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<p>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&#xa0;µg mL⁻¹. NAS-based sensitivity assessment produced detection limits between 0.109 and 0.876&#xa0;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<sup>®</sup> dosage matrices. Multidimensional sustainability appraisal revealed favorable environmental and operational attributes. The method satisfied all National Environmental Methods Index criteria, achieved a Greenness Evaluation Metric for Analytical Methods score of 7.502, and displayed a calculated carbon footprint of 0.021&#xa0;kg CO₂/sample. Complementary operational and innovation assessments yielded Blue Applicability Grade Index and Violet Innovation Grade Index scores of 90.00 and 80.00, respectively, while the integrated Normalized Quality Score reached 83%. Collectively, the developed platform provides a cost-efficient and environmentally considerate analytical approach suitable for pharmaceutical quality control and preliminary bioanalytical screening in fortified plasma matrices, particularly in laboratories lacking access to advanced chromatographic instrumentation.</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月13日

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  • Ahmed Emad F. Abbas,
  • Nisreen F. Abo Talib,
  • Mohamed R. Elghobashy,
  • Omkulthom Al kamaly,
  • Michael K. Halim

摘要

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® dosage matrices. Multidimensional sustainability appraisal revealed favorable environmental and operational attributes. The method satisfied all National Environmental Methods Index criteria, achieved a Greenness Evaluation Metric for Analytical Methods score of 7.502, and displayed a calculated carbon footprint of 0.021 kg CO₂/sample. Complementary operational and innovation assessments yielded Blue Applicability Grade Index and Violet Innovation Grade Index scores of 90.00 and 80.00, respectively, while the integrated Normalized Quality Score reached 83%. Collectively, the developed platform provides a cost-efficient and environmentally considerate analytical approach suitable for pharmaceutical quality control and preliminary bioanalytical screening in fortified plasma matrices, particularly in laboratories lacking access to advanced chromatographic instrumentation.