Tropical Journal of Pharmaceutical Research

Original Research Article | OPEN ACCESS

Artificial Neural Networks and Concentration Residual Augmented Classical Least Squares for the Simultaneous Determination of Diphenhydramine, Benzonatate, Guaifenesin and Phenylephrine in their Quaternary Mixture

Hany W Darwish^1,2 , Fadia H Metwally^2,3, Abdelaziz El Bayoumi², Ahmed A Ashour⁴

¹Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, PO Box 2457, Riyadh 11451, Saudi Arabia; ²Department of Analytical Chemistry, Faculty of Pharmacy, Cairo University, Kasr El - Aini Street, ET 11562, Cairo, Egypt; ³Ibn Sina National College for Medical Studies, AlMahjer Road, Jeddah, Saudi Arabia; ⁴Department of Analytical Chemistry, Faculty of Pharmacy, Misr International University, Cairo, Egypt.

For correspondence:- Hany Darwish Email: hdarwish@ksu.edu.sa

Received: 15 October 2014 Accepted: 12 November 2014 Published: 15 December 2014

Citation: Darwish HW, Metwally FH, Bayoumi AE, Ashour AA. Artificial Neural Networks and Concentration Residual Augmented Classical Least Squares for the Simultaneous Determination of Diphenhydramine, Benzonatate, Guaifenesin and Phenylephrine in their Quaternary Mixture. Trop J Pharm Res 2014; 13(12):2083-2090 doi: 10.4314/tjpr.v13i12.20

© 2014 The authors.
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Abstract

Purpose: To develop two multivariate calibration methods for the simultaneous spectrophotometric determination of a quaternary mixture composed of diphenhydramine HCl, benzonatate, guaifenesin and phenylephrine HCl in Bronchofree ™ capsules in the ratio of 2.5 : 10 : 10 : 1, respectively.

Methods: Novel artificial neural networks (ANNs) and concentration residual augmented classical least squares (CRACLS) methods were developed for the quantitative determination of the quaternary mixture. For proper analysis, a four-level, four-factor experimental design was established resulting in a training set of 16 mixtures containing different ratios of the four analytes. A validation set consisting of six mixtures was used to validate the prediction ability of the suggested models.

Results: ANNs and CRACLS methods were successfully applied for the analysis of raw materials and capsules. For ANNs method, % recovery of diphenhydramine HCl, benzonatate, guaifenesin and phenylephrine HCl in the capsules was 102.21 ± 1.34, 100.30 ± 1.17, 99.31 ± 2.00 and 98.50 ± 1.27, respectively. On the other hand, % recovery of the four analytes by CRACLS was 99.84 ± 2.22, 100.07 ± 0.63, 98.37 ± 1.42 and 97.99 ± 0.96, respectively.

Conclusion: The proposed methods can be applied for the quantitative determination of the four components without interference from excipients, thus obviating the need for preliminary extraction of analytes from the pharmaceutical formulation. The ability of the methods to deconvolute the highly overlapped UV spectra of the four components’ mixtures using low-cost and easy-to-handle instruments such as UV spectrophotometer is also an advantage.

Keywords: Artificial neural networks, Concentration residual augmented classical least squares, Quaternary mixture, Simultaneous determination