A finite element analysis model to predict and optimize the mechanical behaviour of bioprinted scaffolds

TL;DR

This paper presents a finite element model to predict and optimize the mechanical behavior of bioprinted scaffolds, aiding in bioink formulation and reducing experimental trial-and-error.

Contribution

It introduces a finite element modeling approach to analyze how bioink composition and concentration affect cell stress in bioprinted constructs.

Findings

Bioink composition significantly influences cell stress distribution.

Concentrated soft cell regions near pores increase stress by three times.

Finite element models can accelerate bioink development.

Abstract

Bioprinting is an enabling biofabrication technique to create heterogeneous tissue constructs according to patient-specific geometries and compositions. Optimization of bioinks as per requirements for specific tissue applications is a critical exercise in ensuring clinical translation of the bioprinting technologies. Most notably, optimum hydrogel polymer concentrations are required to ensure adequate mechanical properties of bioprinted constructs without causing significant shear stresses on cells. However, experimental iterations are often tedious for optimizing the bioink properties. In this work, a finite element modelling approach has been undertaken to determine the effect of different bioink parameters like composition, concentration on the range of stresses being experienced by the cells in a bioprinting process. The stress distribution of the cells at different parts of the constructs has also been modelled. It is found that both bioink chemical compositions and stoichiometric concentrations can substantially alter the stress effects experienced by the cells. Similarly, concentrated regions of soft cells near the pore regions were found to increase stress concentrations by almost three times compared to the Von-Mises stress generated around the region of cells away from the pores. The study outlines the importance of finite element models in the rapid development of bioinks.