ANALYTICAL QUALITY BY DESIGN APPROACH IN RP-HPLC METHOD DEVELOPMENT FOR THE ASSAY OF PITAVASTATIN IN TABLET DOSAGE FORMHTML Full Text
ANALYTICAL QUALITY BY DESIGN APPROACH IN RP-HPLC METHOD DEVELOPMENT FOR THE ASSAY OF PITAVASTATIN IN TABLET DOSAGE FORM
Aruna Gundala * 1, K. Bharathi 2 and K. V. S. R. G. Prasad 2
Department of Pharmaceutical Analysis & Quality Assurance 1, Krishna Teja Pharmacy College, Tirupati - 517501, Andhra Pradesh, India.
Institute of Pharmaceutical Technology 2, Sri Padmavati Mahila Visvavidyalayam (Women’s University), Tirupati - 517502, Andhra Pradesh, India.
ABSTRACT: Chromatographic method was developed according to Quality by Design (QbD) approach as per ICH Q8 (R2) guidelines for estimation of pitavastatin in pharmaceutical dosage form. By considering the current regulatory requirement for an analytical method development, a reversed phase high performance liquid chromatographic method for routine analysis of Pitavastatin has been optimized using analytical quality by design approach. Unlike routine approach, the present study was initiated with understanding of quality target product profile, analytical target profile and risk assessment for method variables that affect the method response. A liquid chromatography system equipped with a C18 column (250 × 4.6 mm, 5 µ), a binary pump and photodiode array detector were used in this work. The experiments were conducted based on plan by central composite design, which could save time, reagents and other resources. Sigma Tech software was used to plan and analyses the experimental observations and obtain quadratic process model. The process model was used for predictive solution for retention time. The predicted data from contour diagram for retention time were verified actually and it satisfied with actual experimental data. The optimized method was achieved at 1.2 ml/min flow rate of using mobile phase composition of methanol and OPA in water at 80:20% v/v, pH adjusted to 6.5 adjusted with 10% ammonia. The method was validated and verified at flexible input variable level for high degree of robustness and system suitability during method transfer.
Analytical quality by design, Pitavastatin, analytical target profile, HPLC
INTRODUCTION: Pitavastatin is a competitive inhibitor of HMG CoA reductase and was developed for the treatment of hypercholestero-laemia 1-3. It can reduce plasma level of LDL cholesterol by 40% in hypercholesterolaemic patients 4.
Pitavastatin is chemically 3-hydroxy-3-methyl glutaryl coenzyme A reductase inhibitor, it is chemically (3R, 5S)-7-(2- cyclopropyl-4-(4-fluorophenyl) quinolin-3-yl) - 3,5-dihydroxy 6(E) -heptenoic acid calcium salt.
Several analytical methods have been reported for quantitative determination of pitavastatin alone and in combination with other drugs in formulations as well as in biological fluids including high-performance liquid chromatographic methods 5-7, chromatography–mass spectrometric methods (LC-MS) 8-12, HPTLC 13-14, UV Spectroscopy method 15-16 and Ultra-fast performance chromatography 17.
Extensive literature survey revealed that there is no RP HPLC method available for estimation of pitavastatin in bulk and pharmaceutical dosage forms using QbD approach. Hence, it was planned to develop simple, economical, and less time consuming methods including High Performance liquid Chromatographic method for estimation of pitavastatin using QbD approach. Applying the principles of QbD to analytical method could result in more robust method which produce consistent, reliable and quality data throughout the life cycle and in turn will lead to less method incidents when used in the routine environment. This would mean less time spent on investigations and ultimately save time and money.
Quality by Design approach suggests looking into the quality of analytical process during the development stage itself. It says that quality should be built into the process design rather than testing into final results of analytical process 18. QbD is defined as “systematic approach to development that begins with predefined objectives and emphasizes product and process understanding based on sound science and quality risk management” 19. In alignment with the approach proposed in the draft FDA guidance for process validation, a Three-stage approach 20 can be applied to method validation.
Stage 1: Method Design: Define method require-ments and conditions and identify critical controls.
Stage 2: Method Qualification: Confirm that the method is capable of meeting its design intent.
Stage 3: Continued Method Verification: Gain ongoing assurance to ensure that the method remains in a state of control during routine use.
A critical function of Stage 1 is the design of an Analytical Target Profile (ATP) for the method. To design the ATP, it is necessary to determine the characteristics that will be indicators of method performance for its intended use. These are selected from the performance characteristics described in ICH Q2 as per the traditional approach 21. Instead of being applied in a tick box manner, they are investigated by a risk assessment exercise as described in ICH Q9 22 in combination with carefully designed development studies to identify the critical method and sources of variation 23.
Variables are then investigated by robustness and ruggedness experiments to understand the functional relationship between method input variables and each of the method performance characteristics and the results are compared to the desired outcome defined in the ATP. From this, one can identify a set of operational method controls. Also, having evaluated the critical method parameters and gained a better understanding of the method through structured experimentation, a control strategy can be built into the method to ensure a consistent performance throughout its life cycle 24. A key advantage of the QbD approach for all of the above situations is the flexibility to perform a qualification against the specific ATP defined for the intended use of the method 25.
Implementation of QbD Approach: According to ICH Q8 (R2) guidelines, an experimental work was planned and QbD approach was implemented as follows.
Method Design: The method design stage includes establishing the method performance requirements, developing a method that will meet these requirements and then performing appropriate studies to understand the critical method variables that must be controlled to assure the method is robust.
