Abstract
Hydroxychloroquine (HCQ) is prescribed to treat malaria and certain autoimmune diseases. Recent studies questioned its efficiency in relieving COVID-19 symptoms and improving clinical outcomes. This work presents a quality-by-design approach to develop, optimize, and validate a potentiometric sensor for the selective analysis of HCQ in the presence of its toxic impurities (key starting materials), namely 4,7-Dichloroquinoline (DCQ) and hydroxynovaldiamine (HND). The study employed a custom experimental design of 16 sensors with different ion exchangers, plasticizers, and ionophores. We observed the Nernstian slopes, correlation coefficients, quantification limit, response time, and selectivity coefficient for DCQ and HND. The computer software constructed a prediction model for each response. The predicted responses strongly correlate to the experimental ones, indicating model fitness. The optimized sensor achieved 93.8% desirability. It proved a slope of 30.57 mV/decade, a correlation coefficient of 0.9931, a quantification limit of 1.07 × 10(-6) M, a detection limit of 2.18 × 10(-7) M, and a fast response of 6.5 s within the pH range of 2.5-8.5. The sensor was successfully used to determine HCQ purity in its raw material. The sensor represents a potential tool for rapid, sensitive, and selective monitoring of HCQ purity during industrial production from its starting materials.