Abstract: A new, quick, easy, affordable and eco-friendly simultaneous spectrophotometric method for determining a combined sitagliptin and metformin hydrochloride in pharmaceutical formulations was developed and validated using two chemometrics technique. These two methods are the partial least square (PLS) and principal component regression (PCR). They do not need to do a sample preparation or separation before analysis. Various drug concentrations and instrumental spectra of 25 mixed solutions of a combination of sitagliptin and metformin hydrochloride were used for model construction in the range of 200–270 nm. The R. values of 0.9994 and 0.9996 assigned for the PLS of the sitagliptin and metformin hydrochloride and that of 0.9987 and 0.9996 for the PCR of the sitagliptin and metformin hydrochloride, respectively. It is noteworthy that these two models were successfully and effectively used with the commercial pharmaceutical formulations. Finally, the statistical comparison revealed no significant differences with the results of the HPLC reference method. The proposed method is dependable to be adopted as an alternative analytical method in the pharmaceutical industry’s quality control.
Keywords: sitagliptin, metformin hydrochloride, spectrophotometric method, chemometrics technique, validation.
Development and validation of a new spectrophotometric method for simultaneous determination of sitagliptin and metformin hydrochloride in tablet pharmaceutical dosage forms using chemometrics technique in comparison with HPLC
Universidade Estadual Paulista Júlio de Mesquita Filho
Recepción: 21 Agosto 2022
Aprobación: 16 Noviembre 2022
Publicación: 11 Enero 2023
Chemically, sitagliptin is (3R)-3-purcino-l-[3-(trifiuoromethyl)-5,6-dihydro[1,2,4] triazolo [4,3-a]pyrazin-7 (8H)-yl] -4-(2,4,S-trifiuorophenyl) butan-l-one phosphate monohydrate (Fig. 1a). It is used as dipeptidylpeptidase-4 inhibitor; treatment of diabetes mellitus (British Pharmacopoeia Commission, 2020; Swamy et al., 2020).
Metformin HCl is 1,1-dimethylbiguanide hydrochloride and its chemical structure is presented in (Fig. 1b). It is used to treat diabetes mellitus. It is also used to treat polycystic ovarian syndrome. It is taken by mouth and is not linked to weight gain. It is sometimes used as an off-label supplement to help persons who are taking antipsychotics avoid gaining weight (British Pharmacopoeia Commission, 2020).
Figure 1.
Chemical structure of sitagliptin (a) and metformin hydrochloride (b).
Uddin et al. (2019) reported that high-performance liquid chromatography (HPLC) is a technique that collects data from simultaneous separation and determination and is more frequently employed in analytical processes for the analysis of pharmaceutical products. However, it has several disadvantages, including the possibility of being bad for the environment and people’s health. The HPLC assay also needed a lot of costly chemicals and supplies. Furthermore, it takes a lot of time, which delays the marketing and production operations. The expense of HPLC maintenance is likewise substantial. Spectrophotometry, which is simple, dependable, rapid, economical, and most significantly, environmentally benign, may be a useful option for determining a complicated combination in pharmaceutical quality control laboratories. Additionally, the data show that spectrophotometry and chemometrics in conjugation have a promising future and can be used in place of HPLC in both quantitative and qualitative analysis.
The study of chemometrics has significantly influenced analytical chemistry, notably in the field of spectrum analysis, which is crucial for the quality assurance of pharmaceutical formulations including two or more pharmaceuticals with overlapping spectra (K. Patel et al., 2013a; Glavanović et al., 2016).
Chemometrics approaches rely on multivariate analysis, which necessitates that ultraviolet (UV) spectrophotometry methods consider multiple variables at once. The absorbance at each wavelength is taken into account, with many wavelengths being taken into consideration (Gandhi et al., 2017; R. Patel and Mashru, 2019). The principal component regression (PCR) and partial least squares (PLS) are the two most significant chemometrics techniques utilized in multivariate analysis. For the purpose of determining the combination of medications in pharmaceutical formulations, these multivariate calibration methods employ spectrophotometric data coupled with statistical tools, mathematical models, and software (R. Patel and Mashru, 2019). These techniques additionally rely on the mathematical model’s calibration using the absorbance data of calibration standards with known concentrations, which is followed by the prediction of the concentration of unknown samples using those samples’ absorbance data (Gandhi et al., 2017; R. Patel and Mashru, 2019).
