Despite dramatic advances in molecular and imaging technologies and nearly 1 5,000 articles on schizophrenia and depression (S&D) there are few novel treatments. We think this is because of three major “bottle-necks”: a lack of etiologically-driven or pathophysiologically-accurate animal models; a lack of tests that provide indication of efficacy in healthy volunteers; and the reliance of clinical trials on DSM categories which provide a collection of biologically heterogeneous patients. Our broad working hypothesis is that a focus on cross-species endophenotypes, testing in healthy volunteers and finding biologically-homogenous groups of patients will overcome existing limitations in target identification, early triage and clinical trials. To implement this idea six leading European institutions and two SMEs will partner with the EFPIA to: a) develop animal models that carry confirmed genetic risks, and in these animals to focus on cross-species endophenotypes (e.g., cognitive function, electrophysiology) to facilitate new drug discovery; b) validate the use of fMRI-based endopheno types in genetically-selected healthy volunteers and patients as early and surrogate markers for efficacy; and to combine this with PET approaches for plasma-kinetics to brain-dynamics modelling to provide guidance regarding optimal clinical trial design; and c) identify pharmacogenetic and multi-omic biomarkers that can be used to stratify patients within an umbrella DSM-diagnosis, thus allowing for ) targeted clinical trials, individualized treatment and back-translation of subgroup-specific biomarkers into preclinical drug discovery. To increase the chance of a breakthrough we will implement new analytical approaches (e.g. support vector machine learning algorithms; Bayesian analyses) and will
actively collaborate with other ongoing international efforts (esp. the Biomarkers Consortium, NIH).
Field of science
- /medical and health sciences/clinical medicine/psychiatry/schizophrenia
- /natural sciences/computer and information sciences/artificial intelligence/machine learning
Call for proposal
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