Resolving overlapped signals for respiratory drugs: a voltammetric and genetic algorithm-PLS-1 approach with enhanced sustainability for pharmaceutical and doping analysis.

The simultaneous electrochemical determination of co-formulated drugs with overlapping oxidation potentials poses a significant analytical challenge. This work reports the development and validation of a chemometrics-assisted voltammetric method for the concurrent analysis of ipratropium bromide (IPRA) and salbutamol (SAL) in their pharmaceutical combination and for SAL monitoring in urine. Using a copper(II) oxide-modified screen-printed electrode (CuO/SPE) and differential pulse voltammetry (DPV), well-defined but overlapping oxidation signals were obtained for both analytes in 0.1 M phosphate buffer (pH 5.0). To deconvolute these signals, a partial least squares (PLS-1) regression model was constructed. A genetic algorithm (GA) was critically employed as a variable selection tool, successfully identifying the most informative potential windows, reducing the dataset by approximately 30-36% (130 vs. 184 variables for IPRA and 118 vs. 184 for SAL), and simplifying the PLS-1 model from 6 to 5 latent variables for IPRA and from 5 to 4 for SAL. The optimized GA-PLS-1 model accurately quantified both drugs in synthetic mixtures and a commercial inhalant solution. Its practical utility was demonstrated through determination of SAL in spiked urine, achieving sub-picomolar detection in a complex biological matrix. This proof-of-concept application underscores the platform's extraordinary sensitivity and its potential for adaptation to clinical monitoring and anti-doping workflows following validation at regulatory-relevant concentrations. Scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDX), electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV) were utilized to characterize the surface morphology, electrochemical behavior and structural properties of the modified electrode. Furthermore, a comprehensive sustainability and innovation assessment using AGSA, CaFRI, a novel % circularity framework, and VIGI confirms the method's demonstrably superior sustainability profile and innovation, achieving a circularity score of 77.8% compared to 11.1-33.3% for reported chromatographic methods. This work showcases the power of combining intelligent variable selection with electroanalysis to create a precise, rapid, and sustainable tool for pharmaceutical and bioanalytical monitoring.