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Shiny Tools for Sample Size Calculation in Process Performance Qualification of Large Molecules

  • Qianqiu LiEmail author
  • Bill Pikounis
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 218)

Abstract

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.

Keywords

Process performance qualification R Shiny Sample size calculation Tolerance intervals Sampling plans Variance component analysis Normal and Binary attributes 

Notes

Acknowledgements

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 [2] 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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Janssen Research & Development LLCSpring HouseUSA

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