In November 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) issued a revised guidance for Good Clinical Practice (GCP): ICH E6 R2. This guidance laid out a set of guidelines that trial sponsors should consider as they adopt risk-based approaches to trial monitoring and quality management. The guidance called for routine review of data collected, identifying data anomalies, outliers, and deviations, using statistical methods to detect trends, risks, and inconsistencies in data and that all decisions and actions from a centralized monitoring process should be audited throughout the course of the study. Since this guidance, nearly all big and mid-size pharma have begun to embrace the importance of applying centralized data monitoring strategies and techniques in line with this guidance.
But supporting such strategies continues to be a challenge.
Clinical teams are still locked in a painful cycle of static report requests, lengthy clinical data review cycles, and trouble with database infrastructure too limited to handle the data load. Clinical teams also struggle to unify and analyze data across domains, including adverse events (AEs), labs, concomitant medications, demography, and exposure, among others. Incomplete clinical data access disables the ability to see “at risk” patients and make optimal decisions on safety and efficacy, early in the process.