Pharmaceutical Technology - October 2021


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44 Pharmaceutical Technology APIs, EXCIPIENTS, AND MANUFACTURING eBOOK 2021 P h a r mTe c h . c o m Manufacturing I n continuous pharmaceutica l manufacturing, rea l-time assurance of critical quality attributes (CQAs) is highly desired. Among CQAs, control and assurance of tablet potency has been challenging due to lack of real-time measurement sensors tied to optimization programs. A modular self-contained toolbox developed using Python programming has been demonstrated to predict tablet potency based on a residence time distribution (RTD) model and apply a diversion strategy to divert out-of-specification tablets. The modular toolbox can be integrated with any commer- cially availabe tools to control tablet potency, can automatically calibrate the RTD model for different formulations and processes, and can be used to predict outlet concentrations, assuming accu- rately quantified inputs. Using RTD to evaluate quality Quality control is always an important topic in continuous pharmaceu- tical manufacturing. To ensure high quality tablets, examinations after and predictions during production can be applied (1). Using RTD-based control systems to predict the quality of tablets is an efficient method (2). The following describes the theory supporting a toolbox to predict certain component outlet concentrations of a continuous pharmaceutical manufacturing plant and shares simulation results. The toolbox can work in a standalone mode reading and writing data between .csv files and can make real-time predictions based on inlet concentration reads from these .csv file. The toolbox can output a diversion signal for the rejection gate, to divert tablets containing Predicting Tablet Potency in Continuous Manufacturing Revendra Singh A modular toolbox enables residence time distribution-based control for continuous pharmaceutical manufacturing. Revendra Singh is Assistant Research Professor, NSF-ERC C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey. ANTON - STOCK.ADOBE.COM

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