There are many applications for chemometrics in analytical spectroscopy, including UV-visible spectrophotometry (UV-VIS) (Ashour et al., 2015; Attia et al., 2018; Belal et al., 2018; Darbandi et al., 2020; Elfatatry et al., 2016; Gholse et al., 2021; Manouchehri et al., 2016; Moussa et al., 2021; M. Patel et al., 2013b; Phechkrajang et al., 2015; Putri et al., 2021; Sebaiy et al., 2020; 2022; V. D. Singh and V. K. Singh, 2021; Vichare et al., 2010), fluorescence spectroscopy (Manouchehri et al., 2016; Salem et al., 2019; Shinde and Divva, 2015; Walash et al., 2011; Zhu et al., 2016), NIR spectroscopy (Manouchehri et al., 2016; Moroni et al., 2022; Muntean et al., 2017; 2021; Rahman et al., 2020; Sun et al., 2021) and FTIR spectroscopic method (Rahman et al., 2020). Furthermore, chromatography methods like liquid chromatography (Aminu et al., 2019; Mohammed et al., 2021; Tsvetkova et al., 2012; Vu Dang et al., 2020) along with a number of other analytical chemistry methods, such as flow-injection analysis are used for the pharmaceutical formulations (Ortega-Barrales et al., 2002; Silva et al., 2011).
Uddin et al. (2019) reported that the majority of the analytes of interest are accompanied in their dosage forms by other compounds that absorb in the same spectral region, making it impossible to distinguish them using the traditional UV spectral studies. It is challenging to use traditional techniques like extraction because they demand a large amount of solvent, which carries risks of analyte loss or contamination as well as the potential for incomplete separation, which is expensive and time-consuming. However, spectrophotometry, as a quick, accurate, low-cost, and easy technology, may be a wonderful choice when used with chemometric techniques for determining a combined mixture in pharmaceutical quality control. When pharmaceutical product quality monitoring calls for dependable, precise, and quick analytical techniques, they are beneficial. This method, which is quick, accurate, and simple to use, avoids the usage of earlier separation procedures.
Many methods for quantifying sitagliptin and metformin hydrochloride have been published, including chromatographic (Adsul et al., 2018; Krishnan and Mishra, 2020; Kumar et al., 2017) and spectrophotometric approaches (Himabindu et al., 2016; Lotfy et al., 2015). At the time of writing, we had the following information to our knowledge, there is no reference in the analytical literature reviews for the development and validation of simultaneous spectrophotometric method assisted chemometrics methods for the determination of sitagliptin with metformin HCl in pharmaceutical dosage form. This study aims to develop and validate an adequate and reproducible simultaneous spectrophotometric assay method for the determination of sitagliptin and metformin HCl in tablet pharmaceutical dosage forms using chemometrics technique.
The reference standard of sitagliptin (as phosphate monohydrate) and metformin HCl were obtained from Global Pharma Company, Sana’a, Yemen. All reagents and chemicals used for the spectrophotometric methods were of analytical grade and HPLC grade were used for the HPLC method. Deionized water (with specific conductance of 0.05 µS cm–1) was produced in-house and used for the preparation of all samples solutions.
Double beam UV-VIS (AnalytiK Jena) model (SPECORD 200) at Sana’a University-Faculty of Science was used for the absorbance measurements. The HPLC system was from JASCO with detector (UV-2070 Plus), pump (PU-2089), an auto sampler (AS-2055 Plus) and a column oven (CO-2067 Plus). Electronic balance (AA-160), Denver Instrument. Electronic balance (GH-252), AND. Electronic balance (GR-120), AND. pH meter (3520), Jenway. Centrifuge (Z326 K), Hermle were also used.
