Radiolabeling and PET–MRI microdosing of the experimental cancer therapeutic, MN-anti-miR10b, demonstrates delivery to metastatic lesions in a murine model of ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-07-08

AUTHORS

Mariane Le Fur, Alana Ross, Pamela Pantazopoulos, Nicholas Rotile, Iris Zhou, Peter Caravan, Zdravka Medarova, Byunghee Yoo

ABSTRACT

BackgroundIn our earlier work, we identified microRNA-10b (miR10b) as a master regulator of the viability of metastatic tumor cells. This knowledge allowed us to design a miR10b-targeted therapeutic consisting of an anti-miR10b antagomir conjugated to ultrasmall iron oxide nanoparticles (MN), termed MN-anti-miR10b. In mouse models of breast cancer, we demonstrated that MN-anti-miR10b caused durable regressions of established metastases with no evidence of systemic toxicity. As a first step towards translating MN-anti-miR10b for the treatment of metastatic breast cancer, we needed to determine if MN-anti-miR10b, which is so effective in mice, will also accumulate in human metastases.ResultsIn this study, we devised a method to efficiently radiolabel MN-anti-miR10b with Cu-64 (64Cu) and evaluated the pharmacokinetics and biodistribution of the radiolabeled product at two different doses: a therapeutic dose, referred to as macrodose, corresponding to 64Cu-MN-anti-miR10b co-injected with non-labeled MN-anti-miR10b, and a tracer-level dose of 64Cu-MN-anti-miR10b, referred to as microdose. In addition, we evaluated the uptake of 64Cu-MN-anti-miR10b by metastatic lesions using both in vivo and ex vivo positron emission tomography–magnetic resonance imaging (PET–MRI). A comparable distribution of the therapeutic was observed after administration of a microdose or macrodose. Uptake of the therapeutic by metastatic lymph nodes, lungs, and bone was also demonstrated by PET–MRI with a significantly higher PET signal than in the same organs devoid of metastatic lesions.ConclusionOur results demonstrate that PET–MRI following a microdose injection of the agent will accurately reflect the innate biodistribution of the therapeutic. The tools developed in the present study lay the groundwork for the clinical testing of MN-anti-miR10b and other similar therapeutics in patients with cancer. More... »

PAGES

16

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12645-021-00089-5

DOI

http://dx.doi.org/10.1186/s12645-021-00089-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1139555164

