Quality by Design Execution from a CMO Perspective

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 experimental design to support the outer ranges. Timelines are often built without an opportunity to perform range finding experiments, or process optimization. Additionally, fears of making “process changes” within the clinical manufacturing cycle have prevented many processes from being more robust and operational-friendly. Thus, the actual control of the process becomes narrow and / or possibly at risk of appearing out of control with consistent excursions outside of the acceptable ranges. Where molecules progress through the clinical cycle and begin gearing up for commercial submissions, the amount of development and characterization may increase and many experiments repeated, adding to timelines and cost.

By starting process development with the understanding that any molecule is a potential candidate for commercialization, experimental design can be performed such that knowledge is gained in a more rapid manner while significantly decreasing the risk of repeating experiments to obtain more data.

This presentation will cover the comparison of two case studies of Process Development models. The first case involved a traditional characterization approach utilizing minimal DOE strategy and ongoing characterization to support shifts seen at-scale. The second case involves a project whose initial development efforts were designed to ensure that the DOE strategy would result in a robust Design Space for an eventual Design Space submission. The comparison of these two development and manufacturing cases demonstrates that with fractionally more investment up front, smarter DOE’s can be executed, timelines can be significantly reduced and overall cost of commercialization could be less.