Application of Advanced Statistical Tools to Achieve Continuous Analytical Verification: A Risk Assessment Case of the Impact of Analytical Method Performance on Process Performance Using a Bayesian Approach

The criticalness of robust analytical performance is becoming more and more recognized in the pharmaceutical industry. An effective analytical control strategy needs to be defined, along with a process control strategy, to ensure that the measurement uncertainties are controlled to achieve the intended purposes of analytical methods. The principles of Continuous Process Verification (CPV) have been applied to the lifecycle management of analytical robustness, which leads to our vision of Continuous Analytical Verification (CAV) through a product lifecycle. This work proposes to apply advanced statistical tools to deliver on the vision of CAV. A Bayesian hierarchical modeling approach is a potential solution to integrate a risk-based control strategy into the framework of CAV from design, qualification, to continued verification. A case study is included to illustrate the benefits of a Bayesian-based systematic tool in assessing the impact of analytical performance on process performance and in informing decisions related to analytical control strategy, in order to ensure analytical and process robustness.