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3DCanPredict SIGNED

Predicting clinical response to anticancer drugs using 3D-bioprinted tumor models for personalized therapy

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 3DCanPredict project word cloud

Explore the words cloud of the 3DCanPredict project. It provides you a very rough idea of what is the project "3DCanPredict" about.

vascularized    biophysics    cancer    evaluation    structure    create    rapid    predict    functional    heavily    attractive    interactions    platform    cells    therapies    printed    cultured    drugs    drug    preclinical    metastasis    time    invest    2d    predictive    predicting    limit    patients    tumor    vessels    potentially    model    offers    brain    tools    personalized    co    responsiveness    successful    biopsy    ct    connected    techniques    dishes    flow    resembling    companies    microenvironment    3d    mixed    scaffold    types    business    generating    pathological    pharmaceutical    stromal    biotech    significantly    toxicity    reproducible    power    tissue    replacing    suits    societal    benefit    save    mimic    cell    decrease    resemble    hurdle    patient    organ    hence    hydrogels    serum    screening    mechanical    translational    consist    solely    poc    indicate    animal    standard    progression    treatment    constructed    reduce    basis    tumors    origin    strategies    clinical    form    mri    models    pump    bioprinted    adjacent    critical    powerful    physio    library    ecosystem    anticancer    plastic    grow    scans    designed   

Project "3DCanPredict" data sheet

The following table provides information about the project.

Coordinator
TEL AVIV UNIVERSITY 

Organization address
address: RAMAT AVIV
city: TEL AVIV
postcode: 69978
website: http://www.tau.ac.il/

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Israel [IL]
 Total cost 0 €
 EC max contribution 150˙000 € (0%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-PoC
 Funding Scheme ERC-POC-LS
 Starting year 2019
 Duration (year-month-day) from 2019-09-01   to  2021-02-28

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TEL AVIV UNIVERSITY IL (TEL AVIV) coordinator 150˙000.00

Map

 Project objective

Predicting clinical response to novel and existing anticancer drugs remains a major hurdle for successful cancer treatment. Studies indicate that the tumor ecosystem, resembling an organ-like structure, can limit the predictive power of current therapies that were evaluated solely on tumor cells. The interactions of tumor cells with their adjacent microenvironment are required to promote tumor progression and metastasis, determining drug responsiveness. Such interactions do not form in standard research techniques, where cancer cells grow on 2D plastic dishes. Hence, there is a need to develop new cancer models that better mimic the physio-pathological conditions of tumors. Here, we create 3D-bioprinted tumor models based on a library of hydrogels we developed as scaffold for different tumor types, designed according to the mechanical properties of the tissue of origin. As PoC, we bioprinted a vascularized 3D brain tumor model from brain tumor cells co-cultured with stromal cells and mixed with our hydrogels, that resemble the biophysics of the tumor and its microenvironment. Our patient-derived models consist of cells from a biopsy, constructed according to CT/MRI scans, and include functional vessels allowing for patients' serum to flow when connected to a pump. These models will facilitate reproducible, reliable and rapid results, determining which treatment suits best the specific patient's tumor. Taken together, this 3D-printed model could be the basis for potentially replacing cell and animal models. We predict that this powerful platform will be used in translational research for preclinical evaluation of new therapies and for clinical drug screening, which will save critical time, reduce toxicity and significantly decrease costs generating a major societal benefit. Our platform offers a highly attractive business case, as pharmaceutical and biotech companies heavily invest in preclinical predictive tools for novel personalized drug screening strategies.

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The information about "3DCANPREDICT" are provided by the European Opendata Portal: CORDIS opendata.

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