Online - 2455-3891 Vol 10, Issue 11, 2017 Print - 0974-2441 Research Article SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION OF DICLOFENAC SODIUM, PARACETAMOL, AND CHLORZOXAZONE IN TERNARY MIXTURE USING CHEMOMETRIC AND ARTIFICIAL NEURAL NETWORKS TECHNIQUES
RAMZIA I EL-BAGARY1, EHAB F EL-KADY2, AHMED A AL–MATARI3* 1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Future University, New Cairo, Egypt. 2Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., Cairo, Egypt. 3Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Sana’a University, Sana’a, Yemen. Email: [email protected] Received: 05 June 2017, Revised and Accepted: 27 July 2017
ABSTRACT
Objective: The aim of this study is to develop and validate simple, accurate, and precise spectrophotometric methods for the simultaneous determination of diclofenac sodium (DIC), paracetamol (PAR), and chlorzoxazone (CHZ) in ternary mixture using chemometric and artificial neural networks (ANN) techniques.
Methods: Three chemometric techniques include classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS) in addition to cascade-forward backpropagation ANN (CFBP-ANN) were prepared using the synthetic mixtures containing the three drugs in methanol. In CLS, PCR, and PLS, the absorbances of the synthetic mixtures in the range 267-295 nm with the intervals ∆ .2 nm in their zero-order spectra were selected. Then, calibration or regression was obtained using the absorbance data matrix and concentration data matrix for the prediction λ=0 found appropriate for the simultaneous determination of the three drugs in their ternary mixture. of the unknown concentrations of DIC, PAR, and CHZ in their mixtures. In CFBP-ANN, two layers, sigmoid layer with 10 neurons and linear layer were Results: The four proposed methods were successfully applied to the analysis of the three drugs in laboratory prepared mixtures and tablets with
Conclusion:good percentage The recoveriesfour proposed in the methods range of showed 98-102%. simplicity, Relative accuracy, standard precision,deviation forand the rapidity precision making study them was suifoundtable <1%. for quality control and routine analysis of the cited drugs in ternary mixtures and pharmaceutical formulation containing them.
Keywords: Diclofenac sodium, Paracetamol, Chlorzoxazone, Chemometrics, Artificial neural networks.
© 2017 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/) DOI: http://dx.doi.org/10.22159/ajpcr.2017.v10i11.20491
INTRODUCTION HPLC-UV [32,33]. Diclofenac sodium (DIC), sodium [2-(2,6-dichloroanilino) phenyl] combination of the three drugs based on densitometry [30,31] and acetate (Fig. 1a), is a derivative of phenylacetic acid classed as a The UV absorption spectra of DIC, PAR, and CHZ in methanol at non-steroidal anti-inflammatory drug, used to relieve the pain and concentrations corresponding to their ratio in the combined dosage inflammation in many conditions such as rheumatoid arthritis, form reveal strong overlap (Fig. 2). Thus, direct simultaneous osteoarthritis, and ankylosing spondylitis [1,2]. Paracetamol (PAR), spectrophotometric determination of the three drugs in the N-acetyl-p-aminophenol (Fig. 1b), is an analgesic and antipyretic agent mixture is not feasible. To the best of our knowledge, no previous with weak anti-inflammatory property. It is used for the management spectrophotometric method based on chemometric techniques of mild-to-moderate pain and fever [1,3]. Chlorzoxazone (CHZ), and artificial neural networks (ANN) has been published for the 5-chlorobenzoxazol-2(3H)-one (Fig. 1c), is a centrally acting skeletal simultaneous determination of the three drugs. Thus, the main muscle relaxant with sedative properties, used as an adjunct in the aim of this work was to develop and validate simple and accurate symptomatic treatment of painful skeletal muscle conditions [1]. spectrophotometric methods for the simultaneous determination of the three drugs in ternary mixture using chemometric