[en] In recent decades, more than 15% of antimalarials marketed in low- and middle-income countries were of poor quality, in which quinoline derivatives and quinine-based formulations represent 21%. These molecules are used for severe and/or cerebral malaria, as well as for falciparum-resistant malaria treatment [1,2]. This research work deals with the opportunity of using vibrational spectroscopy techniques combined with chemometrics for the quality control of local production of antimalarial drugs. Near Infrared (NIR) spectroscopy was chosen for its low-cost and rapid testing properties as well as the possibility to transfer calibrations to handheld devices to control medicines directly on field [3,4]. The main objective was to develop and validate PLS regression method using NIR spectroscopy to replace the high solvent consuming techniques such as HPLC. Indeed, HPLC analyses are difficult to perform in routine in low- and middle-income countries due to the supply and price of reagents and solvents. Furthermore, liquid chromatography devices often suffer from technical issues needing specialized technicians. The driven idea for the development of the quantitative method was the ease of implementation and use by low skilled staff together with the possibility to analyze different formulations. Multivariate analysis methods based on NIR spectroscopy have been developed and validated for the quality control of quinine sulfate tablets manufactured by a major local manufacturer in the Democratic Republic of Congo (DRC).
The samples are prepared by dissolution of quinine salt in HCl 0.5 M that is a cheap and easily available medium in low- and middle-income countries.
Calibration and validation samples were prepared by dissolving reference quinine sulfate in the presence of excipients using gravimetric data as reference.
After being validated, the method was used to analyze commercial quinine sulfate tablets of 500 mg collected in the local market. The method is considered as valid within the acceptance limits as shown in Fig 1. This approach gives the guarantee that each further measurement of unknown samples is included within the tolerance limits at the +/- 10% acceptance limits with a 5.0 % risk level.