A Robust Spectrophotometric Method using Least Squares Support Vector Machine for Simultaneous Determination of Anti−Diabetic Drugs and Comparison with the Chromatographic Method

Document Type : Research Paper

Authors

1 Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran

2 Faculty of chemistry, Shahrood University of Technology, Shahrood, Iran

Abstract

In the present paper, the simultaneous spectrophotometric estimation of Metformin (MET) and Pioglitazone (PIO) in an antidiabetic drug called Actoplus MET based on least squares support vector machine (LS-SVM) was proposed. The optimum gamma (γ) and sigma (σ) parameters were found to be 825 and 90 with the root mean square error (RMSE) of 0.1343for MET, as well as 1000 and 350 with RMSE=0.4120 for PIO. Also, the mean recovery values of MET and PIO were 99.81% and 100.19%, respectively. Ultimately, the real sample was analyzed by High-Performance Liquid Chromatography (HPLC) reference method and the proposed procedure. Then, one-way analysis of variance (ANOVA) test at the 95 % confidence level was performed on achieved results from HPLC and LS-SVM methods. The statistical data of these methods showed that there were no significant differences between them.

Keywords

References

  1. S. A. Kumar, M. Debnath, J. V. L. N. S. Rao and D. G. Sankar, Simultaneous estimation of metformin, pioglitazone and glimepiride in bulk samples and in tablet dosage forms by using RP-HPLC in an isocratic mode, J. Chem. Pharm. Res. 7 (1) (2015) 941-951.
  2. A. M. I. Mohamed, F. A. F. Mohamed, S. Ahmed and Y. A. S. Mohamed, An efficient hydrophilic interaction liquid chromatographic method for the simultaneous determination of metformin and pioglitazone using high-purity silica column, J. Chromatogr. B. 997 (2015) 16–22.
  3.  M. M. Sebaiy, S. M. El-Adl, M. M. Baraka and A. A. Hassan, Rapid RP-HPLC method for simultaneous estimation of metformin, pioglitazone, and glimepiride in human plasma, Acta Chromatographica. 32 (1) (2019) 1-6.
  4. S. Havele and S. Dhaneshwar, Development and validation of a HPLC method for the determination of metformin hydrochloride, gliclazide and pioglitazone hydrochloride in multicomponent formulation, WebmedCentral. Pharm. Sci. 1 (10) (2010) WMC0010.
  5. A. D. Mali, S. Mali, A. Tamboli and R. Bathe, Simultaneous UV spectrophotometric methods for estimation of metformin HCl and glimepiride in bulk and tablet dosage form, Int. J. Adv. Pharm. 4 (6) (2015) 117-124.
  6. M. Kawaguchi-Suzuki, F. Bril, P. P. Sanchez, K. Cusi and R. F. Frye, A validated liquid chromatography tandem mass spectrometry method for simultaneous determination of pioglitazone, hydroxyl pioglitazone, and keto pioglitazone in human plasma and its application to a clinical study, J. Chromatogr. B. 969 (2014) 219–223.
  7. P. K. Chaturvedi and R. Sharma, Simultaneous spectrophotometric estimation and validation of three component tablet formulation containing pioglitazone hydrochloride, metformin hydrochloride and glibenclamide, Anal. Lett. 41 (12) (2008) 2133–2142.
  8. K. S. Lakshmi, T. Rajesh and S. Sharma, Simultaneous determination of metformin and pioglitazone by reversed phase HPLC in pharmaceutical dosage forms, Int. J. Pharm. Pharm. Sci. 1 (2)(2009) 162-166.
  9. G. S. Talele, D. D. Anghore and P. K. Porwal, Liquid chromatographic method for simultaneous estimation of metformin HCl, pioglitazone HCl and glibenclamide in rat plasma, Pharm Aspire. 10 (2018) 41-47.