For the aim of developing an accurate, precise and dependable simultaneous spectrophotometric methods assisted with the chemometrics technique, the analytical methods were established and developed to get the intended results for quantifying the targeted components.
Literature reviews were conducted to identify the proper solvents that aid in dissolving the desired active pharmaceutical ingredients without excipients. Through a series of trial-and-error attempts, a suitable solvent was chosen. Other advantages for selecting the appropriate solvent such as available, easy to use, a cheap, environmentally friendly and for the spectrophotometric method implementation were given a full consideration.
After the phase of choosing the solvent and before the data is preprocessed, the range of 200–400 nm with a 0.2 nm interval was used to record the individual pure and mixed absorbance spectra of the targeted medicinal components. UV spectra of the mixtures analysis were selected among a suitable wavelength range against a solvent blank providing the greatest amount of information about the two components (Shah and Jasani, 2017).
As the training set (calibration set), twenty-five different concentrations of the binary mixture of sitagliptin and metformin HCl were prepared to construct the model. These mixtures’ absorbencies were measured against a blank at intervals of 0.2 nm between 200 and 400 nm.
The two multivariate calibration models; the PLS and the PCR analysis were established as follows:
· To begin with, binary mixture absorbencies were measured against a blank, and the spectra were saved and extracted into Microsoft Excel in order to develop models;
· Secondly, using absorption data at chosen spectral zones for analysis at intervals of 0.2 nm, the PCR and PLS models were built using the Minitab 17 program;
· Then, the required number of latent variables was obtained using the leave-one-out cross validation method;
· After that, the calibration samples, constants, and coefficients for each wavelength were calculated in order to calculate the predicted concentrations;
· In the end, the predicted concentrations were compared to the actual concentrations in each sample to compute the assay of binary mixture in each sample;
· The root mean square error of cross-validation (RMSECV), which must be as small as possible for a given model, was determined for each method to assess the precision and accuracy of predictions for the models using the following Eq. 1 (Shah and Jasani, 2017):
(1)where RMSECV = Root mean square error of cross validation; Cact = Actual concentration of calibration set; Cpre = predicted concentration of calibration set; and Ic = Total number of samples in calibration set.
In order to validate and assess the performance of the suggested and developed spectrophotometric methods assisted chemometric models, these methods were subjected to validation set. Also, the performance criteria of the developed methods including linearity, accuracy, precision (repeatability) and specificity were validated in accordance with the recommendations of International Conference Harmonization and after that determined.
The performance of the proposed and developed method was determined in accordance with the method validation results. This method was studied and tested for determination of sitagliptin and metformin HCl in marketed pharmaceutical formulations. And they were compared with analysis results of reference method.
Stock solutions of 1670 μg mL–1 of sitagliptin and 1000 μg mL–1 of metformin hydrochloride were individually prepared in a 100 mL volumetric flask by dissolving 167 mg sitagliptin and 100 mg metformin hydrochloride separately in water.
Twenty-five binary mixtures of sitagliptin and metformin hydrochloride were prepared by transferring different aliquots of their standard stock solutions into a series of 50 mL volumetric flasks. The absorbencies of these mixtures were measured between 200 and 400 nm at 0.2 nm intervals against water as a blank.
A set of twelve binary mixtures of sitagliptin and metformin hydrochloride was prepared by transferring different volumes into 50 mL volumetric flasks and the procedure under the construction of the training set was repeated.
Powdered tablets of 25 mg of sitagliptin and 250 mg of metformin hydrochloride were accurately weighed, transferred to a 250 mL volumetric flask and then 200 mL of water was added, the mixture was shaken for 5 min and with frequent shaking the volume completion to 250 mL with the selected solvent was carried out. The solution was then filtered. A 0.5 mL of the filtrate was transferred into 50 mL volumetric flask and calculated amount of sitagliptin and metformin hydrochloride from standard solutions were spiked into sample solution and then diluted with water up to 50 mL. The absorbance was then measured.
