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Kamlesh Palandurkar Department of Biochemistry, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India

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Richie Bhandre Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
Center of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, P.O. Box 346, United Arab Emirates

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Sai H. S. Boddu Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
Center of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, P.O. Box 346, United Arab Emirates

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Minal Harde Department of Pharmaceutical Chemistry, PES's Modern College of Pharmacy, Sector No. 21, Yamuna Nagar, Nigdi, Pune, Maharashtra, India

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Sameer Lakade Department of Pharmaceutics, RMD Institute of Pharmaceutical Education & Research, Pune, Maharashtra, India

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Ujjwala Kandekar Department of Pharmaceutics, JSPMs Rajarshi Shahu College of Pharmacy and Research, Tathawade, Pune, Maharashtra, India

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Prashant Waghmare Department of Pharmaceutical Chemistry, PES's Modern College of Pharmacy, Sector No. 21, Yamuna Nagar, Nigdi, Pune, Maharashtra, India

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Abstract

A systematic DoE and Analytical Quality by Design (AQbD) approach was utilized for the development and validation of a novel stability indicating high-performance thin–layer chromatographic (HPTLC) method for Rivaroxaban (RBN) estimation in bulk and marketed formulation. A D-optimal design was used to screen the effect of solvents, volume of solvents, time from spotting to development and time for development to scanning. ANOVA results and Pareto chart revealed that toluene, methanol, water and saturation time had an impact on retention time. The critical method and material attributes were further screened by Box-Behnken design (BBD) to achieve optimal chromatographic condition. A stress degradation study was carried out and structure of major alkaline degradant was elaborated. According to the design space, a control strategy was used with toluene: methanol: water (6:2:2) and the saturation time was 15 min. A retention factor (RF) of 0.59 ± 0.05 was achieved for RBN using chromatographic plate precoated with silica gel at detection wavelength 282 nm with optimized conditions. The linear calibration curve was achieved in the concentration range of 200–1,200 ng/band with r2 > 0.998 suggesting good coordination between analyte concentration and peak areas. The quadratic model was demonstrated as the best fit model and no interaction was noted between CMAs. The optimized HPTLC method was validated critically as stated in International Conference on Harmonization (ICH) Q2 (R1) guideline and implemented successfully for stress degradation study of RBN. The developed HPTLC method obtained through AQbD application was potentially able to resolve all degradants of RBN achieved through forced degradation study. The obtained results demonstrate that a scientific AQbD approach implementation in HPTLC method development and stress degradation study drastically minimizes the number of trials in experiments, ultimately time and cost of analysis could be minimized.

Abstract

A systematic DoE and Analytical Quality by Design (AQbD) approach was utilized for the development and validation of a novel stability indicating high-performance thin–layer chromatographic (HPTLC) method for Rivaroxaban (RBN) estimation in bulk and marketed formulation. A D-optimal design was used to screen the effect of solvents, volume of solvents, time from spotting to development and time for development to scanning. ANOVA results and Pareto chart revealed that toluene, methanol, water and saturation time had an impact on retention time. The critical method and material attributes were further screened by Box-Behnken design (BBD) to achieve optimal chromatographic condition. A stress degradation study was carried out and structure of major alkaline degradant was elaborated. According to the design space, a control strategy was used with toluene: methanol: water (6:2:2) and the saturation time was 15 min. A retention factor (RF) of 0.59 ± 0.05 was achieved for RBN using chromatographic plate precoated with silica gel at detection wavelength 282 nm with optimized conditions. The linear calibration curve was achieved in the concentration range of 200–1,200 ng/band with r2 > 0.998 suggesting good coordination between analyte concentration and peak areas. The quadratic model was demonstrated as the best fit model and no interaction was noted between CMAs. The optimized HPTLC method was validated critically as stated in International Conference on Harmonization (ICH) Q2 (R1) guideline and implemented successfully for stress degradation study of RBN. The developed HPTLC method obtained through AQbD application was potentially able to resolve all degradants of RBN achieved through forced degradation study. The obtained results demonstrate that a scientific AQbD approach implementation in HPTLC method development and stress degradation study drastically minimizes the number of trials in experiments, ultimately time and cost of analysis could be minimized.

Introduction

Rivaroxaban (RBN) is a novel, direct acting, target-specific, potent oral anticoagulant drug. It acts at a crucial stage in the process of blood-clotting by inhibiting potentially the free and clot-bound coagulation factor Xa (Vitamin K dependent plasma protein) and prothrombinase activity, thus effective blocking of thrombin generation leads to prolongation of clotting time [1]. It is used in treatment and prevention of stroke in adult patients suffered with atrial fibrillation, inhibition of cardiovascular events in association with acute coronary syndrome and the inhibition of venous thromboembolism in patients which undergo selective knee or hip replacement surgery [2]. RBN is an excellent alternative to low molecular weight heparins for the treatment and prevention of cancer-associated deep venous thrombosis/pulmonary embolism [3]. RBN, an oxazolidinone-based anticoagulant is small, water insoluble molecule, has molecular formula C19H18ClN3O5S and chemically it is (S)-5-chloro-N-[2-oxo-3-[4-(3-oxomorpholin-4-yl)phenyl]-1,3-oxazolidin-5-yl-methyl] thiophen-2-carboxamide (Fig. 1) [4]. It is odourless, white to yellowish powder with molecular weight of 435.882 g mol−1 [5].

Fig. 1.
Fig. 1.

Structure of RBN

Citation: Acta Chromatographica 35, 1; 10.1556/1326.2021.00978

The International conference on harmonization of technical requirements for registration of pharmaceuticals for human use (ICH) guidelines on stress testing of new drug substances and product require that stress testing imperative step of drug carried out to determine the stability of the drug and active constituent. It helps to analyze the intrinsic stability of the drug. Identification of degradation products and degradation pathway have a crucial role in drug development [6]. The ICH stability guideline Q1A (R2) necessitates stress study of drugs to be performed under hydrolysis, oxidation, under the exposure to UV light and varied temperature. The degradation products formed during different stress conditions might be unsafe, toxic and results in certain physiological complications. Hence it was utmost necessary to identify and quantify the degradation product.

