Simultaneous estimation of amlodipine and atorvastatin by micelle-augmented first derivative synchronous spectrofluorimetry and multivariate analysis

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 224 (2020) 117430

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Simultaneous estimation of amlodipine and atorvastatin by micelle-augmented first derivative synchronous spectrofluorimetry and multivariate analysis Jenny Jeehan Nasr*, Shereen Shalan Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 July 2019 Received in revised form 25 July 2019 Accepted 25 July 2019 Available online 26 July 2019

Five Selective, rapid and sensitive spectrofluorimetric methods were performed in this study for the simultaneous estimation of amlodipine besylate (AML) and atorvastatin (ATR) in their binary mixtures and combination polypills that are used for management of cardiovascular conditions. The first method depends on micelle-enhanced first derivative synchronous fluorimetric analysis (method I) and the other four methods are multivariate analysis techniques based on the use of factor-based calibration prediction methods comprising partial least squares (PLS), Principal Component Regression (PCR), genetic algorithm PLS (GA-PLS) and genetic algorithm PCR (GA-PCR). The synchronous fluorescence spectra of the solutions were measured at a constant wavelength difference; Dl ¼ 100 nm. The magnitudes of the peaks of the first derivative spectra (1D) were measured at 292 nm and 387 nm for ATR, and AML correspondingly. The multivariate models were constructed utilizing fifteen mixtures as a calibration set and ten mixtures as a validation set. The linearity of all the methods was in the concentration ranges of (0.1e4.0 mg mL1, 0.4e10.0 mg mL1) for AML and ATR, correspondingly. Statistical analysis revealed no significant difference between the proposed methods and the reference method. The validity of the proposed methods allows their suitability for quality control work. All the analysis settings were optimized and all the suggested procedures were applied productively for the determination of both drugs in synthetic mixtures, validation set, and combination polypills. © 2019 Elsevier B.V. All rights reserved.

Keywords: Amlodipine Atorvastatin Synchronous Spectrofluorimetry Multivariate Genetic algorithms

1. Introduction A combined therapy polypill mixing some medicines, that are utilized in medical practice to lessen the risk of cardiovascular diseases; such as blood pressure and lipid-reducing medicines; may be a superior remedy to prevent stroke. Polypills can improve prevention and remedy, increase adherence, and shorten medication regimens [1]. Amlodipine besylate (AML, Fig. 1), designated chemically as; 3-ethyl-5-methyl (±)-2-[(2-aminoethoxy) methyl]4-(o-chlorophenyl)-1,4-dihydro-6-methyl-3,5-pyridine dicarboxylate, monobenzene sulfonate; is a long-acting calcium channel blocker of the dihydropyridine group. It is a peripheral arterial vasodilator which has a direct effect on vascular smooth muscle reducing the peripheral vascular resistance resulting in lowering blood pressure [2,3]. Atorvastatin calcium (ATR, Fig. 1), designated chemically as; [R-(R*,R*)]-2-(4-fluorophenyl)-b,d-dihydroxy-5-(1methyl ethyl)-3-phenyl-4-[(phenylamino)carbonyl]- 1H-pyrrole-1heptanoic acid, calcium salt; is an artificial lipid-lowering drug of * Corresponding author. E-mail address: [email protected] (J.J. Nasr). https://doi.org/10.1016/j.saa.2019.117430 1386-1425/© 2019 Elsevier B.V. All rights reserved.

the statins group. ATR is a discriminating, competing inhibitor of HMG-CoA (3-hydroxy-3-methyl glutaryl-coenzyme A) reductase which is the rate-limiting enzyme that transforms HMG-CoA to mevalonate, a cholesterol precursor [2,3]. Usage of AML-ATR combined tablets might result in a highly integrated approach for the management of cardiovascular diseases [4]. Revising the literature showed that numerous procedures existed for the simultaneous estimation of both amlodipine and atorvastatin in their combined polypill dosage forms. These procedures comprise spectrophotometry [5e8], spectrofluorimetry [9], HPLC [9e12], LC-MS/MS [13e18], voltammetry [4,19] and capillary electrophoresis [10]. Synchronous spectrofluorimetry represents a significant technique for the simultaneous assay of multicomponent preparations because it has notable benefits as simplified spectra, decreased scattering of light, and enhanced selectivity superior to traditional spectrofluorimetry. Resolution and selectivity are additionally boosted by means of combining synchronous fluorescence methodologies with different strategies, including derivative approach, low-temperature technique, and chemometrics [20]. As far as we could possibly know, only one spectrofluorimetric

