Simultaneous spectrophotometric determination of glimepiride and pioglitazone in binary mixture and combined dosage form using chemometric-assisted techniques

Accepted Manuscript Simultaneous spectrophotometric determination of glimepiride and pioglitazone in binary mixture and combined dosage form using chemometric-assisted techniques

Asmaa A. El-Zaher, Ehab F. Elkady, Hanan M. Elwy, Mahmoud Abo El Makarim Saleh PII: DOI: Reference:

S1386-1425(17)30204-4 doi: 10.1016/j.saa.2017.03.028 SAA 15007

To appear in:

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Received date: Revised date: Accepted date:

22 June 2016 7 March 2017 13 March 2017

Please cite this article as: Asmaa A. El-Zaher, Ehab F. Elkady, Hanan M. Elwy, Mahmoud Abo El Makarim Saleh , Simultaneous spectrophotometric determination of glimepiride and pioglitazone in binary mixture and combined dosage form using chemometric-assisted techniques. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Saa(2017), doi: 10.1016/j.saa.2017.03.028

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ACCEPTED MANUSCRIPT Simultaneous spectrophotometric determination of Glimepiride and Pioglitazone in binary mixture and combined dosage form using chemometric-assisted techniques Asmaa A. El-Zahera, Ehab F. Elkadya, Hanan M. Elwyb, Mahmoud Abo El Makarim Salehb,* Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr

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El-Aini St., Cairo 11562, Egypt.

National Organization for Drug Control and Research (NODCAR), Giza 35521,

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Egypt.

______________________________________________________________ * Corresponding author Pharmacist/ Mahmoud Abo El Makarim M. Saleh Postal address: Makram Ebeid, 8th district, Postal code: 11762, Nasr city, Cairo, Egypt E-mail address: [email protected] Tel: +2-01114516158

ACCEPTED MANUSCRIPT Abstract In the present work, pioglitazone and glimepiride, 2 widely used antidiabetics, were simultaneously determined by a chemometric-assisted UV-spectrophotometric method which was applied to a binary synthetic mixture and a pharmaceutical preparation containing both drugs. Three chemometric techniques – Concentration

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residual augmented classical least-squares (CRACLS), principal component

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regression (PCR), and partial least-squares (PLS) were implemented by using the

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synthetic mixtures containing the two drugs in acetonitrile. The absorbance data matrix corresponding to the concentration data matrix was obtained by the

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measurements of absorbencies in the range between 215 and 235 nm in the intervals with Δ λ = 0.4 nm in their zero-order spectra. Then, calibration or regression was

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obtained by using the absorbance data matrix and concentration data matrix for the prediction of the unknown concentrations of pioglitazone and glimepiride in their

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mixtures. The described techniques have been validated by analyzing synthetic

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mixtures containing the two drugs showing good mean recovery values lying between 98-100 %. In addition, accuracy and precision of the three methods have been assured

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by recovery values lying between 98-102 % and R.S.D. % ˂ 0.6 for intra-day precision and ˂ 1.2 for inter-day precision. The proposed chemometric techniques

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were successfully applied to a pharmaceutical preparation containing a combination of pioglitazone and glimepiride in the ratio of 30: 4, showing good recovery values. Finally, statistical analysis was carried out to add a value to the verification of the proposed methods. It was carried out by an intrinsic comparison between the 3 chemometric techniques and by comparing values of present methods with those obtained by implementing reference pharmacopeial methods for each of pioglitazone and glimepiride.

