Traditional Quality Assurance relies heavily on manually conducted audits as a means of detecting quality issues and is based on an annual Quality risk strategy. This is extremely resource intensive and provides a point-in-time snapshot of Quality for the limited scope chosen. The time gap between finalizing an annual risk strategy and the conduct of some of the last audits in an annual audit plan means potentially detecting issues up to 18 months late. This late detection of Quality issues and the higher chances of there being hidden issues that may not be identified by an audit, led us to consider if there was a more proactive approach.
With advanced analytics and the use of descriptive/predictive analysis, it is possible to have a much more comprehensive oversight of Quality issues, enabling near-time early detection, follow-up, and resolution. It also enables the conduct of remote audits (which have been invaluable during the pandemic), targeting audits based on robust data analysis and deploying auditing resources wisely. This allows the paradigm shift in Quality Assurance from late detection of issues in an ever evolving drug development landscape to being a true enabler for faster approval, bringing safer drugs to patients.