Biopharmaceutical manufacturing generates a wealth of data from multiple sensors. Consequently, the industry faces the challenge
of moving from a data-rich environment to one where the data are translated to information that leads to knowledge. If this
knowledge is effectively exploited, it can improve production processes, financial status, and the environmental agenda of
Photo: PT Europe
Addressing the data–information–knowledge chain is not straightforward because there is a series of compounding issues. For
example, with respect to the manufacturing process, the data analysis problem is not restricted to one unit operation: The
interactions between different units need to be extracted and understood. This requires scientists and engineers to apply
advanced statistical methods. This is not easy because the data garnered will have diverse data structures (from batch and
continuous operations) and include both qualitative and quantitative measurements as well as temporal and/or end-point measurements.
From these data structures, potentially valuable sources of knowledge can be extracted to assist developing data-based model
process descriptors. This information can lead to increased process understanding, which allows those working on the design
and operational functions to make more informed decisions.
Although many of the problems of translating data to knowledge in the biopharmaceutical industry have been overcome using
commercially available tools, the introduction of process analytical technology (PAT) has introduced a whole new set of challenges
(1). For example, spectroscopic instruments significantly increase the amount of data and the complexity of the knowledge-mining
problem. The opening image shows a fermentation vessel with an invasive near infrared (NIR) probe as well as the standard
probes. The complexity of this instrument means that the need for data analysis and its interpretation will continue to grow.
Faced with the overwhelming challenge of extracting knowledge from data, a team-based strategic approach is required. The
team needs to include process engineers, scientists, statisticians and, most importantly, a business champion who recognizes
the potential benefits.The approach adopted, and the capability of existing technologies for knowledge extraction, depend
on whether the development or manufacturing environment is being considered.
In development, at the reaction stage, understanding the predominant reactions that are occurring at various stages throughout
the course of a batch is vital. Furthermore, as changes in operating policy are explored, it is necessary to determine when
important reaction pathways are significant and when they are affected by reactant limitations or excessive accumulation of
From such knowledge, operational policy changes can be made, and new avenues of operation explored. The approach that has
been typically adopted uses off-line sample analysis to identify the concentrations of nutrients that process scientists perceive
to be influential. However, off-line sample analysis has its limitations, particularly regarding low sampling frequency affecting
the amount of available data. It is in this situation where PAT, and its related technologies, can increase the frequency
of available information and provide a detailed fingerprint of the chemical composition.
As previously mentioned, a series of recovery operations are executed following the reaction stage. In the development stage,
once it has been determined which unit operations should be used, the standard operating policy for each unit must be specified
and, ideally, the performance of the chain must be considered as a whole, rather than an isolated unit. Again, extracting
information from the available data is crucial to achieving informed design and operational decisions. Figure 1 shows that
although improved instrumentation leads to increased process knowledge during a development process, the real benefit is not
realized until appropriate statistical analyses are applied. The impact this knowledge has on process profitability is important.
As Figure 2 illustrates, improved measurement promotes potentially reduced development times, and if integrated statistical
analyses are used, improved operating policies can lead to yield increases and higher profits.