ACCEPTED MANUSCRIPT Keywords: Chemometric

techniques;

Glimepiride;

Pioglitazone;

UV-

spectrophotometry; Synthetic mixture; Pharmaceutical preparation. Non-standard abbreviations C: Concentration matrix, C*: new concentration matrix, CLS: classical least-squares, CRACLS: Concentration residual augmented classical least-squares, GLM:

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Glimepiride, ILS: inverse least-squares, PCR: principal component regression, PIO:

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Pioglitazone, PLS: partial least-squares, PPAR-γ: Peroxisome proliferation activating

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γ receptor, RMSECV: Root-Mean-Square Error of Cross-Validation, USP: United States Pharmacopeia

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1. Introduction

Glimepiride (GLM) (Fig. 1a) and Pioglitazone (PIO) (Fig. 1b) are well known

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antidiabetics used to treat type 2 diabetes mellitus. GLM belongs to the 2nd generation sulphonylureas, with respect to structure. It is also known to be an insulin

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secratagogue, with respect to pharmacodynamics, as the other sulphonylureas. It acts

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directly on the β-cells of the pancreas through KATP channel inhibition leading eventually to insulin secretion which further increases on acute dosing. PIO, on the

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other hand, belongs to a different chemical class, thiazolidinediones, which are considered as PPAR-γ (Peroxisome proliferation activating γ receptor) activators

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which act through increasing tissue sensitivity to insulin (1). Combination of GLM with PIO in a single dosage form adds a high benefit to the combined dosage form since each component in the dosage form has a different mechanism of action to treat type 2 diabetes mellitus. Literature survey revealed that each of GLM and PIO were determined in the presence of their degradation products (2, 3) and in binary mixture (4-6) by liquid chromatographic methods. They were also incorporated in multi-component mixtures

ACCEPTED MANUSCRIPT (7-11). Basically, the newly developed spectrophotometric method is much simpler than HPLC as UV-spectrophotometric determination doesn't require prior physical separation. Nevertheless, spectrophotometric methods were reported for their simultaneous determination (12-15). A detailed comparison and addressing the rationale of the present work will be discussed further under 'Results and discussion'.

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Recently, multivariate calibrations, such as classical least-squares (CLS),

(PLS) have been applied to the analysis of data obtained from all the

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squares

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inverse least-squares (ILS), principal component regression (PCR) and partial least-

instrumentations. They have been applied to the assay of binary or multi-component

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dosage forms containing two or more compounds with overlapping spectra as they can get selective information from non-selective data; they acquire the advantages of

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higher speed of processing data concerning the values of concentrations and absorbencies of compounds with strongly overlapping spectra and errors of

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calibration model are minimized by measuring the absorbance values at many points

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in the wavelength range of the zero-order or derivative spectra. Dinç, Ustündağ and Onur used these techniques for the simultaneous analysis of binary and ternary

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mixtures (16-18). Analytical methods using multivariate calibrations and their applications include the spectrophotometric, spectrofluorimetric, chromatographic and

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electrochemical methods for determination of analytes in the mixtures (19, 20). In the present work, different chemometric methods were applied for the simultaneous UV-spectrophotometric determination of PIO and GLM. The chemometric methods applied are PCR, PLS and also a modified approach to CLS, under the name: Concentration residual augmented classical least-squares (CRACLS) (21). These multivariate calibrations were useful in spectral analysis because the simultaneous inclusion of many spectral wavelengths instead of a single wavelength

ACCEPTED MANUSCRIPT greatly improved the precision and predictive ability (19). CRACLS was introduced as a theory with an experimental demonstration by David K. Melgaard, David M. Haaland and Christine M. Wehlburg in 2002 (21). Later, it was successfully applied in different mixtures as those resolved by Mostafa A. Shehata and Ahmed Ashour (22, 23). Implementation of the described chemometric methods will be further explained

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under 'Procedures' and 'Results and discussion'.

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2. Experimental

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2.1. Materials and Reagents

GLM (certified to contain 99.46%) was supplied by Hikma Pharmaceutical

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Company-Egypt while PIO-HCl (certified to contain 99.3%) was supplied by Eva Pharma Company-Egypt. Amaglust® 30/4 tablets (Next Pharma Pharmaceutical

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Company) - each labeled to contain 4 mg GLM and 30 mg PIO -were bought from local pharmacy. Acetonitrile was obtained from Avantor Performance Materials-

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2.2. Instrumentation

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Product of Poland.