The Quality-by-Design (QbD) concept is thorough understanding of the process to achieve robust method along with better quality product. According to ICH Q8 guidelines begins with predefined objectives and focused on product and process understanding based on sound science and quality risk management. It is imperative to enhance the efficiency and cost-effectiveness. The principle of QbD when applied to analytical method is termed as analytical QbD (AQbD). The output of AQbD is a well understood and robust method that constantly supplies the desired performance throughout its lifecycle. Factors affecting the robustness are taken into account for the improvement of the analytical method in the QbD [7, 8]. In AQbD robustness and reproducibility of the method build while developing the method while in traditional approach quality is assured at the end of sample testing. AQbD is flexible process and allows continuous improvement [9, 10]. The ease of identification of factors affecting the method performance is enabled by AQbD approach. One of the key area of QbD is the designing of a design space that allows the analytical method to be developed and validated under a varied factor with the minimum number of experiments. Pareto chart are useful to find the critical material attributes and critical processing parameters and response surface methodology was useful to optimize these parameters. In addition, modification within the design space are acceptable by regulatory authorities [11].

Literature survey revealed variety of analytical methods employed for estimation of Rivaroxaban either alone or in combination with other drug as well as on bulk drug, marketed formulations or on human plasma like UV spectrophotometric method [12], HPLC and RP-HPLC method with UV or DAD detector [4, 13–17], LC-MS/MS [18–22], UPLC [23] etc. has been reported. To the best of our knowledge, there is no DoE –practiced, AQbD based stability indicating TLC-densitometric method reported for quantitative estimation of RBN in marketed formulation as well as potentially resolves the degradation products obtained in stress degradation study. Hence, present study was a successful effort, to develop a scientific, validated DoE designed, AQbD based HPTLC method for quantitative estimation of RBN in marketed formulation. Presented method potentially resolves and quantitates the degradation product identified through stress degradation studies on RBN as per ICH-stated conditions. Further degradation pathway was established for isolated alkaline degradation product after characterization with LC-MS and FT-IR technique.

Experimental

Materials and reagents

RBN pure drug (Purity ∼99.8%) was procured from Bayer Pharmaceutical Private Limited, (India) as a gift sample. The solvents methanol, toluene and water used were purchased of HPLC grade (Merck specialties Pvt. Ltd). Tablets of Xarelto® (Bayer HealthCare) were purchased from the local pharmacy. Pre-coated silica gel 60F254 on aluminium sheets was procured from E. Merck Ltd, India.

Instrumentation

The instrument used for analysis was Camag HPTLC system comprising Linomat V automatic sample applicator (Camag, Muttenz, Switzerland), Microsyringe (Linomat 5 syringe, Hamilton-Bonaduzschweiz, Camag, Switzerland), pre-coated silica gel 60 F-254 glass plates (10 × 10 cm with 200 l mm thickness HPTLC; Merck, Germany), twin trough chamber 10 × 10 cm (Camag, Muttenz, Switzerland), UV chamber (Camag, Muttenz, Switzerland), TLC scanner III (Camag, Muttenz, Switzerland), and winCATS version 1.4.0 software (Camag, Muttenz, Switzerland) were used in this study. A precision water bath equipped with MV controller (i-therm, Biomedica, India) was used to carry out selected reactions in solution during stress degradation study. Thermal stability study was carried out in dry hot air oven (Biotechnics BTI–20D, Mumbai, India). Other equipment used were sonicator (Imecoultrasonics, India), analytical balance (Shimadzu AUX 200, Japan) and auto pipettes (Eppendorf, Hamburg, Germany). Design expert software 12® (Trial version-Stat-Ease Inc., Minneapolis) was used for implementing QbD in a present research work.

Preparation of solution

Standard solution of RBN

Accurately weighed quantity (100 mg) of RBN was transferred to a 100 mL volumetric flask, dissolved and diluted up to the mark with methanol. From this solution, 10 mL was transferred to a 100 mL volumetric flask and diluted to the mark with methanol. The solution was mixed and filtered through a 0.45 μ membrane filter.

Forced degradation studies

In order to evaluate the stability indicating property of the developed HPTLC method, stress studies were carried out under ICH recommended conditions. Intentional degradation was tried by exposing the tablet sample to the following stress conditions: acid (0.1 N HCl at 80 °C), base (0.1 N NaOH at 80 °C), oxidation (3% H2O2 at 80 °C), dry heat (80 °C), and UV light (254 nm). The ability of the proposed method to measure the analyte response in the presence of its degradation products was studied [23].

Isolation of alkaline degradation product

An accurately weighed quantity 100 mg of RBN was dissolved in 70 mL methanol. Subsequently, 1.0 N sodium hydroxide was added and volume was made up to 100 mL in a graduated A-grade volumetric flask. The resulted solution was refluxed in round bottom flask on a temperature controlled precision water bath at 80° C for 3.0 h. The alkaline degradation of the RBN was confirmed by newly developed HPTLC method, where the major degradant formed in alkaline stressed condition was isolated through preparative HPTLC technique. The peak area was determined and quantitative estimation of RBN and degradant formed in alkaline stressed condition was carried out from the corresponding regression equation.

Preliminary trials to identify critical method and material parameters (CMPs)

Various preliminary trials were performed to identify critical method and method parameters (CMPs) for the development of a stability-indicating HPTLC method. The amount of toluene, methanol and water, volume of mobile phase, saturation time, time from spotting to development and time from development to scanning were found CMPs for retention factor of drug and its degraded products.

Screening of potential method variables by D-optimal design

A D-Optimal factorial design (Design Expert software 12- Trial version) was applied for the screening of CMPs on retention factor as this design provides optimum combination with minimum runs. Thirty-four experimental runs were performed in the laboratory in triplicate (Table 1). TLC plates were checked for compact and sharp spots of the drug and its degradation products with a better retention factor. All the values for the retention factor were entered against the respective experimental run and analyzed for the effect of CMPs on retention factor. Statistical analysis was carried out by ANOVA and Pareto chart was employed for identification of the effect of CMPs on retention factor.

Table 1.

