High-resolution 3D visualization of nanomedicine distribution in tumors
Open accessAbstract
To improve the clinical translation of anti-cancer nanomedicines, it is necessary to begin building specific insights into the broad concept of the Enhanced Permeability and Retention (EPR) effect, using detailed investigations of the accumulation, distribution and retention of nanomedicines in solid tumors.Nanomedicine accumulation in preclinical tumors has been extensively studied; however, treatment efficacy will be heavily influenced by both the quantity of drug-loaded nanomedicines reaching the tumor as well as their spatial distribution throughout the tumor.It remains a challenge to image the heterogeneity of nanomedicine distribution in 3 dimensions within solid tumors with a high degree of spatial resolution using standard imaging approaches.Methods: To achieve this, an ex vivo micro computed tomography (µCT) imaging approach was developed to visualize the intratumoral distribution of contrast agent-loaded PEGylated liposomes.Using this semi-quantitative method, whole 3-dimensional (3D) tumor liposome distribution was determined with 17 µm resolution in a phenotypically diverse panel of four preclinical xenograft and patient-derived explant (PDX) tumor models.Results: High-resolution ex vivo µCT imaging revealed striking differences in liposome distribution within tumors in four models with different vascular patterns and densities, stromal contents, and microenvironment morphologies.Following intravenous dosing, the model with the highest density of pericyte-supported vessels showed the greatest liposome accumulation, while the model with vessels present in regions of high α-smooth muscle actin (αSMA) content presented with a large proportion of the liposomes at depths beyond the tumor periphery.The two models with an unsupported vascular network demonstrated a more restricted pattern of liposome distribution.Conclusion: Taken together, vessel distribution and support (the latter indicative of functionality) appear to be key factors determining the accumulation and distribution pattern of liposomes in tumors.Our findings demonstrate that high-resolution 3D visualization of nanomedicine distribution is a useful tool for preclinical nanomedicine research, providing valuable insights into the influence of the tumor vasculature and microenvironment on nanomedicine localization.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
High-resolution ex vivo μ-CT was utilized to study spatial distribution of drugs in different tumor models.
“Further, high-resolution ex vivo μ-CT was utilized to study the spatial distribution of drugs in different tumor models [127].”
From Approaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis (2023) · cites this paper as [127]
The Discussion (page not specified) says the study used high-resolution ex vivo μ-CT to visualize liposome distribution across four tumor models. However, the Results section, “Selection of nanomedicine” (page not specified), identifies the liposomes as containing iohexol, a CT contrast agent, rather than a therapeutic drug. The method and range of tumor models are supported, but describing the measured distribution as that of drugs overstates what was imaged. (LLM, conf=0.86)
Experimental — accuracy not yet measuredVessel spread and vessel support are significant factors for effective liposome aggregation in tumors.
“This study identified vessel spread and vessel support as significant factors for effective liposome aggregation in tumors.”
From Approaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis (2023) · cites this paper as [127]
This semiquantitative technique was utilized to determine whole 3D liposome distribution in tumors with 17 μm resolution in different PDX models.
“This semiquantitative technique was utilized to determine whole 3D liposome distribution in tumors with 17 μm resolution in different PDX models.”
From Approaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis (2023) · cites this paper as [127]
The microcomputed tomography imaging approach was established to image intratumoral distribution of contrast agent-loaded PEGylated liposomes.
“The microcomputed tomography (μ-CT) imaging approach was established to image the intratumoral distribution of contrast agent-loaded PEGylated liposomes.”
From Approaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis (2023) · cites this paper as [127]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
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 · see citing works
Prabhakar, U., Maeda, H., Jain, R. K., Sevick-Muraca, E. M., Zamboni, W. C., Farokhzad, O. C., Barry, S. T., Gabizón, A., Grodzinski, P. A., & Blakey, D. C. (2013) · Cancer Research · doi:10.1158/0008-5472.can-12-4561 · Cited by 1,493 · see citing works
Self-citationGabizón, A., Catane, R., Uziely, B., Kaufman, B., Safra, T., Cohen, R., Martin, F., Huang, A., & Barenholz, Y. (1994) · PubMed · Cited by 1,382 · see citing works
Tumor targeting via EPR: Strategies to enhance patient responses
Golombek, S. K., May, J.-N., Theek, B., Appold, L., Drude, N., Kiessling, F., & Lammers, T. (2018) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2018.07.007 · Cited by 1,262 · see citing works
Challenges and strategies in anti-cancer nanomedicine development: An industry perspective
Hare, J. I., Lammers, T., Ashford, M. B., Puri, S., Storm, G., & Barry, S. T. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.04.025 · Cited by 1,246 · see citing works
Self-citationLiposome formulations with prolonged circulation time in blood and enhanced uptake by tumors.
Gabizón, A., & Papahadjopoulos, D. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.18.6949 · Cited by 1,134 · see citing works
Chauhan, V. P., Stylianopoulos, T., Martin, J. D., Popović, Z., Chen, O., Kamoun, W. S., Bawendi, M. G., Fukumura, D., & Jain, R. K. (2012) · Nature Nanotechnology · doi:10.1038/nnano.2012.45 · Cited by 1,101 · see citing works
Chauhan, V. P., Martin, J. D., Liu, H., Lacorre, D. A., Jain, S. R., Kozin, S. V., Stylianopoulos, T., Mousa, A. S., Han, X., Adstamongkonkul, P., et al. (2013) · Nature Communications · doi:10.1038/ncomms3516 · Cited by 1,031 · see citing works
Harrington, K. J., Mohammadtaghi, S., Uster, P. S., Glass, D. M., Peters, A. M., Vile, R. G., & Stewart, J. S. W. (2001) · PubMed · Cited by 575 · see citing works
Facing the Truth about Nanotechnology in Drug Delivery
Park, K. (2013) · ACS Nano · doi:10.1021/nn404501g · Cited by 545 · see citing works
Cancer nanomedicines: So many papers and so few drugs!
Venditto, V. J., & Szoka, F. C. (2012) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2012.09.038 · Cited by 538 · see citing works
Maeda, H., & Khatami, M. (2018) · Clinical and Translational Medicine · doi:10.1186/s40169-018-0185-6 · Cited by 491 · see citing works
Noninvasive Imaging of Nanomedicines and Nanotheranostics: Principles, Progress, and Prospects
Kunjachan, S., Ehling, J., Storm, G., Kiessling, F. M., & Lammers, T. (2015) · Chemical Reviews · doi:10.1021/cr500314d · Cited by 480 · see citing works
Self-citationRobichaud, G., Garrard, K. P., Barry, J. A., et al. (2013) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-013-0607-z · Cited by 392 · see citing works
Current Multistage Drug Delivery Systems Based on the Tumor Microenvironment
Chen, B., Dai, W., He, B., Zhang, H., Wang, X., Wang, Y., & Zhang, Q. (2017) · Theranostics · doi:10.7150/thno.16684 · Cited by 347 · see citing works
Schramm, T., Hester, Z., Klinkert, I., et al. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.07.026 · Cited by 329 · see citing works
Allen, T. M. (1997) · Drugs · doi:10.2165/00003495-199700544-00004 · Cited by 279 · see citing works
Stromal barriers and strategies for the delivery of nanomedicine to desmoplastic tumors
Miao, L., Lin, C. M., & Huang, L. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2015.08.017 · Cited by 264 · see citing works
