Industrial application of QbD and NIR chemometric models in quality improvement of immediate release tablets

Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets

Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by-design, and real-time release testing. The aim of this work is to develop a digital UV/VIS imaging-based system for predicting the in vitro dissolution of meloxicam-containing tablets. The alteration of the dissolution profiles of the samples required different levels of the critical process parameters, including compression force, particle size and content of the API. These process parameters were predicted non-destructively by multivariate analysis of UV/VIS images taken from the tablets. The dissolution profile prediction was also executed using solely the image data and applying artificial neural networks. The prediction error (RMSE) of the dissolution profile points was less than 5%. The alteration of the API content directly affected the maximum concentrations observed at the end of the dissolution tests. This parameter was predicted with a relative error of less than 10% by PLS models that are based on the color components of UV and VIS images. In conclusion, this paper presents a modern, non-destructive PAT solution for real-time testing of the dissolution of tablets.

Chitosan-based buccal mucoadhesive patches to enhance the systemic bioavailability of tizanidine

Tizanidine hydrochloride (TZN) is a muscle relaxant used to treat a variety of disorders such as painful muscle spasms and chronic spasticity. TZN has low oral bioavailability due to extensive first-pass metabolism and is used orally at a dose of 6-24 mg per day. In the present study, buccal patches were prepared by solvent casting method using chitosan glutamate (Chi-Glu) and novel chitosan azelate (Chi-Aze) which was synthesised in-house for the first time, to enhance the bioavailability of TZN by bypassing first-pass metabolism. The characterisation, mucoadhesion and drug release studies were performed. Chi-Aze patches retained their integrity longer in the buccal medium and showed higher ex vivo drug permeability compared to that prepared with Chi-Glu. In vivo studies revealed that buccal formulation fabricated with Chi-Aze (3%) showed approx 3 times more bioavailability than the orally administered commercial product. Results of the studies indicate that Chi-Aze, prepared by conjugation of chitosan and a fatty acid, the patch formulation is a promising buccal mucoadhesive system due to the physical stability in buccal medium, the good mucoadhesiveness and the high TZN bioavailability. Moreover, Chi-Aze patch might be an alternative to oral formulations of TZN to reduce the dose and frequency of drug administration.

UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks

The potential of machine vision systems has not currently been exploited for pharmaceutical applications, although expected to provide revolutionary solutions for in-process and final product testing. The presented paper aimed to analyze the particle size of meloxicam, a yellow model active pharmaceutical ingredient, in intact tablets by a digital UV/VIS imaging-based machine vision system. Two image processing algorithms were developed and coupled with pattern recognition neural networks for UV and VIS images for particle size-based classification of the prepared tablets. The developed method can identify tablets containing finer or larger particles than the target with more than 97% accuracy. Two algorithms were developed for UV and VIS images for particle size analysis of the prepared tablets. According to the applied statistical tests, the obtained particle size distributions were similar to the results of the laser diffraction-based reference method. Digital UV/VIS imaging combined with multivariate data analysis can provide a new non-destructive, rapid, in-line tool for particle size analysis in tablets.