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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.

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

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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