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80 mg) manufactured by Goedecke GmbH, Freiburg, Germany and packed by Pfizer Pharmaceuticals LLC, Caguas, PR, USA. 2.3. Preparation of stock solutions Stock solutions were prepared by dissolution of 10.0 mg of each of AML or ATR in a 25 mL volumetric flask and completed to volume with methanol to obtain a concentration of 400 mg mL1 for each drug. The stock solutions were found to be stable for 10 days when refrigerated. 2.4. General procedures

Fig. 1. Structural formula of Amlodipine besylate and Atorvastatin calcium trihydrate.

method [9] was reported for the simultaneous estimation of AML and ATR employing the conventional technique. But in this published method there is still overlapping between AML at lem (442 nm) and ATR at lem (369 nm) when overlay with each other and this is a problem and does not allow accurate simultaneous determination of AML and ATR. Hence, it was necessary to develop a simple, accurate surfactant-enhanced synchronous fluorescence method with increased selectivity and resolution of this polypill combination. 2. Experimental

2.4.1. Construction of calibration curves for SFS Volumes of appropriate standard solutions of AML and ATR in the concentration ranges of 0.1e4.0 and 0.4e10.0 mg mL1, correspondingly, were delivered to a 10 mL volumetric flasks, then 0.5 mL acetate buffer (pH ¼ 3.6), and 2 mL 1% cetrimide were added and completed to the volume with water. The synchronous fluorescence spectra of the solutions were measured at a constant wavelength difference; Dl ¼ 100 nm. The first derivative synchronous fluorescence spectra of both compounds were then calculated. The magnitudes of the peaks of the first derivative spectra (1D) were measured at 292 nm and 387 nm for ATR, and AML, correspondingly. Then, the conforming regression equation was determined by plotting the peak amplitude of the first derivative spectra (1D) against the concentration of each drug in mg mL1 to acquire the calibration charts. 2.4.2. Multivariate calibration procedures A calibration design of five levels  two factors was utilized [21] to make 25 samples through conveying specified volumes of stock solutions of ATR and AML into 10-mL volumetric flasks then 0.5 mL of acetate buffer of pH 3.6 and 2 mL of 1% cetrimide solution was added and then completed with water to the specified volume with thorough mixing (Table 1). Fifteen of the samples were utilized as the training set to construct the multivariate calibration models whereas ten of the samples were utilized as the validation set to check the predictive capability of the constructed models. The

2.1. Apparatus Fluorimetric measurements were performed by means of an Agilent Cary Eclipse fluorescence spectrophotometer G9800A (Agilent, California, USA) provided with a xenon flash lamp, grating monochromators for excitation and emission using the bandwidth of 10 nm. The acquisition interval was 1 nm and the integration time was preserved at 0.1 s. A 1 cm quartz cuvette was utilized. D l of 100 nm was chosen and the photomultiplier voltage was adjusted to the high degree with a scan rate of 600 nm/min. The instrument linearity was recurrently tested by standard quinine sulfate (0.01 mg mL1). All the measured spectra were transformed into ASCII format by the device software. 2.2. Materials and reagents AML (99.2% purity) and ATR (99.5% purity) analytical standards were generously delivered from Chemipharm for Pharmaceutical Industries, 6th October City, Giza, Egypt. Methanol, ethanol, and acetonitrile (HPLC grade) were purchased from Sigma-Aldrich (Germany). Acetic acid, sodium acetate, sodium hydroxide, boric acid, hydrochloric acid, Sodium dodecyl sulfate (SDS), Tween-80, and carboxymethyl cellulose were all purchased from El-Nasr Pharmaceutical Chemical Co. (ADWIC; Egypt). Cetyl trimethyl ammonium bromide (CTAB) (cetrimide) was procured from Winlab (UK). Caduet ® tablets contain the following dose blends of the active ingredients AML/ATR: (10 mg/10 mg, 10 mg/20 mg, 10 mg/40 mg, and 10 mg/

Table 1 The concentrations of different mixtures of AML and ATR used as calibration and validation sets. Mixture no.

AML (mg mL1)

ATR (mg mL1)

0.15 0.25 0.50 1.00 2.00 0.15 0.25 0.50 1.00 2.00 0.15 0.25 0.50 1.00 2.00 0.15 0.25 0.50 1.00 2.00 0.15 0.25 0.50 1.00 2.00

0.50 0.50 0.50 0.50 0.50 1.00 1.00 1.00 1.00 1.00 2.00 2.00 2.00 2.00 2.00 5.00 5.00 5.00 5.00 5.00 10.00 10.00 10.00 10.00 10.00

1 2a 3 4a 5 6a 7 8a 9 10 11 12 13a 14 15a 16 17a 18 19a 20 21a 22 23 24 25a a

Mixtures of the validation set.

