Akihiro Nishiguchi
(National Institute for Materials Science)
;
Miho Ohta
;
Debabrata Palai
(National Institute for Materials Science)
;
Shima Ito
(National Institute for Materials Science)
;
Kensaku Mori
;
Ryotaro Akagi
;
Christophe Bajan
(National Institute for Materials Science)
;
Guillaume Lambard
(National Institute for Materials Science)
;
Keitaro Sodeyama
(National Institute for Materials Science)
;
Tetsushi Taguchi
(National Institute for Materials Science)
Description:
(abstract)Embolic agents have been widely used to treat blood vessel abnormalities in interventional radiology as a minimally invasive procedure. However, only a few biodegradable liquid embolic agents exhibit high embolization performance, biodegradability, and operability. Herein, we report the design of in situ-forming supramolecular nanofiber (SNF) hydrogels as biodegradable liquid embolic agents with the assistance of Bayesian optimization through an active learning pipeline. Chemically modified gelatin with hydrogen-bonding moieties produced fibrin-inspired nanofiber-based hydrogels with a high blood coagulation capacity. The low viscosity of the SNF hydrogels made them injectable using a microcatheter, and the hydrogel showed sufficient tissue adhesion to the blood vessel walls and very weak adhesion to the catheter tubes. Moreover, the SNF hydrogels exhibited high blood compatibility, cytocompatibility, cell-adhesive properties, and biodegradability (in vitro and in vivo). Intravascularly delivered SNF hydrogels induced embolization of rat femoral arteries. This biodegradable liquid embolic agent could be a powerful tool for interventional radiology in the treatment of various diseases, including aortic aneurysm stent grafting, gynecological diseases, and liver cancer.
Rights:
Keyword: Hydrogel, Embolic agent, Machine learning
Date published: 2024-11-07
Publisher: Wiley
Journal:
Funding:
Manuscript type: Author's original (Submitted manuscript)
MDR DOI: https://doi.org/10.48505/nims.5346
First published URL: https://doi.org/10.1002/adhm.202403784
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Updated at: 2025-02-26 16:30:29 +0900
Published on MDR: 2025-02-26 16:30:30 +0900
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