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/34531932


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1112", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oncology and Carcinogenesis", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
            "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Le Fur", 
        "givenName": "Mariane", 
        "id": "sg:person.0723770445.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723770445.72"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ross", 
        "givenName": "Alana", 
        "id": "sg:person.0773665622.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0773665622.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pantazopoulos", 
        "givenName": "Pamela", 
        "id": "sg:person.0614535026.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614535026.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
            "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rotile", 
        "givenName": "Nicholas", 
        "id": "sg:person.01231046160.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01231046160.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
            "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhou", 
        "givenName": "Iris", 
        "id": "sg:person.01041376113.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041376113.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
            "Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Caravan", 
        "givenName": "Peter", 
        "id": "sg:person.0727346345.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727346345.99"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Medarova", 
        "givenName": "Zdravka", 
        "id": "sg:person.01005511751.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01005511751.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.32224.35", 
          "name": [
            "MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yoo", 
        "givenName": "Byunghee", 
        "id": "sg:person.01321624134.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01321624134.54"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/srep45060", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084132908", 
          "https://doi.org/10.1038/srep45060"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00259-005-1906-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045504659", 
          "https://doi.org/10.1007/s00259-005-1906-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrc.2016.25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050875196", 
          "https://doi.org/10.1038/nrc.2016.25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s40262-019-00769-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1113786189", 
          "https://doi.org/10.1007/s40262-019-00769-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00441-004-0884-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053242624", 
          "https://doi.org/10.1007/s00441-004-0884-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-018-23317-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101582214", 
          "https://doi.org/10.1038/s41598-018-23317-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm1486", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008398170", 
          "https://doi.org/10.1038/nm1486"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/onc.2012.173", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050681272", 
          "https://doi.org/10.1038/onc.2012.173"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.1618", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024956742", 
          "https://doi.org/10.1038/nbt.1618"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41573-020-0080-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1130660527", 
          "https://doi.org/10.1038/s41573-020-0080-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature06174", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014546686", 
          "https://doi.org/10.1038/nature06174"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-07-08", 
    "datePublishedReg": "2021-07-08", 
    "description": "BackgroundIn our earlier work, we identified microRNA-10b (miR10b) as a master regulator of the viability of metastatic tumor cells. This knowledge allowed us to design a miR10b-targeted therapeutic consisting of an\u00a0anti-miR10b\u00a0antagomir conjugated to ultrasmall iron oxide nanoparticles (MN), termed MN-anti-miR10b. In mouse models of breast cancer, we demonstrated that MN-anti-miR10b caused durable regressions of established metastases with no evidence of systemic toxicity. As a first step towards translating MN-anti-miR10b for the treatment of metastatic breast cancer, we needed to determine if MN-anti-miR10b, which is so effective in mice, will also accumulate in human metastases.ResultsIn this study, we devised a method to efficiently radiolabel MN-anti-miR10b with Cu-64 (64Cu) and evaluated the pharmacokinetics and biodistribution of the radiolabeled product at two different doses: a therapeutic dose, referred to as macrodose, corresponding to 64Cu-MN-anti-miR10b co-injected with non-labeled MN-anti-miR10b, and a tracer-level dose of 64Cu-MN-anti-miR10b, referred to as microdose. In addition, we evaluated the uptake of 64Cu-MN-anti-miR10b by metastatic lesions using both in vivo and ex vivo positron emission tomography\u2013magnetic resonance imaging (PET\u2013MRI). A comparable distribution of the therapeutic was observed after administration of a microdose or macrodose. Uptake of the therapeutic by metastatic lymph nodes, lungs, and bone was also demonstrated by PET\u2013MRI with a significantly higher PET signal than in the same organs devoid of metastatic lesions.ConclusionOur results demonstrate that PET\u2013MRI following a microdose injection of the agent will accurately reflect the innate biodistribution of the therapeutic. The tools developed in the present study lay the groundwork for the clinical testing of MN-anti-miR10b and other similar therapeutics in patients with cancer.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12645-021-00089-5", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7519547", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2481655", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.9656121", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7725239", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2671427", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.6664260", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1042272", 
        "issn": [
          "1868-6958", 
          "1868-6966"
        ], 
        "name": "Cancer Nanotechnology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "12"
      }
    ], 
    "keywords": [
      "metastatic breast cancer", 
      "metastatic lesions", 
      "breast cancer", 
      "metastatic lymph nodes", 
      "tomography-magnetic resonance imaging", 
      "positron emission tomography-magnetic resonance imaging", 
      "PET-MRI", 
      "metastatic tumor cells", 
      "durable regression", 
      "lymph nodes", 
      "therapeutic dose", 
      "murine model", 
      "mouse model", 
      "experimental cancer", 
      "clinical testing", 
      "systemic toxicity", 
      "resonance imaging", 
      "ConclusionOur results", 
      "higher PET signal", 
      "similar therapeutics", 
      "cancer", 
      "tumor cells", 
      "different doses", 
      "human metastases", 
      "microRNA-10b", 
      "miR10b", 
      "lesions", 
      "same organ", 
      "PET signal", 
      "metastasis", 
      "microdose", 
      "dose", 
      "comparable distribution", 
      "present study", 
      "biodistribution", 
      "therapeutics", 
      "master regulator", 
      "patients", 
      "lung", 
      "pharmacokinetics", 
      "mice", 
      "ResultsIn", 
      "doses", 
      "administration", 
      "uptake", 
      "antagomir", 
      "Cu-64", 
      "BackgroundIn", 
      "treatment", 
      "injection", 
      "bone", 
      "vivo", 
      "organs", 
      "study", 
      "toxicity", 
      "microdosing", 
      "imaging", 
      "delivery", 
      "cells", 
      "regression", 
      "agents", 
      "evidence", 
      "radiolabeling", 
      "testing", 
      "viability", 
      "regulator", 
      "addition", 
      "knowledge", 
      "iron oxide nanoparticles", 
      "nodes", 
      "model", 
      "results", 
      "first step", 
      "tool", 
      "method", 
      "groundwork", 
      "products", 
      "consisting", 
      "oxide nanoparticles", 
      "signals", 
      "distribution", 
      "step", 
      "nanoparticles", 
      "work", 
      "earlier work"
    ], 
    "name": "Radiolabeling and PET\u2013MRI microdosing of the experimental cancer therapeutic, MN-anti-miR10b, demonstrates delivery to metastatic lesions in a murine model of metastatic breast cancer", 
    "pagination": "16", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1139555164"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12645-021-00089-5"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "34531932"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12645-021-00089-5", 
      "https://app.dimensions.ai/details/publication/pub.1139555164"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:37", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_881.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12645-021-00089-5"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12645-021-00089-5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12645-021-00089-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12645-021-00089-5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12645-021-00089-5'


 

This table displays all metadata directly associated to this object as RDF triples.

