Document Type
Article
Publication Date
2-2022
Department
College of Forest Resources and Environmental Science
Abstract
Fused deposition molding (FDM) can complete most complex preparation of drug delivery implants to meet the personalized needs of patients. However, the drug activity has strict requirements on processing temperature and preparation method of filaments, the implant also has strict biocompatibility requirements for the materials. In this study, a drug delivery implant was prepared with good biocompatibility, controlled and efficient drug release using FDM printing for personalized administration. Drug-loaded filaments were developed for FDM process by hot-melt extrusion (HME). Polycaprolactone was used as a drug delivery carrier, and ibuprofen as the model drug. Notably, chitosan was dissolved to form controlled and efficient release channels. The printability, changes in physical and chemical properties during HME and FDM processes of the filament, and drug release behavior, mechanism and biocompatibility of the implants were investigated. The results showed that the filament tensile strength decreased with the increase of drug and chitosan content. No obvious degradation and chemical change occurred during the whole process. The drug release efficiency could reach>99% and lasted for 120 h mainly via the diffusion - erosion mechanism. The viability of cells cultured for 24 h in 72 h, 100% implant extract was 75.3%.
Publication Title
Materials and Design
Recommended Citation
Yang, Y.,
Wu, H.,
Fu, Q.,
Xie, X.,
Song, Y.,
Xu, M.,
&
Li, J.
(2022).
3D-printed polycaprolactone-chitosan based drug delivery implants for personalized administration.
Materials and Design,
214.
http://doi.org/10.1016/j.matdes.2022.110394
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15666
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Publisher’s version of record: https://doi.org/10.1016/j.matdes.2022.110394