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selection criteria for the concentrations of every component in the twenty-five samples included; the linearity range of each one of the two compounds and the ratio used in the pharmaceutical formulations comprising the two drugs; (1:1, 1:2 and 1:4 for AML and ATR, respectively). The synchronous fluorimetric spectra of the 25 sample mixtures were measured from 235 to 415 nm at 1 nm interval using a constant wavelength difference; Dl ¼ 100 nm and saved in ASCII format. 2.5. Assay of tablet dosage forms of the studied drugs The weight of ten tablets was measured accurately then they were crushed into a fine powder. An accurately weighed amount of the pulverized tablets corresponding to (10 mg þ 10 mg), (10 mg þ 20 mg), (10 mg þ 40 mg), and (10 mg þ 80 mg) of AML and ATR (respectively in their pharmaceutical ratio) were delivered into a 100 mL standard flask and shaken with methanol then they were sonicated for 30 min, then completed to the volume with methanol. Filtration of the resulted solutions was performed. The clear solutions obtained were then used for the application of the developed methods. The procedures under the construction of the calibration curves for SFS and for multivariate calibration were applied. 3. Results and discussion AML is co-formulated with ATR in Caduet® tablets. They were extensively employed in treating hypertension and hypercholesterolemia. There was no synchronous spectrofluorimetric method reported for these combination polypill components. So, it is particularly challenging to provide a simple, precise and selective method for their synchronous spectrofluorimetric determination with no interfering problems. 3.1. For the first derivative synchronous fluorescence spectra (FDSFS) AML and ATR show enhanced native fluorescence at wavelengths of 440 and 388 nm, after excitation at 365 and 248 nm for AML and ATR, correspondingly. Overlapping of both spectra of excitation and emission of AML and ATR happened (Fig. 2).

Fig. 2. Native fluorescence spectra of A, A0 : emission and excitation spectra of (4 mg mL1) ATR B, B0 : emission and excitation spectra of (1 mg mL1) AML C, C0 : emission spectra of (1 mg mL1) AML and (4 mg mL1) ATR, respectively, after micellar enhancement.

Fig. 3. Synchronous spectra for (1 mg mL1) AML (A) and (4 mg mL1) ATR (B).

Therefore, the traditional fluorescence method for the simultaneous estimation of AML and ATR is difficult, particularly, if it is required to estimate these drugs in their co-formulated dosage forms. So, we apply the synchronous technique to resolve this problem, but there is still interference problem of the two drugs as shown in Fig. 3. Thus, the first derivative amplitudes of synchronous fluorimetric spectra of AML and ATR at 387 and 292 nm, respectively were recorded to resolve the problem of overlapping spectra and estimate the drugs simultaneously as shown in Fig. 4.

3.2. For multivariate calibration methods 3.2.1. Chemometric partial least squares (PLS) and principal component regression (PCR) methods Chemometric methods are utilized for handling data with several numerical methods that apply mathematical and statistical methods to plan optimal processes to give maximum information from the chemical data analysis [22]. PLS method implicates experimental data decomposition to latent variables which

Fig. 4. FDSF spectra for (1 mg mL1) AML (A) and (4 mg mL1) ATR (B).

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elucidate the observed data variance. The PLS method objective is to form a calibration model between the concentration of the compounds under investigation (AML and ATR) and the latent variables of the data matrix. PLS executes the decomposition with spectrum data and analyte concentration matrices together [22]. The Combination of principal component analysis (PCA) and inverse least squares regression (ILS) yields a quantitative model to complex samples (PCR). Even though PLS is highly interrelated to PCR, the data matrix of concentration is employed in data decomposition, however, in PCR, the information in the fluorescence matrix only is employed in that stage. Furthermore, PLS has extra merit above PCR in producing a model with added robustness owing to the noise removal [22]. The update of the regression model is concerned with the dissimilarity of various matrices at the time the predicted samples are not present within the domain of the calibration. Comprising an extra number of latent variables in the model results in the problematic situation of overfitting. Also, comprising a low number of latent variables results in missing important information. Hence, optimizing the count of latent variables is a highly crucial subject in PLS and PCR methods. The optimal number of latent variables for PLS and PCR models was six (Fig. 5). Crossvalidation (CV) was used in order to expect the optimal count of PLS

and PCR latent variables. A CV includes repetitively distributing the data between two groups, a training group applied to build a model and a validation group applied to measure the wellness of the performance of the model with the purpose of that every sample is left out of the training group one individual time [22]. CV using leave one out is utilized in the proposed technique to adjust the number of PLS or PCR variables. The root mean square error of CV (RMSECV) is calculated as:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 2 I  u1 X A ci  b RMSECV ¼ t c icv I i¼1