and ANN techniques. The combination of the three drugs is widely prescribed for the alleviation of the pain associated with the muscle spasm. Multivariate calibrations are widely used in quantitative spectral analysis for compounds with highly overlapping spectra. These Literature survey reveals that several methods have been reported for calibrations are characterized by the higher speed of data processing the determination of DIC in pharmaceutical and biological samples. and minimizing of calibration models errors by measuring the These methods include spectrophotometry and multivariate [4-6], absorbance at many points in the wavelength range of the zero- order and derivative spectra. Control analyses on pharmaceutical chromatography-ultraviolet (HPLC-UV) [11-15], liquid chromatography- preparations using multivariate calibration methods have been proven tandemfluorimetry mass [7,8], spectrometry voltammetry (LC-MS/MS) [9,10], high-performance[16-19], capillary liquidzone to be a valid alternative to HPLC [34]. These calibrations have been used in many analysis methods such as spectrophotometric [35], were developed for the simultaneous determination of DIC in binary spectrofluorometric [36], and voltammetric [37] methods. In this combinationelectrophoresis with [20], PAR or and CHZ densitometry that include spectrophotometry [21,22]. Other methods[23-25], work, three multivariate calibration methods have been described to densitometry [26], voltammetry [27], and HPLC-UV [28,29]. Some resolve the overlapping between DIC, PAR, and CHZ in their zero-order methods were reported for the simultaneous determination of ternary spectra. These methods include classical least squares (CLS), principal El-Bagary et al. Asian J Pharm Clin Res, Vol 10, Issue 11, 2017, 225-230 component regression (PCR), and partial least squares (PLS). ANNs are Sample preparation computer programs designed to simulate some human brain functions Twenty tablets were accurately weighed and finely powdered using using different algorithms, which can learn from experience. ANN mortar and pestle. An accurate weight of the powdered tablets analyses are currently perceived as an efficient and advantageous way to handle complex data and solve problems of non-linear calibration, pattern recognition, classification, prediction, and other related fields equivalent to 50 mg of DIC, 325 mg of PAR, and 250 mg of CHZ was in analytical chemistry. Both linear and non-linear mapping functions marktransferred with intomethanol, a 100 mland volumetric filtered flask.through The Whatman powder was filter extracted paper can be modeled by suitably designing the network [38]. In this work, discardingwith 50 ml themethanol first few by sonicationmilliliters tofor produce15 minutes, table completedt stock solution to the cascade-forward backpropagation ANN (CFBP-ANN) was used for the simultaneous determination of the cited three drugs in their ternary accurately measured aliquot (5 ml) from the tablet stock solution was mixture with good accuracy and precision. of 0.5 mg/ml of DIC, 3.25 mg/ml of PAR, and 2.5 mg/ml of CHZ. An with methanol to obtain the table METHODS transferred into a 250 ml volumetric flask and completed to volume t working solution of 10 µg/ml of DIC, Instrumentation Construction65 µg/ml of PAR, of theand training 50 µg/ml set of CHZ. Twelve mixtures of DIC, PAR, and CHZ were prepared by transferring connected to an IBM compatible computer and supported with UV probeShimadzu software ultraviolet/visible version 2.43 was spectrophotometer used. The chemometric 1600 methods, (Japan) CFBP-ANN, and data analysis were performed using Matlab™ software, volumetric flasks and completing the volume with methanol (Table 1). Thedifferent ratio aliquotsof the three of theirdrugs working in their combined solutions intodosage a seriesform was of 10taken ml into consideration during the construction of this set. The absorbances Reagentsversion 7.9.0 and with reference