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,"
www.gartner.com/binaries/content/assets/events/
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)
www.bioprocessintl.com/manufacturing/process-monitoring-
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.
