Issue link: https://www.e-digitaleditions.com/i/1248960
Pharmaceutical Technology BIOLOGICS AND STERILE DRUG MANUFACTURING 2020 7 data are being transferred and stored. "While inter- faces within a piece of equipment are optimized by the supplier, when that piece of equipment must interface with databases, control systems, or other equipment, there are challenges," Botonjic-Sehic observes. Standards describe how best to integrate data across the systems effectively and safely. "There is guidance on communication standards, data formatting, and even coding that helps to facilitate the process from PD to commercial scales," she notes. In addition, the foundation should start with system design based on the International Society for Pharmaceutical Engi- neering's Good Automated Manufacturing Practice (1) and integrate the data standards outlined by reg- ulatory agencies such as 21 Code of Federal Regula- tions Part 11, Annex 11. Supplementary guidance and various industry communities of practice help build a strong solution on that foundation. Comprehensive information technology solutions that provide the quality control necessary but also en- gender compelling and innovative experiences—the third layer—can enable the right quality culture. "Im- plementation of appropriate technical solutions should reduce friction in accomplishing tasks in addition to providing the array of business benefits, including data quality. If a solution is cumbersome or does not fully meet the business need, users will find a way to work around it, which will assuredly lead to less data integrity. The result can include data silos, dirty (i.e., invalid or incomplete) data, and thus, much more onerous data integration, interpretation, and security efforts," Andrews observes. The fourth layer involves validation. "Where pro- cess development data are intended to support key decisions for processing at scale, validation—of the systems used to collate information—or secondary verification—for manual collection—should be per- formed to ensure quality," Seaver explains. Validation requires testing against pre-established criteria to con- firm that the collection system performs as intended to ensure quality of the data. Manual adjustments made to the data set should be visible through an audit trail to protect against fraud and misinterpreta- tion, Seaver adds. Some tactics to avoid Because integration of data is a key component of how any piece of manufacturing equipment works, a strat- egy for integration must be built in from the start. Too often companies will get excited and build a system based on features, only to think about data integration last; this, however, makes it difficult to deliver a strong data solution, says Botonjic-Sehic. "From the launch of design, data tracking, storage, security, and transfer- ability must be engineered into the system. Creating a ground-up strategy for implementation of standards and ease of integration is undoubtedly the best way." It is also important to avoid manipulation or analy- sis of the data in a separate program/system, accord- ing to Seaver. "Doing so invalidates the quality con- trols provided through the original system validation. Secondary validation or manual verification would be required to ensure that all information has been transcribed correctly and is accurately represented following analysis," he explains. Neglecting the quality of "unregulated" data, or those that are not necessarily required per an internal standard operating procedure or an external regula- tory agency, is another common problem. The result, according to Andrews, can be unexpected down- stream quality failures. He also notes that, in general, not ensuring process development monitoring data quality represents a lost opportunity to learn and ben- efit from that data.