Development of a New Separation Method for Escitalopram Oxalate and Its Related Impurities by Capillary Electrophoresis: Application on Valid and Expired Dosage Forms

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Badulla, Arın G Dal Poçan, Sema Koyutürk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7727236/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Capillary Electrophoresis (CE) methods have a wide range of applications in industry and quality control laboratories for routine drug analysis. In the current study, a CE separation method was developed and validated for the concurrent detection of Escitalopram oxalate (ESC-OX) and three related impurities: Citalopram A, C, and D (CIT A, C, D). Additionally, the method was extended for the determination of impurities in both valid and expired dosage forms, Including Tablets and oral solutions. Methods Separation and analysis were accomplished in an untreated fused-silica capillary tube (48.5 cm total, 75 µm i.d.) within 20 minutes under an applied voltage gradient of 25 − 15 kV. Ideal separation was achieved using 40 mM phosphate buffer (pH 2.5) containing 60% Methanol as the background electrolyte. The apparatus was equipped with a diode array detector (DAD) to detect compounds at 210 nm. The established method was validated to accomplish the International Conference on Harmonization (ICH) requirements. Results The limit of detection was 7.44 x 10 − 3 , 7.83 x 10 − 3 , 11.56 x 10 − 3 and 9.19 x 10 − 3 µg mL − 1 for ESC-OX, CIT A, CIT C, and CIT D. The limit of quantitation was 0.025, 026, 0.038, and 031 µg mL − 1 . Conclusion the developed method provides a good complementary analytical method for routine analysis and stability testing. Capillary electrophoresis Citalopram A C and D Dosage form Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Escitalopram Oxalate (ESC) is chemically named [(s)-1-[3-(dimethylamino) propyl]-1-(4-fluorophenyl)-1,3-dihydro isobenzofuran-5-carbonitrile oxalate][1] (Figure 1). It is an antidepressant from the selective serotonin reuptake inhibitor (SSRI) category. It is used in the treatment of major depressive disorder in adolescents and adults. It is a pure S-enantiomer of the racemic mixture of citalopram[2]. Estimation of APIs or drug substances and product impurities has been an imperative demand for almost all APIs by several administrative authorities. Impurities can be defined as any organic or inorganic substance that may interact with the APIs. These impurities can be generated during synthesis, development, formulation, and storage. The impurity level should be under specified limits due to its potential hazard to human health. Various analytical methods have been utilized for their estimation, including HPLC, LC-MS/MS, and capillary electrophoresis [3-12]. Most of the published analytical methods were concerned with the detection of ESC-OX impurities, such as R-enantiomer or other impurities. As well as HPLC methods in USP 2016 for the determination of other ESC-OX impurities [13]. The previous Zone Capillary Electrophoresis (ZCE) methods were limited to the separation of the R-enantiomer. However, in this study, the focus was on impurities named Citalopram A, B, and C (CIT A, B, C) (Figure 1) in various pharmaceutical formulations. The recommended analytical method in many official pharmacopeias is HPLC due to its high sensitivity and versatility in separation modes and stationary phases; however, three distinct methods are offered by the most recent edition of the United States Pharmacopeia (USP 2024) for identifying ESC-OX contaminants in raw materials and various dosage forms. ZCE represents a complementary analytical method and a noteworthy alternative. This valuable characteristic of ZEC is due to several advantages, such as low consumption of organic solvent, and relatively easier than HPLC in optimization of analytical method and instrumental parameters. In addition, different separation mechanisms can be utilized for the separation of closely structure-related impurities with a reasonable selectivity, which results in an acceptable resolution [14]. Of note, the analysis of basic drugs in HPLC can result in peak tailing due to interaction with the stationary phase silanols; however, this situation doesn’t arise in the case of ZCE because most basic drugs were separated in acidic pH 2-4. The use of phosphate buffer provides a low background UV absorbance[15]. Therefore, a low UV range of 190-210 nm can be used, as most organic compounds exhibit distinguishable and intense UV absorbance coefficients in this range. According to the literature survey, no ZCE method has been developed for the determination of ESC-OX and its previously mentioned impurities in pharmaceutical formulations. So, the main aim of this study was to apply an easy, fast, economical, validated, and complementary method to the HPLC. The established ZCE method was utilized for the detection and quantification of ESC-OX-related impurities in tablets and oral solutions. Materials and Methods The reference standards of ESC-OX and CIT A, C, and D were provided by the United States Pharmacopeial Convention, USA, with 99.2% purity. All other compounds were of analytical grade and were purchased from Merck GmbH (Germany). Ultrapure water was purified with a Milli-Q system of Millipore (USA). Trade ESC-OX (tablet, containing 10 mg and 20 mg, and oral drops containing 10 mg mL -1 ESC were obtained from a resident Turkish pharmacy. Instruments The current method was carried out by using an Agilent 7100 model Capillary Electrophoresis (CE) with a diode array detector UV-2401 model spectrophotometer (Shimadzu, Japan). Separation was achieved by a fused silica capillary with 40 cm effective (48.5 cm total, 75 µm i.d.) length. The pH of the solutions was measured with a Mettler Toledo pH meter. All solutions were sonicated in a B-220 model ultrasonic bath (Branson, USA) before injection. Background Electrolyte (BGE) The BGE, 40 mM phosphate buffer (pH 2.5), and 60% methanol were attained by adjusting 40 mM NaH 2 PO 4 accurately to the anticipated pH with diluted phosphoric acid. All solutions were filtered with 0.22 µm Nylon membrane filters (diameter 25 mm) before usage. Standard stock solutions of ESC-OX (120 µg mL -1 ), CIT A (96.40 µg mL -1 ), CIT C (80.80 µg mL -1 ), and CIT D (83.20 µg mL -1 ) were prepared in 30% Acetonitrile. Before analysis, each solution was diluted with 1/10 BGE to a suitable volume. The working solutions were kept at 4ᵒC, and they were stable with no change in the peak area for three days in the acidic BGE. After that, the peak area started to reduce. To verify the utility of the developed method, two different pharmaceutical dosage forms (tablet and oral solution), valid and expired for each dosage form, were