Optimization of a semi-batch tablet coating process for a continuous manufacturing line by design of experiments

AbstractContinuous manufacturing is increasingly used in the pharmaceutical industry, as it promises to deliver better product quality while simultaneously increasing production flexibility. GEA developed a semi-continuous tablet coater which can be integrated into a continuous tableting line, accelerating the switch from traditional batch production to the continuous mode of operation. The latter offers certain advantages over batch production, e.g., operational flexibility, increased process/product quality, and decreased cost. However, process understanding is the key element for process control. In this regard, computational tools can improve the fundamental understanding and process performance, especially those related to new processes, such as continuous tablet coating where process mechanics remain unclear. The discrete element method (DEM) and computational fluid dynamics (CFD) are two methods that allow transition from empirical process design to a mechanistic understanding of the individual process units. The developed coupling model allows to track the heat, mass, and momentum exchange between the tablet and fluid phase. The goal of this work was to develop and validate a high-fidelity CFD-DEM simulation model of the tablet coating process in the GEA ConsiGma® coater. After the model development, simulation results for the tablet movement, coating quality, and heat and mass transfer during the coating process were validated and compared to the experimental outcomes. The experimental and simulation results agreed well on all accounts measured, indicating that the model can be used in further studies to investigate the operating space of the continuous tablet coating process.

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Coating process optimization through in-line monitoring for coating weight gain using Raman spectroscopy and design of experiments

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/j.jpba.2018.03.001 ◽

2018 ◽

Vol 154 ◽

pp. 278-284 ◽

Cited By ~ 7

Author(s):

Byungsuk Kim ◽

Young-Ah Woo

Keyword(s):

Raman Spectroscopy ◽

Weight Gain ◽

Design Of Experiments ◽

Process Optimization ◽

Coating Process ◽

Coating Weight

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Managing API raw material variability in a continuous manufacturing line – Prediction of process robustness

International Journal of Pharmaceutics ◽

10.1016/j.ijpharm.2019.118525 ◽

2019 ◽

Vol 569 ◽

pp. 118525 ◽

Cited By ~ 6

Author(s):

F. Stauffer ◽

V. Vanhoorne ◽

G. Pilcer ◽

Pierre-François Chavez ◽

C. Vervaet ◽

...

Keyword(s):

Raw Material ◽

Continuous Manufacturing ◽

Process Robustness ◽

Manufacturing Line ◽

Raw Material Variability

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Step-wise approach to developing a scale-independent design space for functional tablet coating process

Drug Development and Industrial Pharmacy ◽

10.1080/03639045.2020.1742140 ◽

2020 ◽

Vol 46 (4) ◽

pp. 566-575

Author(s):

Veronika Debevec ◽

Tijana Stanić Ljubin ◽

Žiga Jeraj ◽

Tanja Rozman Peterka ◽

Borut Bratuž ◽

...

Keyword(s):

Design Space ◽

Coating Process ◽

Tablet Coating ◽

Independent Design

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Optimization of a photo resists coating process for photolithography in wafer manufacturing via design of experiments method

Microelectronics International ◽

10.1108/13565360610680730 ◽

2006 ◽

Vol 23 (3) ◽

pp. 26-32 ◽

Cited By ~ 11

Author(s):

Yung‐Kuang Yang

Keyword(s):

Design Of Experiments ◽

Coating Process ◽

Wafer Manufacturing

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On the Hardfacing Performance Optimization after Plasma Transfer Arc Experiments

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.3640 ◽

2011 ◽

Vol 189-193 ◽

pp. 3640-3646

Author(s):

Ming Der Jean ◽

Ming Cheng Li ◽

Tzu Hsuan Chien ◽

Ching Jyi Chen

Keyword(s):

Design Of Experiments ◽

Response Surface ◽

Performance Optimization ◽

Three Dimensional ◽

Surface Hardness ◽

Statistical Design ◽

Yield Enhancement ◽

Coating Process ◽

Hardness Model ◽

Coating Design

The paper describes response surface methodology (RSM) based on design of experiments and analysis of variance (ANOVA) as a statistical design while developing a robust plasma transfer arc (PTA)coating process. Based on ANOVA, The relative important parameters with respect to surface at hardness values were identified in the Taguchi design, where they were further used in predictors. In addition, we applied three-dimensional graphs in RSM to develop a robust PTA response surface yielding the desired-better area of a treated layer. In this study, a quadratic polynomial with a Box-Behnken design is utilized. The results reveal that RSM provides the effective methods as compared to the traditional trial-and-error method for exploring the effects of controlled factors on response. A very good agreement was observed, as evidenced by R-squared value, 90%, between the predicted and the experimental data, and its error percent is found to be approximately 3.801% in the PTA-coating process. It is clear that RSM model demonstrated better accuracy in predicting surface hardness for PTA-coating process. Accordingly, RSM based on design of experiments was used as statistical PTA-coating design tools combined with the hardness model. Device zone optimization and yield enhancement have been demonstrated.

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