Applying Six Sigma
A peptide synthesis and manufacturing process can have upwards of 50 steps, introducing a large margin for error but also a substantial opportunity for process optimization. Compared to some synthetic processes, for which a 96%–98% product yield at each process step might seem sufficient, for a process with 50+ steps, little product would be left to recover at the end of the process.
In peptide manufacturing, therefore, “you need an average 99+ percent yield at each step, and there is little room for error,” said Didier Monnaie, Ph.D., project leader, Lonza, and Six Sigma Black Belt. He described the implementation of Six Sigma at the Lonza Braine peptide manufacturing facility over the past three to four years.
Whereas a quality by design (QbD) approach and intensive process optimization require a great deal of up-front process development, analytical work, and comparative testing—and the associated costs—the ultimate rewards can be substantial in terms of time, labor, resources, and costs saved by maximizing process efficiency, product quality and yield, and overall productivity.
A well-defined process with clearly documented specifications, optimized process parameters, and well-studied operational ranges developed for small-scale production will also minimize potential problems on scale-up, noted Dr. Monnaie. It can help avoid the need for second-generation process development.
Furthermore, a QbD approach provides the opportunity for additional process optimization in the future, within clearly proscribed limits, after regulatory review and product approval, allowing for continued improvements in efficiency and cost savings.
Working with Six Sigma experts, Lonza developed a six-step QbD approach within the framework defined by the FDA. A key facet of this approach is the development of critical quality attributes (CQAs), which relate to the characteristics and constraints of a specific product as required by a particular customer.
Dr. Monnaie emphasized the importance of defining and documenting CQAs from the outset of process development, using them as a guide to help stay focused on the goals of a project.Process development has traditionally been more empirical, based on fixed parameters, and has mainly focused on optimizing one factor at a time.
In contrast, QbD provides a better understanding of process variability, depends on process risk assessments to define an operational range for process parameters, and incorporates a multivariate design of experiments methodology that can optimize multiple parameters in parallel and identify critical interactions between individual factors.
As an example of the value of applying quantitative Six Sigma tools, Dr. Monnaie described how Lonza has used these tools to document a seasonal effect that was compromising process efficiency. The availability of detailed process specifications and statistics led to the identification of a seasonal change in the temperature of incoming solvents as the cause of decrease in efficiency.