examined. To prepare the tablet dosage form, the USP method was used by weighing each tablet and taking its average weight. After grinding to a smooth powder, the amount equivalent to (300 µg mL -1 ) was diluted with 30% Acetonitrile and then diluted with the BGE to (3 µg mL -1 ) was injected into the CE system. In the case of oral solution, the same amount was directly diluted and injected into the system. CE Procedure Before starting to use the new Fused-silica capillary, it was conditioned by washing with 1.0 M NaOH for 30 min, followed by 0.1 M NaOH, ultrapure water, and BGE for 30 min, respectively. Every day, the capillary was cleaned by flushing for 10 min with 0.1N H 3 PO 4 , ultrapure water, and BGE, respectively. The hydrodynamic injection was to introduce a sample to the CE system. Thereby, appalling 50 mbar pressures for 5 sec. Between injections, the capillary was rinsed with 0.01N H 3 PO 4 (2 min), distilled water (2 min), and BGE (2 min). The 0.01 N H 3 PO 4 was used instead of 0.1 N H 3 PO 4 to prolong the age of the capillary because by using 0.1 N H 3 PO 4, the silanol groups of the capillary were quickly destroyed. At the end of each working day, it was washed with 0.1N H 3 PO 4 , ultrapure water for 10 min, and left with aspirated air. Phosphoric acid is used instead of NaOH to get rapid re-equilibrium of the capillary surface. During analysis, a voltage gradient was applied with a potential of + 25 kV for 10 min then changing the applied voltage to +15 kV for one min and continuing at this voltage for the remaining time of the analysis. All CE runs were conducted at 25 °C. Detection was performed at 210 nm. Validation Studies The method was validated according to ICH Q2 (R1), 2005 guidelines [16]. The specificity of the developed method was verified by comparing the reference solutions' electropherogram with the electropherogram of the drug substance spiked with the analytes. The linearity of the method was examined in the range of 2.40-0.054 μg mL -1 for ESC-OX, 4.82-0.043 μg mL -1 for CITT A, 4.04-0.036 μg mL -1 for CIT C, and 4.16-0.03 μg mL -1 for CIT D. The reference solutions were injected into the system for three repeated days for intra- and inter-day replication purposes. The repeatability (intraday) and intermediate precision (interday) of the developed method were evaluated by injecting a middle concentration of 0.218 μg mL -1 for ESC-OX, 0.233 μg mL -1 for CIT A, 0.226 μg mL -1 for CIT C, and 0.206 μg mL -1 for CIT D six times in a day for three consecutive days. The precision was estimated by calculating the RSD%. The percentage of recovery was used to estimate the accuracy of the developed method. Middle concentrations of 0.218 μg mL -1 for ESC-OX, 0.233μg mL -1 for CIT A, 0.226 μg mL -1 for CIT C, and 0.206 μg mL -1 for CIT D were injected six times a day for three consecutive days. Result And Discussion Method Development According to the USP 2024, the impurity CIT A should be lower than 0.1%, 0.5%, and 0.2% while CIT C 0.1%, 0.5%, and 0.3% in raw material, tablet, and oral solution, respectively, and CIT D 0.1% in raw material, tablet, and oral solution as process impurity. Thus, the CE method was developed by taking into consideration LOD in the range that covers the previously mentioned percentage of impurities. Due to limited information about the solubility of the impurities, they were dissolved in 30% acetonitrile to ensure solubilization. A wide range of calibration curves for ESC-OX and its mentioned impurities was constructed, and a calibration curve with low concentrations for impurities was constructed to ensure the high sensitivity of the developed CE method. The critical step in the CE method development is the selection of BGE. Due to the very similar chemical structure and properties between the ESC-OX and its impurities, several BGE were utilized to get separation with good resolution between them. The phosphate buffer at 10 mM at pH 2.5 and 7 (Fig. 2 ), the acetate buffer at pH 4, and the borate buffer at pH 9 were tried, but the separation was not good (Fig. 2 , b). The voltage was 20 kV and 50 mbar for 5 sec. The addition of organic solvents (acetonitrile and methanol) was tried; there was a little improvement in separation by using methanol. Then, Sodium dodecyl sulfate (SDS) was added to the acetate buffer with 30% methanol, and under these conditions, there were no peaks. Also, beta cyclodextrin was used, but the result was similar to the SDS. By increasing the molarity of the phosphate buffer to pH 50 mM, there was an improvement in the separation. The best result was obtained by using 40 mM. Then, the content of methanol gradually increased, and the separation continued to improve to 60%. Using these conditions results in a longer migration time. To decrease the migration time, several voltage gradients were tried, and the acceptable separation with reasonable migration time was attained by changing the voltage from 25 kV after 10 min to 15 kV for 1 min, then continuing at the same voltage for 20 min. i.e., using gradient voltage. Under these conditions, the method displays good robustness. The optimum operating temperature of the capillary cassette was verified by changing the temperature from 20℃ to 25 ℃ to 30 ℃. There was no notified change in the separation with an increase in the baseline noise above 25 ℃. Therefore, 25 ℃ was selected as optimal. To get maximum peak intensity, two detectors were examined: A Diode Array Detector (DAD) and Laser Induced Fluorescence (LIF). In the case of LIF, the peak intensities were lower than the DAD, so the DAD detector at a wavelength of 210 nm was selected for further analysis. The optimal CE conditions are illustrated in Table 1. By using acidic BGE, the electroosmotic mobility is decreased, and the only driving force is electrophoretic mobility. All compounds were positively charged, and the electrophoretic migration depended on the mass of each compound. CIT D has a molecular mass equal to 391.28 g mol − 1 ; it was the first migrating compound, followed by ESC-OX with a molecular mass equal to 414.43 g mol − 1 , then CIT C with a molecular mass equal to 428.42 g mol − 1 , and finally CIT A with a molecular mass equal to 442.41 g mol − 1 . As presented in Fig. 4 . Method validation During method validation, all parameters recommended by International Conference for Harmonization (ICH) guidelines Q2 were verified such as; the specificity, linearity and range, accuracy, precision, detection, and quantitation limits. The method's robustness was confirmed throughout the method development. Confirmation of method specificity was carried out by comparing the electropherograms of the standard solutions of ESC-OX and its impurities with the ESC-OX pharmaceutical dosage forms. The comparison showed that the