The developed method was applied to the measurement of a commercially available samples. It was carried out using the marketed formulation with concentration of 50 mg sitagliptin and 500 mg metformin hydrochloride. The tablets solution prepared in the sample preparation section was diluted with water to prepare solutions with concentration of 10.68 μg mL–1 sitagliptin and of 14 μg mL–1 metformin hydrochloride. The spectra of the prepared solutions were recorded and then the developed multivariate models PCR and PLS were applied to determine the concentrations of the sitagliptin and metformin HCl.
Comparison was carried out with the recovery results of the newly developed methods and that of reference method for each of sitagliptin with metformin hydrochloride according to the United States Pharmacopeia (USP, 43). 80 μg mL–1 sitagliptin was prepared by dissolving 80 mg sitagliptin in acetonitrile: dilute phosphoric acid (5:95) in a 100 mL volumetric flask as standard stock solution; 5 mL of sitagliptin of the standard stock solution was transferred in acetonitrile: dilute phosphoric acid (5:95) in 50 mL volumetric flask. A test sample was prepared by placing 10 tablets containing 500 mg of sitagliptin to 500 mL volumetric flask; 500 mL of acetonitrile: dilute phosphoric acid (5:95) as solvent was added and the solution was shaken for 1 h then a portion of the solution was centrifuged for 10 min; 2 mL of the supernatant solution was transferred into 25 mL volumetric flask and diluted with solvent. The standard and the test sample of sitagliptin were injected through an HPLC system with a mixture of acetonitrile: monobasic potassium phosphate buffer (pH adjusted to 2 with phosphoric acid) (15:85) as the mobile phase at flow rate of 1 mL min–1 through a C8 column (15 cm × 4.6 mm, 5 μm) and column temperature was 30 °C. The UV detection of the sitagliptin was then carried out at 205 nm (United States Pharmacopeia and the National Formulary, 2020).
Metformin hydrochloride was also determined, according to the USP (34), 200 μg mL–1 metformin hydrochloride was prepared by dissolving 40 mg metformin hydrochloride in acetonitrile: dilute phosphoric acid (5:95) in a 200 mL volumetric flask as standard stock solution. A test sample was prepared by placing 10 tablets containing 5,000 mg of metformin hydrochloride to 500 mL volumetric flask; 500 mL of acetonitrile: dilute phosphoric acid (5:95) as solvent was added and the solution was shaken for 1 h then a portion of the solution was centrifuged for 10 min; 2 mL of the supernatant solution was transferred into 100 mL volumetric flask and diluted with solvent. The standard and the test sample of metformin hydrochloride were injected through an HPLC system with a mixture of acetonitrile: monobasic potassium phosphate buffer (pH adjusted to 2 with phosphoric acid) (15:85) as the mobile phase at flow rate of 1 mL min–1 through a C8 column (15 cm × 4.6 mm, 5 μm) and column temperature was 30 °C. The UV detection of the sitagliptin was then carried out at 205 nm.
In order to choose a suitable solvent, solubility was checked in water, methanol, 0.1 mol L–1 NaOH and 0.1 mol L–1 HCl. The drug was found to be soluble in methanol, water, 0.1 mol L–1 NaOH and 0.1 mol L–1 HCl. Therefore, water was selected as diluent that has striking advantages such as easily available, easy to handle, a cheap and environmentally friendly for implementing the spectrophotometric method and Fig. 2 showed the spectra of the sitagliptin and metformin hydrochloride in water.
To determine the overlap spectral zones, the absorbance spectra of the pure sitagliptin and metformin hydrochloride samples, and that sample of the mixed sitagliptin with metformin hydrochloride in water were recorded in the range of 200-400 nm with 0.2 nm interval. For the analysis, the UV spectra of the mixtures were selected for a suitable wavelength range (200–270 nm) against water blank. This range provided a great amount of information about the two components as shown in the sitagliptin with metformin hydrochloride spectra (Fig. 2).
