AccScience Publishing / IJB / Online First / DOI: 10.36922/ijb.3814
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RESEARCH ARTICLE

Machine learning-generated compression modulus database for 3D printing of gelatin methacryloyl

Shiue-Luen Chen1,2 ,  Manisha Senadeera3 ,  Kalani Ruberu4 ,  Johnson Chung4 ,  Santu Rana3 ,  Svetha Venkatesh3 ,  Chong-You Chen1,2 ,  Guan-Yu Chen1,2,5,6* ,  Gordon Wallace4*
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1 Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
2 Department of Electronics and Electrical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
3 Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Victoria, Australia
4 Intelligent Polymer Research Institute, ARC Centre of Excellence for Electromaterial Science, AIIM Facility, Innovation Campus, University of Wollongong, New South Wales, Australia
5 Department of Biological Science and Technology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
6 Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
IJB, 3814 https://doi.org/10.36922/ijb.3814
Submitted: 31 May 2024 | Accepted: 19 July 2024 | Published: 20 September 2024
(This article belongs to the Special Issue Bioprinting of in vitro tissue and disease models)
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

3D bioprinting enables the fabrication of printable tissues, including those for neural, cartilage, and skin repair. The mechanical properties, especially stiffness, of 3D-bioprinted scaffolds are crucial for modulating cell adhesion, growth, migration, and differentiation. The stiffness of a scaffold can be adjusted post-printing by modifying the hydrogel concentration, crosslinker concentration, light intensity during photocrosslinking, and duration of crosslinking. The optimization of these conditions to produce the desired scaffold stiffness for a particular cell type or application is a time-consuming and rigorous process. This study developed an innovative approach to predict the compression modulus of 3D-printed gelatin methacryloyl (GelMA) scaffolds using the Bayesian optimization (BO) algorithm. Through just 10 iterations (75 experimental data points), the model was able to predict > 13,000 possible compression modulus values in a search space comprising four independent variables (GelMA concentration, crosslinker concentration, ultraviolet light [UV] distance, and UV exposure time). This approach can be utilized in other photocrosslinkable bioinks for 3D printing that have a myriad of pre- or post-printing parameters that can affect scaffold stiffness.

 

Keywords
3D bioprinting
Scaffold stiffness
Compression modulus
Bayesian optimization
Gelatin methacryloyl
Funding
The authors acknowledge funding from the Australian Research Council (ARC) (CE140100012) and (FL170100006), and support from the Australian National Fabrication Facility (ANFF) – Materials Node. G.-Y.C. would like to acknowledge funding from The National Science and Technology Council (NSTC113-2321-B-A49-021, NSTC113-2628-B-A49-008-MY3, NSTC113-2823- 8-A49-003, NSTC112-2321-B-A49-015, NSTC 112-2321- B-A49-016), Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) (113W30305), and the Higher Education Sprout Project of the National Yang Ming Chiao Tung University and MOE, Taiwan (113W020211, 113W020211, 113W020214).
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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