In silico modeling of endocrine organ-on-a-chip systems

The organ-on-a-chip (OoC) is an artificially reconstructed microphysiological system that is implemented using tissue mimics integrated into miniaturized perfusion devices. OoCs emulate dynamic and physiologically relevant features of the body, which are not available in standard in vitro methods. Furthermore, OoCs provide highly sophisticated multi-organ connectivity and biomechanical cues based on microfluidic platforms. Consequently, they are often considered ideal in vitro systems for mimicking self-regulating biophysical and biochemical networks in vivo where multiple tissues and organs crosstalk through the blood flow, similar to the human endocrine system. Therefore, OoCs have been extensively applied to simulate complex hormone dynamics and endocrine signaling pathways in a mechanistic and fully controlled manner. Mathematical and computational modeling approaches are critical for quantitatively analyzing an OoC and predicting its complex responses. In this review article, recently developed in silico modeling concepts of endocrine OoC systems are summarized, including the mathematical models of tissue-level transport phenomena, microscale fluid dynamics, distant hormone signaling, and heterogeneous cell-cell communication. From this background, whole chip-level analytic approaches in pharmacokinetics and pharmacodynamics will be described with a focus on the spatial and temporal behaviors of absorption, distribution, metabolism, and excretion in endocrine biochips. Finally, quantitative design frameworks for endocrine OoCs are reviewed with respect to support parameter calibration/scaling and enable predictive in vitro-in vivo extrapolations. In particular, we highlight the analytical and numerical modeling strategies of the nonlinear phenomena in endocrine systems on-chip, which are of particular importance in drug screening and environmental health applications.

Automated summary

The organ-on-a-chip is an artificial microphysiological system that mimics dynamic and physiologically relevant features of the body. It is widely used to simulate complex hormone dynamics and endocrine signaling pathways. Mathematical and computational modeling approaches are crucial for analyzing and predicting the chip's responses.