Modeling a Dynamic Printability Window on Polysaccharide Blend Inks for Extrusion Bioprinting

Abstract

Extrusion-based bioprinting is one of the most widespread technologies due to its affordability, wide range of processable materials, and ease of use. However, the formulation of new inks for this technique is based on time-consuming trial-and-error processes to establish the optimal ink composition and printing parameters. Here, a dynamic printability window was modeled for the assessment of the printability of polysaccharide blend inks of alginate and hyaluronic acid with the intent to build a versatile predictive tool to speed up the testing procedures. The model considers both the rheological properties of the blends (viscosity, shear thinning behavior, and viscoelasticity) and their printability (in terms of extrudability and the ability of forming a well-defined filament and detailed geometries). By imposing some conditions on the model equations, it was possible to define empirical bands in which the printability is ensured. The predictive capability of the built model was successfully verified on an untested blend of alginate and hyaluronic acid chosen to simultaneously optimize the printability index and minimize the size of the deposited filament.

Keywords: extrusion bioprinting; hyaluronic acid; printability; sodium alginate.

Figure 1

Schematic illustration of the definition of the printing parameters evaluated for the printability assessment: uniformity factor (UF), spreading ratio (SR), angle fidelity factor (AF), and printability index (Pr). The nature of all these parameters is thoroughly described in the Printability Characterization section of the Materials and Methods.

Figure 2

Schematic illustration of the image segmentation process and the Matlab functions used.

Figure 3

(A) Mean LVE region moduli acquired from oscillatory stress sweep tests for all tested conditions; (B) oscillatory stress sweep tests for 2ALG8HA, as representative curves for all the results; (C) average yield stress acquired from oscillatory stress sweep; and (D) rotational shear rate sweep average curves for all the tested conditions.

Figure 4

Example of the effect of the increasing extrusion pressure on grids of three different compositions.

Figure 5

Multilayered grids of 6AL-MA6HA-MA printed at 145 kPa.

Figure 6

Contour plots representing the mathematical model built on the rheological analysis.

Figure 7

Contour plot of the model predicting the mean value of 2D printing parameters (Pr, Qm, and SR) varying the Alg-HA amount and the printing pressure.

Figure 8

Contour plot of the model predicting the mean value of AF varying the Alg-HA amount, the printing pressure, and the printed angle.

Figure 9

(A) Desirability function: the predicted best point is shown in the flag (Alg 2.75%, HA 6.4%); the best predicted pressure was 55 kPa. In these specified conditions, the desirability function is equal to 1. (B) Grid, lines, and angles printed under the optimal conditions found through the desirability function.