Advancing green and white assessment: DFT-assisted spectrofluorimetry for accurate favipiravir quantification in...

Recently, the recognition of computational chemistry potential is growing, because of its applicability for design of substances and studying their properties using computer programs and modelling approaches that help solve various problems. Computational chemistry is involved in the design of advanced fluorescent probes which can be employed in sensing of various analytes. Favipiravir (FVR) is an antiviral drug recommended for the treatment of COVID-19, known for its broad-spectrum activity against RNA viruses by inhibiting viral RNA-dependent RNA polymerase. This study introduces the first-ever integration of computational density functional theory (DFT) and experimental spectrofluorimetric approach to design a highly sensitive spectrofluorimetric method for estimation of FVR in its bulk form and human plasma. The DFT analysis was carried out to investigate the affinity of Zirconium (Zr⁴⁺) to FVR in aqueous solution and explore the formation of FVR-Zr⁴⁺ chelate. The combuted formation energy (ΔG = -416.5 kcal/mol) of [Zr (FVR)₄]⁴⁺ complex confirmed the strong of ability of Zr⁴⁺ to recognize FVR in solution and evidenced the strong nature of the Zr⁴⁺- O and N coordination bonds. The results revealed a significant enhancement in the weak native fluorescence of FVR upon formation of the complex. Various experimental parameters were examined, further the established method was validated according to ICH standards where linearity range was achieved in the range of 0.50-200.0 ng mL^-1, with low detection limit reached 32.99 pg mL^-1. The developed DFT-assisted spectrofluorimetric methodology was successfully employed for FVR assessment in human plasma samples with good recoveries (98.74 -100.10 %) and relative standard deviation did not exceed 1.80 %. Moreover, the proposed method's eco-friendliness and sustainability were evaluated through four metrics (Red/Green/Blue 12 Algorithm (RGB12), Green Solvent Selection Tool (GSST), Analytical Greenness Metric (AGREE), and Analytical Greenness Metric for Sample Preparation (AGREEprep)), demonstrating its superiority over the existing methods in terms of using safer solvents, reduced sample preparation procedures, and higher overall greenness. Additionally, the high sensitivity and applicability of the proposed method to the reliable analysis of both bulk drug and plasma samples make it efficient and practical for routine FVR analysis in both pharmaceutical and clinical settings. Furthermore, this study opens new avenues for extending computational and experimental approaches to analyze FVR in real samples and explore other drug-metal interactions, contributing to advancements in drug analysis and mechanistic studies.