Pharmaceutical Technology - May 2018

Pharmaceutical Technology eBook - Biologics and Sterile Drug Manufacturing

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Page 13 of 61

14 Pharmaceutical Technology BIOLOGICS AND STERILE DRUG MANUFACTURING 2018 P h a r mTe c h . c o m Process Modeling A dvanced data analytics tools are used by industry to find the golden nuggets in historical data, to aid in process development, to fine-tune production, and to achieve long-term improvements in product quality and throughput. During recent years, four key stages of data analytics have been defined (1), which can be seen as being part of a data ana- lytical continuum (see Figure 1). Handling those key stages diligently by using advanced data analytics tools is expected to give manufac- turers a competitive edge. Along the data analytics continuum (described in detail in Figure 1), the most advanced challenge is being able to predict what will happen in the future and, in the event of an undesirable outcome, prescribe certain activities or interventions to prevent it from happening. Al- though looking ahead into the future is of greatest commercial inter- est, value is created at every stage of data analytics; it all depends on the specific need and the tools and approach to the analytics process. In the bioprocessing industry, parts or all stages of the data ana- lytics continuum are applicable. In early-stage development, ad- vanced data analytics is used to compare batches, for example, to figure out how to modify cell expansion steps so that they lead to higher cell densities and product titers. In late-stage development, advanced data analytics is used when scaling-up manufacturing processes to verify comparable performance at different scales. And in full production, advanced data analytics is used for real-time bioprocess monitoring and early fault detection of batches deviating from good, normal operating conditions. A primary objective in bioprocess development and scale-up is to accomplish a consistent, uniform, and predictable environment across scales. The following case study describes the essentials of a Characterizing a Bioprocess with Advanced Data Analytics Lennart Eriksson and Chris McCready Modeling at various stages of the data analytics continuum aids scale comparison of a bioreactor. Lennart Eriksson, PhD, is senior lecturer and data scientist, lennart.eriksson@, and Chris McCready is lead data scientist, both at Sartorius Stedim Data Analytics. LEUNGCHOPAN/SHUTTERSTOCK.COM

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