From a CMO perspective, the traditional strategy behind process development has largely been to quickly identify target operational
values that hit a primary quality target. This type of development strategy has been intended to generate at-scale product
for clinical trials as fast as possible. The resultant operational ranges have traditionally been either very narrow because
of lack of supporting experimental studies or statistically derived with little to no data from experimental design to support
the outer ranges. Timelines are often built without an opportunity to identify crucial aspects of the process, perform range
finding experiments, or optimize the process. Additionally, fears of making "process changes" within the clinical-manufacturing
phase have prevented many processes from being more robust and operationally friendly. The resultant process-control strategy
becomes narrow and/or possibly at risk of consistent excursions outside of the acceptable ranges. As molecules progress through
the clinical phase and begin gearing up for commercial submissions, the amount of development and characterization may increase
and many experiments must be repeated, adding to timelines and cost. Thus, by not following a well-defined development plan
up front (i.e., a quality-by-design [QbD] approach), there is a potential increased risk of delaying commercialization.
Although the phrase QbD has been thought of as a new or borrowed concept and discussed in terms of feasability, many CMOs
have been using these concepts, albeit perhaps not in a well-planned, cohesive manner. Now the discussion has turned to breaking
down the reality of a QbD strategy. Implementing the infrastructure of a QbD approach to process development involves formalizing
the commitment and the strategy and integrating execution into quality systems. The end result is a road map for the development
of the product from the very beginning.
The purpose of this paper is to present a scenario based upon a real case study, where the project decision making followed
a "traditional" approach to development and commercialization, which in turn caused longer development timelines and increased
program costs. In this example, the traditional approach involved minimizing development studies to keep costs down, waiting
for clinical data to perform any further optimization, and then performing a long series of univariate characterization studies
to complete the information required for a submission. In contrast to this approach, a second product with a different client
was planned and scheduled against a timeline with a built-in strategy to gain knowledge of the process more quickly. The predicted
result not only lowers the risk of the manufacturing commercialization effort, but should also be a significant time savings.
For a CMO, occupying space in manufacturing or even on the bench costs money; thus, time is money in the truest form.
Figure 1 illustrates the difference in approaches. Both products are similar in complexity; therefore, the total knowledge required
to be "ready to submit" (i.e., the area under the curve in Figure 1) is relatively constant between the two products. The timeline presented in Figure 1 represents time spent in product development at the CMO, not necessarily real time. What it does demonstrate, however, is
that using the aforementioned QbD model, the CMO portion of the product commercialization is unlikely to ever be the bottleneck
for a commercialization timeline. Additionally, the smarter portion of the QbD approach is minimizing redundant or inefficient
characterization studies and reducing the risk of having to repeat characterization based upon late understanding of the process.
Figure 1: Comparison of effort (number of experiments over time) for a traditional approach vs. a quality-by-design (QbD)/design-of-experiments
(DOE) based approach to product development. (ALL FIGURES ARE COURTESY OF THE AUTHORS)