Bridging the gap between visualization and reality: integrating degradation and metabolism data to elucidate recombinant collagen behavior

Abstract

Current methodologies face limitations in elucidating the in vivo behavior of biodegradable protein-based biomaterials, particularly crosslinked recombinant collagen materials. While isotope and fluorescence labeling techniques enable sensitive tracking, they primarily reflect the migration and distribution of labeled molecules rather than the composition and transformation of degradation products. To address this gap, this study proposes an integrated analytical strategy that combines in vitro simulated degradation product characterization with in vivo biological validation. Degradation products are systematically analyzed using an in vitro enzymatic degradation model and further correlated with histological observations, immunological responses, and multi-level toxicological assessments following implantation. The results demonstrate that labeling-based tracing effectively captures early-stage degradation dynamics, whereas the combined analysis of in vitro degradation products and in vivo biological evaluations provides additional insight into mid- to late-stage degradation and product transformation. This work offers a complementary framework for interpreting the in vivo behavior of recombinant collagen-based materials and highlights that, from a biological perspective, the degradation process itself may serve as a more direct indicator of material fate than conventional metabolic pathways.

Graphical abstract: Bridging the gap between visualization and reality: integrating degradation and metabolism data to elucidate recombinant collagen behavior

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Article information

Article type
Paper
Submitted
22 Jan 2026
Accepted
29 Mar 2026
First published
09 Apr 2026

Biomater. Sci., 2026, Advance Article

Bridging the gap between visualization and reality: integrating degradation and metabolism data to elucidate recombinant collagen behavior

C. Huang, Z. Li, S. Wu, P. Shi, Z. Wu, J. Li, X. Fan, Y. Zhai, H. Le, H. Wang, X. Wang, H. Sun, Z. Zhu, J. Lv, L. Wu, Y. Fan, J. Gao, Y. Chu and T. Yuan, Biomater. Sci., 2026, Advance Article , DOI: 10.1039/D6BM00099A

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