D-Optimal design for screening of critical method parameters

Trial NoToluene (mL)Methanol (mL)Water (mL)Volume of mobile phase (mL)Saturation time (Min)Time from spotting to development (Min)Time from development to scanning (Min)Retention factor
1.721915550.5
2.62195550.53
3.7121052050.48
4.71210155200.5
5.71195550.42
6.7119152050.45
7.611952050.47
8.6221015550.57
9.621101520200.56
10.612105550.56
11.6229152050.56
12.7111055200.43
13.611915550.54
14.72291520200.53
15.7119155200.46
16.712915550.49
17.711101520200.47
18.6211055200.49
19.62210520200.55
20.7111015550.43
21.61291520200.5
22.61210152050.5
23.6229155200.56
24.72110155200.51
25.72110520200.45
26.6211052050.49
27.6219520200.52
28.722955200.48
29.722105550.43
30.61110520200.5
31.612955200.51
32.722952050.46
33.72210152050.5
34.7129520200.47
Response surface modeling by Box-Behnken Design (BBD)

The effect of toluene, methanol, water and saturation time was found significant on retention time, hence these CMPs were varied further to achieve the optimum method parameters (Table 2). For response surface Modeling Box-Behnken design was used to study these effects on retention factor (Design-Expert software 12- Trial version). Twenty-five runs were carried out in triplicate by varying solvents and saturation time at three levels. The retention factor was calculated for each run. The retention factor values were entered against each run and analyzed for the impact of CMPs on retention factor.

Table 2.

Box-Behnken design trials to optimized solvents and saturation time

Trial NoToluene (mL)Methanol (mL)Water (mL)Saturation time (Min)Retention factor
1.6.502.003.0015.000.52
2.6.502.001.005.000.44
3.6.001.002.0010.000.48
4.6.002.002.005.000.57
5.6.503.003.0010.000.43
6.6.503.002.0015.000.46
7.6.002.002.0015.000.59
8.6.002.001.0010.000.49
9.6.503.002.005.000.45
10.7.002.002.005.000.43
11.6.502.002.0010.000.44
12.7.002.003.0010.000.45
13.7.003.002.0010.000.42
14.6.501.001.0010.000.46
15.6.501.002.0015.000.5
16.6.502.003.005.000.45
17.6.501.003.0010.000.43
18.6.503.001.0010.000.46
19.6.501.002.005.000.42
20.6.502.001.0015.000.48
21.6.002.003.0010.000.46
22.7.001.002.0010.000.41
23.7.002.001.0010.000.42
24.7.002.002.0015.000.45
25.6.003.002.0010.000.45
Optimized chromatographic conditions

Aluminium plates of dimensions 10 × 10 cm, pre-coated with a 250-μm layer of silica gel 60F254 (E. Merck, Darmstadt, Germany) were used for chromatographic separation. Plates were pre-washed with methanol and dried in an oven at 60 °C for 15 min. The samples were spotted on a TLC plate 15 mm from the bottom edge by a CAMAG Linomat V semi-automatic spotter using a bandwidth of 8 mm and an application rate of 0.3 μL s−1. The TLC plate was developed in the twin-trough chamber using toluene: methanol: water (6:2:2 v/v) as the mobile phase at a chamber saturation time of 15 min, relative humidity of 35 ± 5%, a temperature of 25 ± 2 °C and a migration distance of 80 mm. The bands were scanned using the TLC scanner 3 in the reflectance/absorbance mode at 282 nm and analyzed by win CATS software using a slit dimension of 4 × 0.30 mm and a scanning speed of 20 mm s−1. Concentrations of the sample were determined from the intensity of reflected light and by comparing peak area of sample band with that of the standard band.

Wavelength detection for drug analysis

From both the working standard solution and the forced degraded sample of the RBN, an aliquot of 20 μL was spotted on aluminium plates pre-coated with a 250-μm layer of silica gel 60 F254. The TLC plate was developed and dried and spots were scanned in a range of 200–800 nm for detection of wavelength [24].

Assay of RBN marketed formulation

Twenty tablets of Xarelto® were weighed accurately to obtain the average weight and finely powdered by crushing in mortar and pestle for 10 min. An accurately weighed quantity of tablet powder equivalent to about 10 mg of RBN was transferred to a 10.0 mL volumetric flask, added 3 mL methanol and the contents of the flask were sonicated for about 15 min, volume was then made up to the mark with methanol. The solution was mixed and filtered through the Whatman filter paper no. 41. An aliquot of 1 mL of this solution was diluted to 10.0 mL with methanol. Further, resulting solution (0.3 μl) was applied on HPTLC plates in the form of bands, plates were developed, and TLC-densitometric evaluation was conducted to obtain the results.

Preparation of calibration curve

The working standard solution of 200, 400, 600, 800 and 1,200 ng were spotted on the TLC plate. The plate was developed, dried, and analyzed. The calibration curve was constructed by plotting peak area versus concentration of RBN.

Method validation

The method validation process was carried out by performing the validation parameters as per ICH recommended guidelines. The Linear relationship between peak area and concentration of the drugs was evaluated over a range of concentrations expressed in ng/band, making five measurements at 10 concentration levels in the range of 200 ng/band – 1,200 ng/band. Recovery studies were carried out by spiking three different known amounts of pure drug (at 80%, 100% and 120% of label claim) to the pre-analyzed tablet powder (standard addition method). Hence, 8 mg, 10 mg, and 12 mg of RBN were spiked to the pre-analyzed tablet powder containing 10 mg of RBN. The system precision was evaluated by six replicate analysis of the standard solution. The method precision was studied by analyzing six different standard solutions of same concentration. The results for method precision and system precision were expressed in terms of percent relative standard deviation. The intraday precision was evaluated by analyzing the drug at three different time intervals on the same day. The inter-day precision of the method was studied by analyzing the RBN in three consecutive days. The detection and quantification limits were evaluated from calibration curves. In order to estimate the limit of detection (LOD) and limit of quantitation (LOQ) linearly they were separately determined based on the standard deviation (r) of the response and the slope (S) of the calibration curve and using the formula used were LOD = 3.3 r/S and LOQ = 10 r/S (where r = standard deviation of the y-intercept and S = slope of corresponding calibration curve) [25].

