The application of coating films is an important step in the manufacture of pharmaceutical tablets. Understanding the phenomena taking place during coating spray application provides important information that can be used to reduce the number of defective tablets and select the optimal conditions for the coating process. In this work, we investigate spray impact and film spreading on a tablet while this passes through the spray-zone in a rotating coating drum. To simulate spray impingement, we developed an one-dimensional (1D) spreading model that is based on the mechanical energy equation. We assumed the spray to be uniform and we divided it into arrays of droplets that impinge successively on the substrate orthogonally to its surface. In the mechanical energy equation that describes the coating spreading, we accounted for the rate of work done on the surface of the liquid coating film by the impinging droplets that leads to volume change (film spreading and thickness increase). The novel model we propose in this work can calculate the coating spreading rate and thickness. We implemented the mathematical model employing the gPROMS Modelbuilder platform. To study the effect of coating properties and process parameters on the film spreading rate and on the final liquid film thickness, we performed variance-based sensitivity analysis. The model predictions are in good agreement with experimental data found in the literature.
Mathematical modeling of pharmaceutical manufacturing processes can provide insights and understanding regarding the key factors impacting product quality. In this study, we describe the development of a dynamic model for a stage in an active pharmaceutical ingredient (API) manufacturing process, its calibration and validation versus industrial experimental data, and its use to address three objectives: (1) assessment of process operating parameter criticality on key performance indicators (KPIs); (2) confirming whether the considered process operating space safely respected limits of critical quality attribute (CQA) impurities; and(3) finding process setpoints that can potentially improve the KPIs. Objective 1 used global sensitivity analysis (GSA) to find that only operating parameters associated with the reactor were significant. Objectives 2 and 3 used nonlinear optimization, confirming that impurity limits are respected at any point in the considered process operating space and suggesting a shifted process setpoint that could allow enhanced yield (∼4% absolute increase) and reduced impurity content (∼0.5 mol % absolute reduction).
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