Objective
Our programme will develop an innovative in-silico based approach to improve the efficacy and precision of drug repurposing trials. We have chosen drug repurposing as it has the shortest time for clinical validation and translation. Validation of all putatively de novo discovered drug repositionings within the time-frame of this programme would be unrealistic. To improve efficacy and precision, and to adopt our computer simulation parameters and models, we choose a systems medicine based in-silico approach that identifies mechanistically related disease phenotypes and, as a result, a virtual patient cohort. We then validate this in-silico drug repurposing via high precision clinical trials in patients with cerebro-cardiovascular phenotypes stratified using an exclusive mechanistic biomarker panel. We thus innovate two biomedical product classes, drugs and diagnostics. With this we will establish generally applicable in silico trials for other mechanistically related or defined disease phenotypes, for which size, duration, and risks will be reduced and precision increased. This generates rapid patient benefit, reduces drug development costs as well as risks, and enhances industrial competitiveness. Scientifically, we will contribute to reducing the uncertainty and vagueness of many of our current disease definitions that describe a symptom or apparent phenotype in an organ rather than defining diseases mechanistically as disturbance of self-regulation equilibria of biomolecular processes. Finally, we will reduce animal experimentation and animal numbers in general by applying a preclinical randomised confirmatory trial (pRCTs) concept and preclinical systematic reviews and meta-analyses facilitated by our open access pre-clinicaltrials.org platform, a pendant to clinicaltrials.gov.
Fields of science
- medical and health sciencesbasic medicinepharmacology and pharmacydrug discovery
- medical and health sciencesbasic medicineneurologystroke
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- medical and health sciencesclinical medicinecardiology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
RIA - Research and Innovation action
Coordinator
6200 MD Maastricht
Netherlands
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Participants (12)
Participation ended
5230 Odense M
NE1 7RU Newcastle Upon Tyne
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45147 Essen
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30625 Hannover
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3584 CX Utrecht
6020 Innsbruck
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GU24 8EB Chobham
82256 Furstenfeldbruck
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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80333 Muenchen
6108 Halle
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20148 Hamburg
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