Drug companies want to make products as quickly and efficiently as possible. The faster and more cheaply a medicine can be manufactured, packaged, and brought to market, the higher its revenue generating potential.
Various methods can be used to try and make manufacturing processes as efficient as possible during scaleup. However, the most common and traditional approach has been to look at each critical process parameter (CPP) separately and in isolation.
But efficiency-focused biopharmaceutical firms are missing a trick according to Alice Kasemiire, PhD, a researcher at the University of Liège’s Center for Interdisciplinary Research on Medicines (CIRM), who says modern statistical methods can accelerate and improve process optimization.
“Design of experiments (DOE) is defined as a branch of applied statistics that deals with planning, conducting, analysing, and interpreting controlled tests to evaluate the CPPs that control the value of a parameter or group of parameters,” she says.
“DOE is much more rigorous than traditional methods of experimentation such as one-factor-at-a-time and expert trial-and-error. This rigor allows practitioners to explicitly model the relationships among the numerous variables in any system, make more informed decisions at each stage of the problem-solving process, and ultimately arrive at better solutions in less time.”
Faster, cheaper, higher quality
Kasemiire analyzed the DOE approach in a paper published in September and found that potential benefits include “faster time to market, lower development costs, lower operating costs, and lower cost of poor quality.”
The initial steps involved in implementing a DOE-based process optimization approach will be familiar, according to Kasemiire, with the first stage being to “Identify the variables influencing your outcome of interest for example through doing a literature review, screening designs and/or risk assessment.”
Establishing a “design space,” which the ICH defines as the “multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality,” is also critical, says Kasemiire.
“Biopharmaceutical process development scientists should define a design space with CPP quality attributes with desired quality. As long as we work within the defined design space, we shall always have a robust bioprocess within specifications.”
They will also need to identify potential sources of variability by for example, carrying out validation runs before scaling up, points out Kasemiire.
Another advantage is that investment in technology will likely be minimal as most biopharmaceutical companies already employ the PAT tools needed for DOE-based process optimization.
“Choosing the right software and learning how to use it properly is important,” explains Kasemiire. Several software programs for designing experiments exist on the market but care needs to be taken as some software will actually help you to select the wrong CPPs.
“When selecting ranges of the CPPs care needs to be taken as a narrow range from low to high may give a result that concludes the chosen CPPs do not influence the process, but in reality, the selected CPPs do not influence the process in the range selected. In addition, selecting very wide ranges from low to high may give results where some combinations of the factors yield unstable results.”