Gomez-Benito, M. J. | bioRxiv Science

bioRxiv2025

Controlling Flow Dynamics and Permeability in Perfusable Vascular Constructs Using Volumetric 3D Printing

Creating perfusable vascular networks that replicate physiological flow remains a key challenge in tissue engineering. Here, we present a volumetric 3D printing (Vol3DP) method for fabricating tunable, biologically relevant vascular structures that enable controlled perfusion. A recyclable resin of methacrylated gelatin (GelMA) and polyethylene glycol diacrylate (PEGDA) was optimized for Vol3DP to produce high-fidelity hydrogels with embedded channels. To evaluate the relationships between flow, structure, and function, we designed modular perfusion platforms that offer precise control over physiological shear stress (3-50 dyne/cm{superscript 2}), flow rates (1-15 mL/min), and flow modes, including pulsatile and continuous. These platforms support endothelial cell attachment and spreading under static seeding conditions, sustained perfusion, and permeability assessment and further allow for direct comparisons of flow dynamics and perfusion efficiency in channel-in-hydrogel systems. The finally engineered adaptable platform allowed for independent pressure modulation within the vessel and the outside environment. We conducted simulations of flow and hydrogel permeability that closely resemble experimental results. These results underscore the dominance of diffusive transport through the hydrogel matrix and highlight the ability of our system to simulate physiologically relevant mass transfer phenomena. Finally, we conducted a pilot study using Galleria mellonella larvae to compare active and passive dye transport in vivo, complementing in vitro data and highlighting the importance of perfusion-capable scaffolds for accurately simulating vascular drug delivery environments. O_FIG O_LINKSMALLFIG WIDTH=172 HEIGHT=200 SRC="FIGDIR/small/669590v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1add089org.highwire.dtl.DTLVardef@56057forg.highwire.dtl.DTLVardef@182e989org.highwire.dtl.DTLVardef@1b235a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Tumour growth: Bayesian parameter calibration of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment

To unravel processes that lead to the growth of solid tumours, it is necessary to link knowledge of cancer biology with the physical properties of the tumour and its interaction with the surrounding microenvironment. Our understanding of the underlying mechanisms is however still imprecise. We therefore developed computational physics-based models, which incorporate the interaction of the tumour with its surroundings based on the theory of porous media. However, the experimental validation of such models represents a challenge to its clinical use as a prognostic tool. This study combines a physics-based model with in vitro experiments based on microfluidic devices used to mimic a 3D tumour microenvironment. By conducting a global sensitivity analysis, we identify the most influential input parameters and infer their posterior distribution based on Bayesian calibration. The resulting probability density is in agreement with the scattering of the experimental data and thus validates the modelling approach. Using the proposed workflow, we demonstrate that we can indirectly characterise the mechanical properties of neuroblastoma spheroids that cannot feasibly be measured experimentally.

bioengineering↗