Shimadzu 1650 PC (Japan) double beam UV-Vis spectrophotometer

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connected to IBM compatible computer; assisted with UVPC software-version 2.21 and data processing program (Matlab™) version R (2012B) 8.0.0.783 with (PLS)

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Toolbox, was used in the newly developed spectrophotometric method. 2.3. Preparation of standard stock solutions Solutions of 100 µg/ml of GLM and 100 µg/ml of PIO were each prepared by dissolving 10 mg GLM and 11 mg PIO-HCl in acetonitrile in separate 100 ml volumetric flasks.

ACCEPTED MANUSCRIPT 2.4. Samples preparation Ten tablets of Amaglust® 30/4 were weighed and finely powdered in a mortar. A quantity of powdered tablets equivalent to 30 mg of PIO and 4 mg of GLM was accurately weighed and transferred to a 100 ml volumetric flask, 50 ml of acetonitrile were added and the mixture was sonicated for 15 minutes with frequent shaking then

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completed to volume with the selected solvent. The solution was then filtered through

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0.45 μm nylon filter paper.

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2.5. Procedures: 2.5.1. Construction of the training set

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Eleven binary mixtures of PIO and GLM were prepared by transferring different aliquots of their standard stock solutions into a series of 10 ml volumetric

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flasks (Table 1). The absorbencies of these mixtures were measured between 200 nm and 400 nm at 0.4 nm intervals against acetonitrile as a blank.

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2.5.2. Pre-processing the data

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Regions below 215 nm and regions above 235 nm were rejected in order to represent a common interval between GLM and PIO spectra (Fig. 2) in which

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magnitude is proportional to quantity. In addition, several trials were carried out at

model.

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other likely common intervals; the established one gave the most excellent predictive

2.5.3. Construction of models Three multivariate calibration models (CRACLS, PCR and PLS) were constructed using the data obtained. For the chemometric techniques, the absorbance data matrix for the training set concentration matrix, (Table 1), were obtained by the measurement of absorbencies between 215 and 235 nm in the intervals of 0.4 nm. Calibration or regression was obtained by using the absorbance data matrix and

ACCEPTED MANUSCRIPT concentration data matrix for prediction of the unknown concentrations of PIO and GLM in their binary mixtures and pharmaceutical formulation. CRACLS model substituted the conventional CLS procedure since un-modeled spectrally active components, in case of the former procedure, were spanned by augmented concentration residual vectors, enabling its definition and removal of its subsequent

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error. To build the CRACLS model (21), the computer was fed with the absorbance

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and concentration matrices (C) for the training set. The calculations to obtain the K

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matrix were carried out. Then, from obtained K matrix, new concentration matrix (C*) is calculated in an inverse order of the first process. Concentration residuals are

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then calculated by subtracting C from C*. The process is repeated in an iterative manner that will be discussed later under 'Results and discussion'. For the PCR and

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PLS models, the training set absorbance and concentration matrices together with PLS-toolbox 2.0 software were used for calculations.

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2.5.4. Selection of the optimum number of factors to build the PCR and PLS

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models

To select the optimum number of factors in the PLS and PCR algorithms, a cross-validation method leaving out one sample at a time was employed using

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calibration set of 11 calibration spectra. PLS and PCR calibration on 10 calibration

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spectra were performed and, using this calibration, the concentration of the sample left out during the calibration process was predicted. This process was repeated 11 times until each training sample had been left out once. The predicted concentrations of the components in each sample were compared with the actual concentrations in the calibration samples and Root-Mean-Square Error of Cross-Validation (RMSECV) was calculated for each method. It indicates both of the precision and accuracy of predictions. It was recalculated upon addition of each new factor to the PLS and PCR models. Three factors were found suitable for both PCR and PLS methods.