Structure prediction of alkaline degradant of RBN

The alkaline stress condition was applied and degradation study was carried out on RBN as mentioned previously. In order to isolate the degradant, the prepared alkaline stressed sample was applied on preparative HPTLC plates and allowed to run in a selected mobile phase. After densitometric analysis, the isolation process was followed to get the alkaline degradant. Working standard solution of RBN and isolated degradant was further subjected to FT-IR and LC-MS studies. The obtained FT-IR spectral data provided information regarding functional groups present and mass spectral data provided the information regarding molecular weight of compound.

Results and discussion

To develop a stability-indicating HPTLC method for RBN, the retention factor and degradation products of the drug are crucially important. The current research work was aimed to identify the degradation pathway of RBN degradant, based on the development of a robust stability-indicating HPTLC assay method. To elucidate inherent stability characteristics of the drug, stress conditions were employed and retention factor of the drug was noted. While developing the method certain factors such as type of solvent, solvent volume, saturation time, time from spotting to development, time from development to scanning etc. were found important and should be optimized carefully. The validation parameters such as linearity range, accuracy and precision of the developed RP-HPTLC method were compared with existing UV, HPLC and UPLC methods. The linearity range of RBN reported using UV spectroscopy, HPLC and UPLC methods were found to be inferior to present reversed-phase densitometry, while the accuracy and precision values were comparable. The method validation parameters obtained from RP-HPTLC were found to be within the ICH recommendation limits [23]. The quantitative methods reported in literature are not as green compared to the present method. Overall, green RP-HPTLC technique was found to be reliable and superior to previously reported analytical techniques for the detection and quantification of RBN.

Screening of potential method variables by D-optimal design

A D-Optimal factorial design (Design Expert software 12- Trial version) was applied for the screening of CMPs on retention factor as this design provides optimum combination with minimum runs. Application of other designs resulted into too many runs; hence it was decided to choose D-optimal design for selection of significant CMPs. This design was commonly used to create fractional general factorial experiments. Thirty-four experimental runs were performed in the laboratory in triplicate (Table 1). TLC plates were checked for compact and sharp spots of the drug and its degradation products with a better retention factor. In the present study toluene was chosen as non-polar solvent, water and methanol were selected as polar solvents. The effects of different combination of these solvents on retention factors in combination with other parameters were studied. The effect of chamber saturation time was studied as incomplete saturation yields the diffused peaks of sample. It was found that the all these solvents had significant impact on the retention factor. These results were supported by Pareto chart (Fig. 2A). Pareto chart revealed that the content of toluene was beyond the Bonferroni limit, means it was very crucial factor to affect the retention factor. The amount of methanol, water and saturation was also t-value limit hence these are also significant factors to affect the retention factor. Minimum 9–10 mL of the solvent volume was used to carry out all the trials and it was found sufficient to for each run. The design space for volume of solvent was ±1. The design space for time from spotting to development and development to scanning was varied as 5, 10, 15 and 20 min it was noted that these parameters did not exhibit any significant effect on retention factor. These results were further supported by Pareto chart; all these parameters were found below t-value, indicating that these parameters were non-significant. The D-Optimal design was statistically analyzed by ANOVA and model was found significant as F value was 11.29 and P value was less than 0.001 (Table 3). The predicted R-Squared value of 0.5640 was found reasonable agreement with the adjusted R-Squared of 0.6859. Adequate precision measures the signal to noise ratio. A ratio greater than 4 was desirable. For the current run this value was 12.401 indicates an adequate signal.

Fig. 2.
Fig. 2.

A) Pareto Chart B) Contour plot C) 3D response surface graph for methanol and toluene D) Contour plot E) 3D response surface graph for toluene and water F) Contour plot G) 3D response surface graph for methanol and water H) Contour plot I) 3D response surface graph for toluene and saturation time J) Contour plot K) 3D response surface graph for methanol and saturation time L) Contour plot M) 3D response surface graph for saturation time and water on retention factor

Citation: Acta Chromatographica 35, 1; 10.1556/1326.2021.00978

Table 3.

ANOVA response for D-optimal factorial design and Box-Behnken design

D-optimal factorial design
SourceSum of SquaresdfMean SquareF-ValueP ValueSignificance
Model0.04476.27811.29<0.0001Significant
A-Toluene0.02610.02647.60<0.0001Significant
B-Methanol4.57314.5738.230.0081Significant
C-Water4.39514.3957.900.0093Significant
D-Volume of mobile phase4.63614.6368.3380.9772Non-Significant
E-Saturation time7.37617.37613.270.0012Significant
F-Time from spotting to development2.25912.2590.410.5294Non-Significant
G-Time from development to scanning6.06616.0661.090.3058Non-Significant
Residual0.014265.559
Cor Total0.05833
Adj R-Squared0.6859
Pred R-Squared0.5640
Adeq Precision
12.401

Box-Behnken design
SourceSum of SquaresdfMean SquareF-ValueP ValueSignificance
Model0.038142.7213.560.0118Significant
A-Toluene0.01810.01823.090.0003
B-Methanol7.50017.5000.0980.7586
C-Water8.33318.3330.0110.9183
D-Saturation Time4.80014.8006.290.0251
AB4.00014.0000.520.4812
AC9.00019.0001.180.2960
AD1.38811.3881.8171.0000
BC6.93916.9399.0861.0000
BD1.22511.2251.600.2260
CD2.25012.2500.290.5958
A22.38312.3833.120.0991
B29.47119.4711.240.2842
C25.51815.5180.0720.7920
D28.72118.72111.420.0045
Residual0.011147.637
Lack of Fit0.011101.069
Adj R-Squared0.5617
Pred R-Squared0.2623

Response surface modeling by Box-Behnken design (BBD)

From the data of D-optimal design, it was found that solvents and saturation time had significant impact on retention factor, hence these CMPs was further studied by varying at different levels. Box-Behnken design was used to screen and optimize the chromatographic conditions. Toluene was varied in the range of 6–7 mL, water and methanol was varied in the range of 1–3 mL and saturation time was varied from 5 to 15 min, total 25 trials was carried out in triplicate (Table 2). The design was statistically analyzed by ANOVA (Table 3). Quadratic model was found best fit model, F-value and P-value was 3.56 and 0.0118 respectively. These values proved that model was significant. The interaction between the variables was found insignificant (Fig. 2). The impact of variables was shown by equation
RetentionFactor=+0.440.038A2.500003B8.333004C+0.020D+1.000002AB+0.015AC+0.000 ∗A∗D+0.000BC0.018BD+7.500003CD+0.019A20.012B2+2.917003C2+0.037D2