PLS-toolbox samples 2.0 and neural networks toolbox. of these prepared mixtures were then scanned between 260 and respectively) were kindly supplied from the central laboratory of drug 400 nm with 0.2 nm intervals against methanol as a blank. control-SanaDIC, PAR, and’ CHZ (certified to contain® table 99.20%,ts nominally 99.45%, containing and 99.64%, DIC Construction of CLS, PCR, and PLS models From the obtained data, three multivariate calibration models Pharmaceuticala, Yemen. limited, Intagesic India. Methanol MR used was of analytical grade. (CLS, PCR, and PLS) were constructed. The absorbance data matrix (50 mg), PAR (325 mg), and CHZ (250 mg) were manufactured by G.S. and the concentration data matrix were used for the calibration Standard solutions and regression. The obtained calibrations were then used for determination of the unknown concentrations of DIC, PAR, and CHZ Stock solutions in their ternary mixtures and in pharmaceutical dosage form. For CLS model construction, non-zero intercepts were used. The model was constructed by the feeding of the Matlab™ with the absorbance and Accurately weighed 20 mg of DIC, 100 mg of PAR, and 125 mg of CHZ completed to volumes with methanol to obtain standard stock solutions concentration matrices of the training set, and then, K matrix was were transferred into separate 50 ml volumetric flasks, dissolved in, and containing 0.4 mg/ml of DIC, 2.0 mg/ml of PAR, and 2.5 mg/ml of CHZ. the training set absorbance and concentration matrices was used for Working solutions thecalculated. calculations. For PCR and PLS methods, PLS-toolbox 2.0 software with Accurately measured aliquots (5 ml) were transferred from each Selection of the optimum number of factors to build the PCR and was completed with methanol to obtain working solutions containing PLS models stock solution into separate 100 ml volumetric flasks. The volume For the selection of the optimum number of factors, cross-validation method was used leaving out one sample at a time [39]. PLS and PCR 20 µg/ml of DIC, 100 µg/ml of PAR, and 125 µg/ml of CHZ. calibration on eleven calibration spectra were performed, and using this calibration, the concentration of the sample left out during the calibration process was predicted. This process was repeated 12 times until each training sample had been left out once, and all samples concentrations were predicted. The predicted concentrations of the three drugs in each sample were compared with the true concentrations a b c in this calibration samples, and then, root-mean-square error of cross- Fig. 1: Chemical structures of diclofenac sodium (a), validation (RMSECV) was calculated for each method. The optimum paracetamol (b), and chlorzoxazone (c) number of factors was selected by visual inspection.
Table 1: The concentrations of different mixtures of DIC, PAR, and CHZ used in the training set
Sample No. DIC (µg/ml) PAR (µg/ml) CHZ (µg/ml) 1 1.6 9.4 8.5 2 1.6 3 1.6 11.4 7.5 4 3.2 21.810.4 15.58.0 5 3.2 6 3.2 19.8 16.5 7 4.8 20.8 24.516.0 8 4.8 31.2 9 4.8 32.230.2 23.5 6.4 42.6 31.524.0 Fig. 2: Zero-order absorption spectra of diclofenac sodium 11 6.4 41.6 (1.0 µg/ml) (a), paracetamol (6.5 µg/ml) (b), and chlorzoxazone 1210 6.4 32.5 32.0 (5.0 µg/ml) (c) in methanol 40.6 DIC: Diclofenac sodium, PAR:Paracetamol, CHZ:Chlorzoxazone 226 El-Bagary et al. Asian J Pharm Clin Res, Vol 10, Issue 11, 2017, 225-230
Accuracy PRESS RMSECV = (1) The accuracy was assessed by the recovery study of the three drugs n in their laboratory prepared mixtures (validation set), and good mean percentage recoveries were calculated (Table 2). Accuracy of the Where PRESS is the predicted residual error sum of squares and n is the methods was also confirmed using standard addition technique to Intagesic MR® table indicating the absence of excipients interference and a good accuracy of number of calibration2 samples [40]. ts. Good mean percentage recoveries were obtained, pred true) (2) the methods (Table 3).