Defined as: I is the count of samples of the calibration group, ci is A the concentration added from sample i and b c icv is the anticipated concentration of sample i by A constituents. The mean centering was done on the training group every time consecutive samples were gone out [23]. The predicted concentration for every sample is thus matched with the true concentration of this sample. Plotting the predicted concentration vs the true concentration for each component was used to construct the calibration graph. Table 2 depicts the statistical parameters of PLS and PCR models acquired from the calibration graphs. A satisfactory linear relationship was acquired for every component as denoted by their correlation coefficients (r). 3.2.2. Genetic algorithm The assortment of wavelengths used in multivariate calibration models has various approaches suggested in the literature. Genetic algorithms (GAs) are one of the most utilized approaches for variable selection. GAs represent a directed random exploration method that applies natural assortment mechanisms, discovering the solution space in a competent way appropriate for parallel processing implementations. GAs are intended for use in resolving challenging complications having objective functions which don't retain ‘nice’ characteristics as continuity, differentiability… etc. These algorithms retain and handle a population of solutions and apply a ‘survival of fittest’ approach in searching for superior solutions [24,25]. GA includes five stages: 1. Initiation: it involves random generation of various wavelengths blends; every blend denotes a probable solution. Every wavelength in the band is consigned arbitrarily a number of 0 or 1, as 0 designates omitting and 1 designates inclusion. 2. Evaluation: every altered chromosome is utilized to build the model and cross-validation is employed to assess the error of prediction for every chromosome. 3. Utilization: assortment of valid chromosomes. 4. Investigation: the reunion of valid genes. 5. Mutation: local change of chromosomes to expectantly produce improved chromosomes. The newly-produced chromosomes are retested for performance and the algorithm repeats till a specified generations number is obtained [23,26,27]. 3.3. Optimization of experimental parameters 3.3.1. Optimization of experimental parameters for (FDSFS) Diverse investigational factors influencing the performance of the proposed technique were sensibly studied and enhanced. Adding a surface active agent by a higher concentration than its critical micellar concentration to a certain fluorescing solution is known to boost the molar absorptivity in addition to the fluorescence quantum yield in most situations [28e30].

Fig. 5. RMSEC plot of cross-validation of the training set as a function of the number of latent variables used to construct PLS (A) and PCR (B) for AML (1) and ATR (2).

3.3.1.1. Impact of organized media. Different micellar systems, such as sodium dodecyl sulfate (anionic surfactant), carboxymethyl

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Table 2 Analytical results for the prediction of the training set concentrations by the proposed multivariate calibration methods. Method

PLS

AML

AML

ATR

AML

ATR

AML

ATR

AML

ATR

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

0.147 0.510 1.978 0.246 0.978 1.984 0.148 0.480 0.979 0.146 0.487 2.063 0.246 0.489 0.979

0.487 0.489 0.494 1.040 1.050 1.050 2.100 1.980 1.950 5.010 4.790 4.870 9.650 9.810 9.880

0.152 0.512 1.972 0.249 0.979 1.952 0.152 0.498 0.986 0.147 0.487 2.063 0.248 0.497 0.989

0.517 0.516 0.511 1.010 1.010 1.010 2.020 1.990 2.060 5.080 4.900 4.880 9.880 9.820 9.960

0.152 0.519 1.969 0.248 0.985 1.975 0.153 0.517 0.979 0.147 0.488 2.068 0.239 0.489 0.989

0.509 0.498 0.486 1.035 1.027 0.975 2.068 2.006 1.984 5.168 4.968 4.945 9.628 9.979 9.934

0.147 0.517 1.971 0.245 0.987 1.979 0.154 0.489 0.997 0.153 0.495 2.05 0.255 0.492 0.977

0.514 0.512 0.511 1.034 1.025 1.031 2.041 1.979 2.042 5.154 4.859 4.868 9.738 9.984 9.985

ATR

True (mg mL1) 0.15 0.50 2.00 0.25 1.00 2.00 0.15 0.50 1.00 0.15 0.50 2.00 0.25 0.50 1.00 Mean S.D. r

0.50 0.50 0.50 1.00 1.00 1.00 2.00 2.00 2.00 5.00 5.00 5.00 10.00 10.00 10.00 (%)

GA-PLS

98.0 102.0 98.9 98.4 97.8 99.2 98.7 96.0 97.9 97.3 97.4 103.0 98.4 97.8 97.9 98.6 1.79 0.9995

97.4 97.8 98.8 104.0 105.0 105.0 105.0 98.8 97.5 100.0 95.8 97.4 96.5 98.1 98.8 99.7 3.23 0.9998

PCR

101.3 102.4 98.6 99.6 97.9 97.6 101.3 99.6 98.6 98.0 97.4 103.1 99.2 99.4 98.9 99.5 1.76 0.9994

cellulose (CMC), tween 80 (non-ionic surfactants) and cetrimide (cationic surfactant), were added in a concentration of 1% w/v. Only cetrimide offered the highest relative fluorescence intensity. So, it was chosen as the optimal fluorophore enhancer as illustrated in (Fig. 6). 3.3.1.2. Impact of the volume of organized media. The impact of volume of 1% cetrimide was studied. The result was that by increasing the volume of cetrimide, the intensity of fluorescence was increased till adding 2 mL of 1% cetrimide after that no increase in fluorescence intensity. So, 2 mL 1% cetrimide was chosen as optimum volume.

