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Quantifying the Ligand-Coated Nanoparticle Delivery to Cancer Cells in Solid Tumors
Abstract
Coating the nanoparticle surface with cancer cell recognizing ligands is expected to facilitate specific delivery of nanoparticles to diseased cells in vivo. While this targeting strategy is appealing, no nanoparticle-based active targeting formulation for solid tumor treatment had made it past phase III clinical trials. Here, we quantified the cancer cell-targeting efficiencies of Trastuzumab (Herceptin) and folic acid coated gold and silica nanoparticles in multiple mouse tumor models. Surprisingly, we showed that less than 14 out of 1 million (0.0014% injected dose) intravenously administrated nanoparticles were delivered to targeted cancer cells, and that only 2 out of 100 cancer cells interacted with the nanoparticles. The majority of the intratumoral nanoparticles were either trapped in the extracellular matrix or taken up by perivascular tumor associated macrophages. The low cancer cell targeting efficiency and significant uptake by noncancer cells suggest the need to re-evaluate the active targeting process and therapeutic mechanisms using quantitative methods. This will be important for developing strategies to deliver emerging therapeutics such as genome editing, nucleic acid therapy, and immunotherapy for cancer treatment using nanocarriers.
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Analysis of nanoparticle delivery to tumours
Wilhelm, S., Tavares, A. J., Dai, Q., Ohta, S., Audet, J., Dvorak, H. F., & Chan, W. C. W. (2016) · Nature Reviews Materials · doi:10.1038/natrevmats.2016.14 · Cited by 5,218
Self-citationDrug penetration in solid tumours
Minchinton, A. I., & Tannock, I. F. (2006) · Nature reviews. Cancer · doi:10.1038/nrc1893 · Cited by 2,805
Engineered Nanoparticles for Drug Delivery in Cancer Therapy
Sun, T., Zhang, Y. S., Pang, B., Hyun, D. C., Yang, M., & Xia, Y. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201403036 · Cited by 2,164
Self-citationFormulation of functionalized PLGA-PEG Nanoparticles for in vivo targeted drug delivery
Cheng, J., Teply, B. A., Sherifi, I., Sung, J., Luther, G., Gu, F. X., Levy-Nissenbaum, E., Radovic-Moreno, A. F., Langer, R., & Farokhzad, O. C. (2007) · Biomaterials · doi:10.1016/j.biomaterials.2006.09.047 · Cited by 1,273
Tumour‐associated macrophages act as a slow‐release reservoir of nano‐therapeutic Pt(IV) pro‐drug
Miller, M. A., Zheng, Y.-R., Gadde, S., Pfirschke, C., Zope, H., Engblom, C., Kohler, R. H., Iwamoto, Y., Yang, K. S., Askevold, B., et al. (2015) · Nature Communications · doi:10.1038/ncomms9692 · Cited by 441
Lee, H., Fonge, H., Hoang, B., Reilly, R. M., & Allen, C. (2010) · Molecular Pharmaceutics · doi:10.1021/mp100038h · Cited by 349
Cited by
Engineering precision nanoparticles for drug delivery
Mitchell, M. J., Billingsley, M. M., Haley, R. M., Wechsler, M. E., Peppas, N. A., & Langer, R. (2021) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-0090-8 · Cited by 7,933
Why 90% of clinical drug development fails and how to improve it?
