To ensure a biomanufacturing process is consistent and efficient, scientists use a statistical analysis technique called design-of-experiment (DoE) to understand the relationship between the materials (inputs) and varied parameters tested in separate production runs. But while the approach is effective, it is time consuming according to a Boehringer Ingelheim analytical development scientist, Verena Nold, PhD, who says intensified DoE (iDoE) may be a better fit for drug companies that want to do more in less time, more efficiently.

“In contrast to classical DoE where input factor settings are static and kept on the same level during the entire process duration, iDoE introduces intra-experimental set-point changes along time,” she continues. “Due to the changing of factor levels over the course of the experiment, more combinations of factor levels can be tested within one experiment in a bioreactor. Compared to DoE, this allows biomanufacturers to reduce the number of bioreactors needed to cover the same amount of input factor level combinations.”

Cost and time

Nold, who described the approach in a recent paper, says iDoE has several potential benefits for biopharmaceutical manufacturers.

“Dependent on the research questions that are to be answered, iDoE hold the potential to reduce the cost for experimentation if less bioreactors can be used while the effects of interest can still be resolved,” she explains. “Considering direct manufacturing costs for one 500L batch of about $430,000.00 and using this as a benchmark for smaller development scales, the ecological and economic advantages of needing fewer bioreactor runs becomes apparent.”

Another advantage is that most drug companies that use DoE already have the capability and infrastructure to use the intensified version without additional investment, according to Nold.

“The preparatory steps for intensified DoE are the same as for classical DoE,” she says. The inputs of interest are defined and transferred to a matrix. Process engineers can than easily identify the impact changing each input has on the processes, both individually and in combination.

“No additional technologies or reagents are required to conduct the iDoE compared to classical DoE. However, the changing of the input factor levels at the scheduled time points needs to be performed. and a higher sampling frequency and analytical capacity to improve the resolution of effects over time might be needed.

“Given that no additional reagent or technology is needed to conduct iDoE per se, existing infrastructure and expertise should be sufficient. However, for the planning and analysis of the data, statistical support might be necessary for an optimal exploitation of the experiment. Investment in sampling and analytical capacity could further be beneficial to fully leverage the potential of iDoE.”