developed method was sufficiently specific. The relationship between the ESC-OX and its impurities concentration and analytical response was confirmed using linearity and the construction of a calibration curve. The linear regression equation was used for calculation. The r 2 was near unity, slope, and intercept values indicating that the method is sensitive enough and without systematic error. The calibration curves related information is represented in Table 2 . LOD and LOQ were calculated by signal-to-noise ratio. The ratio of signal-to-noise equal to 3 and 10, for the LOD and LOQ, respectively, was regarded as satisfactory. The LOD was as follows: 7.44×10 − 3 µg mL − 1 for ESC-OX,7.83×10 − 3 µg mL − 1 for CITT A, 11.56 ×10 − 3 µg mL − 1 for CIT C, and 9.19 ×10 − 3 µg mL − 1 for CIT D. The LOQ was also calculated in the same manner; the result was 0.025 µg mL − 1 for ESC-OX, 0.026 for CITT A, 0.038 µg mL − 1 for CIT C, and 0.031 for CIT D. The LOD and LOQ were low enough to determine low concentrations of impurities. The calibration results are represented in Table 2 . Table 2 Validation parameters for the developed method. ESC-OX CIT A Validation parameters Data (Mean ± SD) Validation parameters Data (Mean ± SD) Range (µg/ml) 0.053- 6.0 Range (ppm) 0.04–4.82 Correlation coefficient 0.9997 Correlation coefficient 0.9996 Intercept -0.07951 Intercept -0.0839 Slope 1.01736 Slope 1.18996 LOD (µg/ml) 7.44×10 − 3 LOD (ppm) 7.83×10 − 3 LOQ (µg/ml) 0.025 LOQ (µg/ml) 0.026 CIT C CIT D Validation parameters Data (Mean ± SD) Validation parameters Data (Mean ± SD) Range (µg/ml) 0.036–4.04 Range (µg/ml) 0.037–4.15 Correlation coefficient 0.9994 Correlation coefficient 0.9993 Intercept -0.04690 Intercept -0.05566 Slope 0.78298 Slope 1.01462 LOD (µg/ml) 11.56 ×10 − 3 LOD (µg/ml)) 9.19 ×10 − 3 LOQ (µg/ml) 0.038 LOQ (µg/ml) 0.031 The precision of the developed method was confirmed at two levels: repeatability and intermediate precision for the middle concentration six times for three days. The accuracy was evaluated by the RSD %. The RSD% values were below 2% which indicates that the developed method is accurate according to the ICH guidelines. The results are represented in Table 3 . Table 3 Statistical evaluation of precision and accuracy. Precision Accuracy ESC-OX Parameters Middle Conc. (n = 18) Middle Conc. (n = 18) Average (peak normality) 0.2130 Con.Spiked 0.218 SD 0.003 % of Recovery 101.35 RSD% 1.55 RSD% 1.62 CIT A Parameters Middle Conc. (n = 18) Middle Conc. (n = 18) Average (peak normality) 0.1924 Con.Spiked 0.233 SD 0.002 % of Recovery 102.58 RSD% 1.13 RSD% 2.12 CIT C Parameters Middle Conc. (n = 18) Middle Conc. (n = 18) Average (peak normality) 0.1132 Con.Spiked 0.226 SD 0.002 % of Recovery 100.86 RSD% 1.83 RSD% 1.70 CIT D Parameters Middle Conc. (n = 18) Middle Conc. (n = 18) Average(peak normality) 0.1359 Con.Spiked 0.206 SD 0.002 % of Recovery 99.72 RSD% 1.79 RSD% 1.77 Analysis of the pharmaceutical dosage forms The validated method was applied to analyze and detect the impurities in the pharmaceutical formulations (tablets and oral solutions) that were valid and expired. The content of the ESC-OX was within the recommended range of pharmacopeias. The result is represented in Table 4 . None of the impurities mentioned above was detected. Table 4 Assay Results of ESC-OX 20 mg, 10 mg Film Tablet, and Oral Solution. Dosage Form Contents Tablet 10 mg 97 ± 1.09 Tablet 20 mg 98.75 ± 1.08 Oral Solution 10 mg ml − 1 99.57 ± 1.17 Conclusion Impurity profiling became an imperative requirement by many official authorities to ensure the APIs and finished drug safety and efficiency. In this regard, a sensitive CE method was developed for the detection and quantification of low levels of ESC-OX impurities named CIT A, C, and D in the pharmaceutical dosage forms. The developed method obeys the validation criteria and provides a good complementary analytical method for routine analysis as well as stability testing. Declarations Competing Interests No conflicts of interest were reported by the authors. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Funding: Not applicable. Author Contribution Wafa Farooq Suleman Badulla contributed to conceptualization, methodology, software, formal analysis, investigation, project administration, and drafting of the manuscript.Arın G Dal Poçan contributed to conceptualization, methodology, formal analysis, project administration, supervision, visualization, and manuscript review/editing.Sema Koyutürk contributed to conceptualization, methodology, formal analysis, investigation, and project administration. Data Availability Statement Data will be available upon request. References British Pharmacopoeia Commission. British Pharmacopoeia . London (UK): The Stationery Office; 2003. p. 712-8. Sánchez C, Bøgesø KP, Ebert B, Reines EH, Braestrup C. Escitalopram versus citalopram: the surprising role of the R-enantiomer. Psychopharmacology . 2004;174:163-76. https://doi.org/10.1007/s00213-004-1865-z Rao RN, Raju AN, Nagaraju D. Development and validation of a liquid chromatographic method for determination of enantiomeric purity of citalopram in bulk drugs and pharmaceuticals. J Pharm Biomed Anal . 2006;41(1):280-5. https://doi.org/10.1016/j.jpba.2005.10.025 Sungthong B, Jáč P, Scriba GK. Development and validation of a capillary electrophoresis method for the simultaneous determination of impurities of escitalopram including the R-enantiomer. J Pharm Biomed Anal . 2008;46(5):959-65. https://doi.org/10.1016/j.jpba.2007.05.029 Dhaneshwar SR, Mahadik MV, Kulkarni MJ. Column liquid chromatography-ultraviolet and column liquid chromatography/mass spectrometry evaluation of stress degradation behavior of escitalopram oxalate. J AOAC Int . 2009;92(1):138-47. https://doi.org/10.1093/jaoac/92.1.138 Raman B, Sharma BA, Ghugare PD, Nandavadekar S, Singh D, Karmuse PK, et al. Structural elucidation of process-related impurities in escitalopram by LC/ESI-MS and NMR. J Pharm Biomed Anal . 2010;53(4):895-901. https://doi.org/10.1016/j.jpba.2010.06.019 Kaleemullah T, Ahmed M, Sharma HK, Rajput P. Reverse phase liquid chromatographic method for the quantification of di-p-toluoyl-D-tartaric acid in escitalopram oxalate drug substance. Eurasian J Anal Chem . 2011;6(3):197-205. Soliman S, Youssef N. Enantiomeric thin-layer chromatographic assay of escitalopram in presence of “in-process impurities”. J Planar Chromatogr Mod TLC . 2011;24(6):475-81. https://doi.org/10.1556/JPC.24.2011.6.4 Dighe V, Pawaskar P, Adhyapak S, Shambhu N, Mestry D. Development of normal phase chiral liquid chromatographic method for estimation of escitalopram oxalate and determination of R-citalopram enantiomer from escitalopram oxalate in bulk drug and tablet. J Chem Pharm Res . 