Raman mapping-based non-destructive dissolution prediction of sustained-release tablets

• Published in: Journal of pharmaceutical and biomedical analysis 2022-04, Vol.212, p.114661-114661, Article 114661
• Main Authors: Galata, Dorián László, Zsiros, Boldizsár, Mészáros, Lilla Alexandra, Nagy, Brigitta, Szabó, Edina, Farkas, Attila, Nagy, Zsombor Kristóf
• Format: Article
• Language: English
• Subjects: Artificial neural network; Delayed-Action Preparations - chemistry; Dissolution prediction; Hypromellose Derivatives; Machine learning; Methylcellulose - chemistry; Particle size data; Raman chemical imaging; Solubility; Sustained-release tablets; Tablets - chemistry
• ISSN: 0731-7085; 1873-264X
• DOI: 10.1016/j.jpba.2022.114661

In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f2 similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology. [Display omitted] •HPMC particle size and concentration sustain the release of the drug.•Discretization method and wavelet analysis can extract information from Raman maps.•ANNs can accurately predict in vitro dissolution profiles from histograms.•Raman mapping is useful to gain information about dissolution non-destructively.