Shiny Tools for Sample Size Calculation in Process Performance Qualification of Large Molecules
The regulatory guidance documents on process validation have been recently revised to emphasize the three-stage lifecycle approach throughout validation. As an important milestone within Stage 2: process qualification, the process performance qualification (PPQ) requires taking adequate samples to provide sufficient statistical confidence of quality both within a batch and between batches. To help meet the PPQ requirements and to further support continued process verification for large molecules, for continuous critical quality attributes, Shiny tools have been developed to calculate the minimum numbers of samples within batches to control the batch-specific beta-content tolerance intervals within prespecified acceptance ranges. The tolerance intervals at attribute level are also displayed to assure the suitability of the predefined number of PPQ batches. In addition, another Shiny application for creation and evaluation of the sampling plans for binary attributes will be illustrated in terms of failure rates of future batches and consumer’s and producer’s risk probabilities. The tools for both continuous and binary attributes allow to adjust the sampling plans based on historical data, and are designed with interactive features including dynamic inputs, outputs and visualization.
KeywordsProcess performance qualification R Shiny Sample size calculation Tolerance intervals Sampling plans Variance component analysis Normal and Binary attributes
We thank Paulo Bargo for helping with configuration and installation of the Shiny applications to the Janssen Shiny server. Our gratitude extends to the team led by Kenneth Hinds for developing an internal position paper  regarding statistical based sampling plans for PPQ. The authors also appreciate the comments on the statistical methods and the Shiny applications from manufacturing scientists and statistician colleagues.
- 1.Bolstad, W.M., Curran, J.M.: Introduction to Bayesian Statistics. 2nd edn. John Wiley & Sons Inc (2017)Google Scholar
- 2.DS-VAL-68021: Position Paper to Address Statistically Based Sampling Plan for Process Validation Stage 2 (Process Performance Qualification). Janssen Internal Document (2014)Google Scholar
- 9.Krishnamoorthy, K., Mathew, T.: Statistical Tolerance Regions: Theory, Applications, and Computation. Wiley (2009)Google Scholar
- 12.Puza, B.: Bayesian Methods for Statistical Analysis. ANU eView (2015)Google Scholar
- 13.Resnizky, H.G.: Learning Shiny. Packt Publishing Ltd (2015)Google Scholar
- 15.Schuetzenmeister, A.: VCA: Variance Component Analysis. R package version 1.3.3 (2017)Google Scholar
- 16.West, B.T., Welch, K.B., Galeckl, A.T.: Linear Mixed Models: A Practical Guide Using Statistical Software. Chapman & Hall/CRC (2007)Google Scholar