  10. R. Peraman, K. K. Peruru, P. R. Yiragam and C. S. Gowra, Stability indicating RP-HPLC method for the simultaneous determination of atorvastatin calcium, metformin hydrochloride, and glimepiride in bulk and combined tablet dosage form, Malays. J. Pharm. Sci. 12 (2014) 33–46.
  11. G. S. Sandhu, S. S. Hallan and B. Kaur, Development of RP-HPLC method for simultaneous estimation of glimepiride, pioglitazone hydrochloride and metformin hydrochloride in a combined tablet dosage form, World J. Pharm. Pharm. Sci. 5 (2016) 1278-1285.
  12. S. A. Mulchand and B. R. Balkrishna, Novel RP-HPLC method development and validation for simultaneous estimation of metformin, voglibose and pioglitazone in bulk and triple fixed drug combinations pharmaceutical dosage form, J. Drug. Deliv. Ther. 9 (2019) 30-37.
  13. M. R. Rezk, S. M. Riad, G. Y. Mahmoud and A. A. Aleem, Simultaneous determination of pioglitazone and glimepiride in their pharmaceutical formulations, Der Pharma Chem. 3 (5) (2011) 176-184.
  14. A. Onal, Spectrophotometric and HPLC determinations of anti-diabetic drugs, rosiglitazone maleate and metformin hydrochloride, in pure form and in pharmaceutical preparations, Eur. J. Med. Chem. 44 (12) (2009) 4998–5005.
  15. A. Khorshid, N. S. Abdelhamid, E. A. Abdelaleem and M. M. Amin, Simultaneous Determination of metformin and pioglitazone in presence of metformin impurity by different spectrophotometric and TLC – densitometric methods, SOJ. Pharm. Pharm. Sci. 5 (3) (2018) 1-8.
  16. J. V. Susheel, D. Paul and T. K. Ravi, Development and validation of high-performance thin-layer chromatography method for the simultaneous densitometric determination of metformin and rosiglitazone in tablets, Austin J. Anal. Pharm. Chem. 3 (3) (2016) 1071-1074.
  17. M. A. Hegazy, M. R. El-Ghobashy, A. M. Yehia and A. A. Mostafa, Simultaneous determination of metformin hydrochloride and pioglitazone hydrochloride in binary mixture and in their ternary mixture with pioglitazone acid degradate using spectrophotometric and chemometric methods, Drug Test. Analysis. 1 (7) (2009) 339–349.
  18. H. M. Lotfy, D. Mohamed and S. Mowaka, A comparative study of smart spectrophotometric methods for simultaneous determination of sitagliptin phosphate and metformin hydrochloride in their binary mixture, Spectrochim. Acta A Mol. Biomol. Spectrosc. 149 (2015) 441-451.
  19. J. A. K. Suykens and J. Vandewalle, Least squares support vector machine classifiers, Neural Process. Lett. 9 (3) (1999) 293–300.
  20. Sh. Mofavvaz, M. R. Sohrabi and A. Nezamzadeh-Ejhieh, New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machineby spectrophotometry method, Spectrochim. Acta A Mol. Biomol. Spectrosc. 182 (2017) 105–115.
  21. A. Baghban, M. Bahadori, A. S. Lemraski and A. Bahadori, Prediction of solubility of ammonia in liquid electrolytes using least square support vector machines, Ain Shams Eng. J. 9 (4)(2018) 1303–1312.
  22. H. Han, X. Cui, Y. Fan and H. Qing, Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features, Appl. Therm. Eng. 154 (2019) 540-547.  
  23. M. R. Sohrabi and G. Darabi, The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants, Spectrochim. Acta A Mol. Biomol. Spectrosc. 152 (2016) 443–452.
  24. J. Guan, J. Zurada and A. S. Levitan, An adaptive neuro-fuzzy inference system based approach to real estate property assessment, J. Real Estate Res. 30 (4) (2008) 395-421.
  25. J. N. Miller and J. C. Miller, Statistics and Chemometrics for Analytical Chemistry, 6th Ed. Pearson Education Limited, Essex, England, 2010.
Iranian Journal of Mathematical Chemistry
Volume 11, Issue 1
March 2020
Pages 43-55

Files

Share

How to cite

Statistics