Evidence map›Paper›PMID 28302144›Full record

ArticleRadiation oncology (London, England)2017

A dosimetric model for the heterogeneous delivery of radioactive nanoparticles In vivo: a feasibility study.

Andrew B Satterlee, Peter Attayek, Bentley Midkiff, Leaf Huang

Open access · goldAbstract read
In one paragraph

Article in Radiation oncology (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 44% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Andrew B SatterleeDivision of Molecular Pharmaceutics and Center for Nanotechnology in Drug Delivery, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7571, USA. satterle@email.unc.edu.
Peter AttayekUNC and NCSU Joint Department of Biomedical Engineering, Chapel Hill, NC, 27599, USA.
Bentley MidkiffTranslational Pathology Laboratory, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7571, USA.
Leaf HuangDivision of Molecular Pharmaceutics and Center for Nanotechnology in Drug Delivery, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7571, USA. leafh@unc.edu.
UNC/NCSU Joint Department of Biomedical Engineering · USUniversity of North Carolina at Chapel Hill · US

Funding

Translational Nanosystems for Improved Lung Cancer Treatment with Small MoleculesU54CA151652 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HAROON, ZISHAN A, TEPPER, JOEL E · 2010 to 2014
$12.1M
Small Animal Imaging CoreU54CA198999 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, ZIBO · 2015 to 2019
$11.9M
Nanocrystals for the Treatment of Multidrug Resistance in CancerR01CA149387 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HUANG, LEAF · 2011 to 2015
$1.5M
Novel nanoparticles for siRNA deliveryR01CA149363 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HUANG, LEAF · 2010 to 2014
$1.3M
NCI NIH HHS R01 CA149363NCI NIH HHS R01 CA149387NCI NIH HHS U54 CA151652NCI NIH HHS U54 CA198999
6 · The paper itself

Abstract

ᅟ: Accurate and quantitative dosimetry for internal radiation therapy can be especially challenging, given the heterogeneity of patient anatomy, tumor anatomy, and source deposition. Internal radiotherapy sources such as nanoparticles and monoclonal antibodies require high resolution imaging to accurately model the heterogeneous distribution of these sources in the tumor. The resolution of nuclear imaging modalities is not high enough to measure the heterogeneity of intratumoral nanoparticle deposition or intratumoral regions, and mathematical models do not represent the actual heterogeneous dose or dose response. To help answer questions at the interface of tumor dosimetry and tumor biology, we have modeled the actual 3-dimensional dose distribution of heterogeneously delivered radioactive nanoparticles in a tumor after systemic injection.

methods24 h after systemic injection of dually fluorescent and radioactive nanoparticles into a tumor-bearing mouse, the tumor was cut into 342 adjacent sections and imaged to quantify the source distribution in each section. The images were stacked to generate a 3D model of source distribution, and a novel MATLAB code was employed to calculate the dose to cells on a middle section in the tumor using a low step size dose kernel.

resultsThe average dose calculated by this novel 3D model compared closely with standard ways of calculating average dose, and showed a positive correlation with experimentally determined cytotoxicity in vivo. The high resolution images allowed us to determine that the dose required to initiate radiation-induced H2AX phosphorylation was approximately one Gray. The nanoparticle distribution was further used to model the dose distribution of two other radionuclides.

conclusionsThe ability of this model to quantify the absorbed dose and dose response in different intratumoral regions allows one to investigate how source deposition in different tumor areas can affect dose and cytotoxicity, as well as how characteristics of the tumor microenvironment, such as hypoxia or high stromal areas, may affect the potency of a given dose.

Indexed as

NanoparticlesAnimalsBrachytherapyCell Line, TumorFeasibility StudiesFemaleHumansImaging, Three-DimensionalLutetiumMiceMice, NudeModels, TheoreticalNeoplasms, ExperimentalRadioisotopesRadiometryRadiotherapy Planning, Computer-AssistedLutetiumRadioisotopesDosimetryHeterogeneousInternal Radiation TherapyNanoparticleTumor

Identifiers

PMID28302144
PMCPMC5356254
OpenAlexW2596733305

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.