Figure 2.
UV absorbance spectra of the pure and mixed samples of sitagliptin and metformin hydrochloride in water solvent.
To determine the linear, range from measuring the absorbance at different concentrations for sitagliptin with metformin hydrochloride, the response was found to be linear in the range of 13.36–26.72 μg mL–1 for sitagliptin and 8–16 μg mL–1 for metformin hydrochloride using 25 different concentrations of sitagliptin and metformin hydrochloride mixtures were prepared to construct the models as shown in Table 1.
Composition of calibration set.
The spectra were saved and extracted into Microsoft Excel for model generation. The PCR and PLS models were developed utilizing the absorption data for the selected spectral zones using Minitab 17 software program. After the PCR and PLS models have been constructed, the optimum number of principal components of sitagliptin and metformin hydrochloride were obtained and given in Tables A1–4 of the Appendix.
Selecting the proper number of principal components for the development of model was necessary to obtain good prediction. Leave-one-out cross validation method was used to obtain the necessary optimum number of the principal factors for the PLS model. It was found that the optimum number of the principal components were eight for sitagliptin and eight for metformin hydrochloride as mentioned above and as given in Table A1 and A2 of the Appendix.
The constant and coefficients at each wavelength were calculated using Minitab 17 program as illustrated in Table A1 of the Appendix.
The predicted or calculated concentrations in μg mL–1 of the sitagliptin and metformin hydrochloride were worked out from the multiple regression Eq. 2:
(2)The predicted or calculated concentrations of the components were compared with the actual concentrations and the assay of binary mixture were calculated. RMSECV was calculated and found to be low. The low values of RMSECV in Table 2 indicate both the precision and accuracy of PLS model for sitagliptin and metformin hydrochloride were very high and the R. values in Fig. 3 were also of very high linearity.
Results of the predicted concentrations with the recovery of sitagliptin and metformin hydrochloride in the binary mixture in each sample for PLS model.
The linearity of the developed method was tested by constructing a cross-validation of the data in Table 2. The results obtained in Fig. 3 indicated that the developed method possessed high linearity with R2 = 1 within the method linear range (13.36–26.72 μg mL–1) for sitagliptin and R2 = 1 within the method linear range (8–16 μg mL–1) for metformin hydrochloride. The linearity of the developed method was very high and most importantly, environmentally friendly with respect to the solvent (water) used. In comparison, Adsul et al. (2018) revealed that the linearity of the HPLC methods which carried out in non-eco-friendly solvents and mobile phases was almost similar to our eco-friendly (water) developed method and better than another HPLC method (Kumar et al., 2017).
Figure 3.
The PLS cross validation for the calibration set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
The PCR was computed by using a few principal components and performed regression analysis of these components with concentration in order to determine the principal components coefficients of sitagliptin and metformin hydrochloride for PCR model as illustrated in Table A4 of the Appendix. From the treatment of the principal component’s coefficients in (Table A4 of the Appendix) using Minitab 17 program. Regression equations (Eqs. 3 and 4) of sitagliptin and metformin hydrochloride were obtained and used to calculate the predicted concentration as shown below.
Regression equation of sitagliptin
(3)Regression equation of metformin hydrochloride
(4)where Z is the principal components coefficients.
The predicted or calculated concentrations in μg mL–1 of the sitagliptin and metformin hydrochloride were calculated from multiple regression Eq. 5:
(5)The predicted or calculated concentrations of the sitagliptin and metformin hydrochloride were compared with the actual concentrations and the assay for binary mixture were calculated in each sample. RMSECV was calculated and found to be low. The RMSECV low values in Table 3 indicate that both the precision and accuracy of PCR model for sitagliptin and metformin hydrochloride were very high, with the R. values in Fig. 4 of very high linearity.
Results of the predicted concentrations with recovery of sitagliptin and metformin hydrochloride in binary mixture in each sample for PCR models.
Figure 4.