The response surface quadratic model was used for the desired retention factor of near about 0.5–0.6 to optimize the experimental conditions. The suggested experimental runs were performed in a laboratoryto examine the validity of the model. The retention factor of all performed experiments was in agreement with the predicted responses, suggesting that the model was valid for defining design space for the development of a robust HPTLC method According to the design space, a control strategy was implemented for the development of an analytical method with a retention factor of 0.5–0.6 was 6 mL of toluene, 2 mL of methanol, 2 mL water and a saturation time of 10 min. By application of these condition densitogram of drug was obtained as shown in Fig. 3.

Fig. 3.
Fig. 3.

A) Densitogram of RBN B) Standard calibration curve of RBN C) 3D Linearity spectrum of RBN

Citation: Acta Chromatographica 35, 1; 10.1556/1326.2021.00978

Wavelength selection for analyte detection

Wavelength selection study was carried out by spotting 20 μL of working standard solution and the forced degradation sample of RBN on aluminium plates pre-coated with a 250-μm layer of silica gel 60F254. The spots were scanned between the range 200–800 nm for detection of wavelength. The scanned samples showed maximum absorbance at 282 nm. Hence, 282 nm was selected as a detection wavelength for scanning the samples.

Method validation

HPTLC method was validated according to the ICH guidelines. The parameters such as linearity, limit of quantitation (LOQ), limit of detection (LOD), precision, accuracy and specificity were studied (Fig. 4).

Fig. 4.
Fig. 4.

HPTLC Densitogram of RBN in (A) Acidic condition (B) Alkaline condition (C) Neutral condition (D)Thermal condition (E)Oxidation condition (F) Photolytic condition

Citation: Acta Chromatographica 35, 1; 10.1556/1326.2021.00978

Linearity

Appropriate volume of aliquot from standard RBN stock solution was filled in the syringe and under nitrogen stream by a semiautomatic sample applicator; it was applied in form of band on a single plate having concentration 200–1,200 ng/spot of RBN (Table 4). Plate was developed using toluene: methanol: water (6:2:2 v/v) at ambient temperature and dried in air. The plate was subjected to measurements in absorbance mode at wavelength 282 nm. A plot of peak area vs. concentration for drug was obtained (Fig. 3). The 3D spectrum of drug was recorded in the range of 200–400 nm and purity of chromatographic peak was checked by scanning individual peak (Fig. 3).

Table 4.

Summary of linear regression and validation

ParametersRBN
Linearity range (ng/band)200–1,200
Linear regression equationY = 8.845x − 50.13
Correlation coefficient (r2)0.999
Standard error of slope0.085
Standard error of intercept0.075
Standard error of residual0.092
System suitability parameters -
Symmetry Factor (As)1.01
Capacity Factor (K)0.83
Selectivity factor (α)1.44
Limit of detection (ng/band)1.12
Limit of Quantitation (ng/band)3.40
Intraday precision (% mean ± SD)99.86 ± 0.305
Interday Precision (% mean ± SD)99.74 ± 0.124
Recovery (mean ± SD)99.8 ± 0.5
Amount of drug quantified in formulation (%)99.94

Accuracy (Recovery) study

Accuracy studies were performed by spiking test solution with standard solution. Recovery studies were carried out by spiking three different known amounts of pure drug (at 80%, 100% and 120% of label claim) to the pre-analyzed tablet powder (standard addition method). The result of recovery study obtained within the range of 98–102% indicates the accuracy of the proposed method. The statistical analysis of recovery study was presented in Table 4.

Precision

For intermediate precision and repeatability studies the linearity was repeated for 6 times without changing the syringe and position of plates on the same day. The intermediate precision of the method was checked by repeating the study on three different days. Data were collected from each set and mean area, standard deviation (S.D) and Coefficient of variance (C.V) were calculated. The results of precision study were expressed in terms of standard deviation (SD) (Table 4). The RSD for both intra-day and inter-day precision study was found to be less than 2 indicating the repeatability and reproducibility of the method.

Limit of detection and quantitation

In order to estimate the limit of detection (LOD) and limit of quantitation (LOQ), determinations were carried out based on the standard deviation (r) of the response and the slope (S) of the calibration curve and using the formula LOD = 3.3 r/S and LOQ =10r/S, the LOD and LOQ for RBN were estimated. Where, r was the standard deviation of the y-intercepts and S was the slope of the calibration curve. The limits of detection and quantification were found to be 1.12 and 3.40 ng/band, respectively as shown in Table 4.

System suitability

System suitability is an important parameter of chromatographic method development and validation. It was prominently utilized to identify the resolution, accuracy and reproducibility of the chromatographic system. Parameters such as peak symmetry, resolution, capacity factor and selectivity factor were analyzed. All obtained results were within the range and represent in Table 4.

Analysis of commercial tablet

The commercial tablet dosage form, Xarelto® (10 mg) was analyzed using the developed HPTLC method. The analysis was based on comparing the mean peak area of sample band with that of the standard band. The results of tablet analysis were in good agreement with the label claims. The statistical result analysis of the commercial dosage form was presented in Table 4.

Degradation behaviour

In the forced degradation studies, RBN was found sensitive and susceptible to degradation under employed acidic, alkaline, oxidative, neutral, thermal and photolytic stress conditions. The results of forced degradation studies were included in Table 5. Typical densitograms obtained for RBN under different stress conditions were shown in Fig. 3. The developed HPTLC method could effectively resolve the drugs from their degradation products which confirmed the stability indicating power of the developed method. Our reversed-phase HPTLC method for the RBN quantification was compared with analytical methods reported in the literature (Table 6).

Table 5.

