IMI2 Project: #821528
Name: “Neuroderisk” (Neurotoxicity De-Risking in Preclinical Drug Discovery)
Start: 1 Mar 2019
End: 31 Aug 2022
The adverse effects of pharmaceuticals on the central or peripheral nervous systems are poorly predicted by the current in vitro and in vivo preclinical studies performed during Research and Development (R&D) process. Therefore, increasing the predictivity of the preclinical toolbox is a clear need, and would benefit to human volunteers/patients (safer drugs) and Pharmaceutical Industry (reduced attrition). By combining top level scientists in neurobiology/toxicology with successful software developers, the NeuroDeRisk | Neurotoxicity De-Risking in Preclinical Drug Discovery Consortium will aim at tackling three of the most challenging adverse effects: seizures, psychological/psychiatric changes, and peripheral neuropathies. Our approach will be global, starting with an in-depth evaluation of knowledge on mechanisms of neurotoxicity (biological pathways as well as chemical structures and descriptors, using in particular historical data). Then we will search for innovative tools, assays and studies covering in silico, in vitro and in vivo approaches. This will include in particular: molecular design platform, artificial intelligence, human induced pluripotent stem cells, blood-brain-barrier models, immunohistochemistry, transcriptomics, RNA editing biomarkers, video-monitoring and telemetry of animals, pharmacokinetics, etc. The last step will aim at combining these tools in an integrated platform for better risk-assessment and decision-points throughout R&D process, and thus better protection of human volunteers and patients.
Together with EFPIA members Sanofi, Novartis, MSD, Pfizer UCB Biopharma, AZ, Fujifilm Cellular Dynamics and a group of leading EU academic institutions, Biovista will work to identify novel mechanisms of neurotoxicity using historical data to inform the development of in vitro and in vivo models. Additionally, it will further develop its own AI-platform supporting the prediction of AEs and decision-support throughout the R&D process.