254 TRIPLES      22 PREDICATES      122 URIs      103 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12645-021-00089-5 schema:about anzsrc-for:11
2 anzsrc-for:1112
3 schema:author N291f8ec9f0334d098b18651bef873a73
4 schema:citation sg:pub.10.1007/s00259-005-1906-9
5 sg:pub.10.1007/s00441-004-0884-8
6 sg:pub.10.1007/s40262-019-00769-x
7 sg:pub.10.1038/nature06174
8 sg:pub.10.1038/nbt.1618
9 sg:pub.10.1038/nm1486
10 sg:pub.10.1038/nrc.2016.25
11 sg:pub.10.1038/onc.2012.173
12 sg:pub.10.1038/s41573-020-0080-x
13 sg:pub.10.1038/s41598-018-23317-2
14 sg:pub.10.1038/srep45060
15 schema:datePublished 2021-07-08
16 schema:datePublishedReg 2021-07-08
17 schema:description BackgroundIn our earlier work, we identified microRNA-10b (miR10b) as a master regulator of the viability of metastatic tumor cells. This knowledge allowed us to design a miR10b-targeted therapeutic consisting of an anti-miR10b antagomir conjugated to ultrasmall iron oxide nanoparticles (MN), termed MN-anti-miR10b. In mouse models of breast cancer, we demonstrated that MN-anti-miR10b caused durable regressions of established metastases with no evidence of systemic toxicity. As a first step towards translating MN-anti-miR10b for the treatment of metastatic breast cancer, we needed to determine if MN-anti-miR10b, which is so effective in mice, will also accumulate in human metastases.ResultsIn this study, we devised a method to efficiently radiolabel MN-anti-miR10b with Cu-64 (64Cu) and evaluated the pharmacokinetics and biodistribution of the radiolabeled product at two different doses: a therapeutic dose, referred to as macrodose, corresponding to 64Cu-MN-anti-miR10b co-injected with non-labeled MN-anti-miR10b, and a tracer-level dose of 64Cu-MN-anti-miR10b, referred to as microdose. In addition, we evaluated the uptake of 64Cu-MN-anti-miR10b by metastatic lesions using both in vivo and ex vivo positron emission tomography–magnetic resonance imaging (PET–MRI). A comparable distribution of the therapeutic was observed after administration of a microdose or macrodose. Uptake of the therapeutic by metastatic lymph nodes, lungs, and bone was also demonstrated by PET–MRI with a significantly higher PET signal than in the same organs devoid of metastatic lesions.ConclusionOur results demonstrate that PET–MRI following a microdose injection of the agent will accurately reflect the innate biodistribution of the therapeutic. The tools developed in the present study lay the groundwork for the clinical testing of MN-anti-miR10b and other similar therapeutics in patients with cancer.
18 schema:genre article
19 schema:inLanguage en
20 schema:isAccessibleForFree true
21 schema:isPartOf N82796c17b09e4ed885262404895b7b48
22 N8f3b1a9a5c014042aa0c0a64b95d0427
23 sg:journal.1042272
24 schema:keywords BackgroundIn
25 ConclusionOur results
26 Cu-64
27 PET signal
28 PET-MRI
29 ResultsIn
30 addition
31 administration
32 agents
33 antagomir
34 biodistribution
35 bone
36 breast cancer
37 cancer
38 cells
39 clinical testing
40 comparable distribution
41 consisting
42 delivery
43 different doses
44 distribution
45 dose
46 doses
47 durable regression
48 earlier work
49 evidence
50 experimental cancer
51 first step
52 groundwork
53 higher PET signal
54 human metastases
55 imaging
56 injection
57 iron oxide nanoparticles
58 knowledge
59 lesions
60 lung
61 lymph nodes
62 master regulator
63 metastasis
64 metastatic breast cancer
65 metastatic lesions
66 metastatic lymph nodes
67 metastatic tumor cells
68 method
69 miR10b
70 mice
71 microRNA-10b
72 microdose
73 microdosing
74 model
75 mouse model
76 murine model
77 nanoparticles
78 nodes
79 organs
80 oxide nanoparticles
81 patients
82 pharmacokinetics
83 positron emission tomography-magnetic resonance imaging
84 present study
85 products
86 radiolabeling
87 regression
88 regulator
89 resonance imaging
90 results
91 same organ
92 signals
93 similar therapeutics
94 step
95 study
96 systemic toxicity
97 testing
