Four chemometric models enhanced by Latin hypercube sampling design for quantification of anti-COVID drugs: sustainability profiling through multiple greenness, carbon footprint, blueness, and whiteness metrics

Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210-400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.

A Green-and-White Integrative Analytical Strategy Combining Univariate and Chemometric Techniques for Quantifying Recently Approved Multi-Drug Eye Solution and Potentially Cancer-Causing Impurities: Application to the Aqueous Humor

BACKGROUND

Drug impurities are now seen as a major threat to the production of pharmaceuticals around the world and a major part of the global contamination problem, especially when it comes to carcinogenic impurities.

OBJECTIVE

We present the first spectrophotometric strategy based on a combination of univariate and multivariate methods as impurity profiling methods for the estimation of lignocaine (LIG) and fluorescein (FLS) with their carcinogenic impurities: 2,6-xylidine (XYL) and benzene-1,3-diol (BZD).

METHOD

The data processing strategy depends on overcoming unresolved bands by employing five affordable, accurate, selective, and sensitive methods. The methods applied were a direct UV univariate spectrophotometric analysis (D0) and four multivariate chemometric methods, including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm (GA-PLS). FLS analysis (1-16 μg/mL) was performed using the D0 method at 478 nm; then, the application of the ratio subtraction method (RSM) allowed the removal of interference caused by the FLS spectrum. From the resulting ratio spectra, LIG, XYL, and BZD can be efficiently determined by chemometrics. The calibration set was carefully selected at five concentration levels using a partial factorial training design, resulting in 25 mixtures with central levels of 160, 40, and 3 μg/mL for LIG, XYL, and BZD, respectively. Another 13 samples were applied to validate the predictive ability.

RESULTS

The statistical parameters demonstrated exceptional recoveries and smaller prediction errors, confirming the experimental model's predictive power.

CONCLUSIONS

The proposed approach was effectively tested using newly FDA-approved LIG and FLS pharmaceutical preparation and aqueous humor. Additionally, it was effectively assessed for whiteness, greenness, and sustainability using five assessment tools.

HIGHLIGHTS

With its remarkable analytical performance, sustainability, affordability, simplicity, and cost-efficiency, the proposed strategy is an indispensable tool for quality control and in situ analysis in little-equipped laboratories, increasing the proposed approach's surveillance ability.

Environmentally sustainable DRS-FTIR probe assisted by chemometric tools for quality control analysis of cinnarizine and piracetam having diverged concentration ranges: Validation, greenness, and whiteness studies

A novel diffuse reflectance fourier transform infrared spectroscopic method accompanied by chemometrics was optimized to fulfill the white analytical chemistry and green analytical chemistry principles for the quantification of cinnarizine and piracetam for the first time without any prior separation in their challenging pharmaceutical preparation, which has a pretty substantial difference in the concentration of cinnarizine/piracetam (1:16). Furthermore, the suggested method was used for cinnarizine/piracetam dissolution testing as an effective alternative to traditional methods. For the cinnarizine/piracetam dissolution tests, we used a dissolution vessel with 900 mL of phosphate buffer pH 2.5 at 37 °C ± 0.5 °C, then the sampling was carried out by frequent withdrawal of 20 µl samples from the dissolution vessel at a one-minute interval, over one hour, then representative fourier transform infrared spectra were recorded. To create a partial-least-squares regression model, a fractional factorial design with 5 different levels and 2 factors was used. This led to the creation of 25 mixtures, 15 as a calibration set and 10 as a validation set, with varying concentration ranges: 1-75 and 16-1000 μg/mL for cinnarizine/piracetam, respectively. Upon optimization of the partial-least-squares regression model, in terms of latent variables and spectral region, root mean square error of cross-validation of 0.477 and 0.270, for cinnarizine/piracetam respectively, were obtained. The optimized partial-least-squares regression model was further validated, providing good results in terms of recovery% (around 98 to 102 %), root mean square error of prediction (0.436 and 3.329), relative root mean square error of prediction (1.210 and 1.245), bias-corrected mean square error of prediction (0.059 and 0.081), and limit of detection (0.125 and 2.786) for cinnarizine/piracetam respectively. Ultimately, the developed method was assessed for whiteness, greenness, and sustainability using five assessment tools. the developed method achieved a greener national environmental method index and complementary green analytical procedure index quadrants with higher eco-scale assessment scores (91), analytical greenness metric scores (0.87), and red-greenblue 12 algorithm scores (89.7) than the reported methods, showing high practical and environmental acceptance for quality control of cinnarizine/piracetam.

An innovative nanoparticle-modified carbon paste sensor for ultrasensitive detection of lignocaine and its extremely carcinogenic metabolite residues in bovine food samples: Application of NEMI, ESA, AGREE, ComplexGAPI, and RGB12 algorithms

Nowadays, veterinary medicine residues have been viewed as a major threat to food safety worldwide, especially when dealing with carcinogenic residues. Herein, we present the first differential pulse voltammetric method for the quantification of lignocaine and its carcinogenic metabolite 2,6-xylidine residues in bovine food samples, aided by five greenness and whiteness assessment tools, including NEMI, ESA, ComplexGAPI, AGREE, and RGB12. The method depends on the electrochemical oxidation after modification of the carbon paste sensor with recycled Al₂O₃-NPs functionalized multi-walled carbon nanoparticles. The produced sensor (Al₂O₃-NPs/MWCNTs/CPE) was characterized using XRD, FT-IR, EDX, SEM, and TEM. As expected, the active surface area and electron transfer processes were accelerated by the modification, resulting in ultra-sensitive quantification with detection limits of 19.00 and 13.94 nM for lignocaine and 2,6-xylidine, respectively. In terms of greenness, whiteness, sustainability, analytical effectiveness, and economic and practical considerations, the proposed method outperforms the reported methods.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture

Background

Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form.

Objective

In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation.

Method

For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 μg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS.

Results

The models’ validation criteria displayed excellent recoveries and lower errors of prediction.

Conclusions

The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results.

Highlights

Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.