Result of stress degradation study of RBN

Sr. No.Stress ConditionTemperature and TimePercent degradationRf Value of degraded product
1.Acid (0.1 N HCl)80 °C for 1 h11.88%0.47
2.Alkali (0.1 N NaOH)80 °C for 1 h13.09%0.92
3.Neutral (H2O)80 °C for 1 h6.76%0.20
4.Thermal80 °C for 1 h4.49%0.72
5.Oxide (3% H2O2)80 °C for 24 h13.09%0.24, 0.38
6.Photolytic Degradation24 h2.90%0.31
Table 6.

Comparison of presented RP-HPTLC method with previous reported methods for quantification of RBN

Analytical methodsLinearity RangeAccuracy (%)Precision (% CV)Reference
UV2–20 (μg mL-1)97.82–100.41.0712
RP-HPLC5–40 (μg mL-1)97.89–99.51.3413
HPLC-UV5–50 (μg mL-1)98.84–101.50.9614
UPLC50–150 (μg mL-1)99.4–101.10.4023
RP-HPTLC200–1,200 (ng band-1)98.19–102.010.65Our method

Forced (Stress) degradation study

Identification of alkaline degradation of RBN

RBN was hydrolysed smoothly with 0.1 N sodium hydroxide after 1 h at 80 °C. Further, alkaline degradant was isolated using preparative TLC technique. The scheme of major alkaline degradation product (DP) achieved of RBN were represented in Fig. 5A, which was supported with analytical data. The structural elucidation of the degradation product was confirmed by the IR and mass spectral data. The IR spectrum Fig. 5C of DP was characterized by the absorption frequency of NH stretch at 3,354, CH stretch at 2,937, C=O stretch at 1,653, C=C aromatic stretch at 1,512, C-O stretch at 1,070, C-Cl stretch and C-S stretch band at 831 and 738 respectively confirms the isolated alkaline degradant when compared with RBN standard in Fig. 5B. The mass spectral data of degradant (Fig. 5D) showed tallest signal (base peak) at 327.07 characterized by the breaking of the amine group of RBN along with the peak obtained at 144.1. Mass data and IR data revealed the confirmation of major structure of alkaline degradant was depicted in scheme of degradation.

Fig. 5.
Fig. 5.

A) Fragmentation scheme of alkaline degradation of RBN B) FT-IR spectra of RBN C) FT-IR spectra of alkali induced DP D) LC-MS of alkaline degradant

Citation: Acta Chromatographica 35, 1; 10.1556/1326.2021.00978

Conclusion

A simple, sensitive and selective AQbD based stability indicating, validated HPTLC method was developed as per ICH guidelines for estimation of RBN in the presence of its degradation products, which provided useful information regarding degradation behaviour of RBN using different stress conditions. As method was based on application of systematic, scientific analytical tool, it reduces the time and cost of analysis and ultimately become a cost effective and less time consuming, it may be more advantageous for routine analysis of drug in marketed formulation. The above mentioned study was able to explore the useful information which has not yet been reported in the literature of RBN. The various degradation products of RBN along with the nature of degradant products and fragmentation pathway of degradant formed under alkaline stress studies were also not reported earlier.

Conflict of interest

The authors do not have any conflict of interest to declare in present research work and manuscript preparation.

Data availability statement

All the important data generated has been utilized in writing the manuscript and there is no any other additional data.

References

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    De Oliveira, A. C.; Davanço, M. G.; De Campos, D. R.; Sanches, P. H. G.; Cirino, J. P. G.; Carvalho, P. D. O.; Antônio, M. A.; Coelho, E. C.; Porcari, A. M. Sensitive LC–MS/MS method for quantification of Rivaroxaban in plasma: application to pharmacokinetic studies. Biomed. Chromatogr. 2021, 35(9), e5147.

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    Rao, P.; Cholleti, V.; Reddy, V. Stability-indicating UPLC method for determining related substances and degradants in Rivaroxaban. Int. J. Res. Pharm. Sci. 2015, 5(2), 1724.

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    Prajapati, P. B.; Bodiwala, K. B.; Shah, S. A. Analytical quality-by-design approach for the stability study of thiocolchicoside by eco-friendly chromatographic method. J. Planar Chromat. 2018, 31(6), 477487.

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  • 1.

    Samama, M. M. The mechanism of action of rivaroxaban--an oral, direct Factor Xa inhibitor--compared with other anticoagulants. Thromb. Res. 2011, 127(6), 497504.

    • Search Google Scholar
    • Export Citation
  • 2.

    Iram, F.; Iqbal, M.; Husain, A. A review on rivaroxaban: a prominent oral anti-coagulant agent. Int. J. Pharma Chem. Res. 2015, 1, 140148.

    • Search Google Scholar
    • Export Citation
  • 3.

    Singh, A. K.; Noronha, V.; Gupta, A.; Singh, D.; Singh, P.; Singh, A.; Singh, A. Rivaroxaban: drug review. Cancer Res. Stats. Treat. 2020, 3(2), 264269.

    • Search Google Scholar
    • Export Citation
  • 4.

    Arous, B.; Al-Mardini, M. A.; Karabet, F.; Daghestani, M.; Al-Lahham, F.; Al-Askar, A. Development and validation of a liquid chromatography method for the analysis of rivaroxaban and determination of its production related impurities. Pharma. Chem. J. 2018, 52(5), 483490.

    • Search Google Scholar
    • Export Citation
  • 5.

    Reçber, T.; Haznedaroğlu, İ. C.; Çelebier, M. Review on characteristics and analytical methods of Rivaroxaban. Crit. Rev. Anal. Chem. 2020, 113.

    • Search Google Scholar
    • Export Citation
  • 6.

    Harde, M. T.; Wankhede, S. B.; Chaudhari, P. D. A validated inherent stability indicating HPTLC method for estimation of cyclobenzaprine hydrochloride in tablets and use of MS–QTOF in characterization of its alkaline stress degradation product. Bull. Fac. Pharm.Cairo Univ. 2016, 54(2), 145156.

    • Search Google Scholar
    • Export Citation
  • 7.

    Vya, A. J.; Visana, N. M.; Patel, A. I.; Patel, A. B.; Patel, N. K.; Shah, S. R. Analytical quality by design in stress testing or stability-indicating method. Asian J. Pharm. Anal. 2021, 11(2), 170178.

    • Search Google Scholar
    • Export Citation
  • 8.

