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REVIEW ARTICLE

Stimuli-responsive bioinks for bioprinted cancer models: Toward personalized drug screening and resistance modeling

Jing Zeng1* ,  Wei-Jian Ni2,3*
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1 Department of Pharmacy, Wenzhou People’s Hospital, The Third Affiliated Hospital of Shanghai University, Wenzhou, Zhejiang , China
2 Department of Pharmacy, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui , China
3 Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui , China
IJB, 026350377 https://doi.org/10.36922/IJB026350377
Received: 27 August 2026 | Revised: 21 September 2026 | Accepted: 22 September 2026 | Published online: 24 September 2026
© 2026 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

Drug resistance remains the leading cause of treatment failure in solid tumors. Conventional two-dimensional (2D) cultures and animal models cannot reproduce the dynamic tumor microenvironment (TME) cues driving resistance evolution—matrix stiffening, acidification, hypoxia, and enzymatic remodeling. Most 3D-bioprinted constructs remain static matrices and share this limitation. This review proposes the spatiotemporal dynamics of the TME as a design criterion for stimuli-responsive ("smart") bioinks, whose stiffness, degradability, drug-release kinetics, and geometry change in response to endogenous TME cues or external triggers. This review distills the TME into a quantitative design blueprint; examines the printability, cytocompatibility, and responsiveness requirements of smart inks, together with the dynamic chemistries enabling them; and summarizes strategies for reconstructing an evolving TME, using pancreatic ductal adenocarcinoma and glioblastoma as anchor case studies. The scope is restricted to cancer modeling and drug screening, excluding drug delivery and tissue regeneration. Current evidence remains proof-of-concept: printed models recapitulate resistance-associated phenotypes such as stiffness-mediated chemoresistance, cancer stem cell enrichment, dormancy, and phenotypic plasticity, whereas prospective validation of resistance models in patients has not been achieved. This review outlines the path from phenotype recapitulation to clinical prediction and analyzes prerequisites including vascularization, standardization, regulatory translation, and artificial intelligence (AI)-guided ink design.

Graphical abstract
Keywords
3D bioprinting
Stimuli-responsive bioink
Tumor microenvironment
Drug screening
Drug resistance
Patient-derived tumor models
Funding
This work was supported by the Research Funds of the Centre for Leading Medicine and Advanced Technologies of IHM (No. 2023IHM02003), the Clinical and Translational Research Project of Anhui Province (No. 202427b10020150), and the Anhui Provincial Health Commission Science and Technology Project (No. AHWJ2025BAm20013).
Conflict of interest
The authors declare no competing interests.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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