Comparison of several strategies for the deployment of a multivariate regression model on several handheld NIR instruments. Application to the quality control of medicines.

Chemometrics applied to spectroscopic measurements such as near-infrared are gaining more and more importance for quality control of pharmaceutical products. Handheld near-infrared devices show great promise as a medicines quality screening technique for post-marketing surveillance. These devices are able to detect substandard and falsified medicines in pharmaceutical supply chains and enable rapid action before these medicines reach patients. The instrumental and environmental changes, expected or not, can adversely affect the analytical performances of prediction models developed for routine applications. Based on a previous study, PLS prediction models were developed and validated on three similar handheld NIR transmission spectrophotometers of the same model and from same company. These models have shown to be effective in analyzing metformin tablet samples, but significant spectral differences between handheld systems complicated their deployment for routine analysis. In this study, different strategies have been applied and compared to correct the instrumental variations, including global modelling (GM) and calibration transfer methods (Direct Standardization, DS; Spectral Space Transformation, SST and Slope/Bias correction, SBC), considering the RMSEP and the accuracy profile as assessment criteria. The transfer methods showed good capabilities to maintain the predictive performances comparable to that of the global modelling approach, except for a remaining slight bias. This approach is interesting since very few standardization samples are required to develop an adequate transfer model. GM, SST and SBC were able to correct/handle drifts in the spectral responses of different handheld instruments and thus may help to avoid the need for a long, laborious, and costly full recalibration process due to inter-instrument variations.

Composition analysis of falsified chloroquine phosphate samples seized during the COVID-19 pandemic.

The proliferation of falsified medicines can cause serious public health issues, particularly in the context of a global pandemic such as the actual COVID-19 pandemic. Our study involved eight chloroquine phosphate medicines seized in Cameroon, Democratic Republic of Congo and Niger during March and May 2020. These suspect samples were first analyzed in a screening phase using field tools such as handheld Raman spectroscopy (TruScan) and then in a confirmation phase using laboratory tools such as hyperspectral Raman imaging and High Performance Liquid Chromatography (HPLC). The results confirmed the falsified nature of the samples, highlighting the presence of metronidazole at low dose in four samples (16.6, 15.2, 15.2 and 14.5 mg/tab), too low levels of chloroquine in two samples (2.4 and 20.2 mg/tab), and substitution of chloroquine phosphate by paracetamol in one sample (255.7 mg/tab). The results also confirmed that four samples had been adulterated with paracetamol in trace amounts and two of them presented traces of chloramphenicol.

Comparing the qualitative performances of handheld NIR and Raman spectrophotometers for the detection of falsified pharmaceutical products.

Over the last decade, the growth of the global pharmaceutical market has led to an overall increase of substandard and falsified drugs especially on the African market (or emerging countries). Recently, several methods using handheld/portable vibrational spectroscopy have been developed for rapid and on-field drug analysis. The objective of this work was to evaluate the performances of various NIR and Raman handheld spectrophotometers in specific brand identification of medicines through their primary packaging. Three groups of drug samples (artemether-lumefantrine, paracetamol and ibuprofen) were used in tablet or capsule forms. In order to perform a critical comparison, the analytical performances of the two analytical systems were compared statistically using three methods: hierarchical clustering algorithm (HCA), data-driven soft independent modelling of class analogy (DD-SIMCA) and hit quality index (HQI). The overall results show good detection abilities for NIR systems compared to Raman systems based on Matthews's correlation coefficients, generally close to one. Raman systems are less sensitive to the physical state of the samples than the NIR systems, it also suffers of the auto-fluorescence phenomenon and the signal of highly dosed active pharmaceutical ingredient (e.g. paracetamol or lumefantrine) may mask the signal of low-dosed and weaker Raman active compounds (e.g. artemether). Hence, Raman systems are less effective for specific product identification purposes but are interesting in the context of falsification because they allow a visual interpretation of the spectral signature (presence or absence of API).