98 therapeutic dose
99 therapeutics
100 tomography-magnetic resonance imaging
101 tool
102 toxicity
103 treatment
104 tumor cells
105 uptake
106 viability
107 vivo
108 work
109 schema:name Radiolabeling and PET–MRI microdosing of the experimental cancer therapeutic, MN-anti-miR10b, demonstrates delivery to metastatic lesions in a murine model of metastatic breast cancer
110 schema:pagination 16
111 schema:productId N62d33f78f66549a0a3f36bfd77b03c0b
112 N78f8c7135d574ae7aea0ceef77d1b8f4
113 Nb45ad3a32f26427d95ac63baa211dc86
114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1139555164
115 https://doi.org/10.1186/s12645-021-00089-5
116 schema:sdDatePublished 2022-05-20T07:37
117 schema:sdLicense https://scigraph.springernature.com/explorer/license/
118 schema:sdPublisher Nadea5ebcfcb44f45b1edda5e1848ac9c
119 schema:url https://doi.org/10.1186/s12645-021-00089-5
120 sgo:license sg:explorer/license/
121 sgo:sdDataset articles
122 rdf:type schema:ScholarlyArticle
123 N291f8ec9f0334d098b18651bef873a73 rdf:first sg:person.0723770445.72
124 rdf:rest N4f86485860cb40d78093fbf38b91d704
125 N4e6c7a538ea64159a928c673351edc4c rdf:first sg:person.01231046160.11
126 rdf:rest Nc63ff70db62949fe8beb53efa41c0a5a
127 N4f86485860cb40d78093fbf38b91d704 rdf:first sg:person.0773665622.07
128 rdf:rest N6a82220b0c7f4c1ba200f9cce4a7e43c
129 N62d33f78f66549a0a3f36bfd77b03c0b schema:name dimensions_id
130 schema:value pub.1139555164
131 rdf:type schema:PropertyValue
132 N64b8cbb6cdfd46888e17f6e2add95b08 rdf:first sg:person.01005511751.27
133 rdf:rest Ne88d1dc6a650446594298185655ebe8d
134 N6a82220b0c7f4c1ba200f9cce4a7e43c rdf:first sg:person.0614535026.51
135 rdf:rest N4e6c7a538ea64159a928c673351edc4c
136 N78f8c7135d574ae7aea0ceef77d1b8f4 schema:name doi
137 schema:value 10.1186/s12645-021-00089-5
138 rdf:type schema:PropertyValue
139 N82796c17b09e4ed885262404895b7b48 schema:volumeNumber 12
140 rdf:type schema:PublicationVolume
141 N8f3b1a9a5c014042aa0c0a64b95d0427 schema:issueNumber 1
142 rdf:type schema:PublicationIssue
143 Nac4ed7c08b9a49f58d3902329112a013 rdf:first sg:person.0727346345.99
144 rdf:rest N64b8cbb6cdfd46888e17f6e2add95b08
145 Nadea5ebcfcb44f45b1edda5e1848ac9c schema:name Springer Nature - SN SciGraph project
146 rdf:type schema:Organization
147 Nb45ad3a32f26427d95ac63baa211dc86 schema:name pubmed_id
148 schema:value 34531932
149 rdf:type schema:PropertyValue
150 Nc63ff70db62949fe8beb53efa41c0a5a rdf:first sg:person.01041376113.53
151 rdf:rest Nac4ed7c08b9a49f58d3902329112a013
152 Ne88d1dc6a650446594298185655ebe8d rdf:first sg:person.01321624134.54
153 rdf:rest rdf:nil
154 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
155 schema:name Medical and Health Sciences
156 rdf:type schema:DefinedTerm
157 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
158 schema:name Oncology and Carcinogenesis
159 rdf:type schema:DefinedTerm
160 sg:grant.2481655 http://pending.schema.org/fundedItem sg:pub.10.1186/s12645-021-00089-5
161 rdf:type schema:MonetaryGrant
162 sg:grant.2671427 http://pending.schema.org/fundedItem sg:pub.10.1186/s12645-021-00089-5
163 rdf:type schema:MonetaryGrant
164 sg:grant.6664260 http://pending.schema.org/fundedItem sg:pub.10.1186/s12645-021-00089-5
165 rdf:type schema:MonetaryGrant
166 sg:grant.7519547 http://pending.schema.org/fundedItem sg:pub.10.1186/s12645-021-00089-5
167 rdf:type schema:MonetaryGrant
168 sg:grant.7725239 http://pending.schema.org/fundedItem sg:pub.10.1186/s12645-021-00089-5
169 rdf:type schema:MonetaryGrant
170 sg:grant.9656121 http://pending.schema.org/fundedItem sg:pub.10.1186/s12645-021-00089-5
171 rdf:type schema:MonetaryGrant
172 sg:journal.1042272 schema:issn 1868-6958
173 1868-6966
174 schema:name Cancer Nanotechnology
175 schema:publisher Springer Nature
176 rdf:type schema:Periodical
177 sg:person.01005511751.27 schema:affiliation grid-institutes:grid.32224.35
178 schema:familyName Medarova
179 schema:givenName Zdravka