103.4 103.2 102.2 101.0 101.0 101.0 101.0 99.4 103.0 101.6 97.9 97.5 98.8 98.2 99.6 101.0 1.96 0.9999

GA-PCR

101.3 103.8 98.4 99.2 98.5 98.7 102.0 103.4 97.9 98.0 97.6 103.4 95.6 97.8 98.9 99.6 2.50 0.9994

101.8 99.6 97.2 103.5 102.7 97.5 103.4 100.3 99.2 103.4 99.4 98.9 96.3 99.8 99.3 100.0 2.33 0.9996

98.0 103.4 98.6 98.0 98.7 99.0 102.7 97.8 99.7 102.0 99.0 102.5 102.0 98.4 97.7 99.8 2.05 0.9996

102.8 102.4 102.2 103.4 102.5 103.1 102.0 98.9 102.1 103.1 97.2 97.4 97.4 99.8 99.9 101.0 2.29 0.9997

while it causes to decrease the fluorescence intensity of ATR. Acetonitrile and ethanol decrease fluorescence intensity (Fig. 8). 3.3.1.5. Choice of optimal Dl. The best Dl is carefully studied for obtaining optimum shape and well-resolved spectra with the highest sensitivity. Altered Dl values were studied fluctuating from

3.3.1.3. Impact of pH. Various buffer solutions were studied as acetate buffer pH (2.5e5.5) and borate buffer pH (6e9). It was found that acetate buffer pH 3.5 increase the fluorescence intensity, while borate buffer cause decrease in fluorescence intensity. Therefore acetate buffer pH 3.5 is the optimum buffer (Fig. 7). 3.3.1.4. Impact of the solvent for dilution. Regarding the impact of various solvents for dilution, such as water, methanol, acetonitrile, and ethanol, water was the optimal diluting solvent where it provided the maximum fluorescence intensity of the studied drugs. Methanol causes an increase in fluorescence intensity of AML only,

Fig. 6. Effect of organized media on (1 mg mL1) AML and (4 mg mL1) ATR.

Fig. 7. Effect of pH on (1 mg mL1) AML and (4 mg mL1) ATR.

Fig. 8. Effect of diluting solvent on (1 mg mL1) AML and (4 mg mL1) ATR.

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Fig. 9. Effect of Dl on (1 mg mL1) AML (A) and (4 mg mL1) ATR (B) Mixture of both (C).

20 nm to 120 nm. It was found that Dl of 100 nm was the optimum for this work. As shown in Fig. 9, where the synchronous spectra of AML and ATR are represented as surface projection and contour plots, where synchronous spectra at gradual rises of Dl have been recorded and graphed. 3.3.2. Optimizing the parameters of genetic algorithm Proper fine-tuning of GA parameters constitutes a critical subject to ensure fruitful GA performance. The GA parameters include the percentage of genes comprised at initiation, the highest number of

generations, the count of wavelengths included in a window, the rate of mutation, cross-over rule either single or double and percentage of the population at convergence. Additional parameters are adjusted through the operator as the highest number of latent variables used for PLS, the kind of cross-validation either random or contiguous block, count of splits used for division of data for crossvalidation, count of iterations for cross-validation at every generation. The GA parameters outline is illustrated in Table 3. In GA-PLS and GA-PCR techniques, the models were constructed utilizing the same measures nevertheless variable selection by GA

J.J. Nasr, S. Shalan / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 224 (2020) 117430 Table 3 Optimal parameters for genetic algorithms (GA). Parameter

Value

Population size Window width (number of variables in a window) % initial terms (% wavelengths used at initiation) Maximum generations % of population at convergence Mutation rate Crossover type Regression choice Maximum number of latent variables Type of cross-validation Number of splits (number of subsets to divide data into for crossvalidation) Number of iterations for cross validation at each generation

