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6 Pharmaceutical Technology BIOLOGICS AND STERILE DRUG MANUFACTURING 2020 P h a r mTe c h . c o m Development at Pall Biotech. "Feeding process data into a database immediately enables quicker validation and builds in an element of safety and security that does not come with flat file formats," she observes. Unfortunately, many analytical instruments default to providing data in flat files, and the responsibility to build and connect to databases falls to the user. "Wher- ever possible," Botonjic-Sehic says, "this issue should be addressed from the beginning to avoid creating more work or risking loss of critical data on the back end." Approaches to data quality management There are many approaches to data quality manage- ment, running the gamut from minimal to strict. The importance of the approach is relative to the critical- ity of the process being developed, according to An- drews. For example, he notes that "monitoring data for low-risk, well-controlled unit operations or those with historic reliability would require less stringent control and quality management. For new or less well- controlled steps or techniques or otherwise high-risk operations, however, a more robust data quality regi- men is called for." "Quality oversight is required for laboratory/pilot- scale studies where process monitoring data will be used to develop the strategy for in-process control and/ or support key components of regulatory submissions," Seaver adds. "The design space defined through these studies provides insight into the criticality and ac- ceptable ranges for controlled parameters and per- formance or quality attributes. Therefore, interpre- tation of the results determines the materials, run conditions, and testing strategies for scaling-up and the final commercial process, to ensure the safety, ef- ficacy, and potency of the final product," he continues. As a result, Andrews asserts that for scale-up and commercial processing, data quality is very impor- tant. "While not required for licensing, the integra- tion of upstream development data with engineering runs, scale-up, and production data has exceptional business value. There is a wealth of information to be drawn from all phases of the product development lifecycle," he says. It is essential, though, according to Botonjic-Sehic, to not only understand the instrument being used and the data it produces, but also the deeper context behind each data point pulled and stored (metadata). "Beyond tracking a number, a user needs to be able to understand what part of the process gave that number and how it relates to the process performance. If there is inadequate metadata available from the PD scale, then the usefulness of that process data during scale up and commercial processing will be limited. Properly contextualized data is critical for troubleshooting and success as the process is transferred from process devel- opment to commercial processing scale," she explains. Layers of best practice Quality is achieved through a multi-layered approach. Encompassing all activities and at all stages of the ther- apeutic product lifecycle should be a culture of quality and commitment to the ultimate endpoint—the pa- tient. "Fostering such a culture of quality is the best way to ensure high-quality process monitoring and control during process development," Andrews says. The next layer involves the leveraging of standards to drive the way that data are stored, transferred, and applied to ensure a coordinated approach. Botonjic- Sehic notes that within the pharmaceutical industry, additional guidance that governs data systems helps manufacturers meet regulatory requirements. Standards are important because they help mini- mize the risk to risk to quality, integration, interpre- tation, and protection, which primarily occurs when