Approximating scaffold printability utilizing computational methods

Abstract

Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for various applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of materials (bioinks) that can be utilized, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that affect bioprinting utility, accuracy, and precision. Here, we have extended our previous work to achieve high precision (i.e. repeatability) and printability across samples by optimizing bioink-specific printing parameters. Specifically, we hypothesized that a fuzzy inference system (FIS) could be used as a computational method to address the imprecision in 3D bioprinting test data and uncover the optimal printing parameters for a specific bioink that result in high accuracy and precision. To test this hypothesis, we have implemented a FIS model consisting of four inputs (bioink concentration, printing flow rate, speed, and temperature) and two outputs to quantify the precision (scaffold bioprinted linewidth variance) and printability. We validate our use of the bioprinting precision index with both standard and normalized printability factors. Finally, we utilize optimized printing parameters to bioprint scaffolds containing up to 30 × 106 cells ml−1 with high printability and precision. In total, our results indicate that computational methods are a cost-efficient measure to improve the precision and robustness of extrusion 3D bioprinting.