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MAR-APR 2019

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18 INTECH MARCH/APRIL 2019 WWW.ISA.ORG PROCESS AUTOMATION Examples of using spectroscopic technologies for determining material characteristics in situ for traditional pharmaceutical processing include the determination of drug content uniformity during powder blending and tablet manufacture, and drug- layering during pellet coating. Active pharmaceutical ingredient manu- facture in reactors and cell growth/ protein expression in bioreactors use in situ methods like focused-beam re flectance and dielectric spectros- copy, respectively, to monitor product attributes in real time. Another example of innovation in process analytical instrumentation and methodology is the recent implemen- tation of a mass spectrometry-based approach to simultaneously monitor the extensive array of product qual - ity attributes present with therapeutic molecules. This approach has success- fully enabled the real-time monitor- ing of bioreactors and quality control release, and it has the potential to re place several conventional electro- phoretic and chromatographic meth- ods currently used to release therapeu- tic molecules. The development of this new method is a prime example of pro- actively reevaluating the desired data source, and then transitioning away from using earlier PAT measurements that are less directly connected to the protein attributes. Deploying these and other types of process analytical instrumentation to gather data is the first step. The second is using advanced analytics software to derive insights and improve opera- tions. This data analytics component of a strong PAT methodology consists of empirical, multivariate, and first-prin- ciples modeling techniques, including mechanistic modeling, chemometrics, and statistics packages. Challenges: Data access, ease of analysis and company culture Although not always accurate, histori- cal data can often bring insight into the future performance of a process. In development, for example, identi - fying which unit operations are robust and which are not, along with the ef - fect on quality metrics, is key for de- fining the required work to shorten the scale-up process while ensuring a quality-by-design approach ready for filing. For example, the relationships of the process inputs to the respective critical quality attributes must be determined to define the design space of a process. General challenges in using PAT effec- tively include: l Access to all the relevant data: Data connectivity continues to be a tre - mendous source of frustration for getting the most value out of PAT. For example, an important aspect of PAT is its role in supporting an ef - fective quality-by-design approach for therapeutic molecule manu - facturing, which requires a deep, molecular-level understanding of the attributes crucial to the safety and efficacy of the medicine. Quite often, these datasets are either main- tained off-line or trapped in a pro- cess data historian. In either case, it takes extra effort, often in the form of spreadsheet gymnastics, to bring the datasets together and make them available to the engineer or scientist Figure 3. Use capsules to easily identify periods of interest during a run, from which pre- dictive models can be quickly developed. (a) Overview of using capsule logic to isolate specific conditions (e.g., by time, limit, pattern) to create combined conditions, and (b) Seeq helps identify only the periods of time where the process analytical instrument is functioning properly, and the process is in the proper mode of operation.

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