Traditionally, studies are performed and analysed following a univariate (one variable at a time) approach. The big advantage
of this is that simple 2D plots can be used to assess cause and effect relationships, and the corresponding statistics are
straightforward too. The production environment, however, is never univariate, and interactions between parameters should
be expected. In the pharmaceutical arena, this situation has been well recognised — guidelines such as ICH Q8 on Pharmaceutical
Development and ICH Q10 on Pharmaceutical Quality System explicatively mention the multidimensional design space in which
product performance should be tested to assure quality.1,2 In this context, trend analyses of the manufacturing process performance and its products have been mentioned as an important
tool for innovation and continuous improvements.3 A potential complicating factor with multidimensional data, however, is that it is not possible to visually inspect such
data and so other ways are needed to represent the results.
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This is where multivariate statistics can help; it facilitates the powerful analysis of multidimensional data and, simultaneously,
amasses knowledge at a single glance.
From dull tables to essential information
For a process running for several years, a wealth of data is usually stored in databases containing continuously measured
data and routine-based quality control (QC) data, but it is extremely difficult to obtain useful information from such an
intimidating amount of numbers and other data. Multivariate data analysis (MVA) is an approach that converts data into knowledge
by using data exploration techniques, without narrowing down solely on allegedly unknown aspects. Representing this knowledge
for human interpretation can be done visually.
Historical data can be analysed using MVA to learn from the past, which can be useful to solve current problems, avoid future
ones or to make a validation study of a similar production process or compound quicker and cheaper. Analysing historical data
can also avoid, or shorten, new studies, which are often expensive. When visualised properly, extended sets of data, such
as dull and perhaps confusing tables, can be changed into spatial representations that clearly depict essential information
that is not visible a-priori. The methods are widely applicable and can be used, for instance, for measuring the quality and authenticity of samples,
or for monitoring a production process.
MVA for a pharmaceutical quality system
MVA helps to transfer data into knowledge, which is very useful in a pharmaceutical quality system, as mentioned in ICH Q10.
In this scenario, the analysis of historical data can play an important role: these data are already available and very often
contain useful information that can be used for innovation and quality improvement. As this has not yet been commonly recognised,
the following case study will demonstrate its advantage.
