Adjusted Green Spectrophotometric Determination of Favipiravir and Remdesivir in Pharmaceutical Form and Spiked Human Plasma Sample Using Different Chemometric Supported Models

The environmentally friendly design of analytical methods is gaining interest in pharmaceutical analysis to reduce hazardous environmental impacts and improve safety and health conditions for analysts. The adaptation and integration of chemometrics in the development of environmentally friendly analytical methods is strongly recommended in the hope of promising benefits. Favipiravir and remdesivir have been included in the COVID-19 treatment guidelines panel of several countries. The main objective of this work is to develop green, tuned spectrophotometric methods based on chemometric based models for the determination of favipiravir and remdesivir in spiked human plasma. The UV absorption spectra of favipiravir and remdesivir has shown overlap to some extent, making simultaneous determination difficult. Three advanced chemometric models, classical least squares, principal component regression, and partial least squares, have been developed to provide resolution and spectrophotometric determination of the drugs under study. A five-level, two-factor experimental design has been used to create the described models. The spectrally recorded data of favipiravir and remdesivir has been reviewed. The noise region has been neglected as it has a negative impact on the significant data. On the other hand, the other spectral data provided relevant information about the investigated drugs. A comprehensive evaluation and interpretation of the results of the described models and a statistical comparison with accepted values have been considered. The proposed models have been successfully applied to the spectrophotometric determination of favipiravir and remdesivir in pharmaceutical form spiked human plasma. In addition, the environmental friendliness of the described models was evaluated using the analytical eco-scale, the green analytical procedure index and the AGREE evaluation method. The results showed the compliance of the described models with the environmental characteristics.