scholarly journals Simultaneous Determination of Drugs Affecting Central Nervous System (CNS) in Bulk and Pharmaceutical Formulations Using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS)

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Heba Shaaban ◽  
Ahmed Mostafa ◽  
Bushra Al-Zahrani ◽  
Bushra Al-Jasser ◽  
Raghad Al-Ghamdi
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Least Squares ◽  
Pharmaceutical Formulations ◽  
Pharmaceutical Products ◽  
Alternating Least Squares ◽  
Multivariate Curve Resolution ◽  
Pharmaceutical Dosage Forms ◽  
Curve Resolution ◽  
Significant Difference

The quality of medications is important to maintain the overall health care of patients. This study aims to develop and validate a spectrophotometric method using multivariate curve resolution-alternating least squares (MCR-ALS) with correlation constraint for simultaneous resolution and quantification of selected drugs affecting the central nervous system (imipramine, carbamazepine, chlorpromazine, haloperidol, and phenytoin) in different pharmaceutical dosage forms. Figures of merit such as root-mean-square error of prediction, bias, standard error of prediction, and relative error of prediction for the developed method were calculated. High values of correlation coefficients ranged between 0.9993 and 0.9998 reflected high predictive ability of the developed method. The results are linear in the concentration range of 0.3–5 μg/mL for carbamazepine, 0.3–15 μg/mL for chlorpromazine, 0.5–10 μg/mL for haloperidol, 0.5–10 μg/mL for imipramine, and 3–20 μg/mL for phenytoin. The optimized method was successfully applied for the analysis of the studied drugs in their pharmaceutical products without any separation step. The optimized method was also compared with a reported HPLC method using Student’s t test and F ratio at 95% confidence level, and the results showed no significant difference regarding accuracy and precision. The proposed chemometric method is fast, reliable, and cost-effective and can be used as an eco-friendly alternative to chromatographic techniques for the analysis of the studied drugs in commercial pharmaceutical products.

scholarly journals Simultaneous Determination of Over-the-Counter Pain Relievers in Commercial Pharmaceutical Products Utilizing Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) Multivariate Calibration Model

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Heba Shaaban ◽  
Ahmed Mostafa ◽  
Zahra Almatar ◽  
Reem Alsheef ◽  
Safia Alrubh
Keyword(s):  
Least Squares ◽  
Multivariate Calibration ◽  
Pharmaceutical Products ◽  
Alternating Least Squares ◽  
Multivariate Curve Resolution ◽  
Calibration Model ◽  
Over The Counter ◽  
Curve Resolution ◽  
Relative Standard ◽  
Significant Difference

The quality of over-the-counter (OTC) pain relievers is important to ensure the safety of the marketed products in order to maintain the overall health care of patients. In this study, the multivariate curve resolution-alternating least squares (MCR-ALS) chemometric method was developed and validated for the resolution and quantification of the most commonly consumed OTC pain relievers (acetaminophen, acetylsalicylic acid, ibuprofen, naproxen, and caffeine) in commercial drug formulations. The analytical performance of the developed chemometric methods such as root mean square error of prediction, bias, standard error of prediction, relative error of prediction, and coefficients of determination was calculated for the developed model. The obtained results are linear with concentration in the range of 0.5–7 μg/mL for acetaminophen and 0.5–3.5 and 0.5–3 μg/mL for naproxen and caffeine, respectively, while the linearity ranges for acetyl salicylic acid and ibuprofen were 1–15 μg/mL. High values of coefficients of determination ≥0.9995 reflected high predictive ability of the developed model. Good recoveries ranging from 98.0% to 99.7% were obtained for all analytes with relative standard deviations (RSDs) not higher than 1.62%. The optimized method was successfully applied for the analysis of the studied drugs either in their single or coformulated pharmaceutical products without any separation step. The optimized method was also compared with a reported HPLC method using paired t-test and F-ratio at 95% confidence level, and the results showed no significant difference regarding accuracy and precision. The developed method is eco-friendly, simple, fast, and amenable for routine analysis. It could be used as a cost-effective alternative to chromatographic techniques for the analysis of the studied drugs in commercial formulations.

