DATA QUALITY IN PRECLINICAL RESEARCH AND DEVELOPMENT

Topic description

Specific Challenge:

Reproducibility and relevance of research findings represent the pillars of the scientific method. For drug development, robust data and scientific rigor are key drivers for decision making, determining patent strength, time-to-market and consequently availability of new treatments to patients. Substantial evidence has accumulated that robustness, rigor and validity of research data can be problematic. While the issues at hand concern all areas of Research & Development, the impact of unreliable data, as well as the potential benefit of intervention, is greatest in areas already facing additional challenges, such as Neuroscience.

Starting with a first pilot project focused on Neuroscience and Safety, the goal is to advance the quality and efficiency of Discovery R&D data. This action should provide evidence/data to develop quality criteria for new and/or improved preclinical tests, develop consensus quality management recommendations in non-regulated R&D to enhance the quality of decisions made based on experimental data, and by developing an educational course on data quality as a major contributor to enhance the quality culture in preclinical research. The action should also contribute to the development of a proficiency test system (ring tests) in preclinical research and to the implementation and testing of the quality principles developed by the consortium in day-to-day research settings, both in academia and industry, to achieve maximal cross fertilization and cultural exchange.

These efforts can be expected to result in an improvement in the data quality of pre-clinical studies via the delivery of reliable and reproducible models with harmonized and standardized protocols and procedures. There will also be a significant contribution to the 3Rs (replacement, reduction and refinement) in the use of experimental animals in preclinical research, to IP protection and regulatory success by ensuring validity and traceability of data. Through dissemination of the scientific quality principles a cultural change and ripple effect should be triggered. Accreditation, consensus quality management recommendations, and an education module on data quality will further facilitate the implementation of innovation from academia and SMEs into the R&D process and will obviate the need for duplicate assessment of external partners.