Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Analysis

Editor-in-Chief

ISSN (Print): 1573-4129
ISSN (Online): 1875-676X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Chemometrics-assisted Spectrophotometric Method for Simultaneous Estimation of Antipsychotic Drugs in Biological Fluid

Author(s): Mojdeh Alibakhshi, Mahmoud Reza Sohrabi* and Mehran Davallo

Volume 17, Issue 5, 2021

Published on: 27 January, 2020

Page: [655 - 667] Pages: 13

DOI: 10.2174/1573412916666200127150554

Price: $65

Abstract

Background: Haloperidol (HP) and Risperidone (RIS) are antipsychotic drugs and the simultaneous determination of these drugs is important. Estimation of HP and RIS alone or in combination with other drugs has been performed in a variety of ways.

Objective: The aim of this paper was to propose a rapid, simple, accurate, and robust method for the simultaneous determination of HP and RIS using Artificial Neural Networks (ANNs), Partial Least Squares (PLS), and Principal Component Regression (PCR) methods along with spectrophotometry technique.

Methods: The simultaneous spectrophotometric determination of HP and RIS in synthetic mixtures and biological fluid was performed by applying ANNs Containing Feed-forward Backpropagation (FFBP) and Radial Basis Function (RBF) networks as intelligent methods, as well as PLS, and principal component regression PCR as multivariate calibration methods. The Levenberg- Marquardt (LM), Scaled Conjugate Gradient (SCG), and Resilient Back-propagation (RP) algorithms with different layers and neurons were used in FFBP network and obtained results were compared with each other.

Results: Among various algorithms of the FFBP network, the LM algorithm was selected as the best model with a lower Mean Square Error (MSE). MSE of the RBF model was 1.46×10-25 and 1.62×10-23 for HP and RIS, respectively. On the other hand, the mean recovery of PLS and PCR was 99.91%, 100.01% and 98.60%, 101.90% for HP and RIS, respectively.

Conclusion: The proposed models and High-Performance Liquid Chromatography (HPLC) as a reference method were compared with each other by one-way Analysis of Variance (ANOVA) test at the 95 % confidence level for the urine sample. It was observed that the developed methods presented comparable results for the simultaneous determination of HP and RIS.

Keywords: Risperidone, haloperidol, intelligent methods, multivariate calibration methods, spectrophotometry, biological fluid.

