Removing Subjectivity from the Assessment of Critical Process Parameters and Their Impact

January 2, 2018
Ke Wang

Ke Wang, PhD, is director of Pfizer’s department of pharmaceutical science and manufacturing statistics.

,
Fangfang Liu

Fangfang Liu, PhD, is manager of Pfizer’s department of pharmaceutical science and manufacturing statistics.

,
Fasheng Li

Fasheng Li, PhD, is director of Pfizer’s department of pharmaceutical science and manufacturing statistics.

,
Aili Cheng

Aili Cheng, PhD, is director of Pfizer’s department of pharmaceutical science and manufacturing statistics.

,
Brad Evans

Brad Evans, PhD, is associate director of Pfizer’s department of pharmaceutical science and manufacturing statistics.

,
Jingnan Zhang

Jingnan Zhang, PhD, is manager of Pfizer’s department of pharmaceutical science and manufacturing statistics.

Pharmaceutical Technology, Pharmaceutical Technology-01-02-2018, Volume 42, Issue 1
Page Number: 46-54

A new algorithm uses a statistical approach to critical process parameter assessment, allowing for faster, more consistent, and less subjective critical process parameter quantification, visualization, and documentation.

Determining critical process parameters (CPPs) is vital to defining the control strategy for drug substance and drug product manufacturing processes (1). Deciding the criticality of a process parameter, however, can often become a subjective exercise, resulting in long discussions within product development teams as well as back-and-forth communication with government regulators during new drug application review. 

A data-driven statistical approach was developed (2) to help reduce subjectivity and debate in determining the criticality of process parameters in the manufacturing of drug substance and drug product. The approach uses the distance of a critical quality attribute (CQA) from its quality limit, together with the estimated parameter effect size, to designate the criticality of a process parameter. 

The method relies on straightforward calculations. However, performing these calculations manually can be time-consuming and error-prone when the statistical model involves multi-factor interactions and higher order parameter effects. To incorporate this systematic approach into daily practice, statisticians have developed a web-based computational tool that uses this method.

This article examines how the algorithm was developed, focusing on the calculation of process parameter effect size on a CQA. It also discusses how analytical and numerical solutions of parameter effect size resulted from conventional linear models. 

Peer-Reviewed

Submitted: July 31, 2017.
Accepted: August 11, 2017

About the Authors

Fasheng Li, PhD, is director; Brad Evans, PhD, is associate director; Fangfang Liu, PhD, is manager; Jingnan Zhang, PhD, is manager; Ke Wang*, PhD (ke.wang2@pfizer.com), is director, and Aili Cheng, PhD, is director, all within Pfizer’s department of pharmaceutical science and manufacturing statistics. Ms. Wang can be reached at 860-686-2888.

*To whom correspondence should be addressed.

Article Details

Pharmaceutical Technology
Vol. 42, No. 1
January 2018
Page: 46-54

Citation

When referring to this article, please cite it as F. Li et al., "Removing Subjectivity from the Assessment of Critical Process Parameters and Their Impact," Pharmaceutical Technology 42 (1) 2018.

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