2012;4(11):4804-9. Deng X, De Wolf J, Vervoort R, Pamperin D, Adams E, Van Schepdael A. Development and validation of a capillary electrophoresis method for the determination of escitalopram and sensitive quantification of its enantiomeric impurity in formulations. Electrophoresis . 2012;33(11):1648-51. https://doi.org/10.1002/elps.201100580 Vaghela BK, Rao SS. Development and validation of stability indicating RP-LC, short runtime method for the estimation of escitalopram in escitalopram dosage form. World J Pharm Res . 2013;2:1018-30. Badulla WF, Can NÖ, Atkosar Z, Arli G, Aboul-Enein HY. Comparative study of different chemistries and particle properties, high-performance liquid chromatography stationary phases in separation of escitalopram oxalate and its impurities in different pharmaceutical dosage forms. Sep Sci Plus . 2019;2(8):268-83. https://doi.org/10.1002/sscp.201900021 United States Pharmacopeial Convention. USP 39–NF 34 . Rockville (MD): United States Pharmacopeial Convention; 2016. p. 838. Hilhorst M, Somsen G, De Jong G. Choice of capillary electrophoresis systems for the impurity profiling of drugs. J Pharm Biomed Anal . 1998;16(7):1251-60. https://doi.org/10.1016/S0731-7085(97)00205-7 Yesilada A, Tozkoparan B, Gökhan N, Öner L, Ertan M. Development and validation of a capillary electrophoretic method for the determination of degradation product in naphazoline HCl bulk drug substance. J Liq Chromatogr Relat Technol . 1998;21(17):2575-88. https://doi.org/10.1080/10826079808003408 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). Validation of analytical procedures: text and methodology Q2 (R1). 2005. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7727236","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534694567,"identity":"8bafaa57-e7ed-4bfa-ae9b-cfedcfc4fe20","order_by":0,"name":"Wafa F.S. 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19:35:38","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78887,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7727236/v1/c12db98bf4c3c8b7d72b2b0f.html"},{"id":94700401,"identity":"481bdd11-2298-4282-abfc-4315b7dc32a5","added_by":"auto","created_at":"2025-10-29 19:35:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67562,"visible":true,"origin":"","legend":"\u003cp\u003eChemical Structure of ESC and Its Impurities\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7727236/v1/297d377943cb25927dd55cb3.png"},{"id":94700402,"identity":"ad78a00d-c3f2-4257-8537-caf08449ba32","added_by":"auto","created_at":"2025-10-29 19:35:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82708,"visible":true,"origin":"","legend":"\u003cp\u003eThe Electropherogram for ESC-OX and its impurities by using (a) 10 mM phosphate Buffer pH 2.5, (b) Phosphate buffer pH 7.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7727236/v1/c0b8b5cc3643fffcb8dbafc5.png"},{"id":94700408,"identity":"e4469a95-3473-4b34-976a-4600fe0bb97b","added_by":"auto","created_at":"2025-10-29 19:35:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110187,"visible":true,"origin":"","legend":"\u003cp\u003eThe Electropherogram for ESC-OX and its impurities by using, (a) 10mM acetate Buffer pH 4, (b) borate buffer pH 9.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7727236/v1/aeb4b93f2ac72e6ce2dadf1a.png"},{"id":94700405,"identity":"f3dd8af6-bc15-4158-ba13-48be5441fb9f","added_by":"auto","created_at":"2025-10-29 19:35:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127624,"visible":true,"origin":"","legend":"\u003cp\u003eThe Electropherogram for ESC-OX and its impurities at optimum conditions.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7727236/v1/0c866d79bb82792a4e7df5d9.png"},{"id":103398202,"identity":"90a031c2-610a-4ce7-ba21-4c762c13bea4","added_by":"auto","created_at":"2026-02-25 08:59:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1190148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7727236/v1/4fb2b1ad-cbb8-4a7c-b957-b4a3651cbce8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a New Separation Method for Escitalopram Oxalate and Its Related Impurities by Capillary Electrophoresis: Application on Valid and Expired Dosage Forms","fulltext":[{"header":"Introduction ","content":"\u003cp\u003eEscitalopram Oxalate (ESC) is chemically named [(s)-1-[3-(dimethylamino)\u0026nbsp;propyl]-1-(4-fluorophenyl)-1,3-dihydro isobenzofuran-5-carbonitrile oxalate][1] (Figure 1). It is an antidepressant from the selective serotonin reuptake inhibitor (SSRI) category. It is used in the treatment of major depressive disorder in adolescents and adults. It is a pure S-enantiomer of the racemic mixture of citalopram[2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEstimation of APIs or drug substances and product impurities has been an imperative demand for almost all APIs by several administrative authorities. Impurities can be defined as any organic or inorganic substance that may interact with the APIs. These impurities can be generated during synthesis, development, formulation, and storage. The impurity level should be under specified limits due to its potential hazard to human health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVarious analytical methods have been utilized for their estimation, including HPLC, LC-MS/MS, and capillary electrophoresis [3-12]. Most of the published analytical methods were concerned with the detection of ESC-OX impurities, such as R-enantiomer or other impurities. As well as HPLC methods in USP 2016 for the determination of other ESC-OX impurities [13]. The previous Zone Capillary Electrophoresis (ZCE) methods were limited to the separation of the R-enantiomer. \u0026nbsp;However, in this study, the focus was on impurities named Citalopram A, B, and C (CIT A, B, C) (Figure 1) in various pharmaceutical formulations. The recommended analytical method in many official pharmacopeias is HPLC due to its high sensitivity and versatility in separation modes and stationary phases; however, three distinct methods are offered by the most recent edition of the United States Pharmacopeia (USP 2024) for identifying ESC-OX contaminants in raw materials and various dosage forms.\u003c/p\u003e\n\u003cp\u003eZCE represents a complementary analytical method and a noteworthy alternative. This valuable characteristic of ZEC is due to several advantages, such as low consumption of organic solvent, and relatively easier than HPLC in optimization of analytical method and instrumental parameters. In addition, different separation mechanisms can be utilized for the separation of closely structure-related impurities with a reasonable selectivity, which results in an acceptable resolution [14]. Of note, the analysis of basic drugs in HPLC can result in peak tailing due to interaction with the stationary phase silanols; however, this situation doesn’t arise in the case of ZCE because most basic drugs were separated in acidic pH 2-4. The use of phosphate buffer provides a low background UV absorbance[15]. \u0026nbsp;Therefore, a low UV range of 190-210 nm can be used, as most organic compounds exhibit distinguishable and intense UV absorbance coefficients in this range. