Mechanical modeling of the maturation process for tissue-engineered implants: application to biohybrid heart valves

TL;DR

This work develops a thermodynamically consistent, energy-based continuum model for the maturation of textile-reinforced tissue-engineered implants, integrating an anisotropic ECM (collagen) with a textile scaffold via generalized structural tensors. Collagen density in the reference state evolves through biologically driven and mechanically driven terms, enabling prediction of collagen growth and the resulting deformations under maturation boundary conditions using a solid-shell FE formulation. The model is calibrated against biaxial scaffold tests and collagen-density measurements, and demonstrated on a pressurized shell and a tubular biohybrid heart valve, illustrating maturation-induced stiffening and spatially heterogeneous collagen deposition. The framework supports design optimization of biohybrid implants by linking maturation protocols to mechanical performance and provides a foundation for future coupling with hemodynamics and more complex growth mechanisms.

Abstract

The development of tissue-engineered cardiovascular implants can improve the lives of large segments of our society who suffer from cardiovascular diseases. Regenerative tissues are fabricated using a process called tissue maturation. Furthermore, it is highly challenging to produce cardiovascular regenerative implants with sufficient mechanical strength to withstand the loading conditions within the human body. Therefore, biohybrid implants for which the regenerative tissue is reinforced by standard reinforcement material (e.g. textile or 3d printed scaffold) can be an interesting solution. In silico models can significantly contribute to characterizing, designing, and optimizing biohybrid implants. The first step towards this goal is to develop a computational model for the maturation process of tissue-engineered implants. This paper focuses on the mechanical modeling of textile-reinforced tissue-engineered cardiovascular implants. First, we propose an energy-based approach to compute the collagen evolution during the maturation process. Then, we apply the concept of structural tensors to model the anisotropic behavior of the extracellular matrix and the textile scaffold. Next, the newly developed material model is embedded into a special solid-shell finite element formulation with reduced integration. Finally, we use our framework to compute two structural problems: a pressurized shell construct and a tubular-shaped heart valve. The results show the ability of the model to predict collagen growth in response to the boundary conditions applied during the maturation process. Consequently, we can predict the implant's mechanical response, such as the deformation and stresses of the implant.

Figures (16)

• Figure 1: (a) Schematic representation for the entire structure of the biaxial tensile test experiment; (b) Boundary value problem for the symmetric part.
• Figure 2: These plots for the engineering stress $(F/A_{0})$ against the engineering strain $(\Delta L/L_{0})$, where $F$ is the applied load, $A_{0}$ is the initial cross-sectional area, $\Delta L$ is the change in length and $L_{0}$ is the initial length. The relation between the displacement boundary conditions along the two directions is (a) $u_{1} = u_{2}$; and (b) $u_{1} = 3 u_{2}$.
• Figure 3: (a) In-vitro cultivated tissue-engineered construct. (b) Image taken by 2-photon microscopy for in-vitro cultivated collagenous tissue. The collagen fibers are indicated by a green stain, and cell nuclei by a blue stain.
• Figure 4: These plots for the engineering stress $(F/A_{0})$ against the stretch $(L/L_{0})$, where $F$ is the applied load, $A_{0}$ is the initial cross-sectional area, $L$ is the current length and $L_{0}$ is the initial length. The plots are for samples after (a) 14, (b) 21, and (c) 28 days of maturation.
• Figure 5: Relative collagen density measured after 7, 14, 21 and 28 days of maturation. The experimental data are fitted by a Weibull cumulative distribution curve.