PRESS = ∑ (Y –Y pred true Precision respectively. The precision of the methods was assessed by studying intra- and inter- Where Y and Y are predicted and true concentrations in µg/ml, Construction of CFBP-ANN The same training set (Table 1) that used in CLS, PCR, and PLS was also day variation using three concentrations (2.4, 3.0, and 3.6 µg/ml) of DIC, during(15.6, 19.5, the sameand 23.4 day µg/ml)and on of three PAR, consecutiveand (12.0, 15.0, days. and The 18.0 calculated µg/ml) of CHZ, representing 80%, 100% and 120%, respectively, in triplicate linearused for layer training was found the CFBP-ANN appropriate model for created the simultaneous in Matlab™, determination version 7.9.0. ofCFBP-ANN the three which drugs containsin their ternary sigmoid mixture layer with (Fig 10. 3). neurons and another values of % relative standard deviation for the three drugs concentrations were found to be <1% for intra- and inter-day precision, Construction of the validation set To evaluate the prediction performance of the proposed four methods (CLS, PCR, PLS, and CFBP-ANN), nine different mixtures of DIC, PAR, and CHZ were prepared by transferring different aliquots of their volume with methanol. The ratio of the three drugs in tablets was takenworking into solutions consideration into 10during ml volumetricthe preparation flasks of andthese completing mixtures. The the concentrations of the three drugs in these prepared mixtures were Fig. 3: Architecture of the used cascade-forward backpropagation predicted using the suggested techniques. network
Analysis of DIC, PAR, and CHZ in Intagesic MR® tablets The four methods were applied to the determination of DIC, PAR, and CHZ in commercial tablets. Further dilutions of the working tablet solution with methanol were carried out to obtain concentrations of CHZ. Standard addition technique was carried out to prove the accuracyof 1.6-4.4 of µg/ml the proposed of DIC, 10.4-28.6 methods. µg/ml The percentage of PAR, and recoveries 8.0-22.0 µg/mlof the drugs in the pharmaceutical dosage form and added standards were a b calculated.
RESULTS AND DISCUSSION
CLS, PCR, and PLS The absorbance data in the wavelength range 267-295 nm with the .2 nm were chosen as it provided the highest amount of information about the three drugs of this ternary mixture while the c dataintervals below 0 267 nm and above 295 nm were rejected. Fig. 4: Root-mean-square error of cross-validation plot as a CLS model was constructed with non-zero intercept. The non-zero function of the number of principle components used to construct intercept allows an additional degree of freedom when k matrix is the principal component regression model (a) diclofenac sodium, calculated. This provides an additional opportunity to adjust the (b) paracetamol, and (c) chlorzoxazone effects of the extraneous substances [41]. The CLS method requires all components in the calibration samples to be known. For the PCR and PLS techniques, selection of the optimum number of factors was a very important step before constructing the models. If the number of factors retained was more than the number required, number retained was less than the number required, meaningful more noise would be added to the data. On the other hand, if the data that could be necessary for the calibration might be ignored. a b The optimum number of factors was selected by visual inspection. According to the Figs. 4 and 5, two factors were found suitable for both PCR and PLS methods.