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;According to the literature survey, no ZCE method has been developed for the determination of ESC-OX and its previously mentioned impurities\u0026nbsp;in pharmaceutical formulations. So, the main aim of this study was to apply an easy, fast, economical, validated, and complementary method to the HPLC. The established ZCE method was utilized for the detection and quantification of ESC-OX-related impurities in tablets and oral solutions.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe reference standards of ESC-OX and CIT A, C, and D were provided by the United States Pharmacopeial Convention, USA, with 99.2% purity.\u0026nbsp;All other compounds were of analytical grade and were purchased from Merck GmbH (Germany). Ultrapure water was purified with a Milli-Q system of Millipore (USA). Trade ESC-OX (tablet, containing 10 mg and 20 mg, and oral drops containing 10 mg mL\u003csup\u003e-1\u003c/sup\u003e ESC were obtained from a resident Turkish pharmacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current method was carried out by using an Agilent 7100 model Capillary Electrophoresis (CE) with a diode array detector UV-2401 model spectrophotometer (Shimadzu, Japan). Separation was achieved by a fused silica capillary with 40 cm effective (48.5 cm total, 75 µm i.d.) length. The pH of the solutions was measured with a Mettler Toledo pH meter. All solutions were sonicated in a B-220 model ultrasonic bath (Branson, USA) before injection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBackground Electrolyte (BGE)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BGE, 40 mM phosphate buffer (pH 2.5), and 60% methanol were attained by adjusting 40 mM NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e accurately to the anticipated pH with diluted phosphoric acid. All solutions were filtered with 0.22 µm Nylon membrane filters (diameter 25 mm) before usage.\u003c/p\u003e\n\u003cp\u003eStandard stock solutions of ESC-OX (120 µg mL\u003csup\u003e-1\u003c/sup\u003e), CIT A (96.40 µg mL\u003csup\u003e-1\u003c/sup\u003e), CIT C (80.80 µg mL\u003csup\u003e-1\u003c/sup\u003e), and CIT D (83.20 µg mL\u003csup\u003e-1\u003c/sup\u003e) were prepared in 30% Acetonitrile. Before analysis, each solution was diluted with 1/10 BGE to a suitable volume. The working solutions were kept at 4ᵒC, and they were stable with no change in the peak area for three days in the acidic BGE. After that, the peak area started to reduce.\u003c/p\u003e\n\u003cp\u003eTo verify the utility of the developed method, two different pharmaceutical dosage forms (tablet and oral solution), valid and expired for each dosage form, were examined. To prepare the tablet dosage form, the USP method was used by weighing each tablet and taking its average weight. After grinding to a smooth powder, the amount equivalent to (300 µg mL\u003csup\u003e-1\u003c/sup\u003e) was diluted with 30% Acetonitrile and then diluted with the BGE to (3 µg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e) was injected into the CE system. In the case of oral solution, the same amount was directly diluted and injected into the system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCE Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore starting to use the new Fused-silica capillary, it was conditioned by washing with 1.0 M NaOH for 30 min, followed by 0.1 M NaOH, ultrapure water, and BGE for 30 min, respectively. Every day, the capillary was cleaned by flushing for 10 min with 0.1N H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, ultrapure water, and BGE, respectively. The hydrodynamic injection was to introduce a sample to the CE system. Thereby, appalling 50 mbar pressures for 5 sec.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBetween injections, the capillary was rinsed with 0.01N H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (2 min), distilled water (2 min), and BGE (2 min). The 0.01 N H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e was used instead of 0.1 N H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e to prolong the age of the capillary because by using 0.1 N H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4,\u003c/sub\u003e the silanol groups of the capillary were quickly destroyed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the end of each working day, it was washed with 0.1N H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, ultrapure water for 10 min, and left with aspirated air. Phosphoric acid is used instead of NaOH to get rapid re-equilibrium of the capillary surface. During analysis, a voltage gradient was applied with a potential of + 25 kV for 10 min then changing the applied voltage to +15\u0026nbsp;kV for one min and continuing at this voltage for the remaining time of the analysis. All CE runs were conducted at 25 °C. Detection was performed at 210 nm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation Studies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe method was validated according to ICH Q2 (R1), 2005 guidelines [16]. The specificity of the developed method was verified by comparing the reference solutions' electropherogram with the electropherogram of the drug substance spiked with the analytes. \u0026nbsp;The linearity of the method was examined in the range of 2.40-0.054 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor ESC-OX, 4.82-0.043 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CITT A, 4.04-0.036 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT C, and 4.16-0.03 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT D.\u0026nbsp;The reference solutions were injected into the system for three repeated days for intra- and inter-day replication purposes.\u003c/p\u003e\n\u003cp\u003eThe repeatability (intraday) and intermediate precision (interday) of the developed method were evaluated by injecting a middle concentration of 0.218 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e\u0026nbsp; for ESC-OX, 0.233 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT A, 0.226 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT C, and 0.206 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT D six times in a day for three consecutive days. The precision was estimated by calculating the RSD%. The percentage of recovery was used to estimate the accuracy of the developed method. Middle concentrations of 0.218 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e\u0026nbsp; for ESC-OX, 0.233μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT A, 0.226 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT C, and 0.206 μg mL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor CIT D were injected six times a day for three consecutive days.