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Cited by 300

The DOI matches its Crossref record; 185 of the 185 references it cites were found in a registry or an index.

pH-Responsive Polymer Nanomaterials for Tumor Therapy

Open access
Chu, Shunli, Shi, Xiaolu, Tian, Ye, Gao, Fengxiang·2022·Frontiers in Oncology

Abstract

The complexity of the tumor microenvironment presents significant challenges to cancer therapy, while providing opportunities for targeted drug delivery. Using characteristic signals of the tumor microenvironment, various stimuli-responsive drug delivery systems can be constructed for targeted drug delivery to tumor sites. Among these, the pH is frequently utilized, owing to the pH of the tumor microenvironment being lower than that of blood and healthy tissues. pH-responsive polymer carriers can improve the efficiency of drug delivery in vivo, allow targeted drug delivery, and reduce adverse drug reactions, enabling multifunctional and personalized treatment. pH-responsive polymers have gained increasing interest due to their advantageous properties and potential for applicability in tumor therapy. In this review, recent advances in, and common applications of, pH-responsive polymer nanomaterials for drug delivery in cancer therapy are summarized, with a focus on the different types of pH-responsive polymers. Moreover, the challenges and future applications in this field are prospected.

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Metadata

Authors
Chu, S., Shi, X., Tian, Y., & Gao, F.
Venue
Frontiers in Oncology
Published
2022-03-22
Volume
12
Pages
855019-855019
ISSN
2234-943X (ISSN-L)
OpenAlex
W4221030462
Cited by
300
References
185
FWCI
10.61
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: pH-Responsive Polymer Nanomaterials for Tumor Therapy. https://app.cochat.ai/papers/10.3389/fonc.2022.855019
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Chu, S., Shi, X., Tian, Y., & Gao, F. (2022). pH-Responsive Polymer Nanomaterials for Tumor Therapy. Frontiers in Oncology. https://doi.org/10.3389/fonc.2022.855019

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.