ACCEPTED MANUSCRIPT 2.5.5. Validation (Construction of the validation set) To evaluate the prediction performance of the proposed chemometric models, a set of five synthetic validation mixtures of PIO (24-60 µg/ml) and GLM (3.2-8 µg/ml) was prepared by transferring different volumes of their stock solutions into 10 ml volumetric flasks and the procedure under 'Construction of the training set' was

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repeated. The suggested models were applied to these mixtures to predict the

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concentrations of PIO and GLM. Intra-day and inter-day accuracy and precision

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tests were also carried out for PIO and GLM in bulk using 3 different concentrations (µg/ml) (33.6/4.48, 42/5.6 and 50.4/6.7 of PIO/GLM, respectively) in triplicates for

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three consecutive days. Results are shown in tables 2 and 3.

2.6. Application of the developed spectrophotometric method for the

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determination of PIO and GLM in Amaglust® 30/4 The tablets solution prepared under section 'Samples preparation' was serially diluted with acetonitrile to prepare solutions with concentrations ranging from 18-30

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g/ml PIO and 2.4-4 g/ml GLM. The spectra of the prepared solutions were scanned, as an example shown in Figure 3, then the developed multivariate models;

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CRACLS, PCR and PLS, were applied to calculate the concentrations of PIO and GLM. The experiment was repeated applying the standard addition technique. The

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recovered concentrations of labeled and added PIO and GLM were then computed by applying the different chemometric techniques (Table 4 and 5) 3. Results and discussion UV absorption spectra of GLM and PIO in acetonitrile at their nominal concentrations ratio in tablets (4: 30) showed overlap in common intervals (Fig. 2) preventing the possibility of multi-component mode of analysis for their simultaneous spectrophotometric determination. Otherwise, UV-spectrophotometric methods were reported for their simultaneous determination using simultaneous equation method,

ACCEPTED MANUSCRIPT isosbestic point and orthogonal polynomial method (12-14) which are also considered mathematical-related separation methods, so they don't pioneer them over chemometric methods. From a different perspective, GLM and PIO were simultaneously determined by a spectrophotometric method in the visible spectrum at 645 nm after the oxidative coupling of each drug with 3-methylbenzothiazolinone

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hydrazone / cerric sulphate system, of which the kinetics was studied (15). Not only

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do chemometric methods offer a new robust mathematical-related separation

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procedure, but it also acquires some advantages over reported mathematicalspectrophotometric methods since its application doesn't require prior knowledge of

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certain specific values as that of 'absorption maximum wavelength' and 'isosbestic point' (12, 13) in addition to minimizing the errors of calibration model since

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absorbance values are measured at many points in the wavelength range of the zeroorder spectrum. This fact improved the precision and predictive ability of

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chemometric techniques in comparison with the other methods. Also, chemometric

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methods can be considered much simpler in its application than orthogonal polynomial method which requires many alternatives and miscellaneous data (14); it

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is regarded by the authors as a complicated procedure. Hence, the main task of the present work was to develop and validate simple, accurate, precise and selective

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methods based on spectrophotometric measurements and capable of determining the two drugs simultaneously with the help of different chemometric techniques. Eleven different concentrations mixtures of PIO and GLM were used (Table 1) as the calibration samples (concentration matrix or training set) to construct the models. They were chosen to give the best predictive model through trial and error. The wavelength range 215–235 nm in the intervals with Δ λ = 0.4 nm was chosen as it provided the greatest amount of information about mixture components.

ACCEPTED MANUSCRIPT Concentration residual augmented CLS (CRACLS) is considered as an extension to the classical least-squares algorithm in which calibration matrix of reference concentrations is augmented with concentration residuals, which represent linear combinations of the un-modeled spectrally active component concentrations, estimated during CLS prediction in an iterative process (21). In each round of this

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process, concentration matrix is augmented with one row of concentration residuals. It

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was repeated till reaching best predictive model that spans all the un-modeled sources

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of spectral variation, which was suggested in the present work by good recovery values of nominal concentration values in validation set and non-significant difference

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in a statistical comparison (F-test) between CRACLS, PLS and PCR. Training set, in the present work, was augmented with two rows of concentration residuals.