180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01005511751.27
181 rdf:type schema:Person
182 sg:person.01041376113.53 schema:affiliation grid-institutes:grid.32224.35
183 schema:familyName Zhou
184 schema:givenName Iris
185 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041376113.53
186 rdf:type schema:Person
187 sg:person.01231046160.11 schema:affiliation grid-institutes:grid.32224.35
188 schema:familyName Rotile
189 schema:givenName Nicholas
190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01231046160.11
191 rdf:type schema:Person
192 sg:person.01321624134.54 schema:affiliation grid-institutes:grid.32224.35
193 schema:familyName Yoo
194 schema:givenName Byunghee
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01321624134.54
196 rdf:type schema:Person
197 sg:person.0614535026.51 schema:affiliation grid-institutes:grid.32224.35
198 schema:familyName Pantazopoulos
199 schema:givenName Pamela
200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614535026.51
201 rdf:type schema:Person
202 sg:person.0723770445.72 schema:affiliation grid-institutes:grid.32224.35
203 schema:familyName Le Fur
204 schema:givenName Mariane
205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723770445.72
206 rdf:type schema:Person
207 sg:person.0727346345.99 schema:affiliation grid-institutes:grid.32224.35
208 schema:familyName Caravan
209 schema:givenName Peter
210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727346345.99
211 rdf:type schema:Person
212 sg:person.0773665622.07 schema:affiliation grid-institutes:grid.32224.35
213 schema:familyName Ross
214 schema:givenName Alana
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0773665622.07
216 rdf:type schema:Person
217 sg:pub.10.1007/s00259-005-1906-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045504659
218 https://doi.org/10.1007/s00259-005-1906-9
219 rdf:type schema:CreativeWork
220 sg:pub.10.1007/s00441-004-0884-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053242624
221 https://doi.org/10.1007/s00441-004-0884-8
222 rdf:type schema:CreativeWork
223 sg:pub.10.1007/s40262-019-00769-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1113786189
224 https://doi.org/10.1007/s40262-019-00769-x
225 rdf:type schema:CreativeWork
226 sg:pub.10.1038/nature06174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014546686
227 https://doi.org/10.1038/nature06174
228 rdf:type schema:CreativeWork
229 sg:pub.10.1038/nbt.1618 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024956742
230 https://doi.org/10.1038/nbt.1618
231 rdf:type schema:CreativeWork
232 sg:pub.10.1038/nm1486 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008398170
233 https://doi.org/10.1038/nm1486
234 rdf:type schema:CreativeWork
235 sg:pub.10.1038/nrc.2016.25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050875196
236 https://doi.org/10.1038/nrc.2016.25
237 rdf:type schema:CreativeWork
238 sg:pub.10.1038/onc.2012.173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050681272
239 https://doi.org/10.1038/onc.2012.173
240 rdf:type schema:CreativeWork
241 sg:pub.10.1038/s41573-020-0080-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1130660527
242 https://doi.org/10.1038/s41573-020-0080-x
243 rdf:type schema:CreativeWork
244 sg:pub.10.1038/s41598-018-23317-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101582214
245 https://doi.org/10.1038/s41598-018-23317-2
246 rdf:type schema:CreativeWork
247 sg:pub.10.1038/srep45060 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084132908
248 https://doi.org/10.1038/srep45060
249 rdf:type schema:CreativeWork
250 grid-institutes:grid.32224.35 schema:alternateName Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA
251 MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA
252 schema:name Institute for Innovation in Imaging, Massachusetts General Hospital, 02129, Boston, MA, USA
253 MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA
254 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...