CFBP-ANN The choice of the suitable ANN was done by trying different types of ANNs that included CFBP, Elman back propagation, and radial basis networks. The CFBP-ANN was selected as it gave good results concerning accuracy c and precision. Bayesian regulation training function (TRAINBR), Fig. 5: Root-mean-square error of cross-validation plot as a function of the number of principle components used to were optimal for the simultaneous determination of the three drugs in construct the partial least squares model (a) diclofenac sodium, theirgradient ternary descent mixture. learning function (LEARNGD), and MSE performance (b) paracetamol, and (c) chlorzoxazone
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ANN ‑ CFBP 99.263 99.371 100.717 100.845 100.680 99.359 99.732 100.338 100.077 100.042±0.634 CHZ 99.860±0.872 99.219±0.762 99.227±0.763 99.394±0.684 PLS 99.933 ‑ 100.492± 0.022 ‑ 0.083 0.022 ‑ 0.083 100.505 ‑ 100.767± 0.231 ‑ 0.498 0.229 ‑ 0.495
ANN techniques ‑ ANN techniques ‑ CHZ 99.934 ‑ 100.636±0.018 0.063 0.018 ‑ 0.063 100.476 ‑ 100.781±0.311 0.490 0.309 ‑ 0.487 PCR 99.480 99.406 100.561 100.682 100.640 99.433 99.738 100.279 99.926 100.016±0.534 PCR 99.932 ‑ 100.485± 0.022 ‑ 0.083 0.022 ‑ 0.082 100.503 ‑ 100.762± 0.236 ‑ 0.500 0.234 ‑ 0.497
ANN technique PLS 99.473 99.400 100.554 100.680 100.637 99.432 99.739 100.275 99.924 100.013±0.534 ‑ PAR 100.610±0.770 100.612±0.771 100.602±0.769 99.938±0.730 CHZ CLS 100.064 ‑ 101.081± 0.030 ‑ 0.097 0.030 ‑ 0.096 100.630 ‑ 101.161± 0.413 ‑ 0.475 0.409 ‑ 0.470
CHZ CLS 98.891 98.947 100.139 100.566 100.414 99.265 99.762 100.023 99.805 99.757±0.608 ANN ‑ ANN: Cascade forward back propagation artificial neural network artificial neural back propagation ANN: Cascade forward network artificial neural back propagation ANN: Cascade forward ‑ ‑ CFBP 99.688 99.884 100.668 100.416 100.742 99.780 100.213 100.075 99.868 100.148±0.387 PLS 100.177 ‑ 100.391± 0.068 ‑ 0.157 0.067 ‑ 0.157 100.959 ‑ 101.687± 0.370 ‑ 0.633 0.364 ‑ 0.627 Added mean recovery% ± SD mean recovery% Added DIC 100.156±0.800 99.768±0.734 99.769±0.735 99.681±0.686
PAR 99.899 ‑ 100.029±0.048 0.121 0.048 ‑ 0.121 100.368 ‑ 100.681±0.790 0.995 0.787 ‑ 0.990 PCR 98.965 99.448 100.422 100.299 100.403 99.475 100.064 99.443 99.629 99.794±0.519 PCR 100.181 ‑ 101.238± 0.068 ‑ 0.158 0.067 ‑ 0.157 100.964 ‑ 101.693± 0.365 ‑ 0.631 0.359 ‑ 0.625
PLS 98.974 99.455 100.429 100.303 100.407 99.478 100.063 99.447 99.631 99.799±0.518 CHZ 101.709±0.392 101.570±0.398 101.572±0.397 101.559±0.383 PAR CLS 100.177 ‑ 101.233± 0.068 ‑ 0.158 0.067 ‑ 0.158 100.962 ‑ 101.692± 0.371 ‑ 0.635 0.365 ‑ 0.629
PAR CLS 98.913 99.457 100.483 100.334 100.407 99.444 100.041 99.443 99.654 99.797±0.543 ANN: Cascade forward back propagation artificial neural network artificial neural back propagation forward ANN: Cascade ‑ ANN ‑ DIC 100.278 ‑ 101.019±0.036 0.06 0.036 ‑ 0.06 100.740 ‑ 101.167±0.417 0.541 0.412 ‑ 0.537 CFBP 99.183 99.613 100.978 100.771 100.637 99.354 99.780 100.017 99.980 100.035±0.635 PLS 100.114 ‑ 100.782± 0.036 ‑ 0.110 0.036 ‑ 0.110 100.703 ‑ 101.130± 0.284 ‑ 0.530 0.281 ‑ 0.526
PAR 101.327±0.487 101.330±0.488 101.326±0.486 100.731±0.499 PCR 99.290 99.422 100.502 100.525 100.547 99.449 99.863 99.949 99.805 99.928±0.498 PCR 100.114 ‑ 100.782± 0.036 ‑ 0.110 0.036 ‑ 0.110 100.703 ‑ 101.129± 0.284 ‑ 0.530 0.281 ‑ 0.526
PLS 99.289 99.422 100.502 100.525 100.547 99.449 99.863 99.949 99.805 99.928±0.498 Parameters Mean±SD RSD% Mean±SD RSD% 5: Intra-day and inter-day precision results of the simultaneous determination of DIC, PAR, and CHZ using CFBP and CHZ using PAR, of DIC, determination of the simultaneous results precision inter-day and 5: Intra-day Table Tablets mean recovery% ± SD mean recovery% Tablets DIC 101.559±0.428 101.475±0.401 101.476±0.401 101.524±0.414 DIC CLS 100.194 ‑ 101.141± 0.045 ‑ 0.118 0.045 ‑ 0.118 100.780 ‑ 101.370± 0.295 ‑ 0.449 0.291 ‑ 0.446 DIC CLS 98.899 99.148 100.283 100.471 100.411 99.333 99.869 99.794 99.744 99.772±0.560 4: Intra-day and inter-day precision results of the simultaneous determination of DIC, PAR, and CHZ using CLS, PCR, and PLS chemometric techniques PLS and PCR, CLS, CHZ using and of DIC, PAR, determination of the simultaneous results precision and inter-day 4: Intra-day Table CHZ 12.5 12.0 11.5 19.5 20.0 20.5 28.5 28.0 27.5