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Augmentation with more than two rows resulted in erroneous results. Spectral decomposition and principal component analysis is an essential and

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integral step in multivariate regression analytical methods; namely: partial least

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square (PLS) and principal component regression (PCR) analysis. It was carried out through Matlab™ software in the present work. A subsequent step involves selection

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of the optimum number of principal components (or latent variables or factors) which is carried out through a cross-validation statistical test leaving out one sample at a

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time. It was a critical step in proceeding through the PLS and PCR algorithms before constructing the models since it was reported that if the number of components retained was more than the number required, more noise would be added to the data. On the other hand, if the number retained was less than the number required, meaningful data that could be necessary for the calibration might be ignored (19). Cross-validation method was employed using calibration set of eleven calibration spectra. PLS and PCR calibration on ten calibration spectra were performed and,

ACCEPTED MANUSCRIPT using this calibration, the concentration of the sample left out during the calibration process was predicted. This process was repeated eleven times until each training sample had been left out once. The predicted concentrations of the components in each sample were compared with the actual concentrations in the calibration samples and the root mean squares error of cross-validation (RMSECV) was calculated for

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each method. It indicates both of the precision and accuracy of predictions. It was

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recalculated upon addition of each new factor to the PLS and PCR models. Three

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factors were found suitable for both PCR and PLS methods. Despite having only two components present in the constructed model with no interferents, number of latent

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variables chosen was three instead of two since the choice of 3 factors during calibration was corresponding to the least RMSECV value during the cross validation

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analysis so that issue doesn't contradict data analysis and regression conclusions which doesn't necessarily cope with the ideal theoretical understanding of the present

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case having only two components in the constructed model. In addition, to validate

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the prediction ability of the suggested models, they were used to predict the concentration of PIO and GLM in their laboratory prepared mixtures, where

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satisfactory results were obtained (Table 2). Also, the chemometric methods (CRACLS, PCR and PLS) were applied successfully to the analysis of PIO and GLM

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in their combined tablet. Acetonitrile was chosen as the solvent, rather than methanol, for implementing the spectrophotometric method since it was found more efficient in dissolution and extraction of bulk drugs and pharmaceutical dosage form respectively. To assess the accuracy of the method, standard addition technique was carried out. The results are given in tables (4 and 5). Also, for qualitative purposes, spectrum of sample solution was overlaid with that of a training sample solution to show similarity (Fig. 3). Hence, results were found satisfactory indicating that the additives of the

ACCEPTED MANUSCRIPT tablets did not interfere with the active principles' characteristic absorbance spectrum or quantitative results. Also, intra-day and inter-day accuracy and precision were assessed by measuring 3 concentrations in triplicates for three consecutive days for PIO and GLM in binary mixtures (33.6/4.48 µg/ml, 42/5.6 µg/ml and 50.4/6.7 µg/ml of PIO/GLM, respectively) representing 80 %, 100 % and 120 %, respectively of the

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training set. Good values for intra-day and inter-day precision were obtained (˂ 0.6

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for intra-day R.S.D. % and ˂ 1.2 for inter-day R.S.D. %) -Table 3. Also, accuracy was

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assured by recovery values lying between 98-102 % (Table 3). 4. Statistical analysis

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Statistical analysis was carried out, with the aid of IBM SPSS statistical package, by applying ANOVA analysis for a statistical comparison between the three

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techniques (CRACLS, PLS and PCR) revealing no significant difference which adds to the verification of the proposed techniques and suggested designs. Also, it was

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carried out between CRACLS, PLS and PCR models in the manner of pairs. Non-

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significant difference was proved by the calculated F-value since it was always less than theoretical F-value (accepting null hypothesis), after calculating recovery values

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for both PIO and GLM in synthetic mixtures (Table 2), dosage form and added standards (Tables 4 and 5).

