation is also discussed. The review concludes with recommendations for refining existing modelling techniques, improving transparency in AI applications, and supporting interdisciplinary collaboration to drive standardization and regulatory acceptance. These efforts are essential for realizing the full potential of OoCs in biomedical research and preclinical drug development.