Pretzner Barbara, Taylor Christopher, Dorozinski Filip, Dekner Michael, Liebminger Andreas, Herwig Christoph
Exputec GmbH, Mariahilfer Straße 88A/1/9, 1070 Vienna, Austria.
Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorferstrasse 1a, 1060 Vienna, Austria.
Bioengineering (Basel). 2020 Jun 3;7(2):50. doi: 10.3390/bioengineering7020050.
Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring in FFF for a daily work-routine as well as for continued process verification (CPV).
过程监控是确保生物制药制剂、灌装和包装（FFF）过程中最终药品质量一致性的关键任务。FFF监控过程中生成的数据包括多个时间序列和高维数据，这些数据通常以有限的方式进行研究，很少使用多变量数据分析（MVDA）工具进行检查，以最佳地区分正常和异常观测值。对各种FFF来源的时间序列进行数据对齐、数据清理和正确的特征提取是资源密集型任务，但它们对进一步的数据分析至关重要。此外，大多数商业统计软件程序仅提供非稳健的MVDA，使得多变量异常值的识别容易出错。为了解决这个问题，我们旨在为FFF过程开发一种新颖的、自动化的多变量过程监控工作流程，该流程能够稳健地识别与过程相关的FFF特征中的根本原因。我们展示了能够对来自各种FFF数据源的时间序列数据进行对齐和清理的算法的成功实施，随后将时间序列数据与与过程相关的阶段设置进行互连，从而能够无缝提取与过程相关的特征。这种工作流程允许在FFF中引入高效的高维监控，用于日常工作以及持续过程验证（CPV）。