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5. Comparing with reference methods A statistical comparison was carried out, with the aid of IBM SPSS statistical package, to assure non-significant difference between recovery results of the newly developed methods and that of reference methods for each of PIO and GLM. Variance ratio F-test proved that null hypothesis since obtained F-value was smaller than tabulated (or theoretical) F-value (Table 6). As for reference methods, GLM was determined, according to United States Pharmacopeia (USP) (24), at 5 concentration

ACCEPTED MANUSCRIPT levels ranging from 30-90 µg/ml by injecting its solution in Acetonitrile: water (4:1; v/v) through an HPLC system having a mixture of monobasic sodium phosphate buffer (pH adjusted to 2.5 with phosphoric acid): Acetonitrile (500: 500; v/v) flowing through a C18 column (250 mm × 4 mm, 5 μm) at a rate of 1 ml/min. UV detection for GLM was then carried out at 228 nm. PIO was also determined, according to USP

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(24), at 5 concentration levels ranging from 27-82 µg/ml by injecting its solution in

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mobile phase through an HPLC system consisting of acetonitrile: 0.1 M Ammonium acetate: glacial acetic acid (25:25:1; v/v) as the mobile phase which flew at a rate of

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0.7 ml/min. through a C18 column (150 mm × 4.6 mm, 3.5 μm), then the effluent was

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detected by UV detector at 254 nm.

6. Novelty of the present work and essential advantages:

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 1st chemometric experiment for glimepiride-pioglitazone binary mixture; a widely used combination product for type 2 diabetes  Error is significantly removed through the 'principle component analysis' step

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in principle component regression (PCR) and partial least squares (PLS)

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techniques. Even in classical least squares (CLS) technique, the submitted work presents a modified approach for CLS, presented as a theory in 2002, under the name 'Concentration residual augmented classical least-squares'

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(CRACLS), in which error is also significantly removed to produce a technique with results comparable to PCR and PLS techniques; which are

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more advanced. This is done through augmentation of the calibration set with concentration residuals, calculated in an iterative process, to span all the unmodeled sources of spectral variation so that we can eventually correlate absorbance values solely to the concentrations of targeted active ingredients of the dosage form without interference with other sources of error.  Results are rapidly calculated and no complex mathematical equations; only data is entered through Matlab™ software by which subsequent results are then computed immediately.  A proven high accuracy and precision  A highly robust method

ACCEPTED MANUSCRIPT  No laborious sample preparation; only a simple solution of bulk drug mixture or dosage form in acetonitrile and without the need for sample enrichment technique  Cheap instrumentation; applicable in many ordinary QC laboratories.  No prior physical separation  Simple in operation

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7. Conclusion The proposed chemometric designs and models (CRACLS, PCR and PLS) can

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be used for simultaneous determination of PIO and GLM in binary mixtures and

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pharmaceutical dosage forms containing them without interference with each other or from excipients and without the need for previous physical separation of the two

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drugs. Spectral and concentration data matrices were used to build multivariate

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calibration models. Verification of the calibrations, carried out with the aid of a synthetic set of mixtures of the two compounds, standard addition technique and

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application to a marketed pharmaceutical dosage form, produced satisfactory results.

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Hence, the proposed methods can be used for routine quality control of the cited drugs, in their combined dosage form, in ordinary laboratories. 8. Acknowledgment

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We are very grateful to all managers and professors in the National

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Organization for Drug control and Research, for giving us the opportunity to proceed through this work in QC labs. We would also like to express special thanks to our professors and colleagues who supported us during this work, either by hardware or by technical advises. 9. References (1)

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ACCEPTED MANUSCRIPT Method with Advantages over Partial Least Squares, Appl. Spectrosc. 56 (2002) 615-624. (22)

M.A. Shehata, A. Ashour, N.Y. Hassan, A.S. Fayed and B.A. El-Zeany, Liquid chromatography and chemometric methods for determination of rofecoxib in presence of its photodegradate and alkaline degradation products,

A.S. Fayed, M.A. Shehata, A. Ashour, N.Y. Hassan and S.A. Weshahy,

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Anal. Chim. Acta 519 (2004) 23–30.

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Validated stabilityindicating methods for determination of cilostazol in the presence of its degradation products according to the ICH guidelines, J.

Authority of the United States Pharmacopeial Convention (2011) The United

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States Pharmacopoeia (USP 34), National Formulary (NF 29), United States

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Pharmacopeial Convention, Maryland, USA

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(24)

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Pharm. Biomed. Anal. 45 (2007) 407-416.

