Journal of Pharmaceutical Innovation

, Volume 12, Issue 4, pp 357–366 | Cite as

A Risk-Based Approach to Setting Acceptance Criteria for Pharmaceutical Process Comparability

  • Binbing YuEmail author
  • Lingmin Zeng
  • Harry Yang
Original Article



Throughout the life cycle of a biotechnological drug product, changes and improvements of manufacturing processes are common. It is required by the regulatory bodies that manufacturers establish adequate and appropriate comparability between pre-change and post-change products. The goals of comparability assessments are to demonstrate the comparability and consistency of product quality before and after change and to demonstrate that the changes do not have an adverse effect on safety and efficacy of the drug products.


A well-planned and detailed comparability protocol may facilitate the approval of process changes and expedite the production and distribution of lifesaving drug products to patients. Analytical comparability is the foundation of all comparability exercise. Therefore, a sound comparability protocol should carefully select relevant critical quality attributes, prepare a flexible testing plan, and predefine appropriate acceptance criteria. Selection of analytical methods and acceptance criteria for determining comparability may be the most challenging step in the comparability study.


In this article, a risk-based approach is proposed to setting the acceptance criteria for analytical comparability during the pharmaceutical process change. The proposed method is applied to a late-phase process comparability study, illustrating the value of using prior information and historical data.


The Bayesian methodology allows the manufacturer to utilize accumulated scientific knowledge and manufacture experience during the life cycle of pharmaceutical development and the risk assessment enables control of the probability of out-of-specification, thus protecting the patients’ risks.


Acceptance criteria Bayesian method Comparability Process change Quality attributes 



The authors would like to thank Dr. Rick Burdick for his helpful comments and suggestions, which greatly helped improve the paper.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Statistical SciencesMedImmune LLC, An AstraZeneca CompanyGaithersburgUSA

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