Sun, D., Gao, W., Hu, H., & Zhou, S. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.02.002 · Cited by 1,763
The entry of nanoparticles into solid tumours
Sindhwani, S., Syed, A. M., Ngai, J., Kingston, B. R., Maiorino, L., Rothschild, J., MacMillan, P., Zhang, Y., Rajesh, N. U., Hoang, T., et al. (2020) · Nature Materials · doi:10.1038/s41563-019-0566-2 · Cited by 1,732
Self-citationConquering the Hypoxia Limitation for Photodynamic Therapy
Wan, Y., Fu, L., Li, C., Lin, J., & Huang, P. (2021) · Advanced Materials · doi:10.1002/adma.202103978 · Cited by 705
The Use of Alternative Strategies for Enhanced Nanoparticle Delivery to Solid Tumors
Izci, M., Maksoudian, C., Manshian, B. B., & Soenen, S. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00779 · Cited by 506
Towards principled design of cancer nanomedicine to accelerate clinical translation
Souri, M., Soltani, M., Moradi Kashkooli, F., Kiani Shahvandi, M., Chiani, M., Shariati, F. S., Mehrabi, M. R., & Munn, L. L. (2022) · Materials Today Bio · doi:10.1016/j.mtbio.2022.100208 · Cited by 159
Advancing engineering design strategies for targeted cancer nanomedicine
Gomerdinger, V. F., Nabar, N., & Hammond, P. T. (2025) · Nature Reviews Cancer · doi:10.1038/s41568-025-00847-2 · Cited by 146
Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures
Zhu, M., Zhuang, J., Li, Z., Liu, Q., Zhao, R., Gao, Z., Midgley, A. C., Qi, T., Tian, J., Zhang, Z., et al. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01323-4 · Cited by 140
Nexus between in silico and in vivo models to enhance clinical translation of nanomedicine
Moradi Kashkooli, F., Soltani, M., Souri, M., Meaney, C., & Kohandel, M. (2021) · Nano Today · doi:10.1016/j.nantod.2020.101057 · Cited by 120
Nanoparticle Interactions with the Tumor Microenvironment
Huai, Y., Hossen, M. N., Wilhelm, S., Bhattacharya, R., & Mukherjee, P. (2019) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.9b00448 · Cited by 113
Self-citationNanoengineered mesenchymal stem cell therapy for pulmonary fibrosis in young and aged mice
Han, M.-M., He, X.-Y., Tang, L., Qi, L., Yang, M.-Y., Wang, Y., Xing, L., Jeong, J.-H., & Jiang, H.-L. (2023) · Science Advances · doi:10.1126/sciadv.adg5358 · Cited by 86
Wu, D., Shi, X., Zhao, F., Chilengue, S. T. F., Deng, L., Dong, A., Kong, D., Wang, W., & Zhang, J. (2019) · Acta Biomaterialia · doi:10.1016/j.actbio.2019.06.033 · Cited by 81
de Maar, J. S., Sofias, A. M., Porta Siegel, T., Vreeken, R. J., Moonen, C., Bos, C., & Deckers, R. (2020) · Theranostics · doi:10.7150/thno.38625 · Cited by 71
Souri, M., Soltani, M., & Moradi Kashkooli, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-98554-z · Cited by 61
Engineering extracellular matrix to improve drug delivery for cancer therapy
He, X., Yang, Y., Li, L., Zhang, P., Guo, H., Liu, N., Yang, X., & Xu, F. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.06.029 · Cited by 54
Unraveling the Role of the Tumor Extracellular Matrix to Inform Nanoparticle Design for Nanomedicine
Cassani, M., Fernandes, S., Pagliari, S., Cavalieri, F., Caruso, F., & Forte, G. (2025) · Advanced Science · doi:10.1002/advs.202409898 · Cited by 48
Souri, M., Moradi Kashkooli, F., & Soltani, M. (2022) · Pharmaceutical Research · doi:10.1007/s11095-022-03255-5 · Cited by 44
Maxwell, T., Nogueira Campos, M. G., Smith, S., Doomra, M., Thwin, Z., & Santra, S. (2020) · Nanoparticles for Biomedical Applications · doi:10.1016/b978-0-12-816662-8.00015-1 · Cited by 43
Kiani Shahvandi, M., Souri, M., Tavasoli, S., Moradi Kashkooli, F., Kar, S., & Soltani, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107574 · Cited by 37
Metadata
- Authors
- Dai, Q., Wilhelm, S., Ding, D., Syed, A. M., Sindhwani, S., Zhang, Y., Chen, Y. Y., MacMillan, P., & Chan, W. C. W.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-07-17
- Volume
- 12
- Issue
- 8
- Pages
- 8423-8435
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2884304605
- Cited by
- 658
- References
- 6
- FWCI
- 19.59
- Type
- journal article
- Language
- en
- Open access
- closed
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