The PCR cross validation for calibration set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
The results of prediction and the percentage recoveries are represented in Table 4. The predictive abilities of the models were evaluated by plotting the actual known concentrations against the predicted concentrations that shown in Fig. 5 and 6. A tremendous agreement between the predicted (calculated) and actual concentration of sitagliptin and metformin hydrochloride for PLS and PCR models can be observed in Fig. 5 and 6.
Results of validation set of sitagliptin and metformin HCl for PLS and PCR model.
Figure 5.
The PLS cross-validation for validation set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
Figure 6.
The PCR cross-validation for validation set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
The repeatability (intraday precision) of the developed method was carried out by determining the binary mixture at three different concentrations for sitagliptin and metformin hydrochloride in bulk using three different concentrations (i.e., 13.36/10, 20.04/12 and 26.72/16 μg mL–1 of sitagliptin/metformin hydrochloride, respectively) in triplicates sequentially. The results were reported as %RSD. The low values of %RSD were indicative of the high precision of the method. The %RSD values of the developed method were within the acceptable limit as suggested by the USP and the results are presented in Table 5.
Results of repeatability and intraday precision using the developed PLS and PCR models.
% Recovery = (predicted conc. in μg mL–1 /Actual conc. in μg mL–1) ×100.
Accuracy of the method was investigated using standard addition method for three different percentage levels (i.e., 80, 100, and 120%) by recovery experiments. Known amounts of standard solutions containing sitagliptin and metformin hydrochloride were added to sample solutions under investigation to make up solutions of 80, 100, and 120% levels in triplicates and scanned at the range 200–400 nm. The amount of the drugs recovered at each percentage level were determined by using the developed PCR and PLS models. The mean percentage recovery for each percentage level was showed low values of %RSD and the percentage recovery was within the acceptable limit (90–110%) as suggested by the USP. This indicates a high accuracy method at all the three levels and the accuracy data are given in Tables 6 and 7.
Accuracy data of sitagliptin by PCR and PLS models.
Accuracy data of metformin hydrochloride by PCR and PLS models.
The specificity of the method was checked by adding a certain amount of sitagliptin and metformin hydrochloride standard into known amount of marketed sample solution as described in the Methodology section. Specificity data are shown in Tables 8 and 9.
Results of specificity for sitagliptin using the developed PCR and PLS models.
Results of specificity for metformin HCl using the developed PCR and PLS models.
As it can be appeared from these data, recovery for sitagliptin and metformin hydrochloride using the developed PCR and PLS models are within the acceptable limit (90–110%) This suggests that the methods are free from interference due to the excipients used in the commercial formulation.
The above validation results indicate that method is simple, rapid, economical, precise and accurate beside being eco-friendly. Therefor it can be used for a routine analysis in quality control of mixtures and commercial products containing sitagliptin and metformin hydrochloride.
The applicability of the developed methods for the quantification of sitagliptin and metformin hydrochloride in marketed formulations was carried out using the marketed formulation of 50 mg sitagliptin with 500 mg metformin hydrochloride concentration collected from the local pharmacies in the capital Sana’a. Tables 10 and 11 summarized the data obtained for the sitagliptin and metformin hydrochloride in the analyzed marketed formulations.
Assay result for sitagliptin and metformin hydrochloride in tablet (marketed sample) by PLS proposed method.
Assay result for sitagliptin and metformin hydrochloride in tablet (Marketed Sample) by PCR proposed method.
As it can be seen from these data, the sitagliptin and metformin hydrochloride concentrations were within the acceptable limit (90–110%) according to the USP.
Comparison was carried out, with the aid of SPSS program using F-Test to assure non-significant difference between the recovery results of the newly developed methods and that of reference method for both the sitagliptin and metformin hydrochloride. Significance level indicated that null hypothesis was acceptable since the P-value was greater than significance level (Table 12). As for reference methods, sitagliptin and metformin hydrochloride were determined according to the USP as described in the Methodology section.
Results of statistical comparison between newly developed method and reference method.
