Integrating computational fluid dynamics into organ-on-chip systems: a glioblastoma-centred design and validation framework

TL;DR

This paper explores how computational fluid dynamics can improve organ-on-chip models of glioblastoma, making them more accurate and biologically relevant.

Contribution

The paper introduces a structured workflow for integrating computational fluid dynamics into GBM-on-chip design and validation.

Findings

Current organ-on-chip platforms lack predictive control over flow and mechanical cues.

CFD can enhance the biological fidelity and design of GBM-on-chip systems.

Integration of CFD with AI-based optimization can improve in vitro tumor models.

Abstract

Glioblastoma GBM: Glioblastoma multiforme (GBM) remains one of the most lethal and treatment-resistant brain cancers, driven in part by the complexity of its tumour microenvironment (TME). While organ-on-chip (OoC) platforms offer more physiologically relevant models than traditional 2D or static 3D systems, their design remains largely empirical, lacking predictive control over flow conditions, biochemical gradients, and mechanical cues. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to enhance the design, precision, and biological fidelity of OoC platforms. This comprehensive review highlights current limitations in replicating GBM’s biological complexity and technical constraints in device fabrication and maintenance, mapping them to specific CFD strategies. It synthesises current strategies into a structured workflow for integrating CFD into the design, optimisation, and validation of microfluidic tumour models—bridging engineering precision with biological complexity. In addition, validation frameworks reported in the literature are highlighted and mapped onto GBM-on-chip applications have been recommended, drawing on widely recognised international standards for engineering validation and regulatory modelling practices. Finally, this review positions CFD as a core component of GBM-on-chip development and explores how its integration with AI-based optimisation can advance the creation of more predictive, scalable, and biologically relevant in vitro tumour models.

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