According to the syllabus, the course will be set up with six modules:
Module 1: Overview and history of the FDA
Module 2: Drug development and approval
Module 3: Drug pricing in the United States
Module 4: Marketing strategies
Module 5: Post-approval evaluation
Module 6: Emerging medical technologies
Data Integrity definitely continues to be a theme, and I agree that we are seeing a growing trend around process validation. I also think root cause investigations was a theme of 2018 that we are going to be seeing a lot more of.
The FDA has announced a pilot program to “propose explicit established conditions (ECs) as part of an original new drug application (NDA), abbreviated new drug application (ANDA), biologics license application (BLA), or as a prior approval supplement (PAS) to any of these.”
With data integrity on everyone’s mind the last few years, the role of a data steward is being more and more discussed. Putting aside my amusement on the proliferation of stewards and champions across our quality systems, the idea of data stewards is a good one.
Data steward is someone from the business who handle master data. It is not an IT role, as a good data steward will truly be invested in how the data is being used, managed and groomed. The data steward is responsible and accountable for how data enters the system and ensure it adds value to the process.
The job revolves around, but is not limited to, the following questions:
Why is this particular data important to the organization?
How long should the particular records (data) be stored or kept?
Measurements to improve the quality of that analysis
Data stewards do this by providing:
Operational Oversight by overseeing the life cycle through defining and implementing policies and procedures for the day-to-day operational and administrative management of systems and data — including the intake, storage, processing, and transmission of data to internal and external systems. They are accountable to define and document data and terminology in a relevant glossary. This includes ensuring that each critical data element has a clear definition and is still in use.
Data quality, including evaluation and root cause analysis
Risk management, including retention, archival, and disposal requirements and ensuring compliance with internal policy and regulations.
With systems being made up of people, process and technology, the line between data steward and system owner is pretty vague. When a technology is linked to a single system or process it makes sense for them to be the same person (or team), for example a document management system. However, most technology platforms are across multiple systems or processes (for example an ERP or Quality Management System) and it is critical to look at the technology holistically as the data steward. I think we are all familiar with the problems that can be created by the same piece of data being treated differently between workflows in a technology platform.
As organizations evolve their data governance I think we will see the role of the data steward become more and more part of the standard quality toolbox, as the competencies are pretty similar.