Pharmaceutical Technology - May 2018

Pharmaceutical Technology eBook - Biologics and Sterile Drug Manufacturing

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18 Pharmaceutical Technology BIOLOGICS AND STERILE DRUG MANUFACTURING 2018 P h a r mTe c h . c o m Process Modeling organized and chronological work order. Along- side this continuum, the dimensions of "business value" and "complexity" are often appended to fur- ther enrich the understanding of the concept. The continuum is general in the sense that its stages apply to any type of data (e.g., production, finance, medical), and the different stages may be addressed using different types of data analytical tools. For example, as shown in this article, the various stages of bioprocess characterization and monitoring are stratifiable according to the steps of the continuum. This article illustrates how evaluation of batch type data, including initial and final conditions and batch evolution trajectories, can be accomplished using projection methods such as PCA, PLS, and OPLS. Such methods, preferably prudently combined with DOE, enable powerful scale-up modeling possibili- ties of bioreactors to support process development and improvement in a predictable, timely, and cost- effective manner. The main conclusion of the use case cited was that micro- and pilot-scale batches performed similarly, albeit with some variability in peak VCD, and that the batches of the micro+ scale deviated substantially and with a strong clustering among themselves. By using contribution-based di- agnostics, reasons for scale differences and clustering were visualized, interpreted, and mitigated. References 1. Gartner, "2017 Planning Guide for Data and Analytics," keywords/catalyst/catus8/2017_planning_guide_ for_data_analytics.pdf (accessed Jan. 31, 2018). 2. L. Eriksson, et al., Design of Experiment: Principles and Applications, (MKS Umetrics AB, Sweden, 3rd Ed., January 2008). 3. L. Eriksson, et al., Multi- and Megavariate Data Analysis Basic Principles and Applications (MKS Umetrics AB, Sweden, 3rd Ed., May 2013). 4. C. McCready, Bioprocess International (November 2017) and-controls/model-predictive-control-for-bioprocess- forecasting-and-optimization/ (accessed Jan. 31, 2018). PT Figure 4: Control chart from a data analytics model capturing batch evolution. The horizontal axis shows batch lifetime in days. The vertical axis is the charted statistic (t), which is a parameter of the data analytics model. The red dashed lines represent upper and lower control limits. Each batch is represented by a single line. As long as a batch line stays within the control limits, normal operating conditions are inferred.

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