Multivariate Monitoring Workflow for Formulation, Fill and Finish Processes

Abstract

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).

Data-Driven Approach toward Long-Term Equipment Condition Assessment in Sterile Drug Product Manufacturing

“…Currently, in addition to backtracking purposes, the recorded process data can be used for process monitoring and fault detection in batch and change-over processes. , A monitoring workflow for the formulation, fill, and finish processes was published . Data-driven batch monitoring in drug product manufacturing for change-over processes has been suggested with a focus on cleaning and sterilization in place (C/SIP) and decontamination .…”

“… 16 , 17 A monitoring workflow for the formulation, fill, and finish processes was published. 18 Data-driven batch monitoring in drug product manufacturing for change-over processes has been suggested with a focus on cleaning and sterilization in place (C/SIP) 17 and decontamination. 19 The latter work showed that deteriorations could be observed across multiple batches before a failure occurs.…”