\u0026nbsp;\u003c/p\u003e"},{"header":"Result And Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eMethod Development\u003c/h2\u003e\u003cp\u003eAccording to the USP 2024, the impurity CIT A should be lower than 0.1%, 0.5%, and 0.2% while CIT C 0.1%, 0.5%, and 0.3% in raw material, tablet, and oral solution, respectively, and CIT D 0.1% in raw material, tablet, and oral solution as process impurity. Thus, the CE method was developed by taking into consideration LOD in the range that covers the previously mentioned percentage of impurities. Due to limited information about the solubility of the impurities, they were dissolved in 30% acetonitrile to ensure solubilization. A wide range of calibration curves for ESC-OX and its mentioned impurities was constructed, and a calibration curve with low concentrations for impurities was constructed to ensure the high sensitivity of the developed CE method.\u003c/p\u003e\u003cp\u003eThe critical step in the CE method development is the selection of BGE. Due to the very similar chemical structure and properties between the ESC-OX and its impurities, several BGE were utilized to get separation with good resolution between them. The phosphate buffer at 10 mM at pH 2.5 and 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the acetate buffer at pH 4, and the borate buffer at pH 9 were tried, but the separation was not good (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, b). The voltage was 20 kV and 50 mbar for 5 sec.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe addition of organic solvents (acetonitrile and methanol) was tried; there was a little improvement in separation by using methanol. Then, Sodium dodecyl sulfate (SDS) was added to the acetate buffer with 30% methanol, and under these conditions, there were no peaks. Also, beta cyclodextrin was used, but the result was similar to the SDS.\u003c/p\u003e\u003cp\u003eBy increasing the molarity of the phosphate buffer to pH 50 mM, there was an improvement in the separation. The best result was obtained by using 40 mM. Then, the content of methanol gradually increased, and the separation continued to improve to 60%. Using these conditions results in a longer migration time. To decrease the migration time, several voltage gradients were tried, and the acceptable separation with reasonable migration time was attained by changing the voltage from 25 kV after 10 min to 15 kV for 1 min, then continuing at the same voltage for 20 min. i.e., using gradient voltage. Under these conditions, the method displays good robustness.\u003c/p\u003e\u003cp\u003eThe optimum operating temperature of the capillary cassette was verified by changing the temperature from 20℃ to 25 ℃ to 30 ℃. There was no notified change in the separation with an increase in the baseline noise above 25 ℃. Therefore, 25 ℃ was selected as optimal. To get maximum peak intensity, two detectors were examined: A Diode Array Detector (DAD) and Laser Induced Fluorescence (LIF). In the case of LIF, the peak intensities were lower than the DAD, so the DAD detector at a wavelength of 210 nm was selected for further analysis. The optimal CE conditions are illustrated in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"695\" height=\"390\"\u003e\u003c/p\u003e\u003cp\u003eBy using acidic BGE, the electroosmotic mobility is decreased, and the only driving force is electrophoretic mobility. All compounds were positively charged, and the electrophoretic migration depended on the mass of each compound. CIT D has a molecular mass equal to 391.28 g mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; it was the first migrating compound, followed by ESC-OX with a molecular mass equal to 414.43 g mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, then CIT C with a molecular mass equal to 428.42 g mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and finally CIT A with a molecular mass equal to 442.41 g mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMethod validation\u003c/h3\u003e\n\u003cp\u003eDuring method validation, all parameters recommended by International Conference for Harmonization (ICH) guidelines Q2 were verified such as; the specificity, linearity and range, accuracy, precision, detection, and quantitation limits. The method's robustness was confirmed throughout the method development.\u003c/p\u003e\u003cp\u003eConfirmation of method specificity was carried out by comparing the electropherograms of the standard solutions of ESC-OX and its impurities with the ESC-OX pharmaceutical dosage forms. The comparison showed that the developed method was sufficiently specific.\u003c/p\u003e\u003cp\u003eThe relationship between the ESC-OX and its impurities concentration and analytical response was confirmed using linearity and the construction of a calibration curve. The linear regression equation was used for calculation. The r\u003csup\u003e2\u003c/sup\u003e was near unity, slope, and intercept values indicating that the method is sensitive enough and without systematic error. The calibration curves related information is represented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eLOD and LOQ were calculated by signal-to-noise ratio. The ratio of signal-to-noise equal to 3 and 10, for the LOD and LOQ, respectively, was regarded as satisfactory. The LOD was as follows: 7.44\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for ESC-OX,7.83\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for CITT A, 11.56 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for CIT C, and 9.19 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for CIT D. The LOQ was also calculated in the same manner; the result was 0.025 \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for ESC-OX, 0.026 for CITT A, 0.038 \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for CIT C, and 0.031 for CIT D. The LOD and LOQ were low enough to determine low concentrations of impurities. The calibration results are represented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValidation parameters for the developed method.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eESC-OX\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCIT A\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValidation parameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eValidation parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange (\u0026micro;g/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.053- 6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eRange (ppm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u0026ndash;4.