ACCEPTED MANUSCRIPT Figure captions: Figure (1): The chemical structures of glimepiride (a) and pioglitazone (b). Figure (2): Zero order absorption UV spectra of PIO (- - - -) and of GLM () in acetonitrile in their dosage form ratio at full scale (top) and magnified (bottom) views.

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Figure (3): UV spectrum showing identical spectrum of Amaglust® 30/4 sample

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solution (- - - -) and a training set mixture solution ()

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'N.B.: If article is accepted, I recommend the colors to be reproduced only online and to be in black and white version in print so that there are no costs.'

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Figure 1

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Figure 2

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Figure 3

ACCEPTED MANUSCRIPT Table (1): The concentrations of different mixtures of PIO and GLM used in the training set for the chemometric techniques PIO Conc. (μg/ml)

GLM Conc. (μg/ml)

1

18

2.2

2

18

2.4

3

30

3.8

4

30

5

42

6

54

7

54

8

54

9

66

10

66

11

66

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Sample no.

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7.2 7.4 8.6 8.8 9

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Mixture no.

Table (2): Results of the analysis of the mixtures of the validation set of PIO and GLM by the proposed chemometric techniques Concentration (µg/ml)

Recovery % of PIO

Recovery % of GLM

PIO

GLM

CRACLS

PLS

PCR

1

24.00

3.20

101.66

101.53

101.54

2

36.00

4.80

97.36

99.25

3

48.00

6.40

98.50

99.66

4

60.00

7.80

96.43

96.77

5

60.00

8.00

99.23

C C

S.E. F-value (5.32)1 F-value (3.89)2

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C S U

98.64

98.39

98.41

99.34

100.5

100.01

100.04

99.71

99.77

100.31

100.32

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96.84

101.13

99.82

99.86

97.75

97.79

99.68

98.26

98.28

98.99

99.04

99.94

99.36

99.38

2.001

1.832

1.817

0.938

0.961

0.961

0.895

0.819

0.812

0.419

0.43

0.43

98.63

S.D.

A

0.086

With PCR

0.114

With PLS

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PLS

D E

T P E

Mean

T P

CRACLS

N A

0.953 0.002

0.002 0.875

0.069

Theoretical F-value at p=0.05 for comparing between values 1: in manner of pairs and 2: between 3 techniques

0.606

ACCEPTED MANUSCRIPT Table (3): Intraday and Interday accuracy and precision results for PIO and GLM by the proposed chemometric techniques PIO

GLM CRACLS

Conc. (g/ml) 33.6

Intra-day R.S.D % 0.06-0.16

Inter-day assay (Recovery % ± R.S.D %) 99.43 ± 0.96

Conc. (g/ml) 4.48

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0.15-0.37

98.76 ± 0.63

5.6

50.4

0.35-0.53

98.83 ± 1.15

6.7

Mean ± S.D.

99.00 ± 0.372

Conc. (g/ml) 33.6

Intra-day R.S.D % 0.03-0.05

Inter-day assay (Recovery % ± R.S.D %) 100.89 ± 0.898

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0.08-0.11

100.24 ± 0.67

50.4

0.10-0.32

99.94 ± 0.86

Conc. (g/ml) 33.6

Intra-day R.S.D % 0.04-0.07

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100.36 ± 0.485

Inter-day assay (Recovery % ± R.S.D %) 101.42 ± 0.98

0.13-0.49

100.92 ± 0.95

0.22-0.39

100.32 ± 0.81

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Mean ± S.D. PLS

Mean ± S.D.

Intra-day R.S.D % 0.06-0.2

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100.89 ± 0.5496

Conc. (g/ml) 4.48

Intra-day R.S.D % 0.04-0.06

Inter-day assay (Recovery % ± R.S.D %) 101.61 ± 0.87

5.6

0.08-0.09

100.99 ± 0.66

6.7

0.06-0.26

100.93 ± 0.80

Mean ± S.D.

