In-Line Film Coating Thickness Estimation of Minitablets in a Fluid-Bed Coating Equipment
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Film coating thickness of minitablets was estimated in-line during coating in a fluid-bed equipment by means of visual imaging. An existing, commercially available image acquisition system was used for image acquisition, while dedicated image analysis and data analysis methods were developed for this purpose. The methods were first tested against simulated minitablet’s images and after that examined on a laboratory-scale fluid-bed Wurster coating process. An observation window cleaning mechanism was developed for this purpose. Six batches of minitablets were coated in total, using two different dispersions, where for the second dispersion coating endpoint was determined based on the in-line measurement. Coating thickness estimates were calculated from the increasing size distributions of the minitablet’s major and minor lengths, assessed from the acquired images. Information on both the minitablet’s average band and average cap coating thicknesses was obtained. The in-line coating thickness estimates were compared to the coating thickness weight gain calculations and the optical microscope measurements as a reference method. Average band coating thickness estimate was found the most accurate in comparison to microscope measurements, with root mean square error of 1.30 μm. The window cleaning mechanism was crucial for the accuracy of the in-line measurements as was evident from the corresponding decrease of the root mean square error (9.52 μm, band coating thickness). The presented visual imaging approach exhibits accuracy of at least 2 μm and is not susceptible to coating formulation or color variations. It presents a promising alternative to other existing techniques for the in-line coating thickness estimation.
KEY WORDSminitablets fluid-bed coating film coating thickness visual imaging PATVIS APA
The authors would like to thank Lek Pharmaceuticals d.d. (Sandoz Development Center Slovenia) for generously providing the minitablet cores used in this study.
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