scholarly journals Eco-Friendly Pharmaceutical Analysis of Multicomponent Drugs Coformulated in Different Dosage Forms Using Multivariate Curve Resolution and Partial Least Squares: A Comparative Study

2019 ◽  
Vol 102 (2) ◽  
pp. 465-472 ◽  
Author(s):  
Ahmed Mostafa ◽  
Heba Shaaban ◽  
Mishal Almousa ◽  
Mishal Al Sheqawi ◽  
Muntdher Almousa
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Dosage Forms ◽  
Multivariate Curve Resolution ◽  
Control Analysis ◽  
Least Squares Regression ◽  
Pharmaceutical Dosage Forms ◽  
Curve Resolution ◽  
Target Analytes ◽  
Significant Difference

Abstract Background: Considering the environmental impact of analytical procedures necessitates replacing the polluting analytical methods with green alternatives. Objective: This study aims to develop and validate a multivariate curve resolution–alternating least-squares (MCR-ALS) method with correlation constraint for the simultaneous determination of theophylline, ambroxol, and guaifenesin as target analytes in the presence of methylparaben and propylparaben as interfering components. In addition, a partial least-squares regression (PLSR) method was also developed andoptimized. Method: The developed methods were validated according to International Conference on Harmonization guidelines and successfully applied for the quantification of the target analytes in different pharmaceutical dosage forms. Results: Figures of merit such as root mean square error of prediction, bias, standard error of prediction, and relative error of prediction for both models were calculated, and they showedsimilar and satisfactory results. Correlation coefficients ranged between 0.9988 and 0.9992, reflectinghigh predictive ability. The optimized methods werecompared with a reported HPLC method using one-wayanalysis of variance and showed no significant difference regarding accuracy and precision. Conclusions: The proposed chemometrics methods can be used as an eco-friendly alternative for chromatographic techniques for the quality control analysis of the studied mixture in different pharmaceutical dosage forms. Highlights: An MCR-ALS model was developed. The developed model was compared with a PLSR model. Both models were validated and successfully used for the determinationof a multicomponent pharmaceutical mixture. The developed method is eco-friendly, fast, reliable, and cost-effective.

scholarly journals Quantitative analysis and resolution of pharmaceuticals in the environment using multivariate curve resolution-alternating least squares (MCR-ALS)

2019 ◽  
Vol 69 (2) ◽  
pp. 217-231 ◽  
Author(s):  
Ahmed Mostafa ◽  
Heba Shaaban
Keyword(s):  
Least Squares ◽  
Water Samples ◽  
Alternating Least Squares ◽  
Multivariate Curve Resolution ◽  
Least Squares Regression ◽  
Environmental Matrices ◽  
Curve Resolution ◽  
Real Water Samples ◽  
Target Analytes ◽  
Spectrophotometric Data

Abstract The study presents the application of multivariate curve resolution alternating least squares (MCR-ALS) with a correlation constraint for simultaneous resolution and quantification of ketoprofen, naproxen, paracetamol and caffeine as target analytes and triclosan as an interfering component in different water samples using UV-Vis spectrophotometric data. A multivariate regression model using the partial least squares regression (PLSR) algorithm was developed and calculated. The MCR-ALS results were compared with the PLSR obtained results. Both models were validated on external sample sets and were applied to the analysis of real water samples. Both models showed comparable and satisfactory results with the relative error of prediction of real water samples in the range of 1.70–9.75 % and 1.64–9.43 % for MCR-ALS and PLSR, resp. The obtained results show the potential of MCR-ALS with correlation constraint to be applied for the determination of different pharmaceuticals in complex environmental matrices.

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