64 1 30 100 50 0.005 Double PLS 10 Random 5 1

was performed for the choice of the most correlated wavelengths to the analytes' concentrations before starting PLS or PCR model construction. After the run of GA, the matrix for fluorescence intensity was lessened to about one third for both drugs (49 nm). The wavelengths selected were 250, 254, 261, 265e266, 268e270, 277e283, 290 301, 320, 339, 342e344, 347, 349, 351e352, 355e358, 361, 374, 376e377, 379e380, 383e384, 386, 388, 391, 396e403 (wholly, 49 nm). 4. Validation of the method 4.1. Validation of the FDSFS method Agreeing with ICH recommendations [31], validating the proposed method was accomplished. 4.1.1. Linearity The proposed method was found to be rectilinear over the range of (0.1e4.0 mg mL1) and (0.4e10 mg mL1) for AML and ATR, correspondingly. The linear regression equations are:

1D ¼ 1:589 þ 5:332C for AML at 387 nm ðr ¼ 0:9999Þ and 1D ¼ 0:684 þ 1:167C for ATR at 292 nm ðr ¼ 0:9999Þ The detection and quantification limits were calculated. They were 0.0135 mg mL1and 0.0409 mg mL1 for AML and 1 0.0863 mg mL and 0.261 mg mL1 for ATR, respectively. The results were cited in Table 4. The proposed method results were statistically compared with those of the reference one [7], and it was found that there was no significant difference between them utilizing Student's t-test and variance ratio F-test [32] as shown in Table 5. The reference method used was adopted by three spectrophotometric methods which were the first derivative of the ratio spectra (1DD), ratio subtraction and the third one is the mean centering of ratio spectra method. The linearity of the calibration graph lies in Table 4 Performance data of the proposed FDSFS method. Parameter

Concentration range (mg mL1) LOD (mg mL1) LOQ (mg mL1) Correlation coefficient (r) Slope Intercept Sy/x Sa Sb % RSD pffiffiffi % Er (% RSD/ n)

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Table 5 Statistical analysis of the results obtained by the proposed and comparison methods for pure samples of AML and ATR. Parameter

AML

% Recovery

X ±S. D. Student's t-valuea Variance ratio F-valuea a

ATR

FDSF

Comparison method [7]

FDSF

Comparison method [7]

98.91 99.21 101.03 99.68 100.37 99.91 99.85

98.08 99.81 98.15

98.99 99.89 100.02 100.85 99.62 100.33 99.89

100.15 99.83 99.56

±0.770

98.68 ±0.970 0.217 (5.05) 1.259 (2.228)

±0.685

99.96 ±0.480 0.159 (5.05) 1.420 (2.228)

Tabulated t- and F-values at p ¼ 0.05 are given in parentheses.

the concentration range of 3e40 and 8e32 mg mL1 for AML and ATR, correspondingly [7]. 4.1.2. Accuracy and precision The repeatability of the proposed method was estimated using three concentrations along three successive days (intra-day precision) and three concentrations within the same day (inter-day precision). The small values of standard deviation and %RSD reveal the high value of precision of the proposed method. The results were depicted in Table 6. 4.2. Validation of PLS and PCR models The prediction error sum of squares (PRESS) for all of the calibration samples is a way of measuring of the wellness of the built PLS and PCR models to turn the concentration data. The root mean squares of error of cross-validation (RMSECV) values of various models were compared to select the optimal number of factors (Fig. 5). The PRESS and RMSEP (root mean square error of prediction) values of true and predicted concentrations are utilized to validate the PLS and PCR models. PRESS and RMSEP are calculated as follows:

PRESS ¼

n  X

b c i  ci

2

i¼1

RMSEP ¼

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP u n u ðc  b c Þ2 ti¼1 n

where: ci is the true concentration of the analyte and ^ci is the predicted concentration of the analyte in sample I and n is the number of samples in the validation set. Table 6 Accuracy and precision data for AML and ATR using the FDSF method.

FDSF AML

ATR

0.1e4.0 0.0135 0.0409 0.9999 5.332 1.589 0.0384 0.0218 0.0115 0.772 0.315

0.4e10.0 0.0863 0.2617 0.9999 1.167 0.6846 0.0484 0.0305 0.0055 0.686 0.279

Parameter Concentration (mg/ Intra-daya mL) Recovery (mean ± SD) AML

ATR

0.5 1.0 2.0 2.0 4.0 8.0

99.94 ± 0.81 99.26 ± 0.62 100.53 ± 1.11 98.37 ± 0.82 99.29 ± 1.91 98.95 ± 1.05

Inter-dayb %Er Recovery (mean ± SD) 0.41 0.52 0.48 0.72 0.69 0.51

Each result is the average of three separate determinations. a Intra-day: within the day. b Inter-day: three consecutive days.