Dong, X., Liu, H., Feng, H., Yang, S., Liu, X., Lai, X. P., Lu, Q., Lovell, J. F., Chen, H., & Fang, C. (2019) · Nano Letters · doi:10.1021/acs.nanolett.8b04236 · Cited by 224 · see citing works
Aurora kinase inhibitor nanoparticles target tumors with favorable therapeutic index in vivo
Ashton, S. E., Song, Y. H., Nolan, J., Cadogan, E. B., Murray, J. A. H., Odedra, R., Foster, J. R., Hall, P. A., Low, S. C., Taylor, P., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad2355 · Cited by 218 · see citing works
Self-citationIoannidis, J. P. A., Kim, B. Y. S., & Trounson, A. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0314-y · Cited by 175 · see citing works
Micro-CT Imaging of Tumor Angiogenesis
Ehling, J., Theek, B., Gremse, F., Baetke, S. C., Möckel, D., Maynard, J., Ricketts, S., Grüll, H., Neeman, M., Knuechel, R., et al. (2013) · American Journal Of Pathology · doi:10.1016/j.ajpath.2013.10.014 · Cited by 151 · see citing works
Self-citationImaging Nanomedicine-Based Drug Delivery: a Review of Clinical Studies
Man, F., Lammers, T., & de Rosales, R. T. M. (2018) · Molecular Imaging and Biology · doi:10.1007/s11307-018-1255-2 · Cited by 135 · see citing works
Self-citationZheng, J., Liu, J., Dunne, M., Jaffray, D. A., & Allen, C. J. (2007) · Pharmaceutical Research · doi:10.1007/s11095-006-9220-1 · Cited by 115 · see citing works
Gao, W., Wang, Z., Lv, L., Yin, D., Chen, D., Han, Z., Ma, Y., Zhang, M., Yang, M. G., & Gu, Y. (2016) · Theranostics · doi:10.7150/thno.15262 · Cited by 109 · see citing works
Size Dependent Kinetics of Gold Nanorods in EPR Mediated Tumor Delivery
Tong, X., Wang, Z., Sun, X., Song, J., Jacobson, O., Niu, G., Kiesewetter, D. O., & Chen, X. S. (2016) · Theranostics · doi:10.7150/thno.17098 · Cited by 103 · see citing works
Theek, B., Gremse, F., Kunjachan, S., Fokong, S., Pola, R., Pechar, M., Deckers, R., Storm, G., Ehling, J., Kiessling, F. M., et al. (2014) · Journal of Controlled Release · doi:10.1016/j.jconrel.2014.03.007 · Cited by 102 · see citing works
Self-citationZhang, L., Su, H., Wang, H., Li, Q., Li, X., Zhou, C., Xu, J., Chai, Y., Liang, X., Xiong, L., et al. (2019) · Theranostics · doi:10.7150/thno.30523 · Cited by 96 · see citing works
Zheng, J., Perkins, G., Kirilova, A., Allen, C. J., & Jaffray, D. A. (2006) · Investigative Radiology · doi:10.1097/01.rli.0000186568.50265.64 · Cited by 95 · see citing works
Ekdawi, S. N., Stewart, J., Dunne, M., Stapleton, S., Mitsakakis, N., Dou, Y. N., Jaffray, D. A., & Allen, C. J. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2015.04.006 · Cited by 93 · see citing works
Chauhan, V. P., Stylianopoulos, T., Martin, J. D., Popović, Z., Chen, O., Kamoun, W. S., Bawendi, M. G., Fukumura, D., & Jain, R. K. (2020) · doi:10.1201/9780429399039-10 · Cited by 81 · see citing works
Stapleton, S., Milosevic, M., Tannock, I. F., Allen, C. J., & Jaffray, D. A. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2015.06.008 · Cited by 73 · see citing works
Zheng, J., Jaffray, D. A., & Allen, C. J. (2009) · Molecular Pharmaceutics · doi:10.1021/mp800234r · Cited by 69 · see citing works
Abbassi‐Ghadi, N., Jones, E. A., Veselkov, K., Huang, J., Kumar, S., Strittmatter, N., Golf, O., Kudo, H., Goldin, R. D., Hanna, G. B., et al. (2014) · Analytical Methods · doi:10.1039/c4ay01770f · Cited by 69 · see citing works
Self-citationSatterlee, A., Rojas, J. D., Dayton, P. A., & Huang, L. (2016) · Theranostics · doi:10.7150/thno.16681 · Cited by 56 · see citing works
