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Predictive Ppk calculations for biologics and vaccines using a Bayesian approach - a tutorial.

Jos Weusten¹, Jianfang Hu²

¹Center for Mathematical Sciences, MSD, Oss, The Netherlands.

Pharmaceutical Statistics

|April 11, 2024

Summary

This study introduces a Bayesian method to predict manufacturing process capability early in development. This approach enhances process robustness and reduces risks before commercialization, improving pharmaceutical product quality.

Keywords:

Bayesian statistics R, Rstan SAS process capability

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Area of Science:

Pharmaceutical Manufacturing
Biopharmaceutical Process Development
Statistical Process Control

Background:

Speedy process development in biologics and vaccines can compromise robustness, leading to issues post-launch.
Current methods assess process capability (Ppk) late in commercialization, hindering timely adjustments.
Biopharmaceutical development often focuses on Critical Quality Attributes without fully assessing process robustness.

Purpose of the Study:

To propose a Bayesian-based methodology for predicting manufacturing process performance at scale during development.
To enable early identification of process vulnerabilities and areas for improvement.
To enhance process robustness and knowledge prior to product launch.

Main Methods:

Utilizing Bayesian statistics to incorporate limited development data, similar product data, and Subject Matter Expert (SME) knowledge.
Formulating informative priors within the Bayesian framework to predict process capability.
Applying the predictive Ppk approach at key development stage-gates.

Main Results:

Demonstrated a method to predict long-term process capability using limited data and prior knowledge.
Provided early insights into potential process vulnerabilities before commercialization.
Enabled data-driven prioritization of development efforts for enhanced robustness.

Conclusions:

A Bayesian approach can predict manufacturing process performance before commercial data is available.
Integrating predictive Ppk assessments at stage-gates facilitates continuous process improvement.
This methodology leads to more robust manufacturing processes and increased process knowledge at launch.