Issue link: https://www.e-digitaleditions.com/i/1248960
4 Pharmaceutical Technology BIOLOGICS AND STERILE DRUG MANUFACTURING 2020 P h a r mTe c h . c o m Development A s biopharma manufacturers incorporate more data- driven monitoring and control systems in production processes, the quality of the data, as well as integrating, interpreting, and protecting it, become more important. One solution is to build quality features into these systems during process development. Importance of quality management As a process scales, "there is a need to transfer or integrate process devel- opment (PD) data; therefore, building data quality into these activities right from the start is important. Management of data quality always facilitates integration with other systems irrespective of the scientific or business purpose," observes Chris Andrews, a senior solution consultant with Dassault Systèmes BIOVIA. "In the case of monitoring data during process development, it is criti- cal," Andrews says, "to identify and segregate valid data intended for technical transfer and to ensure that data are clean (i.e., have few invalid results or records) as possible. Higher quality data will minimize integra- tion time, improve the ability to quickly interpret those data, and offer assurance of data integrity and provenance." Establishing a common format for data, units, and terminology will also simplify integration with other systems and prevent confusion dur- ing interpretation, adds Kevin Seaver, general manager for bioprocess automation and digital at GE Healthcare Life Sciences. In addition, working with secure databases and historians, rather than in flat files such as comma-separated values (CSV) or extensible markup language (XML), allows easier data retrieval and integration along the way, according to Edita Botonjic-Sehic, global PAT manager Cynthia A. Challener, PhD, is a contributing editor to Pharmaceutical Technology. YOUR123 - STOCK.ADOBE.COM Building Data Quality In Generates Quality Data Out Cynthia A. Challener Ensuring the quality of data in process monitoring and control systems starts in process development phases. Maintaining data integrity during scale-up and technical transfer phases is crucial.