The DOI matches its Crossref record; 181 of the 181 references it cites were found in a registry or an index.
Tailoring Nanomaterials for Targeting Tumor‐Associated Macrophages
Abstract
Advances in the field of nanotechnology together with an increase understanding of tumor immunology have paved the way for the development of more personalized cancer immuno‐nanomedicines. Nanovehicles, due to their specific physicochemical properties, are emerging as key translational moieties in tackling tumor‐promoting, M2‐like tumor‐associated macrophages (TAMs). Cancer immuno‐nanomedicines target TAMs primarily by blocking M2‐like TAM survival or affecting their signaling cascades, restricting macrophage recruitment to tumors and re‐educating tumor‐promoting M2‐like TAMs to the tumoricidal, M1‐like phenotype. Here, the TAM effector mechanisms and strategies for targeting TAMs are summarized, followed by a focus on the mechanistic considerations in the development of novel immuno‐nanomedicines. Furthermore, imaging TAMs with nanoparticles so as to forecast a patient's clinical outcome, describing treatment options, and observing therapy responses is also discussed. At present, strategies that target TAMs are being investigated not only at the basic research level but also in early clinical trials. The significance of TAM‐targeting biomaterials is highlighted, with the goal of facilitating future clinical translation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The blockade of immune checkpoints in cancer immunotherapy
Pardoll, D. M. (2012) · Nature Reviews Cancer · doi:10.1038/nrc3239 · Cited by 13,838
Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma
Larkin, J., Chiarion-Sileni, V., Gonzalez, R., Grob, J. J., Cowey, C. L., Lao, C. D., Schadendorf, D., Dummer, R., Smylie, M., Rutkowski, P., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504030 · Cited by 8,157
Principles of nanoparticle design for overcoming biological barriers to drug delivery
Blanco, E., Shen, H., & Ferrari, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3330 · Cited by 7,002
Oncology meets immunology: the cancer-immunity cycle.
Chen, D. S., & Mellman, I. (2013) · Immunity · doi:10.1016/j.immuni.2013.07.012 · Cited by 6,929
Cancer immunotherapy using checkpoint blockade
Ribas, A., & Wolchok, J. D. (2018) · Science · doi:10.1126/science.aar4060 · Cited by 6,791
Cancer Immunoediting: Integrating Immunity’s Roles in Cancer Suppression and Promotion
Schreiber, R. D., Old, L. J., & Smyth, M. J. (2011) · Science · doi:10.1126/science.1203486 · Cited by 6,474
Macrophage plasticity and polarization: in vivo veritas
Sica, A., & Mantovani, A. (2012) · Journal of Clinical Investigation · doi:10.1172/jci59643 · Cited by 6,335
Cancer nanomedicine: progress, challenges and opportunities
Shi, J., Kantoff, P. W., Wooster, R., & Farokhzad, O. C. (2017) · Nature Reviews Cancer · doi:10.1038/nrc.2016.108 · Cited by 5,873
Regulation of microRNA biogenesis
Ha, M., & Kim, V. N. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3838 · Cited by 5,462
Cancer immunoediting: from immunosurveillance to tumor escape
Dunn, G. P., Bruce, A. T., Ikeda, H., Old, L. J., & Schreiber, R. D. (2002) · Nature Immunology · doi:10.1038/ni1102-991 · Cited by 5,393
Cited by
The role of macrophages in the resolution of inflammation
Watanabe, S., Alexander, M., Misharin, A. V., & Budinger, G. R. S. (2019) · Journal of Clinical Investigation · doi:10.1172/jci124615 · Cited by 925
Harnessing tumor‐associated macrophages as aids for cancer immunotherapy
Li, X., Liu, R., Su, X., Pan, Y., Han, X., Shao, C., & Shi, Y. (2019) · Molecular Cancer · doi:10.1186/s12943-019-1102-3 · Cited by 364
Zhou, Y., Chen, X., Cao, J., & Gao, H. (2020) · Journal of Materials Chemistry B · doi:10.1039/d0tb00649a · Cited by 213
Wu, P., Luo, X., Sun, M., Sun, B., & Sun, M. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121492 · Cited by 25
Metadata
- Authors
- Ovais, M., Guo, M., & Chen, C.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2019-03-18
- Volume
- 31
- Issue
- 19
- Pages
- e1808303-e1808303
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2923186456
- Cited by
- 298
- References
- 181
- FWCI
- 14.10
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Tailoring Nanomaterials for Targeting Tumor‐Associated Macrophages. https://app.cochat.ai/papers/10.1002/adma.201808303
- The paper
- Ovais, M., Guo, M., & Chen, C. (2019). Tailoring Nanomaterials for Targeting Tumor‐Associated Macrophages. Advanced Materials. https://doi.org/10.1002/adma.201808303
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.