MAR-APR 2019

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INTECH MARCH/APRIL 2019 17 PROCESS AUTOMATION In this article, case studies will illustrate an ef fective use of a PAT methodology for optimiz- ing the facility operations and active pharma- ceutical manufacturing processes required to produce quality medicines for patients. But first, let's look at why PAT is important for improving pharmaceutical manufacturing processes. Background: Why PAT? The demand for new therapies, loss of revenue from biosimilar and generic competition, and the rapid growth of emerging markets are forcing manufacturers to be more productive. To meet these challenges, pharmaceutical companies must capture process parameters and real-time measurements of critical quality attributes using analytical tech- niques and data analysis/model - ing techniques, often referred to as PAT. A robust PAT methodology is essential to achieve: l cost effectiveness: —increase system automation and control —reduce or eliminate produc- tion of waste —enable the use of alternative or less expensive raw materials —reduce production cycle times —reduce energy use —support a semi- to fully continuous processing approach l reliability: —achieve target quality consistently —prevent rejection of batches —reduce time from manu- facture to product release —demonstrate confidence in product quality l productive system perfor- mance: —simplify and shorten development cycles through increased process understanding —enable extended automation capability —achieve greater throughput with minimal additional time or resources —incorporate real-time release to reduce or eliminate many testing requirements PAT can help achieve these goals through pro- cess analysis, including the application of field- deployable instrumentation and chemometrics to monitor chemical or physical attributes, and the detection of events that cannot be derived from conventional physical measurements like temperature or pressure. Process analytical in- strumentation can be organized into many cat- egories: (1) physical property analyzers measur- ing attributes like viscosity, refractive index, and thermal conductivity, (2) combustion analyzers, (3) electrochemical analyzers measuring voltage or current that correlates with concentration, and (4) spectrophotometers that measure an at- tribute via electromagnetic interactions. Figure 1. Major elements of an effective PAT methodology include data cleansing, advanced analytics, reports and dashboards, and analytics workflows. Figure 2. An effective PAT methodology can be used to provide diagnostic, predictive, monitoring, prescriptive, and descriptive benefits. Advanced analytics Enabling capabilities

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