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrelation coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCorrelation coefficient\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.07951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSlope\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOD (\u0026micro;g/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.44\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLOD (ppm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.83\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOQ (\u0026micro;g/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLOQ (\u0026micro;g/ml)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIT C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCIT D\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValidation parameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eValidation parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange (\u0026micro;g/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.036\u0026ndash;4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eRange (\u0026micro;g/ml)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u0026ndash;4.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrelation coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCorrelation coefficient\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.05566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSlope\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOD (\u0026micro;g/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.56 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLOD (\u0026micro;g/ml))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.19 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOQ (\u0026micro;g/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLOQ (\u0026micro;g/ml)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe precision of the developed method was confirmed at two levels: repeatability and intermediate precision for the middle concentration six times for three days. The accuracy was evaluated by the RSD %. The RSD% values were below 2% which indicates that the developed method is accurate according to the ICH guidelines. The results are represented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical evaluation of precision and accuracy.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eESC-OX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eParameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage (peak normality)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCon.Spiked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% of Recovery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e101.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eCIT A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eParameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage (peak normality)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCon.Spiked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% of Recovery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e102.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIT C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eParameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage (peak normality)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCon.Spiked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% of Recovery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIT D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eParameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMiddle Conc.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage(peak normality)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCon.Spiked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% of Recovery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRSD%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eAnalysis of the pharmaceutical dosage forms\u003c/h3\u003e\n\u003cp\u003eThe validated method was applied to analyze and detect the impurities in the pharmaceutical formulations (tablets and oral solutions) that were valid and expired. The content of the ESC-OX was within the recommended range of pharmacopeias. The result is represented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. None of the impurities mentioned above was detected.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssay Results of ESC-OX 20 mg, 10 mg Film Tablet, and Oral Solution.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage Form\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContents\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTablet 10 mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTablet 20 mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e98.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOral Solution 10 mg ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e99.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eImpurity profiling became an imperative requirement by many official authorities to ensure the APIs and finished drug safety and efficiency. In this regard, a sensitive CE method was developed for the detection and quantification of low levels of ESC-OX impurities named CIT A, C, and D in the pharmaceutical dosage forms. The developed method obeys the validation criteria and provides a good complementary analytical method for routine analysis as well as stability testing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eNo conflicts of interest were reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eWafa Farooq Suleman Badulla contributed to conceptualization, methodology, software, formal analysis, investigation, project administration, and drafting of the manuscript.Arın G Dal Poçan contributed to conceptualization, methodology, formal analysis, project administration, supervision, visualization, and manuscript review/editing.Sema Koyutürk contributed to conceptualization, methodology, formal analysis, investigation, and project administration.\u003c/p\u003e\n\u003ch2\u003eData Availability Statement\u003c/h2\u003e\n\u003cp\u003eData will be available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBritish Pharmacopoeia Commission. \u003cem\u003eBritish Pharmacopoeia\u003c/em\u003e. London (UK): The Stationery Office; 2003. p. 712-8.