Monitoring critical process parameters and ensuring that they are within predefined specifications are essential tasks when establishing a robust biomanufacturing process and developing high-quality drug substances and products. Innovative technologies such as bioprocess sensors are used to measure such parameters, thus generating large amounts of data over time. Those data then can be used to predict trends, suggest patterns among different batches, and establish ideal process conditions. Other innovations for handling process data and ensuring that future batches are controlled include the use of monitoring software, mechanistic and data-driven modeling, and statistical analysis to establish process models. The articles in this report focus especially on data-driven, statistical, and mathematical modeling strategies for monitoring bioprocessing stages, especially chromatography-based purification, and for estimating long-term process variability when two separate data sources are pooled.
Development of Standalone Monitoring Application for Purification Processes
Konstantinos Spetsieris, Michal Mleczko, Shreya Maiti, Shyam Panjwani
Process monitoring provides process scientists with the ability to detect trends and patterns in near-real time and support root-cause analysis. It also enables the quantification of process variations so that scientists can understand further their bioprocess operations and make improvements. The authors present a case study on the development of a stand-alone, data-driven, process-monitoring application for a biomanufacturing purification process. They implemented a stand-alone graphic user interface application for monitoring chromatography columns used to purify therapeutic proteins. The application enabled automatic processing of in-line data from a process historian and generated quantitative metrics that facilitate the comparison of a purification batch in the context of historical data.
Mechanistic Modeling for Hydrophobic Interaction Chromatography Process: For Vaccine Antigen Purification
Angela Shaoxu Li, Lars Biebinger, Jacob Trombley, Shilin Cheung, Tao Yuan
Bioprocess models have been shown to be useful applications in nearly all stages of a product development life cycle. Mechanistic modeling, a first-principles technique for mathematically describing physiochemical phenomena, in particular has been used as the main method of chromatography modeling, and it provides a full description of a system. Several commercial chromatography mechanistic modeling software are available that do not require coding, making them practical for bioprocess scientists. The authors present a study using chromatography modeling software to model a hydrophobic-interaction chromatography process used to purify a vaccine antigen with a complex feed stock. They describe methods for column characterization, ultrahigh performance chromatography analysis, model calibration and application, and model development and validation.
Appropriate Estimation of Long-Term Variability from Biopharmaceutical Release and Stability Data
Keith M. Bower
Quality attributes testing of drug product and drug substance lots generate numeric results that can be used for statistical analyses, including the determination of process variability. The author addresses potential concerns associated with pooling disparate data sources and illustrates a technique to perform appropriate calculations using statistical software. His study pooled reportable values from two distinct data sources (lot release and lots that have been placed on a stability study and are stored at a recommended storage condition) to estimate long-term manufacturing variability. He demonstrates methods for calculating method variance and estimates of long-term variation. He highlights the random coefficients model, which allows individual lots to have different intercepts and slopes. That model can be fitted to combined stability and lot-release data, and the residuals can be studied to ensure that standard model assumptions of independence, normality, linearity, and homogeneity of variance are met.
Increasing Reproducibility of Cell Culture Bioprocesses
Ulrike Rasche, with Amanda Suttle and Robert Glaser
Upstream production cell quality, media, upstream production methods, and other factors can influence the quality of cell growth and viability. Experts at Eppendorf describe the factors that can contribute to inconsistent results and explain how to increase the reproducibility of cell culture bioprocesses. Amanda Suttle is a bioprocess senior research scientist in the Eppendorf applications laboratory in Enfield, CT. Robert Glaser is an applications laboratory manager at the Eppendorf Bioprocess Center in JÃ¼lich, Germany. They discuss methods for improving quality consistency of cells used to inoculate a bioreactor, how bioprocess equipment can increase reproducibility of an upstream process, the use of sensors to process monitoring and control, and troubleshooting steps.