Application of quality by design principles to the development and technology transfer of a major process improvement for the manufacture of a recombinant protein

Abstract

This study describes the application of quality by design (QbD) principles to the development and implementation of a major manufacturing process improvement for a commercially distributed therapeutic protein produced in Chinese hamster ovary cell culture. The intent of this article is to focus on QbD concepts, and provide guidance and understanding on how the various components combine together to deliver a robust process in keeping with the principles of QbD. A fed-batch production culture and a virus inactivation step are described as representative examples of upstream and downstream unit operations that were characterized. A systematic approach incorporating QbD principles was applied to both unit operations, involving risk assessment of potential process failure points, small-scale model qualification, design and execution of experiments, definition of operating parameter ranges and process validation acceptance criteria followed by manufacturing-scale implementation and process validation. Statistical experimental designs were applied to the execution of process characterization studies evaluating the impact of operating parameters on product quality attributes and process performance parameters. Data from process characterization experiments were used to define the proven acceptable range and classification of operating parameters for each unit operation. Analysis of variance and Monte Carlo simulation methods were used to assess the appropriateness of process design spaces. Successful implementation and validation of the process in the manufacturing facility and the subsequent manufacture of hundreds of batches of this therapeutic protein verifies the approaches taken as a suitable model for the development, scale-up and operation of any biopharmaceutical manufacturing process.

Identification of Growth Phases and Influencing Factors in Cultivations with AGE1.HN Cells Using Set-Based Methods

“…This cell counter discriminates dead and viable cells using the trypan blue method. Concentrations of main metabolites were determined enzymatically with the bioprofile 100plus (Nova Biomedical, Waltham (MA), USA) [40], [41].…”

7.3 Process Characterization for Upstream and Downstream Process Development

“…The training set used for the PCA model mainly consists of data from manufacturing, and a 95 % confidence ellipse is generated. In instances where manufacturing data may not be available, one may use data from the next highest scale (typically pilot scale) that compares well with manufacturing [14]. When the small scale data set is subsequently overlaid on the training model, if the component scores are such that the small scale batches fall within the ellipse, then we could say with 95 % confidence that the scale-down model is statistically similar to the manufacturing scale.…”

“…Sometimes it is easier to perform an OFAT study for such factors, especially when there is a low likelihood of interaction with other factors, based on prior process knowledge. Examples of such cell culture parameters include medium hold times and feed addition timings [14]. Some other parameters such as pCO 2 levels and culture osmolality, which are outputs dependent on process inputs but can have a direct impact on process performance, are independently studied.…”

“…In addition, the FDA guidance for industry on process validation has been revised and updated in January 2011 to integrate QbD principles [5] and with an emphasis placed on continuous verification and use of QbD principles, post approval in lifecycle management. Numerous publications have been written on QbD, in addition to recognized industry/regulatory collaborations for biopharmaceuticals [9][10][11][12][13][14][15][16][17][18][19][20].…”

Industry 4.0: a vision for personalized medicine supply chains?

“…Embracing Industry 4.0 will facilitate the biotech sector's move towards QbD, including a more flexible process based on having enhanced product and process understanding that can be used to adapt the process to manage critical sources of variability [8,9]. This is because Industry 4.0 will connect Process Analytical Technologies (PAT) comprised of sensors with multivariate data analytics and control algorithms as well as electronic data records and enterprise systems to enable efficient knowledge sharing across components in the supply chain.…”