101.18 ± 0.379

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Inter-day assay (Recovery % ± R.S.D %) 100.96 ± 0.93

Conc. (g/ml) 4.48

Intra-day R.S.D % 0.04-0.06

Inter-day assay (Recovery % ± R.S.D %) 101.64 ± 0.88

0.08-0.1

100.32 ± 0.68

5.6

0.08-0.09

101.01 ± 0.67

0.09-0.31

99.98 ± 0.86

6.7

0.06-0.26

100.92 ± 0.81

C A

100.42 ± 0.502

Mean ± S.D.

101.19 ± 0.393

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Table (4): Determination of PIO in Amaglust® 30/4 tablets by the proposed chemometric techniques

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Concentration of PIO (µg/ml) Added

CRACLS

PLS

PCR

18

21

104.30

104.64

104.93

104.44

104.15

3

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4

23 30

105.80

5

21

Mean

D E

C C

S.E.

A

CRACLS

PLS

PCR

98.73

100.03

100.05

97.19

98.96

98.99

97.69

98.26

98.22

97.39

96.71

96.64

98.35

98.39

98.38

104.44

N A

M

106.05

106.32

104.85

104.94

105.23

97.87

98.47

98.46

0.829

0.988

0.977

0.651

1.206

1.244

0.479

0.570

0.564

0.291

0.5395

0.556

T P E

S.D.

T P

I R

C S U

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F-value (5.14)3, (3.89)4

Recovery % of PIO (Added)

Tablet

2

F-value (7.71)1, (5.32)2

Recovery % of PIO (Tablets)

With PLS

0.018

With PCR

0.269

0.958 0.125

0.000 0.872

0.136

0.513

Theoretical F-value at p=0.05 for comparing between 1: recovery % in tablets in manner of pairs, 2: recovery % in added standard in manner of pairs, 3: recovery % in tablets in 3 techniques, 4: recovery % in added standard in 3 techniques

ACCEPTED MANUSCRIPT Table (5): Determination of GLM in Amaglust® 30/4 tablets by the proposed

Recovery % of GLM (Tablets)

Recovery % of GLM (Added)

Added

CRACLS

PLS

PCR

CRACLS

PLS

PCR

2.4

2.9

105.68

105.47

105.7

96.41

97.098

97.07

104.82

2

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Tablet

99.54

99.62

99.57

98.15

99.99

99.94

97.06

99.13

99.07

98.14

98.77

98.75

105.86

97.86

98.92

98.88

2.6 2.8

104.22

3

3.7

4

3 4

106.85

2.8

107.06

105.64

S.D.

1.342

1.135

1.129

1.197

1.120

1.110

S.E.

0.775

0.652

0.535

0.501

0.496

F-value (7.71)1, (5.32)2

With PLS

0.002

With PCR

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Concentration of GLM (µg/ml)

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0.655

2.097

0.057

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F-value (5.14)3, (3.89)4

0.066

0.003 1.953

0.040

1.384

Theoretical F-value at p=0.05 for comparing between 1: recovery % in tablets in

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Table (6): Results of statistical comparison between newly developed method and reference methods GLM

PGZ

Component CRACLS

PLS

PCR

R.M.

CRACLS

PLS

PCR

Mean

99.77

99.94

99.36

99.38

98.68

98.64

98.99

99.04

S.D.

0.731

0.938

0.960

0.962

1.287

2.000

1.833

1.816

S.E.

0.327

0.419

0.429

0.430

0.575

0.895

0.820

0.812

R.S.D. %

0.733

0.938

0.966

0.968

1.304

2.028

1.851

1.834

0.002

0.095

0.131

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F-value 0.002 (5.32)* * Theoretical F-value at p=0.05

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Graphical abstract

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ACCEPTED MANUSCRIPT Highlights

 Spectrophotometric method to determine pioglitazone and glimepiride simultaneously.

 Validation and statistical analysis were carried out.

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 Different chemometric algorithms for processing obtained data.

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 The method was applied successfully for combined dosage form.

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