QbD-steered HPTLC approach for concurrent estimation of six co-administered COVID-19 and cardiovascular drugs in different matrices: greenness appraisal.

Many COVID-19 sufferers have a history of cardiovascular illnesses, which makes them more likely to develop severe COVID-19. Such patients were advised by experts to prioritize drug therapies based on their doctor's commendations to avoid exacerbating their basic illnesses. Therefore, developing an analytical methodology for the concurrent estimation of medications prescribed for co-treating cardiovascular and COVID-19 illnesses is becoming critical in both bioavailability hubs and QC units. Herein, an inventive, rapid, and affordable HPTLC approach was developed, and its conditions were optimized employing the full factorial design approach for the concurrent estimation of aspirin, atorvastatin, atenolol, losartan, remdesivir, and favipiravir as co-administered medications, either with salicylic acid or not. Using the desirability function, the experimental design approach could forecast the best eluent system for optimal resolution results. On HPTLC-silica plates, the above-mentioned medications were separated utilizing an eluent system of ethyl acetate, methylene chloride, methanol, and ammonia (6:4:4:1 by volume), and their spots were detected at 232 nm. The proposed methodology was evaluated following ICH prerequisites and applied successfully to the medications' dosage forms, human plasma, and buffered dissolution media with superb recovery proportions and no intrusiveness from formulations' additives or plasma matrices. Five metrics were employed to appraise the suggested technique's greenness: AGREE, eco-scale, Raynie and Driver, GAPI, and NEMI. The sensitivity, large sample capacity, and short run duration (15 min) of the proposed methodology confirm its appositeness for regular estimation of the above-mentioned medications.