« Previous Next »
Graphical Abstract

[1]
Wate, S.P.; Borkar, A.A. Simultaneous spectrophotometric estimation of haloperidol and trihexyphenidyl in tablets. Indian J. Pharm. Sci., 2010, 72(2), 265-267.
[http://dx.doi.org/10.4103/0250-474X.65016] [PMID: 20838539]
[2]
Petkovska, R.; Dimitrovska, A. Use of chemometrics for development and validation of an RP-HPLC method for simultaneous determination of haloperidol and related compounds., Acta Pharm., 2008, 58(3), 243-256. http://dx.doi.org/10.2478/v10007-008-0019-y.
[PMID: 19103562]
[3]
Higashi, Y.; Kitahara, M.; Fujii, Y. Simultaneous analysis of haloperidol, its three metabolites and two other butyrophenone-type neuroleptics by high performance liquid chromatography with dual ultraviolet detection. Biomed. Chromatogr., 2006, 20(2), 166-172.
[http://dx.doi.org/10.1002/bmc.547] [PMID: 16034821 ]
[4]
Abu Shawish, H.M.; Tamous, H.; Shaheen, A.A.; Abed Almonem, K.I.; Awad Elgamel, A.; Allham, W.S. Determination of haloperidol drug in ampoules and in urine samples using a potentiometric PVC-membrane and graphite coated wire electrodes. Marmara Pharm. J., 2017, 21, 110-120.
[http://dx.doi.org/10.12991/marupj.259888]
[5]
Hasan, M.; Al Masud, A.; Ahmed, J. development and validation of spectrophotometric method for the determination of risperidone in bulk drug and pharmaceutical formulation. Int. J. Pharm. Sci. Res., 2011, 2, 378-382.
[6]
Locatelli, I.; Mrhar, A.; Grabnar, I. Simultaneous determination of risperidone and 9-hydroxyrisperidone enantiomers in human blood plasma by liquid chromatography with electrochemical detection. J. Pharm. Biomed. Anal., 2009, 50(5), 905-910.
[http://dx.doi.org/10.1016/j.jpba.2009.06.013] [PMID: 19589654]
[7]
Kokane, B.; Kokane, V.J.; Dabhade, P.S.; Kawade, S.N. RP-HPLC method development and validation for estimation of risperidone in bulk & dosage forms. Int. J. Pharm. Sci. Rev. Res., 2019, 54, 29-32.
[8]
Patel, D.; Patel, J. development and validation of RP-HPLC method for simultaneous estimation of risperidone and trihexyphenidyl hydrochloride in tablet dosage forms. Int. J. Pharm. Sci. Rev. Res., 2010, 4, 85-88.
[9]
Lakshmi Prasanna, I.; Naidu, G.T.; Fathima, N.; Chakravarthy, I.E.; Abdul Huq, G. spectrophotometric oxidation method for the determination of risperidone in the presence of trihexyphenydyl HCl by using bromate-bromide mixture as an oxidant Int. J. Res. Trends. Innov. (Camb., Mass.), 2018, 3, 28-32.
[10]
Nejedly, T.T.; Pilarova, P.; Kastner, P.; Blazkova, Z.; Klimes, J. development and validation of rapid UHPLC method for determination of risperidone and its impurities in bulk powder and tablets. Int. J. Res. Pharm. Chem., 2014, 4, 261-266.
[11]
Prasad, C.V.N.; Parihar, C.; Rama Chowdhary, T.; Purohit, S.; Parimoo, P. Simultaneous determination of atenolol-amlodipine and haloperidol-trihexyphenidyl in combined tablet preparations by derivative spectroscopy. Pharm. Pharmacol. Commun., 1998, 4, 325-330.
[12]
Amulya, E.; Naveen Kumar, N.; Mounika, C.H.; Kowmudi, V.; Supriya, N.; Ramya Madhuri, K. Development and validation of rp-hplc method for the simultaneous estimation of haloperidol and trihexyphenidyl in API and combined tablet dosage form. Int. J. Appl. Pharm., 2018, 3, 36-40.
[13]
Bommella, M.; Mukkanti, K.; Sarbani, P.; Priyanka, P. development and validation of a stability indicating RP-HPLC method for simultaneous determination of haloperidol and benzhexol in pharmaceutical combined dosage forms. Int. J. Dev. Res., 2016, 6, 8828-8836.
[14]
Dharmaraj Santhosam, S.; Kannan, S. An HPLC method for the simultaneous estimation of risperidone and trihexyphenidyl hydrochloride from bulk and dosage forms. Hygeia. J.D. Med, 2011, 3, 29-33.
[15]
Ashour, S.; Kattan, N. Sensitive method for the quantitative determination of risperidone in tablet dosage form by high-performance liquid chromatography using chlordiazepoxide as internal standard. Int. J. Biomed. Sci., 2013, 9(2), 91-97.
[PMID: 23847459]
[16]
Eddington, N.D.; Young, D. Sensitive electrochemical highperformance liquid chromatography assay for the simultaneous determination of haloperidol and reduced haloperidol. J. Pharm. Sci.,1988, 77(6), 541-543. .
[http://dx.doi.org/10.1002/jps.2600770617] [PMID: 3171937]
[17]