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez C, B\u0026oslash;ges\u0026oslash; KP, Ebert B, Reines EH, Braestrup C. Escitalopram versus citalopram: the surprising role of the R-enantiomer. \u003cem\u003ePsychopharmacology\u003c/em\u003e. 2004;174:163-76. https://doi.org/10.1007/s00213-004-1865-z\u003c/li\u003e\n\u003cli\u003eRao RN, Raju AN, Nagaraju D. Development and validation of a liquid chromatographic method for determination of enantiomeric purity of citalopram in bulk drugs and pharmaceuticals. \u003cem\u003eJ Pharm Biomed Anal\u003c/em\u003e. 2006;41(1):280-5. https://doi.org/10.1016/j.jpba.2005.10.025\u003c/li\u003e\n\u003cli\u003eSungthong B, J\u0026aacute;č P, Scriba GK. Development and validation of a capillary electrophoresis method for the simultaneous determination of impurities of escitalopram including the R-enantiomer. \u003cem\u003eJ Pharm Biomed Anal\u003c/em\u003e. 2008;46(5):959-65. https://doi.org/10.1016/j.jpba.2007.05.029\u003c/li\u003e\n\u003cli\u003eDhaneshwar SR, Mahadik MV, Kulkarni MJ. Column liquid chromatography-ultraviolet and column liquid chromatography/mass spectrometry evaluation of stress degradation behavior of escitalopram oxalate. \u003cem\u003eJ AOAC Int\u003c/em\u003e. 2009;92(1):138-47. https://doi.org/10.1093/jaoac/92.1.138\u003c/li\u003e\n\u003cli\u003eRaman B, Sharma BA, Ghugare PD, Nandavadekar S, Singh D, Karmuse PK, et al. Structural elucidation of process-related impurities in escitalopram by LC/ESI-MS and NMR. \u003cem\u003eJ Pharm Biomed Anal\u003c/em\u003e. 2010;53(4):895-901. https://doi.org/10.1016/j.jpba.2010.06.019\u003c/li\u003e\n\u003cli\u003eKaleemullah T, Ahmed M, Sharma HK, Rajput P. Reverse phase liquid chromatographic method for the quantification of di-p-toluoyl-D-tartaric acid in escitalopram oxalate drug substance. \u003cem\u003eEurasian J Anal Chem\u003c/em\u003e. 2011;6(3):197-205.\u003c/li\u003e\n\u003cli\u003eSoliman S, Youssef N. Enantiomeric thin-layer chromatographic assay of escitalopram in presence of \u0026ldquo;in-process impurities\u0026rdquo;. \u003cem\u003eJ Planar Chromatogr Mod TLC\u003c/em\u003e. 2011;24(6):475-81. https://doi.org/10.1556/JPC.24.2011.6.4\u003c/li\u003e\n\u003cli\u003eDighe V, Pawaskar P, Adhyapak S, Shambhu N, Mestry D. Development of normal phase chiral liquid chromatographic method for estimation of escitalopram oxalate and determination of R-citalopram enantiomer from escitalopram oxalate in bulk drug and tablet. \u003cem\u003eJ Chem Pharm Res\u003c/em\u003e. 2012;4(11):4804-9.\u003c/li\u003e\n\u003cli\u003eDeng X, De Wolf J, Vervoort R, Pamperin D, Adams E, Van Schepdael A. Development and validation of a capillary electrophoresis method for the determination of escitalopram and sensitive quantification of its enantiomeric impurity in formulations. \u003cem\u003eElectrophoresis\u003c/em\u003e. 2012;33(11):1648-51. https://doi.org/10.1002/elps.201100580\u003c/li\u003e\n\u003cli\u003eVaghela BK, Rao SS. Development and validation of stability indicating RP-LC, short runtime method for the estimation of escitalopram in escitalopram dosage form. \u003cem\u003eWorld J Pharm Res\u003c/em\u003e. 2013;2:1018-30.\u003c/li\u003e\n\u003cli\u003eBadulla WF, Can N\u0026Ouml;, Atkosar Z, Arli G, Aboul-Enein HY. Comparative study of different chemistries and particle properties, high-performance liquid chromatography stationary phases in separation of escitalopram oxalate and its impurities in different pharmaceutical dosage forms. \u003cem\u003eSep Sci Plus\u003c/em\u003e. 2019;2(8):268-83. https://doi.org/10.1002/sscp.201900021\u003c/li\u003e\n\u003cli\u003eUnited States Pharmacopeial Convention. \u003cem\u003eUSP 39\u0026ndash;NF 34\u003c/em\u003e. Rockville (MD): United States Pharmacopeial Convention; 2016. p. 838.\u003c/li\u003e\n\u003cli\u003eHilhorst M, Somsen G, De Jong G. Choice of capillary electrophoresis systems for the impurity profiling of drugs. \u003cem\u003eJ Pharm Biomed Anal\u003c/em\u003e. 1998;16(7):1251-60. https://doi.org/10.1016/S0731-7085(97)00205-7\u003c/li\u003e\n\u003cli\u003eYesilada A, Tozkoparan B, G\u0026ouml;khan N, \u0026Ouml;ner L, Ertan M. Development and validation of a capillary electrophoretic method for the determination of degradation product in naphazoline HCl bulk drug substance. \u003cem\u003eJ Liq Chromatogr Relat Technol\u003c/em\u003e. 1998;21(17):2575-88. https://doi.org/10.1080/10826079808003408\u003c/li\u003e\n\u003cli\u003eInternational Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). Validation of analytical procedures: text and methodology Q2 (R1). 2005.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Capillary electrophoresis, Citalopram A, C, and D, Dosage form","lastPublishedDoi":"10.21203/rs.3.rs-7727236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7727236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eCapillary Electrophoresis (CE) methods have a wide range of applications in industry and quality control laboratories for routine drug analysis. In the current study, a CE separation method was developed and validated for the concurrent detection of Escitalopram oxalate (ESC-OX) and three related impurities: Citalopram A, C, and D (CIT A, C, D). Additionally, the method was extended for the determination of impurities in both valid and expired dosage forms, Including Tablets and oral solutions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eSeparation and analysis were accomplished in an untreated fused-silica capillary tube (48.5 cm total, 75 \u0026micro;m i.d.) within 20 minutes under an applied voltage gradient of 25\u0026thinsp;\u0026minus;\u0026thinsp;15 kV. Ideal separation was achieved using 40 mM phosphate buffer (pH 2.5) containing 60% Methanol as the background electrolyte. The apparatus was equipped with a diode array detector (DAD) to detect compounds at 210 nm. The established method was validated to accomplish the International Conference on Harmonization (ICH) requirements.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe limit of detection was 7.44 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, 7.83 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, 11.56 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003eand 9.19 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for ESC-OX, CIT A, CIT C, and CIT D. The limit of quantitation was 0.025, 026, 0.038, and 031 \u0026micro;g mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ethe developed method provides a good complementary analytical method for routine analysis and stability testing.\u003c/p\u003e","manuscriptTitle":"Development of a New Separation Method for Escitalopram Oxalate and Its Related Impurities by Capillary Electrophoresis: Application on Valid and Expired Dosage Forms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 19:35:32","doi":"10.21203/rs.3.rs-7727236/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6222d55-fa28-43db-99a9-2fedd8117afe","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T08:58:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 19:35:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7727236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7727236","identity":"rs-7727236","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}