Human relevant platforms for cutaneous wound healing research: current landscape, translational gaps, and emerging frontiers.

Authors

Fidha Latheef, K Suthindhiran

Published in

Frontiers in bioengineering and biotechnology. Volume 14. Pages 1917429. Epub Jul 31, 2026.

Abstract

Cutaneous wound healing is a dynamic, multicellular process that unfolds across four interrelated phases - haemostasis, inflammation, proliferation, and remodellingeach governed by precise intercellular signalling that remains incompletely understood in its human context. Animal models and two-dimensional cell cultures have shaped much of what we know about wound biology, yet both consistently fall short when the question moves from mechanism to translation. They fail to capture the structural organisation of human skin, the particular rhythms of human immune activation and resolution, and above all the multifactorial pathology that makes chronic wounds-diabetic foot ulcers especially, so resistant to treatment. This review traces the development of human-relevant alternative models as a coherent scientific response to those failures: from scratch assays and monocultures through to three-dimensional reconstructed equivalents, organoid platforms, ex vivo tissue preparations, and skin-on-a-chip systems capable of dynamic perfusion and real-time wound monitoring. We assess each class of model not simply on its merits but on what specific biological gap it was designed to close and what gaps remain. Emerging analytical frameworks-multi-omics integration, spatial transcriptomics, microbiome and biofilm modelling, multi-organ-on-a-chip architectures, artificial intelligence, and neuro-immune crosstalk -are examined as the next Frontier. These findings show that no single platform will resolve the translational deficit; what is required is a deliberately combinatorial paradigm in which complementary systems are deployed in tiered sequence, each contributing the biological information it is best positioned to generate.

PMID:
42601972
Bibliographic data and abstract were imported from PubMed on 15 Aug 2026.

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