99.79 ± 0.75 100.61 ± 0.92 99.43 ± 1.22 99.89 ± 0.71 100.21 ± 0.69 99.71 ± 0.59

%Er 0.65 1.23 1.57 0.52 0.53 0.47

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Table 7 Analytical results for the prediction of the validation set concentrations by the proposed multivariate calibration methods. Method

PLS

AML ATR

AML

ATR

AML

ATR

AML

ATR

AML

ATR

True (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

Found %R (mg mL1)

0.258 1.022 0.145 0.529 1.984 2.079 0.254 1.003 0.148 2.061

0.511 0.515 1.03 0.97 1.05E 2.11 4.85 4.92 10.2 10.2

0.253 1.033 0.147 0.51 1.952 2.014 0.258 1.014 0.149 2.044

0.25 0.50 1.00 0.50 0.15 1.00 0.50 1.00 2.00 1.00 2.00 2.00 0.25 5.00 1.00 5.00 0.15 10.00 2.00 10.00 Mean (%) S.D. PRESS RMSEP

GA-PLS

103.0 102.0 96.7 106.0 99.2 104.0 102.0 100.0 98.7 103.0 101.0 2.76 0.012 0.034

102.0 103.0 103.0 97.0 105.0 106.0 97.0 98.4 102.0 102.0 101.0 2.98 0.103 0.101

PCR

101.2 103.3 98.0 102.0 97.6 100.7 103.2 101.4 99.3 102.2 101.0 2.00 0.006 0.024

0.505 101.0 0.516 103.2 1.018 101.8 1.027 102.7 1.006 100.6 1.973 98.6 4.877 97.5 5.038 100.7 10.148 101.48 10.347 103.5 101.0 1.89 0.161 0.127

GA-PCR

0.251 1.020 0.148 0.515 1.975 2.012 0.254 0.974 0.149 2.049

100.4 102.0 98.7 103.0 98.7 100.6 101.6 97.4 99.3 102.4 100.0 1.85 0.004 0.021

0.513 0.488 1.024 0.986 0.975 2.049 4.858 4.865 10.137 10.150

102.6 97.6 102.4 98.6 97.5 102.4 97.2 97.3 101.4 101.5 99.8 2.39 0.084 0.091

0.256 1.007 0.153 0.495 1.979 2.035 0.256 0.971 0.147 2.041

102.4 100.7 102.0 99.2 98.9 101.7 102.4 97.1 98.0 102.0 100.0 1.99 0.004 0.021

0.496 99.2 0.515 103.0 1.032 103.2 0.985 98.5 1.031 103.1 2.055 102.75 4.868 97.4 4.993 99.9 10.322 103.2 10.191 101.9 101.0 2.25 0.163 0.128

5. Application in pharmaceutical dosage forms

40 mg, 10 mg/80 mg AML/ATR respectively). No interference was observed from the common excipients present in the tablets as lactose, starch, talc, and magnesium stearate. The developed technique results showed good accuracy and precision as designated from % recovery, SD and %RSD values (Table 8).

5.1. Application of FDSFS

5.2. Application of multivariate calibration methods

The presented technique was applied for the estimation of these drugs in the pharmaceutical formulations present in the market (Caduet ® tablets in ratios 10 mg/10 mg, 10 mg/20 mg, 10 mg/

The proposed multivariate calibration techniques used to determine AML and ATR by resolution of their synchronous fluorescence spectra were successfully applied for their estimation in

To evaluate the predictive capability of the constructed models, each was applied to a validation set to determine the components. The obtained recoveries were illustrated in Table 7.

Table 8 Statistical comparison of the results obtained by proposed methods and the reference method [7] for the analysis of Caduet® tablets in different concentration ratios. Preparation

Parameters

Caduet® tablets %R (AML 10 mg þ ATR 10 mg/tablet)

Caduet® tablets (AML 10 mg þ ATR 20 mg/tablet)

Caduet® tablets (AML 10 mg þ ATR 40 mg/tablet)

Caduet® tablets (AML 10 mg þ ATR 80 mg/tablet)

Mean S.D. Variance Student's t-testa F-testa %R

Mean S.D. Variance Student's t-testa F-testa %R

Mean S.D. Variance Student's t-testa F-testa %R

Mean S.D. Variance Student's t-testa F-testa a

Conc. taken (mg mL1)

FDSF

AML ATR

AML

1.0 2.0 4.0

1.0 2.0 4.0

99.15 100.71 99.87 99.91 0.781 0.610 0.440 2.737 1.0 2.0 98.46 2.0 4.0 99.63 4.0 8.0 99.41 99.17 0.623 0.387 1.055 2.350 1.0 4.0 100.27 2.0 8.0 99.64 2.5 10.0 100.19 100.03 0.343 0.118 0.243 3.028 0.25 2.0 99.60 0.50 4.0 100.44 1.0 8.0 99.90 99.98 0.426 0.181 0.601 2.258

Tabulated t- and F-values at p ¼ 0.05 are 2.78 and 19.00, respectively.

