Incorporating quality by design into process development in a contract manufacturing environment can be challenging. In this case study, design of experiments was used to identify the key and critical operational parameters and their targets for hydrophobic interaction chromatography (HIC) used in the purification of an antibody. Six parameters that typically influence HIC performance were assessed for their effect on elution profile, product yield, and resolution between product and product-related impurities. Statistical software was used to design experiments and analyze the data. The data established wide ranges for some parameters and tighter controls were required for others that had a significant effect on product yield. Some of the parameters also influenced the elution profile and the resolution of product from product-related impurities. These bench-scale studies established a design space that delivered product with reproducible quality and yields during manufacturing.
Process development in a contract manufacturing organization (CMO) is a delicate balance between various and often conflicting demands. The CMO strives to deliver defined, quality processes and product to a client, while meeting predetermined budget and timeline constraints. These limits are particularly narrow for early phase clinical programs before the candidate drug has proven its value. Although the specific deliverables for the CMO are dependent on the client requirements and clinical phase of the product, all campaigns seek to deliver robust processes that satisfy client and regulatory requirements.
The concept of quality by design is not new. It was best explained by Juran1 as a means of efficient quality control that has been applied to a wide range of goods and services. Quality by design was introduced to pharmaceuticals in the International Conference on Harmonization (ICH) Q8 guideline, approved in May 2006, which provides guidance for pharmaceutical process development.2
The aim of pharmaceutical development is to design a manufacturing process to consistently deliver a quality product. The knowledge gained from development studies and manufacturing experience provide scientific understanding to support the establishment of design space.2 The immediate benefit of a well-established design space is knowledge of process parameters that need to be monitored or controlled, which results in a robust manufacturing process.2 Product quality attributes can be accurately and reliably predicted over the design space.3 This allows continuous improvement, which, in turn, improves efficiency by optimizing a process and eliminating wasted efforts in production.4 Furthermore, the process can be optimized using less time, effort, and cost because changes in the design space do not require regulatory pre-approval.
Developing a design space requires extensive process characterization studies, and may seem an unwieldy task for a CMO, given the cost and time constraints. Design of experiments (DOE) is an efficient experimental approach that can reduce the number of runs required as compared to testing each parameter individually, and gives information on the interaction between parameters.4 In this case study, DOE was used to determine the key and critical operational parameters and their targets for a hydrophobic interaction chromatography (HIC) step in the purification process of a monoclonal antibody. Critical parameters are variables that are known to affect product quality and are difficult to control. Key parameters can also affect product quality, but are well-controlled and present less risk than critical parameters. A model for the process was created that generated targets for the key and critical parameters. Using this information, four 2,000-L scale runs were performed. Analysis of the runs demonstrated the predictive power of the bench-scale model.