Academic Leads: Prof. Sabine Bahn, The University of Cambridge & Dr. Paul Rodgers, Psynova Neurotech Ltd, UK
EFPIA Lead: Dr. Christian Czech, F. Hoffmann-La Roche AG, Switzerland
There is a major synergy between academia and industry in this workpackage. The overarching goal of WP09 is to provide tools that enable the assessment of a wide selection of potential preclinical models of schizophrenia and, subsequently, depression in terms of the overlap of biomarker profiles with the clinical condition. This is of vital importance to the drug discovery industry as it is clear that current preclinical models of psychiatric disease have been very poor at enabling the development of truly novel therapeutics.
This collaboration between Psynova, the Bahn lab, the academics leading other WPs and EFPIA members enables a close interaction between experts with mutually complementary capabilities and resources. Psynova and the Bahn lab have biomarker discovery, validation and assay development expertise, as well as an existing database of profiling data from clinically defined cohorts (e.g. protein, RNA & metabolomic profiling of prefrontal cortex from schizophrenia patients and carefully matched controls; extensive serum profiling data from multiplexed ELISA assays in drug naive, first onset schizophrenic patients as well as acutely ill depressed patients before and after treatment, etc) relevant to NEWMEDS. EFPIA members bring considerable expertise in using preclinical models for drug discovery. They have worked together to generate a collection of the most interesting preclinical models actively being used by EFPIA members currently and a plan to compare their underlying molecular signatures with those observed in the disease state. This will lead to more insight about which aspects of the disease are best recapitulated in which models and allow for a tailoring of models depending on the goals of the drug developer. The use of similar models from different EFPIA members and from both mice and rats will add to the robustness of the data if it is possible to show similar biomarker signatures from these models. Appealingly, this strategy also provided the tools to allow for interrogation of the wealth of new genetic data appearing in schizophrenia and depression which will spur the development of many new genetically defined preclinical models in the coming years. Once the best models are identified, the biomarker panels also will be used to monitor response to existing and novel drug candidates. In particular, this approach should be very useful in predicting differential efficacy between compounds – a major gap in current models.
Finally, there are also significant synergies with other workpackages in NEWMEDS. Preclinical models prioritised in WP09 will also be studied in other WPs, enabling better translation of clinical endpoints and symptoms (via correlated electrophysiological measures, MRI, metabolism and biomarker changes, etc) between human and preclinical models. Similar combined analysis of the effects on novel drug intervention will aid the development of new therapies targeting the features of the diseases least efficiently addressed by current models (e.g. targeting negative symptoms/cognitive dysfunction in schizophrenia rather than positive symptoms; or looking for more rapid efficacy in antidepressant treatments rather than mechanisms with delayed onset of action).
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