Title |
Theranostic barcoded nanoparticles for personalized cancer medicine
|
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Published in |
Nature Communications, November 2016
|
DOI | 10.1038/ncomms13325 |
Pubmed ID | |
Authors |
Zvi Yaari, Dana da Silva, Assaf Zinger, Evgeniya Goldman, Ashima Kajal, Rafi Tshuva, Efrat Barak, Nitsan Dahan, Dov Hershkovitz, Mor Goldfeder, Janna Shainsky Roitman, Avi Schroeder |
Abstract |
Personalized medicine promises to revolutionize cancer therapy by matching the most effective treatment to the individual patient. Using a nanoparticle-based system, we predict the therapeutic potency of anticancer medicines in a personalized manner. We carry out the diagnostic stage through a multidrug screen performed inside the tumour, extracting drug activity information with single cell sensitivity. By using 100 nm liposomes, loaded with various cancer drugs and corresponding synthetic DNA barcodes, we find a correlation between the cell viability and the drug it was exposed to, according to the matching barcodes. Based on this screen, we devise a treatment protocol for mice bearing triple-negative breast-cancer tumours, and its results confirm the diagnostic prediction. We show that the use of nanotechnology in cancer care is effective for generating personalized treatment protocols. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 21% |
Spain | 2 | 8% |
Israel | 2 | 8% |
France | 2 | 8% |
Ireland | 1 | 4% |
Greece | 1 | 4% |
Switzerland | 1 | 4% |
Serbia | 1 | 4% |
Germany | 1 | 4% |
Other | 0 | 0% |
Unknown | 8 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 21 | 88% |
Scientists | 3 | 13% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | <1% |
Turkey | 1 | <1% |
Australia | 1 | <1% |
Unknown | 205 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 58 | 28% |
Researcher | 30 | 14% |
Student > Master | 25 | 12% |
Student > Bachelor | 14 | 7% |
Student > Doctoral Student | 12 | 6% |
Other | 34 | 16% |
Unknown | 36 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 32 | 15% |
Chemistry | 30 | 14% |
Agricultural and Biological Sciences | 22 | 11% |
Engineering | 19 | 9% |
Pharmacology, Toxicology and Pharmaceutical Science | 18 | 9% |
Other | 42 | 20% |
Unknown | 46 | 22% |