3D printing PLGA: a quantitative examination of the effects of polymer composition and printing parameters on print resolution. (12th April 2017)

• Record Type: Journal Article
• Authors: Guo, Ting; Holzberg, Timothy R; Lim, Casey G; Gao, Feng; Gargava, Ankit; Trachtenberg, Jordan E; Mikos, Antonios G; Fisher, John P
• Is Part Of: Biofabrication. Volume 9:Number 2(2017)
• Publication Date: 2017-04-12
• Subjects: scaffold -- biomaterial -- 3D printing -- statistical analysis -- poly(lactic-co-glycolic acid); Biomedical engineering -- Periodicals; Tissue engineering -- Periodicals; Biomedical materials -- Microstructure -- Periodicals; Bioengineering -- Periodicals; 610.28
• DOI: 10.1088/1758-5090/aa6370
• Languages: English
• ISSNs: 1758-5082

Abstract

Abstract: In the past few decades, 3D printing has played a significant role in fabricating scaffolds with consistent, complex structure that meet patient-specific needs in future clinical applications. Although many studies have contributed to this emerging field of additive manufacturing, which includes material development and computer-aided scaffold design, current quantitative analyses do not correlate material properties, printing parameters, and printing outcomes to a great extent. A model that correlates these properties has tremendous potential to standardize 3D printing for tissue engineering and biomaterial science. In this study, we printed poly(lactic-co-glycolic acid) (PLGA) utilizing a direct melt extrusion technique without additional ingredients. We investigated PLGA with various lactic acid:glycolic acid (LA:GA) molecular weight ratios and end caps to demonstrate the dependence of the extrusion process on the polymer composition. Micro-computed tomography was then used to evaluate printed scaffolds containing different LA:GA ratios, composed of different fiber patterns, and processed under different printing conditions. We built a statistical model to reveal the correlation and predominant factors that determine printing precision. Our model showed a strong linear relationship between the actual and predicted precision under different combinations of printing conditions and material compositions. This quantitative examination establishes a significant foreground to 3D print biomaterials following a systematic fabrication procedure. Additionally, our proposed statistical models can be applied to couple specific biomaterials and 3D printing applications for patient implants with particular requirements.