PLGA from PolySciTech used in optimizing 3D printing techniques for tissue engineering

A relatively recent and powerful tool for both manufacturing and research has been developed in 3D printing. Despite it’s advantages, 3D printing is restricted based on the polymeric material’s melt and processing properties. Recently, researchers working jointly at University of Maryland, Cornell University, and Rice University screened through a series of PLGA materials in order to define the optimal printing procedures for each. The utilized a series of PLGA’s from PolySciTech ( (PolyVivo AP039, AP137, AP076, and AP024) and optimized their printing configurations for bone-tissue engineering. This research holds promise for the capability to print biocompatible, biodegradable parts for tissue engineering and other applications. Read more at: Guo, Ting, Timothy Holzberg, Casey Lim, Feng Gao, Ankit Gargava, Jordan Trachtenberg, Antonios Mikos, and John Fisher. “3D printing PLGA: a quantitative examination of the effects of polymer composition and printing parameters on print resolution.” Biofabrication (2017).

“Abstract: In the past few decades, 3D printing has played a significant role in fabricating scaffolds with consistent, complex structure that meets 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 (microCT) 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.”

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