Collecting unit operation performance data on manufacturing processes has many benefits. Besides assessing the health of assets, the data can also be analyzed to drive process improvement activities, to assist in establishing practical operating ranges for new products, and can be used to facilitate process development activities in the laboratory ultimately leading to more robust tech transfers of processes to the manufacturing facility. In this paper, we will describe how to obtain operational variability on the basis of performance data extracted from current and/or historical manufacturing unit operations and its practical uses. Although, the concept was developed initially as a small molecule process design tool, its flexibility is underscored by including an example of how a parenteral manufacturing site used it to identify and correct an issue before it translated into unplanned downtime or product loss.
During the development of a pharmaceutical chemical process, it is vital to establish a control strategy that will ensure the process performance and fitness for use of the active pharmaceutical ingredient (API), which in turn is essential to the drug product performance and its fitness for use. As part of the control strategy, it is very important to understand and establish critical elements of the process, one of which is the establishment of the critical process parameters that impact the critical quality attributes (CQAs) of the API. In this paper, we are proposing a method for determining the criticality of a process parameter and whether it should be listed in the common technical document as critical. By using routine process control capability across a variety of operating conditions and equipment configurations, a risk-based approach is used to identify parameters that could have a potential of impacting the CQAs of the API. Beyond establishing criticality, and understanding the operational variability, the knowledge gathered from these approaches can also be used to facilitate the efficient mapping of a multivariate design space.
The principles of quality by design (QbD) have been applied in cell culture manufacturing process development and characterization in the biotech industry. Here we share our approach and practice in developing and characterizing a cell culture manufacturing process using QbD principles for establishing a process control strategy. Process development and characterization start with critical quality attribute identification, followed by process parameter and incoming raw material risk assessment, design of experiment, and process parameter classification, and conclude with a design space construction. Finally, a rational process control strategy is established and documented.
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