Bagheri, H.; Afkhami, A.; Panahi, Y.; Khoshsafar, H.; Shirzadmehr, A. Facile stripping voltammetric determination of haloperidol using a high performance magnetite/carbon nanotube paste electrode in pharmaceutical and biological samples. Mater. Sci. Eng. C, 2014, 37, 264-270.
[http://dx.doi.org/10.1016/j.msec.2014.01.023] [PMID: 24582248]
[18]
Oloyede, R.B.; Idris, A.Y.; Usman, M.A.; Musa, A. simple spectrophotometric method for determination of risperidone in pure and tablet dosage forms by formation of a coloured ion-pair complex. Niger. J. Pharm. Sci. March, , 2016, 15, 32-40.
[19]
Zhang, Y.; Chen, H.; Yang, B.; Fu, S.; Yu, J.; Wang, Z. Prediction of phosphate concentrate grade based on artificial neural network modeling. Results Phys., 2018, 11, 625-628.
[http://dx.doi.org/10.1016/j.rinp.2018.10.011]
[20]
Cranenburgh, S.V.; Alwosheel, A. An artificial neural network based approach to investigate travellers’ decision rules. Transp. Res., Part C Emerg. Technol., 2019, 98, 152-166.
[http://dx.doi.org/10.1016/j.trc.2018.11.014]
[21]
M, feed forward back propagation artificial neural network based faulty switch identification of the three phase three level converter based drive for the three phase induction motor Asian. J. Inform Technol., 2016, 15, 2108-2115.
[22]
Goyal, S.; Kumar Goyal, G. Cascade and feedforward backpropagation artificial neural network models for prediction of sensory quality of instant coffee flavoured sterilized drink. Can. J. Artif. Intell. Mach. Learn. Patt. Recog., 2011, 2, 78-82.
[23]
Lv, C.Q.; Ma, C.F. A Levenberg–Marquardt method for solving semi-symmetric tensor equations. J. Comput. Appl. Math., 2018, 332, 13-25.
[http://dx.doi.org/10.1016/j.cam.2017.10.005]
[24]
Lia, J.; Zheng, X.W.; Gu, J.; Hua, L. Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements. J. Franklin Inst., 2017, 354, 316-331.
[http://dx.doi.org/10.1016/j.jfranklin.2016.10.002]
[25]
Batra, D. Comparison between Levenberg-Marquardt and scaled conjugate gradient training algorithms for image compression using MLP. Int. J. Image Process., 2014, 8, 412-422.
[26]
Orozco, J.; Reyes Garcia, C.A. Detecting pathologies from infant cry applying scaled conjugate gradient neural networks; Eur. Sympos. Art. Neural Net: Bruges, Belgium, 2003, pp. 23-25.
[27]
Nayak, S.; Kumar, N.; Choudhury, B.B. Scaled conjugate gradient backpropagation algorithm for selection of industrial robots. Int. J. Comput. Appl., 2017, 7, 92-101.
[28]
Babani, L.; Jadhav, S.; Chaudhari, B. Scaled conjugate gradient based adaptive ANN control for SVM-DTC induction motor drive. IFIP, 2016, 475, 384-395.
[http://dx.doi.org/10.1007/978-3-319-44944-9_33]
[29]
Abdul-Wahed Salman, M. adaptive learning rate versus resilient backpropagation for numeral recognition. J. Uni. Anbar. Pure. Sci., 2008, 2, 94-105.
[30]
Ayoub, M.A.; Demiral, B.M. Application of resilient back-propagation neural networks for generating a universal pressure drop model in pipelines. Uni. Khartoum Eng. J., 2011, 1, 9-21.
[31]
Mofavvaz, S.; Sohrabi, M.R.; Nezamzadeh-Ejhieh, A. New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2017, 182, 105-115.
[http://dx.doi.org/10.1016/j.saa.2017.04.001] [PMID: 28412664]
[32]
Zhang, W.; Zhou, R.; Yang, P.; Liu, K.; Yan, J.; Gao, P.; Tang, Z.; Li, X.; Lu, Y.; Zeng, X. Determination of chlorine with radical emission using laser-induced breakdown spectroscopy coupled with partial least square regression. Talanta, 2019, 198, 93-96.
[http://dx.doi.org/10.1016/j.talanta.2019.01.102] [PMID: 30876608]
[33]
Mou, Y.; Zhou, L.; You, X.; Lu, Y.; Chen, W.; Zhao, X. Multiview partial least squares. Chemom. Intell. Lab. Syst., 2017, 160, 13-21.
[http://dx.doi.org/10.1016/j.chemolab.2016.10.013]
[34]
Kavaklioglu, K. Robust modeling of heating and cooling loads using partial least squares towards efficient residential building design. J. Build. Eng., 2018, 18, 467-475.
[http://dx.doi.org/10.1016/j.jobe.2018.04.018]
[35]
Zhao, Z.; Wang, J.; Sun, B.; Arowo, M.; Shao, L. Mass transfer study of water deoxygenation in a rotor-stator reactor based on principal component regression method. Chem. Eng. Res. Des., 2018, 132, 677-685.
[http://dx.doi.org/10.1016/j.cherd.2018.02.007.]

Rights & Permissions Print Cite
Article Metrics
16
1
© 2024 Bentham Science Publishers | Privacy Policy