PLS

PCR

AML

GA-PLS

ATR

AML

GA-PCR

ATR

AML

Reference

ATR

AML

ATR

98.54 101.18 99.79 99.84 1.321 1.744 0.018 2.233 100.83 98.41 99.29 99.51 1.225 1.500 0.352 1.615 100.38 99.13 100.52 100.01 0.765 0.586 0.950 2.391 100.37 98.44 100.07 99.63 1.039 1.079 0.032 1.038

100.32 100.92 99.24 100.16 0.851 0.725 0.709 2.302 100.73 99.42 98.95 99.7 0.922 0.851 0.209 1.067 98.74 100.61 99.24 99.53 0.968 0.937 0.619 2.631 99.41 98.02 99.83 99.09 0.947 0.897 0.949 2.193

98.97 99.76 99.83 100.98 101.22 97.85 102.23 99.76 101.29 100.86 99.87 100.89 99.54 101.61 100.74 98.71 98.46 97.49 99.75 98.23 100.87 99.82 99.92 99.72 99.45 100.62 98.54 101.57 0.887 1.297 1.204 1.783 0.771 0.887 0.681 0.786 1.683 1.450 3.179 0.595 0.786 0.464 0.005 0.372 0.120 0.063 1.181 1.091 2.717 1.006 1.009 1.856 1.906 1.313 2.122 1.685 100.78 98.44 100.45 101.24 99.29 98.45 101.95 98.88 99.78 101.5 100.92 101.27 100.59 100.47 101.02 98.59 99.17 99.46 100.44 99.15 100.17 100.23 98.94 100.37 100.54 100.33 99.40 100.86 1.172 0.734 1.167 0.949 0.994 1.091 0.953 1.375 0.539 1.362 0.900 0.989 1.191 0.908 0.456 1.329 0.626 0.876 0.634 0.554 1.325 1.479 1.685 1.465 1.009 1.064 1.310 1.023 100.17 99.47 98.53 98.66 100.81 100.49 99.92 99.24 100.28 100.22 100.7 99.17 99.28 98.82 100.96 101.1 99.06 100.48 100.11 98.49 98.41 100.12 100.28 99.27 99.95 100.03 99.42 99.05 0.861 0.815 0.865 1.120 0.823 1.007 0.781 0.741 0.664 0.747 1.254 0.677 1.015 0.610 1.070 0.594 0.417 0.014 0.938 0.764 0.862 3.027 1.864 3.051 3.519 2.765 2.848 2.490 101.17 98.88 99.17 100.74 101.57 98.36 99.92 98.71 99.16 101.33 99.36 100.06 97.99 101.11 101.38 100.6 98.11 98.32 99.20 100.45 100.85 100.42 99.55 99.54 99.47 100.28 98.93 100.63 1.485 0.923 1.641 1.214 1.200 1.326 0.626 2.204 0.852 2.693 1.474 1.440 1.759 0.391 0.789 0.257 0.057 0.303 0.744 0.917 1.487 2.121 2.081 2.591 3.601 1.385 4.299 2.655

ATR

AML

ATR

98.51 99.04 100.98 100.78 99.09 99.64 99.53 99.82 1.292 0.884 1.668 0.781

99.34 98.73 99.28 100.54 100.96 100.21 99.86 99.83 0.953 0.964 0.908 0.929

100.59 99.8 99.42 99.94 0.597 0.356

99.76 99.83 98.94 99.51 0.495 0.245

100.43 99.76 99.20 98.51 99.51 100.53 99.71 99.60 0.640 1.019 0.409 1.039

J.J. Nasr, S. Shalan / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 224 (2020) 117430

pharmaceutical dosage forms by different ratios. The obtained results were compared to the reference method [7] where it was observed that there is no significant difference as shown by student's t-test and variance ratio F-test [32] as illustrated in Table 8.

[13]

6. Conclusion [14]

Five simple, rapid and sensitive analytical synchronous spectrofluorimetric techniques were established for the simultaneous determination of polypills containing AML and ATR. The studied methods allow estimation of these drugs in their dosage forms in different ratios. The suggested procedures were validated by using the International Conference on Harmonization guidelines. The acquired validation meters such as linearity, accuracy, precision, LOD, and LOQ were in reasonable limits, and these procedures were accordingly highly regarded to be reliable and feasible. The Assessment outcomes of the suggested procedures were not significantly different from the reference spectrophotometric method. As a consequence, these procedures are applicable to determine the studied medicines in their raw materials and in tablet dosage forms. As a result of the simplicity of the methods, this work is suitable for routine quality control work.

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