Data Quality and Robustness (DQaR)
Several steps in drug discovery and development need to be compliant with established GxP-based quality requirements such as GLP toxicology, but analogous standards for non-regulated areas of drug discovery and target assessment are not available. A specialized set of quality guidelines is needed that specifically focuses on study design, unbiased conduct, statistical analysis and transparent reporting and that will support academic-industry interactions by aligning quality criteria in preclinical research.
Consequently, questions related to data quality are crucial for the success of translational projects and need to be addressed especially for decision-enabling processes.
The data quality questions highlight the importance of increasing the internal validity of key experiments, including crucial processes such as blinding and randomisation, appropriate statistical power analyses and primary endpoint definitions. They also emphasize the need to establish external validity by multiple independent replicates as well as several orthogonal technologies, which provide greater confidence and converging evidence for the therapeutic relevance of a target.
In addition, a major requirement to ensure robust research outcomes is that researchers routinely question reagent purity, authenticate cell lines, validate antibodies and animal models, and include appropriate controls when planning and conducting an experiment.
