Machine learning-based design strategy for 3D printable bioink: elastic modulus and yield stress determine printability. (13th May 2020)

• Record Type: Journal Article
• Authors: Lee, Jooyoung; Oh, Seung Ja; An, Sang Hyun; Kim, Wan-Doo; Kim, Sang-Heon
• Is Part Of: Biofabrication. Volume 12:Number 3(2020)
• Publication Date: 2020-05-13
• Subjects: atelocollagen -- 3D bioprinting -- bioinks -- hydrogel -- machine learning -- rheological properties; Biomedical engineering -- Periodicals; Tissue engineering -- Periodicals; Biomedical materials -- Microstructure -- Periodicals; Bioengineering -- Periodicals; 610.28
• DOI: 10.1088/1758-5090/ab8707
• Languages: English
• ISSNs: 1758-5082

Abstract

Abstract: Although three-dimensional (3D) bioprinting technology is rapidly developing, the design strategies for biocompatible 3D-printable bioinks remain a challenge. In this study, we developed a machine learning-based method to design 3D-printable bioink using a model system with naturally derived biomaterials. First, we demonstrated that atelocollagen (AC) has desirable physical properties for printing compared to native collagen (NC). AC gel exhibited weakly elastic and temperature-responsive reversible behavior forming a soft cream-like structure with low yield stress, whereas NC gel showed highly crosslinked and temperature-responsive irreversible behavior resulting in brittleness and high yield stress. Next, we discovered a universal relationship between the mechanical properties of ink and printability that is supported by machine learning: a high elastic modulus improves shape fidelity and extrusion is possible below the critical yield stress; this is supported by machine learning. Based on this relationship, we derived various formulations of naturally derived bioinks that provide high shape fidelity using multiple regression analysis. Finally, we produced a 3D construct of a cell-laden hydrogel with a framework of high shape fidelity bioink, confirming that cells are highly viable and proliferative in the 3D constructs.