Application of multivariate data analysis in biopharmaceutical production

Abstract

In 2004, the FDA launched the Process Analytical Technology (PAT) initiative to support product and process development. Even before this, the biologics manufacturing industry was working to implement PAT. While a strong focus of PAT is the implementation of new monitoring technologies, there is also a strong emphasis on the use of multivariate data analysis (MVDA). Effective implementation and integration of MVDA is of particular interest as it can be applied retroactively to historical datasets in addition to current datasets. However translation of academic research into industrial ways of working can be slowed or prevented by many obstacles, from proposed solutions being workable only by the original academic to a need to prove that time invested in developing MVDA models and methodologies will result in positive business impacts (e.g. reduction of costs or man hours). The presented research applied MVDA techniques to datasets from three scales typically encountered during investigations of biologics manufacturing processes: a single product, dataset; a single product, multi-scale dataset; a multi-product, multi-scale, single platform dataset. These datasets were interrogated in multiple approaches and multiple objectives (e.g. indictors/causes of productivity variation, comparison of pH measurement technologies). Individual project outcomes culminated in the creation of a robust statistical toolbox. The toolbox captures an array of MVDA techniques from PCA and PLS to decision trees employing k-NN. These are supported by frameworks and guidance for implementation based on interrogation aims encountered in a contract manufacturing environment. The presented frameworks ranged from extraction of indirectly captured information (Chapter 4) to meta-analytical strategies (Chapter 6). Software-based tools generated during research ranged from translation of high frequency online monitoring data as robust summary statistics with intuitive meaning (Appendix A) to tools enabling potential reduction in confounding underlying variation in dataset structures through the use of alternative progression variables (Chapter 5). Each tool was designed to fit into current and future planned ways of working at the sponsor company. The presented research demonstrates a range of investigation aims and challenges encountered in a contract manufacturing organisation with demonstrated benefits from ease of integration into normal work process flows and savings in time and human resources.