Parameters Mean±SD RSD% Mean±SD RSD% PAR 14.6 15.6 16.6 27.0 26.0 25.0 35.4 36.4 37.4 (validation set) using CLS, PCR, PLS, and CFBP PLS, PCR, set) using CLS, (validation mixtures and CHZ in synthetic of DIC, PAR, obtained of the simultaneous determination results 2: Recovery Table ANN ‑ 3: The mean percentage recoveries of the simultaneous determination of DIC, PAR, and CHZ in the tablets and added standards using CLS, PCR, PLS, and CFBP PLS, PCR, using CLS, and CHZ in the tablets added standards of DIC, PAR, of the simultaneous determination recoveries 3: The mean percentage Table Precision Intraday Interday least squares Partial PLS: component regression, Principal PCR: least squares, Classical sodium, CLS: Diclofenac DIC: deviation, standard Relative RSD: deviation, SD: Standard Precision Intraday Interday sodium, CFBP Diclofenac DIC: deviation, standard Relative RSD: deviation, SD: Standard Concentration (µg/ml) DIC 2.4 2.4 2.4 4.0 4.0 4.0 5.6 5.6 5.6 Mean±SD CFBP least squares, Partial PLS: PCR: Principal component regression, Classical least squares, sodium, CLS: DIC: Diclofenac deviation, SD: Standard Method CLS PCR PLS CFBP CFBP least squares, Partial PLS: PCR: Principal component regression, Classical least squares, sodium, CLS: DIC: Diclofenac deviation, SD: Standard
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indicating that intra- and inter-day precisions of the methods as shown in Tables 4 and 5. ANN ‑ Selectivity
CFBP 100.094± 0.623 0.279 0.623 5 0.388 The selectivity of the four methods was evident by the good mean percentage recoveries obtained from the laboratory prepared mixtures (validation set) and from the combined dosage form (Intagesic MR® tablets) without any interference from the tablets excipients. PLS 100.065± 0.511 0.228 0.511 5 0.261 Statistical analysis RSD: Relative standard deviation, deviation, standard Relative RSD: f Statistical analysis of the recovery results obtained from the laboratory prepared mixtures using the four proposed methods and reference PCR 100.069± 0.511 0.229 0.511 5 0.261
confirmingmethod for the each absence drug was of performedsignificant usingdifference one-way between ANOVA. them The concerningcalculated Faccuracy values wereand precision ˂ the critical as shown one andin Table p values 6. were ˃0.05, CLS 99.802± 0.574 0.257 0.575 5 0.330 SE: Standard error, error, SE: Standard e
CONCLUSION c The four proposed methods (CLS, PCR, PLS, and CFBP-ANN) can USP34NF29: United States Pharmacopoeia and National States USP34NF29: United b be used for the simultaneous determination of DIC, PAR, and CHZ in CHZ Reference Reference method 100.786± 0.895 0.400 0.888 5 0.801 1.657 0.199>0.05* synthetic ternary mixtures and pharmaceutical dosage form containing
them without interference and without the need for previous physical separation of them. Multivariate calibration models were built from ANN SD: Standard deviation, deviation, SD: Standard ‑ d the spectral and concentration data matrices. Verification of the calibrations, carried out with the aid of a synthetic set of mixtures of CFBP 100.236± 0.469 0.210 0.468 5 0.220
the three drugs, produced satisfactory results showing simplicity, selectivity, and rapidity. Hence, the proposed methods can be used for quality control of the cited drugs. PLS 99.901± 0.611 0.273 0.612 5 0.373 REFERENCES 1. Sweetman SC. Martindale: The Complete Drug Reference. 36th ed.
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