The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The Binding Site Barrier Elicited by Tumor-Associated Fibroblasts Interferes Disposition of Nanoparticles in Stroma-Vessel Type Tumors
Open accessAbstract
The binding site barrier (BSB) was originally proposed to describe the binding behavior of antibodies to cells peripheral to blood vessels, preventing their further penetration into the tumors. Yet, it is revisited herein to describe the intratumoral cellular disposition of nanoparticles (NPs). Specifically, the BSB limits NP diffusion and results in unintended internalization of NPs by stroma cells localized near blood vessels. This not only limits the therapeutic outcome but also promotes adverse off-target effects. In the current study, it was shown that tumor-associated fibroblast cells (TAFs) are the major component of the BSB, particularly in tumors with a stroma-vessel architecture where the location of TAFs aligns with blood vessels. Specifically, TAF distance to blood vessels, expression of receptor proteins, and binding affinity affect the intensity of the BSB. The physical barrier elicited by extracellular matrix also prolongs the retention of NPs in the stroma, potentially contributing to the BSB. The influence of particle size on the BSB was also investigated. The strongest BSB effect was found with small (∼18 nm) NPs targeted with the anisamide ligand. The uptake of these NPs by TAFs was about 7-fold higher than that of the other cells 16 h post-intravenous injection. This was because TAFs also expressed the sigma receptor under the influence of TGF-β secreted by the tumor cells. Overall, the current study underscores the importance of BSBs in the delivery of nanotherapeutics and provides a rationale for exploiting BSBs to target TAFs.
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
Kalluri, R., & Zeisberg, M. (2006) · Nature Reviews Cancer · doi:10.1038/nrc1877 · Cited by 4,538
Özdemir, B. C., Pentcheva-Hoang, T., Carstens, J. L., Zheng, X., Wu, C.-C., Simpson, T. R., Laklai, H., Sugimoto, H., Kahlert, C., Novitskiy, S. V., et al. (2014) · Cancer Cell · doi:10.1016/j.ccr.2014.04.005 · Cited by 2,681
Accumulation of sub-100 nm polymeric micelles in poorly permeable tumours depends on size
Cabral, H., Matsumoto, Y., Mizuno, K., Chen, Q., Murakami, M., Kimura, M., Terada, Y., Kano, M. R., Miyazono, K., Uesaka, M., et al. (2011) · Nature Nanotechnology · doi:10.1038/nnano.2011.166 · Cited by 2,429
Multicellular tumor spheroids: An underestimated tool is catching up again
Hirschhaeuser, F., Menne, H., Dittfeld, C., West, J., Mueller-Klieser, W., & Kunz-Schughart, L. A. (2010) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2010.01.012 · Cited by 1,674
Sun, Y., Campisi, J., Higano, C., Beer, T. M., Porter, P., Coleman, I., True, L., & Nelson, P. S. (2012) · Nature Medicine · doi:10.1038/nm.2890 · Cited by 793
Combining two strategies to improve perfusion and drug delivery in solid tumors
Stylianopoulos, T., & Jain, R. K. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1318415110 · Cited by 508
Lee, H., Fonge, H., Hoang, B., Reilly, R. M., & Allen, C. (2010) · Molecular Pharmaceutics · doi:10.1021/mp100038h · Cited by 349
Cited by
Quantifying the Ligand-Coated Nanoparticle Delivery to Cancer Cells in Solid Tumors
Dai, Q., Wilhelm, S., Ding, D., Syed, A. M., Sindhwani, S., Zhang, Y., Chen, Y. Y., MacMillan, P., & Chan, W. C. W. (2018) · ACS Nano · doi:10.1021/acsnano.8b03900 · Cited by 658
Engineered nanomedicines with enhanced tumor penetration
Ding, J., Chen, J., Gao, L., Jiang, Z., Zhang, Y., Li, M., Xiao, Q., Lee, S. S., & Chen, X. (2019) · Nano Today · doi:10.1016/j.nantod.2019.100800 · Cited by 420
Bridging Bio–Nano Science and Cancer Nanomedicine
Björnmalm, M., Thurecht, K. J., Michael, M., Scott, A. M., & Caruso, F. (2017) · ACS Nano · doi:10.1021/acsnano.7b04855 · Cited by 382
Nanoparticles for modulating tumor microenvironment to improve drug delivery and tumor therapy
Yang, S., & Gao, H. (2017) · Pharmacological Research · doi:10.1016/j.phrs.2017.05.004 · Cited by 256
Nanoformulations for combination or cascade anticancer therapy
Miao, L., Guo, S., Lin, C. M., Liu, Q., & Huang, L. (2017) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2017.06.003 · Cited by 178
Self-citation
Metadata
- Authors
- Miao, L., Newby, J. M., Lin, C. M., Zhang, L., Xu, F., Kim, W. Y., Forest, M. G., Lai, S. K., Milowsky, M. I., Wobker, S. E., & Huang, L.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-09-28
- Volume
- 10
- Issue
- 10
- Pages
- 9243-9258
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2522400683
- Cited by
- 221
- References
- 7
- FWCI
- 8.14
- Type
- journal article
- Language
- en
- Open access
- green
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
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.