(306c) Process Robustness Aided by Retrospective Multivariate Data Analysis of Factory Data - a Case Study

Process robustness aided by retrospective multivariate
data analysis of factory data ? a case study  
  

Sankar
Raghavan, Cherokee Pharmaceuticals L.L.C. a wholly owned subsidiary of Merck
& Company,  Sankar.Raghavan@merck.com , 570-271-2106

Jeff
Foster, Cherokee Pharmaceuticals L.L.C. a wholly owned subsidiary of Merck
& Company, Jeffrey.Foster@merck.com,
570-271-2173

Dale Kopas, dakopas@gmail.com, 609-417-1707

Ed Warner, Merck & Company, edwarner4@gmail.com, 908-670-1902

Presenter:  Sankar
Raghavan

Keywords:  Multistep
process, Noisy data, Multivariate analysis, Continuous improvement

Purpose:  Illustrate
the power of combining process knowledge and advanced statistical analysis in
addressing intricate process issues

Abstract

Quality by Design is a powerful tool that can be applied to continuously
improve performance and robustness of processes.  However, with complex processes, it can be
effort intensive and costly to do particularly when the process has multiple
steps and the scale-up and the interaction effects are not fully understood.  Since most pharmaceutical processes are
batch, factory data with its normal variability provides a vehicle to generate
such data without incurring excessive cost. 
Such a case study is presented ? The example involves a manufacturing
process for an antibiotic drug substance that involved four reactions and
several down stream purification steps.  Multiple
factors influenced each of these steps. 
Identification of key factors that affected the process proved difficult
as univariate and bivariate approaches such as SQC/SPC control charts and
simple linear regression models explained only a small fraction of the process
variability. Multiple interactions added to the complexity, and the presence of
a high degree of co-linearity between process variables in the X-matrix lead to
problems with matrix inversion in traditional multivariate regression models
such as stepwise regression.  To correct
the situation, data mining was attempted by advanced multivariate methods,
specifically Random Forest and Partial Least
Squares (PLS) to supplement process knowledge.

The paper describes the analysis of inherently noisy factory
data for an entire campaign consisting of over 200 individual batches and more
than 100 variables by a combination of advanced statistical methods and physical
reasoning based on expert knowledge of the process.   The
method was effective in explaining the broad factory trends.  Key outcomes were:

1. The methodology helped rank factors in terms of their importance.  This made it possible to focus on a subset of few factors.
2. Implementation of lessons derived from the studies has resulted in a more robust process with improvements in purity, yield and productivity.
3. The work pointed to areas of opportunity to improve the process. 

The method aligns well with the F&DA initiative to
continuously improve robustness of processes through the life cycle of a process.  The methodology should be broadly applicable for
the continuous improvement of complex processes.