Evidence map›Paper›PMID 28165700›Full record

ArticleACS applied materials & interfaces2017

Intrinsic and Stable Conjugation of Thiolated Mesoporous Silica Nanoparticles with Radioarsenic.

Paul A Ellison, Feng Chen, Shreya Goel, Todd E Barnhart, Robert J Nickles, Onofre T DeJesus, Weibo Cai

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Article
  8. Mesoporous Silica Nanoparticles in Bioimaging.Materials (Basel, Switzerland) · 2020
    Review
  9. Article
  10. Medical imaging modalities using nanoprobes for cancer diagnosis: A literature review on recent findings.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2019
    Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
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

7 authors.

Paul A EllisonDepartment of Medical Physics and ‡Department of Radiology, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin 53726, United States.ORCID http://orcid.org/0000-0002-8379-7419
Feng ChenDepartment of Medical Physics and ‡Department of Radiology, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin 53726, United States.
Shreya GoelDepartment of Medical Physics and ‡Department of Radiology, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin 53726, United States.
Todd E BarnhartDepartment of Medical Physics and ‡Department of Radiology, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin 53726, United States.
Robert J NicklesDepartment of Medical Physics and ‡Department of Radiology, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin 53726, United States.
Onofre T DeJesusDepartment of Medical Physics and ‡Department of Radiology, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin 53726, United States.
Weibo CaiDepartment of Medical Physics and ‡Department of Radiology, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin 53726, United States.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Christian Capitini · 1985 to 2026
$142.6M
U.W. RADIOLOGICAL SCIENCES TRAINING PROGRAMT32CA009206 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Bryan Patrick Bednarz · 1985 to 2026
$12.1M
Novel Combination Therapy for Prostate CancerR01CA169365 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI CAI, WEIBO · 2013 to 2015
$903k
NCI NIH HHS P30 CA014520NCI NIH HHS R01 CA169365NCI NIH HHS T32 CA009206
6 · The paper itself

Abstract

The development of new image-guided drug delivery tools to improve the therapeutic efficacy of chemotherapeutics remains an important goal in nanomedicine. Using labeling strategies that involve radioelements that have theranostic pairs of diagnostic positron-emitting isotopes and therapeutic electron-emitting isotopes has promise in achieving this goal and further enhancing drug performance through radiotherapeutic effects. The isotopes of radioarsenic offer such theranostic potential and would allow for the use of positron emission tomography (PET) for image-guided drug delivery studies of the arsenic-based chemotherapeutic arsenic trioxide (ATO). Thiolated mesoporous silica nanoparticles (MSN) are shown to effectively and stably bind cyclotron-produced radioarsenic. Labeling studies elucidate that this affinity is a result of specific binding between trivalent arsenic and nanoparticle thiol surface modification. Serial PET imaging of the in vivo murine biodistribution of radiolabeled silica nanoparticles shows very good stability toward dearsenylation that is directly proportional to silica porosity. Thiolated MSNs are found to have a macroscopic arsenic loading capacity of 20 mg of ATO per gram of MSN, sufficient for delivery of chemotherapeutic quantities of the drug. These results show the great potential of radioarsenic-labeled thiolated MSN for the preparation of theranostic radiopharmaceuticals and image-guided drug delivery of ATO-based chemotherapeutics.

Indexed as

NanoparticlesAnimalsDrug Delivery SystemsMicePorositySilicon DioxideTheranostic NanomedicineTissue DistributionSilicon Dioxidearsenic trioxideAs-72As-76As-77image-guided drug deliverypositron emission tomographyradioarsenicradiolabeling

Identifiers

PMID28165700
PMCPMC5597940

What OpenQuestion holds

Textmetadata
LicenceTDM
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.