    Gp, B.; Ad, D. Development and validation of stability indicating UPLC method for the simultaneous estimation of drugs in combined dosage forms using quality by design approach. Asian J. Pharm. Anal. 2020, 10(3), 119.

    • Search Google Scholar
    • Export Citation
  • 9.

    Das, P.; Maity, A. Analytical quality by design (AQbD): a new horizon for robust analytics in pharmaceutical process and automation. Int. J. Pharm. Drug Anal. 2017, 324337.

    • Search Google Scholar
    • Export Citation
  • 10.

    Ganorkar, A.; Gupta, K. Analytical quality by design: a mini review. Biomed. J.Sci. Tech. Res. 2017, 1(6), 15551558.

  • 11.

    Krull, I.; Joseph, T.; Lukulay, P.H.; Verseput, R.; Swartz, M. A quality-by-design methodology for rapid LC method development, Part I. LCGC North Am. 2008, 26(12), 11901197.

    • Search Google Scholar
    • Export Citation
  • 12.

    Bala, C. S.; Bind, V. H.; Damayanthi, M. R.; Sireesha, A. Development and validation of UV spectrophotometric method for the determination of Rivaroxaban. Der Pharma Chem. 2013, 5, 15.

    • Search Google Scholar
    • Export Citation
  • 13.

    Manjunatha, D. H. Determination of Rivaroxaban in pure, pharmaceutical formulations and human plasma samples by RP-HPLC. Int. J. Adv. Pharm. Anal. 2015, 5, 6568.

    • Search Google Scholar
    • Export Citation
  • 14.

    Çelebier, M.; Reçber, T.; Koçak, E.; Altinöz, S. RP-HPLC method development and validation for estimation of Rivaroxaban in pharmaceutical dosage forms. Braz. J. Pharm. Sci. 2013, 49, 359366.

    • Search Google Scholar
    • Export Citation
  • 15.

    Sarkis, N.; Bitar, Y.; Sarraj, M. M. Development and validation of RP-HPLC method for simultaneous estimation of aspirin and rivaroxaban in synthetic mixture. Res. J. Pharm. Technol. 2020, 13(11), 54595465.

    • Search Google Scholar
    • Export Citation
  • 16.

    Eswarudu, M. M.; Lalitha Devi, A.; Pallavi, K.; Srinivasa Babu, P.; Nandini Priya, S.; Sabeeha Sulthana, S. K. Novel validated RP-HPLC method for determination of rivaroxaban in bulk and its pharmaceutical dosage form. Int. J. Pharm. Sci. Rev. Res. 2020, 64(1), 183187.

    • Search Google Scholar
    • Export Citation
  • 17.

    Gouveia, F.; Bicker, J.; Santos, J.; Rocha, M.; Alves, G.; Falcão, A.; Fortuna, A. Development, validation and application of a new HPLC-DAD method for simultaneous quantification of apixaban, dabigatran, edoxaban and rivaroxaban in human plasma. J. Pharm. Biomed. Anal. 2020, 181, 113109.

    • Search Google Scholar
    • Export Citation
  • 18.

    Derogis, P. B. M.; Sanches, L. R.; De Aranda, V. F.; Colombini, M. P.; Mangueira, C. L. P.; Katz, M.; Faulhaber, A. C. L.; Mendes, C. E. A.; Ferreira, C. E. D. S.; França, C. N. Determination of rivaroxaban in patient’s plasma samples by anti-Xa chromogenic test associated to High Performance Liquid Chromatography tandem Mass Spectrometry (HPLC-MS/MS). PLoS One 2017, 12(2), e0171272.

    • Search Google Scholar
    • Export Citation
  • 19.

    Baldelli, S.; Cattaneo, D.; Pignatelli, P.; Perrone, V.; Pastori, D.; Radice, S.; Violi, F.; Clementi, E. Validation of an LC–MS/MS method for the simultaneous quantification of dabigatran, rivaroxaban and apixaban in human plasma. Bioanalysis 2016, 8(4), 275283.

    • Search Google Scholar
    • Export Citation
  • 20.

    Gai, S.; Huang, A.; Feng, T.; Gou, N.; Wang, X.; Lu, C.; Tang, H.; Xu, D.; Zhang, B.; Wang, L. LC–MS/MS method for simultaneous determination of Rivaroxaban and metformin in rat plasma: application to pharmacokinetic interaction study. Bioanalysis 2019, 11(24), 22692281.

    • Search Google Scholar
    • Export Citation
  • 21.

    De Oliveira, A. C.; Davanço, M. G.; De Campos, D. R.; Sanches, P. H. G.; Cirino, J. P. G.; Carvalho, P. D. O.; Antônio, M. A.; Coelho, E. C.; Porcari, A. M. Sensitive LC–MS/MS method for quantification of Rivaroxaban in plasma: application to pharmacokinetic studies. Biomed. Chromatogr. 2021, 35(9), e5147.

    • Search Google Scholar
    • Export Citation
  • 22.

    Shaikh, K.; Mungantiwar, A.; Halde, S.; Pandita, N. Liquid chromatography–tandem mass spectrometry method for determination of Rivaroxaban in human plasma and its application to a pharmacokinetic study. Eur. J. Mass Spectrom. 2019, 26(2), 91105.

    • Search Google Scholar
    • Export Citation
  • 23.

    Rao, P.; Cholleti, V.; Reddy, V. Stability-indicating UPLC method for determining related substances and degradants in Rivaroxaban. Int. J. Res. Pharm. Sci. 2015, 5(2), 1724.

    • Search Google Scholar
    • Export Citation
  • 24.

    Validation of Analytical Procedures: Text and Methodology; ICH Harmonized Tripartite GuidelineS Q2 (R1): Geneva, 2005.

  • 25.

    Prajapati, P. B.; Bodiwala, K. B.; Shah, S. A. Analytical quality-by-design approach for the stability study of thiocolchicoside by eco-friendly chromatographic method. J. Planar Chromat. 2018, 31(6), 477487.