Also, the chromatograms in Fig. 7 have showed the results of the analysis for reference method for the determination of sitagliptin and metformin hydrochloride.
Figure 7.
Chromatogram of sitagliptin and metformin HCl standard and commercial samples. (a) Standard sitagliptin; (b) Standard Metformin HCl; (c) Sitagliptin in Jauntab Sample (commercial); (d) Metformin HCl in Jauntab Sample (commercial); (e) Sitagliptin in Jaunmet Sample (commercial); (f) Metformin HCl in Jaunmet Sample (commercial).
The proposed chemometrics models (PLS and PCR) has proven to determine simultaneously sitagliptin and metformin HCl in combined mixtures of pharmaceutical dosage forms without excipients interference or each other, and without prior physical separation of the two drugs. Multivariate calibration models were generated using matrices of spectral and concentration data. The validation of the two models and their application to a commercial pharmaceutical dosage form gave excellent results. As a result, the suggested techniques can be applied to regular quality control of the specified medications in their combination dosage form in standard laboratories.
Conceptualization: Almaqtari, M. A.
Data curation: Almaqtari, M. A.; Al-Odaini, N. A.
Formal Analysis: Alarbagi, F. A.
Funding acquisition: Not applicable.
Investigation: Alarbagi, F. A.; Al-Maydama, H.
Methodology: Alarbagi, F. A.
Project administration: Almaqtari, M. A.; Al-Odaini, N. A.
Resources: Not applicable.
Software: Alarbagi, F. A.
Supervision: Almaqtari, M. A.; Al-Odaini, N. A.
Validation: Alarbagi, F. A.
Visualization: Al-Odaini, N. A.
Writing – original draft: Alarbagi, F. A.
Writing – review & editing: Al-Maydama, H.
Data will be available upon request.
Not applicable
Appendix (html)
The authors would like to thank the Chemistry Department-Factuality of Science, Sana’a University, Global Pharma and Shiba’a pharma Companies, Sana'a, Yemen for providing the laboratory facilities and the reference standards of the samples drugs as a gift.
m.almaqtari@su.edu.ye
Figure 1.
Chemical structure of sitagliptin (a) and metformin hydrochloride (b).
Figure 2.
UV absorbance spectra of the pure and mixed samples of sitagliptin and metformin hydrochloride in water solvent.
Composition of calibration set.
Results of the predicted concentrations with the recovery of sitagliptin and metformin hydrochloride in the binary mixture in each sample for PLS model.
Figure 3.
The PLS cross validation for the calibration set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
Results of the predicted concentrations with recovery of sitagliptin and metformin hydrochloride in binary mixture in each sample for PCR models.
Figure 4.
The PCR cross validation for calibration set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
Results of validation set of sitagliptin and metformin HCl for PLS and PCR model.
Figure 5.
The PLS cross-validation for validation set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
Figure 6.
The PCR cross-validation for validation set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl.
Results of repeatability and intraday precision using the developed PLS and PCR models.
% Recovery = (predicted conc. in μg mL–1 /Actual conc. in μg mL–1) ×100.
Accuracy data of sitagliptin by PCR and PLS models.
Accuracy data of metformin hydrochloride by PCR and PLS models.
Results of specificity for sitagliptin using the developed PCR and PLS models.
Results of specificity for metformin HCl using the developed PCR and PLS models.
Assay result for sitagliptin and metformin hydrochloride in tablet (marketed sample) by PLS proposed method.
Assay result for sitagliptin and metformin hydrochloride in tablet (Marketed Sample) by PCR proposed method.
Results of statistical comparison between newly developed method and reference method.
Figure 7.
Chromatogram of sitagliptin and metformin HCl standard and commercial samples. (a) Standard sitagliptin; (b) Standard Metformin HCl; (c) Sitagliptin in Jauntab Sample (commercial); (d) Metformin HCl in Jauntab Sample (commercial); (e) Sitagliptin in Jaunmet Sample (commercial); (f) Metformin HCl in Jaunmet Sample (commercial).
