The inability of pre-clinical studies to predict clinical efficacy is a major bottleneck in drug development. In severe asthma this bottleneck results from: a lack of useful and validated biomarkers, underperforming pre-clinical models, inadequate and incomplete sub-phenotyping, and insufficient disease understanding.
The use of biomarker profiles comprised of various types of high-dimensional data, integrated with an innovative systems biology approach into distinct phenotype handprints, will enable significantly better prediction of therapeutic efficacy than single or even clustered biomarkers of one data type and will identify novel targets.
U-BIOPRED proposes a staged-strategy:
I)Generating an international consensus on diagnostic criteria for severe asthma.
II)Unbiased discovery of sub-phenotypes of severe asthma in adult and paediatric patient cohorts using biomarker profiles (“fingerprints”) and systems biology to define “phenotype/handprints”.
III)Validating the handprints for exacerbations, disease progression, and experimental challenges.
IV)Using the handprints to improve human/animal in vivo and ex vivo models.
V)Establishing the responsiveness and predictability of the handprints in gold standard and experimental therapeutic interventions.
VI)Refinement of the diagnostic criteria and definition of severe asthma sub-phenotypes by incorporating the handprints, thereby enabling more focused drug development and faster delivery proof of concept for novel drugs.
VII)Education, training and dissemination through a stakeholder platform and academic-industrial exchange, with potential for further growth as appropriate.
We will use a novel systems biology approach to integrate high dimensional data from invasive, non-invasive and patient-reported outcomes. All studies will include academic and EFPIA partners as active contributors.
Field of science
- /medical and health sciences/clinical medicine/pneumology/asthma
Call for proposal
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Funding SchemeJTI-CP-IMI - Joint Technology Initiatives - Collaborative Project (IMI)