Active coating is an important unit operation in the pharmaceutical industry. The quality, stability, safety and performance of the final product largely depend on the amount and uniformity of coating applied. Active coating is challenging regarding the total amount of coating
[...] Read more.
Active coating is an important unit operation in the pharmaceutical industry. The quality, stability, safety and performance of the final product largely depend on the amount and uniformity of coating applied. Active coating is challenging regarding the total amount of coating and its uniformity. Consequently, there is a strong demand for tools, which are able to monitor and determine the endpoint of a coating operation. In previous work, it was shown that Raman spectroscopy is an appropriate process analytical tool (PAT) to monitor an active spray coating process in a pan coater . Using a multivariate model (Partial Least Squares—PLS) the Raman spectral data could be correlated with the coated amount of the API diprophylline. While the multivariate model was shown to be valid for the process in a mini scale pan coater (batch size: 3.5 kg cores), the aim of the present work was to prove the robustness of the model by transferring the results to tablets coated in a micro scale pan coater (0.5 kg). Method:
Coating experiments were performed in both, a mini scale and a micro scale pan coater. The model drug diprophylline was coated on placebo tablets. The multivariate model, established for the process in the mini scale pan coater, was applied to the Raman measurements of tablets coated in the micro scale coater for six different coating levels. Then, the amount of coating, which was predicted by the model, was compared with reference measurements using UV spectroscopy. Results:
For all six coating levels the predicted coating amount was equal to the amounts obtained by UV spectroscopy within the statistical error. Thus, it was possible to predict the total coating amount with an error smaller than 3.6%. The root mean squares of errors for calibration and prediction (root mean square of errors for calibration and prediction—RMSEC and RMSEP) were 0.335 mg and 0.392 mg, respectively, which means that the predictive power of the model is not dependent on the scale or the equipment. Conclusion:
The scale-down experiment showed that it was possible to transfer the PLS model developed on a mini scale coater to a micro scale coater.