Adjusted green HPLC determination of nirmatrelvir and ritonavir in the new FDA approved co-packaged pharmaceutical dosage using supported computational calculations

Abstract

AbstractThe greening of analytical methods has gained interest in the quantitative analysis field to reduce environmental impact and improve safety health conditions for analysts. Nirmatrelvir plus ritonavir is a new FDA approved co-packaged medication developed for the treatment of COVID-19. The aim of this research was to develop green fitted HPLC method using pre experimental computational testing of different stationary phases as well as selecting mobile phase regarding to green analytical chemistry principles.Computational study was designed to test the physical interaction between nirmatrelvir and ritonavir and different columns (C8, C18, Cyano column). The study showed that the C18 column was better for simultaneous HPLC analysis of the cited drugs. Regarding to green point of view, mobile phase consisted of ethanol: water (80:20, v/v) provided an efficient chromatographic separation of nirmatrelvir and ritonavir within a short analytical run time, reasonable resolution and excellent sensitivity. Isocratic elution was performed on a selected C18 column and a green adjusted mobile phase at flow rate of 1 mL/min and UV detection at 215 nm. The chromatographic system allowed complete baseline separation with retention times of 4.9 min for nirmatrelvir and 6.8 min for ritonavir. The method succeeded to determine nirmatrelvir and ritonavir over the concentration range of 1.0–20.0 μg/mL in the pure form and in pharmaceutical dosage form. Greenness profiles of the applied HPLC method was assessed using analytical eco-scale, the green analytical procedure index and the AGREE evaluation method. The results revealed adherence of the described method to the green analytical chemistry principles. The authors hope to provide a promising challenge for achieving green goals through integrating computational tools and applying them with green assessment metrics.