Investigating metformin-active substances from different manufacturing sources by NIR, NMR, high-resolution LC-MS, and chemometric analysis for the prospective classification of legal medicines - Raimondo, Mariangela; Prestinaci, Francesca; Aureli, Federica; D’Ettorre, Giulia; Gaudiano, Maria Cristina (Frontiers in Analytical Science, 2023)
The characterisation of active substances is an essential tool to ensure traceability and authenticity of legal medicines. Metformin is a well established biguanide derivative recommended in oral formulations as first line treatment of type 2 diabetes. With increasing demand, Metformin is likely to be an attractive target for falsification and substandard production, which may pose health risks to consumers. Methods able to point out even small differences in active pharmaceutical ingredients (APIs) are deemed necessary. The detection of fraudulent practices in APIs is not straightforward and a single technique able to provide sufficient number of information to unambiguously address this issue is still not available. This study investigated an integrated analytical platform based on NIR, 1H-NMR, 13C-NMR and high resolution LC-MS combined with chemometrics to profile thirty-two Metformin hydrochloride samples originating from several global authorised manufacturers. The aim of the present study was to explore differences in the chemical characteristics of Metformin hydrochloride APIs to identify or predict a possible classification for each manufacturer’ in view of perspective authenticity studies. Different pre-processing methods were applied. Bucket tables for 1H- and 13C-NMR were obtained, while mass spectrometry data were processed in targeted and untargeted modes. Datasets were analysed individually and merged by multivariate unsupervised method, performing a Principal Component Analysis (PCA). The results evidenced differences in cluster behaviour depending on manufacturers. Each technique has shown a specific clustering tendency, highlighting how different analytical approaches are able to characterise Metformin APIs. Some manufacturers, however, showed the similar behaviour independently of the techniques. NIR and 1H-NMR were confirmed as the more predictive techniques, if taken individually, in particular 1H-NMR provided a good separation between the samples of the two most representative manufacturers. For LC-MS the targeted approach resulted in a separation in groups clearer than that of the untargeted approach. Nevertheless, the untargeted LC-MS approaches presented in this paper could be a possible alternative to obtain different information for drug substances with a number of different and complex synthetic pathways leading to several unknown impurities. Further grouping of manufacturers emerged by data fusion approach, highlighting its potential in the traceability of Metformin.