In summary, multivariate analysis for raw material selection and control serves multiple purposes. First, it categorizes an
incoming product based on the analytical data alone, without the need to evaluate it using time-consuming cell culture studies.
This classification is quite important because scale-down studies may not result in picking up the differences in raw materials
even though they are different analytically. Performing the multivariate analysis on the analytical data provides another
criterion for deciding if a raw material from a particular vendor or source is acceptable for use in manufacturing. Second,
MVDA can be applied to the combined experimental and analytical data to identify the critical components required for desired
outcome, e.g., productivity. After sufficient analytical and experimental data are gathered, multivariate analysis can be
used as the sole criterion for assessing the raw material quality. It can also assist in directing the efforts to improve
the quality of a suboptimal raw material (e.g., product C in the current study). Finally, the multivariate analysis also helps
limit the scope of analytical testing for raw material control. For example, in the case study described here, only two assays
may be needed for future products (or lots) instead of the four used to develop the model ealier.
This article demonstrates the usefulness of the MVDA with respect to various activities involved in biopharmaceutical manufacturing,
including scale up, process comparability, process optimization, process monitoring, and raw material testing. Currently,
a lot of data collected at small and large scale do not undergo the rigorous data analysis presented here. We hope to convince
the readers that MVDA allows us to extract useful process information through analysis of the readily available data, in order
to maximize our understanding of the process. As the biotech industry implements Quality by Design, multivariate analysis
will become a necessity.
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