    • Search Google Scholar
    • Export Citation
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Senior editors

Editor(s)-in-Chief: Kowalska, Teresa (1946-2023)

Editor(s)-in-Chief: Sajewicz, Mieczyslaw

Editors(s)

  • Danica Agbaba (University of Belgrade, Belgrade, Serbia)
  • Łukasz Komsta (Medical University of Lublin, Lublin, Poland)
  • Ivana Stanimirova-Daszykowska (University of Silesia, Katowice, Poland)
  • Monika Waksmundzka-Hajnos (Medical University of Lublin, Lublin, Poland)

Editorial Board

  • R. Bhushan (The Indian Institute of Technology, Roorkee, India)
  • J. Bojarski (Jagiellonian University, Kraków, Poland)
  • B. Chankvetadze (State University of Tbilisi, Tbilisi, Georgia)
  • M. Daszykowski (University of Silesia, Katowice, Poland)
  • T.H. Dzido (Medical University of Lublin, Lublin, Poland)
  • A. Felinger (University of Pécs, Pécs, Hungary)
  • K. Glowniak (Medical University of Lublin, Lublin, Poland)
  • B. Glód (Siedlce University of Natural Sciences and Humanities, Siedlce, Poland)
  • A. Gumieniczek (Medical University of Lublin, Lublin, Poland)
  • U. Hubicka (Jagiellonian University, Kraków, Poland)
  • K. Kaczmarski (Rzeszow University of Technology, Rzeszów, Poland)
  • H. Kalász (Semmelweis University, Budapest, Hungary)
  • K. Karljiković Rajić (University of Belgrade, Belgrade, Serbia)
  • I. Klebovich (Semmelweis University, Budapest, Hungary)
  • A. Koch (Private Pharmacy, Hamburg, Germany)
  • P. Kus (Univerity of Silesia, Katowice, Poland)
  • D. Mangelings (Free University of Brussels, Brussels, Belgium)
  • E. Mincsovics (Corvinus University of Budapest, Budapest, Hungary)
  • Á. M. Móricz (Centre for Agricultural Research, Budapest, Hungary)
  • G. Morlock (Giessen University, Giessen, Germany)
  • A. Petruczynik (Medical University of Lublin, Lublin, Poland)
  • R. Skibiński (Medical University of Lublin, Lublin, Poland)
  • B. Spangenberg (Offenburg University of Applied Sciences, Germany)
  • T. Tuzimski (Medical University of Lublin, Lublin, Poland)
  • Y. Vander Heyden (Free University of Brussels, Brussels, Belgium)
  • A. Voelkel (Poznań University of Technology, Poznań, Poland)
  • B. Walczak (University of Silesia, Katowice, Poland)
  • W. Wasiak (Adam Mickiewicz University, Poznań, Poland)
  • I.G. Zenkevich (St. Petersburg State University, St. Petersburg, Russian Federation)

 

KOWALSKA, TERESA (1946-2023)
E-mail: kowalska@us.edu.pl

SAJEWICZ, MIECZYSLAW
E-mail:msajewic@us.edu.pl

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2022  
Web of Science  
Total Cites
WoS
647
Journal Impact Factor 1.9
Rank by Impact Factor

Chemistry, Analytical (Q3)

Impact Factor
without
Journal Self Cites
1.9
5 Year
Impact Factor
1.4
Journal Citation Indicator 0.41
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Chemistry, Analytical (Q3)

Scimago  
Scimago
H-index
29
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0.28
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Chemistry (miscellaneous) (Q3)

Scopus  
Scopus
Cite Score
3.1
Scopus
CIte Score Rank
General Chemistry 211/407 (48th PCTL)
Scopus
SNIP
0.549

2021  
Web of Science  
Total Cites
WoS
652
Journal Impact Factor 2,011
Rank by Impact Factor Chemistry, Analytical 66/87
Impact Factor
without
Journal Self Cites
1,789
5 Year
Impact Factor
1,350
Journal Citation Indicator 0,40
Rank by Journal Citation Indicator Chemistry, Analytical 72/99
Scimago  
Scimago
H-index
29
Scimago
Journal Rank
0,27
Scimago Quartile Score Chemistry (miscellaneous) (Q3)
Scopus  
Scopus
Cite Score
2,8
Scopus
CIte Score Rank
General Chemistry 210/409 (Q3)
Scopus
SNIP
0,586

2020
 
Total Cites
650
WoS
Journal
Impact Factor
1,639
Rank by
Chemistry, Analytical 71/83 (Q4)
Impact Factor
 
Impact Factor
1,412
without
Journal Self Cites
5 Year
1,301
Impact Factor
Journal
0,34
Citation Indicator
 
Rank by Journal
Chemistry, Analytical 75/93 (Q4)
Citation Indicator
 
Citable
45
Items
Total
43
Articles
Total
2
Reviews
Scimago
28
H-index
Scimago
0,316
Journal Rank
Scimago
Chemistry (miscellaneous) Q3
Quartile Score
 
Scopus
393/181=2,2
Scite Score
 
Scopus
General Chemistry 215/398 (Q3)
Scite Score Rank
 
Scopus
0,560
SNIP
 
Days from
58
submission
 
to acceptance
 
Days from
68
acceptance
 
to publication
 
Acceptance
51%
Rate

2019  
Total Cites
WoS
495
Impact Factor 1,418
Impact Factor
without
Journal Self Cites
1,374
5 Year
Impact Factor
0,936
Immediacy
Index
0,460
Citable
Items
50
Total
Articles
50
Total
Reviews
0
Cited
Half-Life
6,2
Citing
Half-Life
8,3
Eigenfactor
Score
0,00048
Article Influence
Score
0,164
% Articles
in
Citable Items
100,00
Normalized
Eigenfactor
0,05895
Average
IF
Percentile
20,349
Scimago
H-index
26
Scimago
Journal Rank
0,255
Scopus
Scite Score
226/167=1,4
Scopus
Scite Score Rank
Chemistry (miscellaneous) 240/398 (Q3)
Scopus
SNIP
0,494
Acceptance
Rate
41%

 

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Acta Chromatographica
Language English
Size A4
Year of
Foundation
1988
Volumes
per Year
1
Issues
per Year
4
Founder Institute of Chemistry, University of Silesia
Founder's
Address
PL-40-007 Katowice, Poland, Bankowa 12
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2083-5736 (Online)

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