ArticleBMC chemistry2026
Candexch algorithm-enhanced chemometric determination of a novel anti-COVID-19 therapeutics in plasma and paxlovid formulation using advanced multivariate modeling: a sustainability-centered bioanalytical approach.
Article in BMC chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This work reports the development of an algorithm-assisted chemometric spectrophotometric method for the concurrent quantification of anti-COVID-19 therapeutics nirmatrelvir, ritonavir, and the active molnupiravir metabolite N4-hydroxycytidine in pharmaceutical formulations and human plasma. A structured fractional five-level factorial calibration design consisting of 25 mixtures was employed to construct the calibration dataset, while the external validation set was generated using D-optimal sample selection via the Candexch algorithm to ensure uniform coverage of the experimental domain and minimize sampling bias relative to random dataset partitioning. Quantitative modeling was performed using four multivariate regression strategies: Principal Component Regression (PCR), Genetic Algorithm-assisted Partial-Least Squares (GA-PLS), Firefly Algorithm-assisted Partial-Least Squares (FA-PLS), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Model optimization, including latent variable selection, wavelength selection, and parameter tuning, was performed exclusively using the calibration dataset through internal cross-validation (LOO-CV) based on minimum RMSECV, while the external validation set was kept completely independent and used only for final prediction. Among the models that were assessed, the MCR-ALS algorithm demonstrated the best overall predictive performance, yielding correlation coefficients exceeding 0.9997 and root mean square prediction errors ranging from 0.076 to 0.213 µg mL⁻¹. NAS-based sensitivity assessment produced detection limits between 0.109 and 0.876 µg mL⁻¹, demonstrating adequate sensitivity within the investigated concentration ranges. Matrix-matched validation employing 25 calibration and 13 external validation mixtures prepared in fortified human plasma confirmed predictive robustness across both plasma and Paxlovid
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