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MEDIVAC TERMINATED

Machine learning software to design personalized neoantigen vaccines tailored to specific vaccine delivery systems

Total Cost €

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EC-Contrib. €

0

Partnership

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 MEDIVAC project word cloud

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

oi    akey    status    lab    once    immunogenic    samples    destroying    identification    sensitive    fed    avenue    prediction    regarded    patient    machine    fuel    data    intensive    oncoimmunity    victims    exists    therapy    immunotherapies    framework    ngs    expensive    grail    bridge    fight    performance    vaccine    wet    trained    roll    tumor    constructs    subsequently    solution    mass    feared    cure    neoantigen    perform    coupled    company    spectroscopy    medicine    engineering    consuming    prohibits    fold    competitive    nobel    revolutionize    dna    age    diseases    immunotherapy    core    meets    unmatched    accuracy    opportunity    delivering    considering    personalisation    immense    optimise    datasets    time    winning    readily    identifies    business    healthy    reached    cancer    generation    despite    serve    utilize    regardless    arguably    cornerstone    fact    lives    personalised    learning    clinically    candidates    proprietary    neoantigens    algorithms    gap    holy    sequencing    tissue    rna    scalable    public    untapping    vaccines   

Project "MEDIVAC" data sheet

The following table provides information about the project.

Coordinator
ONCOIMMUNITY AS 

Organization address
address: ULLERNCHAUSSEEN 64
city: OSLO
postcode: 379
website: n.a.

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 Norway [NO]
 Total cost 3˙143˙437 €
 EC max contribution 2˙200˙406 € (70%)
 Programme 1. H2020-EU.3. (PRIORITY 'Societal challenges)
2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs)
3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
 Code Call H2020-SMEInst-2018-2020-2
 Funding Scheme SME-2
 Starting year 2019
 Duration (year-month-day) from 2019-05-01   to  2021-10-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ONCOIMMUNITY AS NO (OSLO) coordinator 2˙200˙406.00

Map

 Project objective

Cancer is arguably the most feared of all diseases, destroying lives regardless of the age of its victims. Immunotherapies are currently regarded as the most promising avenue to delivering the holy grail of medicine i.e. providing a cure for cancer. Despite their Nobel-winning status, personalisation of immunotherapies remains akey challenge to which no cost-effective solution currently exists. Current methods for identifying the immunogenic neoantigens required to design patient-specific cancer vaccines typically utilize next generation sequencing (NGS) analysis of DNA and RNA coupled with wet lab methods (e.g. spectroscopy). However, these approaches are time consuming to perform, expensive and not readily scalable–which currently prohibits the mass roll-out of personalised cancer vaccines.

Despite the fact that intensive research has been dedicated to developing prediction algorithms which can identify immunogenic neoantigens from NGS data from tumor samples, their accuracy has not yet reached a competitive performance compared to wet lab methods. To bridge this gap, OncoImmunity (OI) has developed a comprehensive machine learning framework, trained using public and proprietary datasets to optimise performance. Once fed with patient NGS data from healthy and tumor tissue, OI’s algorithms identifies the most clinically relevant neoantigen candidates, with an unmatched accuracy (4-fold increase of prediction accuracy), which can be subsequently engineering into personalised vaccine cancer constructs.

Considering the potential of personalised therapy within cancer immunotherapy, OI’s core technology meets all the requirements to become a key enabling technology, providing cost-effective, scalable and sensitive identification of clinically relevant targets for vaccine development. Thus, it has the potential to serve as a cornerstone to revolutionize the fight against cancer – while untapping an immense business opportunity to fuel our company’s growth.

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

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