Lymphatic Uptake of Pulmonary Delivered Radiolabelled Solid Lipid Nanoparticles
Abstract
UNLABELLED: Lymphatic drainage plays an important role in the uptake of particulates in the respiratory system, being also associated to the spreading of lung cancer through metastasis development. In recent years solid lipid nanoparticles (SLN) have been proposed as carriers of anti-tumoural drugs, for their low toxicity and surface characteristics make them suitable for either imaging (gamma-scintigraphy) or therapy upon encapsulation of cytotoxic drugs. Assessment of inhaled radiolabelled SLN biodistribution is described in the present work. METHODS: Nanoparticles (200 nm) were radiolabelled with 99mTc using the lipophilic chelator D,L-hexamehylpropyleneamine oxime (HMPAO). Biodistribution studies were carried out following aerosolisation and administration of a 99mTc-HMPAO-SLN suspension to a group of adult male Wistar rats. A 60 min dynamic image acquisition was performed in a gamma-camera, followed by static image collection at 30 min intervals up to 4 h postinhalation. Radiation counting was performed in organ samples, collected after the animals were sacrificed. RESULTS: The data show an important and significant uptake of the radiolabelled SLN into the lymphatics after inhalation, and a high rate of distribution in periaortic, axillar and inguinal lymph nodes. CONCLUSION: Results indicate that SLN could be effective colloidal carriers for lymphoscintigraphy or therapy upon pulmonary delivery.
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Lipid nanoparticles show higher stability and drug loading efficiency with appropriate sizes and shapes for direct lung targeting and controlled drug delivery.
“Lipid NPs show higher stability and drug loading efficiency with appropriate sizes and shapes for direct lung targeting and controlled drug delivery.213”
From Targeted therapy in chronic diseases using nanomaterial-based drug delivery vehicles (2019) · cites this paper as [213]
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- Authors
- Videira, M. A., Botelho, M. F., Santos, A. C., Gouveia, L. F., Pedroso de Lima, J. J., & Almeida, A. J.
- Affiliations
- University of Lisbon (PT)
- University of Coimbra (PT)
- Venue
- Journal of Drug Targeting
- Publisher
- Informa UK Limited
- Published
- 2002-01
- Volume
- 10
- Issue
- 8
- Pages
- 607-613
- ISSN
- 1026-7158 (ISSN-L)
- 1061-186X
- 1029-2330
- OpenAlex
- W2115533786
- Cited by
- 213
- References
- 25
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 9 · 2023: 8 · 2022: 10 · 2021: 14 · 2020: 10 · 2019: 7 · 2018: 11 · 2017: 8 · 2016: 14 · 2015: 12 · 2014: 12 · 2013: 14 · 2012: 14
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of drug targeting (publishedVersion)
- PubMed (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Funders
- Fundação para a Ciência e a Tecnologia
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Inhalation and Respiratory Drug Delivery
- Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiopharmaceutical Chemistry and Applications
- Keywords
- solid lipid nanoparticles, lymphatic absorption, pulmonary drug delivery, aerosol inhalation, 99mTc-HMPAO, tissue distribution, lymphoscintigraphy, lymph node distribution, lung cancer metastasis, scintigraphy, nanoparticle drug carriers
- MeSH terms
- Administration, Inhalation, Animals, Colloids, Drug Carriers, Image Processing, Computer-Assisted, Isotope Labeling, Lipids, Lung, Lymphatic System, Male, Microspheres, Spirometry, Tissue Distribution, Rats, Wistar, Radiopharmaceuticals, Technetium Tc 99m Exametazime, Rats, Lymphoscintigraphy
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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.
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