Tumor perfusion imaging predicts the intra-tumoral accumulation of liposomes
Stapleton, S., Allen, C. J., Pintilie, M., & Jaffray, D. A. (2013) · Journal of Controlled Release · doi:10.1016/j.jconrel.2013.08.296 · Cited by 54 · see citing works
Growth hormone, insulin, and somatostatin therapy of cancer cachexia
Bartlett, D. L., Torosian, M. H., & Charland, S. L. (1994) · Cancer · doi:10.1002/1097-0142(19940301)73:5 · Cited by 40 · see citing works
Tumour stromal morphology impacts nanomedicine cytotoxicity in patient-derived xenografts
Delgado-SanMartin, J. A., Hare, J. I., Yates, J. W. T., et al. (2015) · Nanomedicine Nanotechnology Biology and Medicine · doi:10.1016/j.nano.2015.02.007 · Cited by 13 · see citing works
Ehling, J., Theek, B., Gremse, F., et al. (2014) · TU/e Research Portal · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [35]Allen TM Liposomes. Opportunities in drug delivery Drugs 1997 54 Suppl 4 8 14 9361956
Cited by
Sorted by citations
Shi, Y., van der Meel, R., Chen, X., & Lammers, T. (2020) · Theranostics · doi:10.7150/thno.49577 · Cited by 868 · see citing works
Self-citationApproaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis
Subhan, M. A., Parveen, F., Filipczak, N., Yalamarty, S. S. K., & Torchilin, V. P. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13030389 · Cited by 98 · see citing works
“Further, high-resolution ex vivo μ-CT was utilized to study the spatial distribution of drugs in different tumor models [127].”
MethodStrittmatter, N., Moss, J. I., Race, A. M., Sutton, D., Canales, J. R., Ling, S., Wong, E., Wilson, J., Smith, A., Howes, C., et al. (2022) · Theranostics · doi:10.7150/thno.68000 · Cited by 25 · see citing works
- Authors
- Moss, J. I., Barjat, H., Emmas, S.-A., Strittmatter, N., Maynard, J., Goodwin, R. J. A., Storm, G., Lammers, T., Puri, S., Ashford, M. B., & Barry, S. T.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- Utrecht University (NL)
- Corresponding author
- Moss, Jennifer I.
- ORCID
- Venue
- Theranostics
- Publisher
- Ivyspring International Publisher
- Published
- 2020
- Volume
- 10
- Issue
- 2
- Pages
- 880-897
- ISSN
- 1838-7640 (ISSN-L)
- PMC
- PMC6929971
- OpenAlex
- W2993972798
- Cited by
- 19
- References
- 40
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 6 · 2022: 2 · 2021: 3 · 2020: 3
- FWCI
- 0.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Theranostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Twente Research Information (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
- Funders
- AstraZeneca
- European Commission (NSC4DIPG, EuroNanoMed-III: NSC4DIPG)
- Deutsche Forschungsgemeinschaft (SFB 1066, TRR57, 309495, SFB‐TRR57, GRK 2375, 331065168)
- National Science Council
- H2020 European Research Council (813086)
- Topics
- Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
- Physical Sciences › Engineering › Biomedical Engineering › Ultrasound and Hyperthermia Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Angiogenesis and VEGF in Cancer
- Keywords
- enhanced permeability and retention effect, PEGylated liposomes, intratumoral drug distribution, micro-computed tomography, ex vivo imaging, tumor vasculature, tumor microenvironment, patient-derived xenografts, alpha-smooth muscle actin, solid tumors
- MeSH terms
- Animals, Contrast Media, Humans, Liposomes, Male, Neoplasms, Permeability, Mice, SCID, Imaging, Three-Dimensional, Xenograft Model Antitumor Assays, Cell Line, Tumor, Nanomedicine, Mice, Nanoparticles, X-Ray Microtomography, Tumor Microenvironment
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