Method Performance Requirements: Utilizing AQbD approach, it is essential at this stage that sufficient thought be given to the intended use of the method and that the objectives or performance requirements of the method be fully documented. This represents the Analytical Target Profile (ATP) 26 for the method. ATP is the estimation of pitavastatin in tablet dosage form using HPLC method.
Method Development: Once the ATP has been defined, an appropriate technique and method conditions must be selected in order to meet the requirements of the ATP.
Method Understanding: Based on an assessment of risk (i.e., the method complexity and the potential for robustness and ruggedness issues) one can perform an exercise focused on understanding the method to better understand what impact key input variables might have on the method’s performance characteristics as per regulatory requirement thus HPLC is must for specificity. From this, one can identify a set of operational method controls.
Risk Assessment: Experiments can be run to understand the functional relationship between method input variables and each of the method performance characteristics. Knowledge accumu-lated during the development and initial use of the method provides input into a risk assessment which may be used to determine which variables need studying and which require controls.
Design of Experiment: Robustness experiments are typically performed on parametric variables using Design of Experiments (DoE) to ensure that maximum understanding is gained while minimizing the total number of experiments. Depending on the type of method, surrogate measures of characteristics such as accuracy or precision may be evaluated.
Method Design Output: A set of method conditions will have been developed and defined which are expected to meet the ATP. Those conditions will have been optimized based on understanding of their impact on method performance like accuracy and precision. Here method responses are taken as output as they are the indicator of accuracy and precision.
QbD-based treatment of the robustness of an analytical method requires the assessment of all parameters (factors) which most strongly influence selectivity (results) alone and in combination. The experimental verification of many factors simultaneously is impractical and associated with extreme technical difficulties and expense. Some authors, have employed statistical studies, such as Plackett-Burman or fractional factorial designs and risk-based approaches 27-31 to overcome the challenge and reduce the experimental workload. Other procedures include running automated robustness experiments 32-35. The present paper, however, employs statistical analysis that is principal component analysis which exhibits factor extraction of variable parameters to evaluate robustness.
MATERIALS AND METHODS: All reagents used in the experimental work were of HPLC grade. HPLC grade methanol and water were purchased from Merck, Mumbai, India. Pitavastatin of 97.80% purity was supplied by Hetero drugs limited, Hyderabad as gift sample.
Chromatography: Chromatographic separations were carried out using Agilent LC system (LC-1200 series), consisting of a binary pump, a Rheodyne injector with a 20 μl loop and a photodiode array detector (DAD). A chromatographic column used was Qualisil Gold C18 (150 × 4.6 mm, i.d., 5 μ particle size). The output signal was monitored and processed using Ezchrome Elite software resident in a Pentium computer (Digital Equipment). Peak identify was confirmed by retention time comparison. Peak purity was assessed by purity plot. The mobile phase was composed of methanol and OPA in water at variable on experimental design as stated in table. The mobile phases were prepared daily, filtered through a 0.45 μ membrane filter and degassed using sonicator prior to use. The DAD detection was carried out at 245 nm wavelength, and the injection volume was 20 μl. The optimized chromatogram is shown in Fig. 1.
FIG. 1: OPTIMIZED RP-HPLC CHROMATOGRAM OF PITAVASTATIN. OPTIMIZED RP -HPLC CHROMATO-GRAM OF PITAVASTATIN (TR: 3.42 min) ON C18 COLUMN USING MODR CONCEPT (PITAVASTATIN ELUTED AT 3.42 min, AND THE METHOD WAS SELECTED FROM MODR, AND VERIFIED FOR ROBUSTNESS AND VALIDATED)
Standard Stock Solution Preparation: 10 mg of pitavastatin was accurately weighed and transferred into a clean dry 10 ml volumetric flask, 5 ml of diluent (mobile phase) was added, sonicated for 5 minutes and made up to the final volume with diluent. Further 1 ml from the above stock solution was taken into a 10 ml volumetric flask and made up to 10 ml with diluent.
Sample Solution Preparation: 20 tablets were weighed and crushed. A powder equivalent to 10 mg of pitavastatin was transferred into a clean dry 10 ml volumetric flask, 5 ml of diluent was added, sonicated for 5 min and made up to the final volume with diluent. Further 1 ml from the above stock solution was taken into a 10 ml volumetric flask and made up to 10 ml with diluent.
Experimental Design: The present AQbD work was carried out as per the cited literatures 36-38 to investigate the impact of different variables on retention time (as method response) and to verify method performances. The levels of these variables are as follows: proportion of the aqueous (X1) in the mobile phase (5% and 25%), pH (X 2) of aqueous phase used in the mobile phase (5.0 and 7.0), and the flow rate (X3) of mobile phase (0.8 and 1.2 ml/min), which are given in Table 1.
The retention time (Y1) and number of theoretical plates (Y2) were used as response in experimental design as controlling response, which is expected to affect and control method responses. A 2 3 factorial design consisting of 3 factors at 2 levels was considered for experimental plan initially and after confirming that the process is a nonlinear central composite design (CCD) was used. The experimental observations along with Factorial Design (DOE) plan are shown in Table 2 and the statistical analysis is given in Table 3 and 4. The MODR was defined using all three variables. From MODR suitable method conditions were selected and subjected to verification for method performance like accuracy and precision (less than 2% RSD) and robustness as targeted response.
TABLE 1: LEVELS IN DESIGN OF EXPERIMENTS
|% Aqueous (X1)||%||25||30||35||40||45|
|Flow rate (X3)||Ml/min||0.8||0.9||1||1.1||1.2|
TABLE 2: CENTRAL COMPOSITE DESIGN PLAN AND OBSERVED DATA (Y1) AND (Y2)
|S. no.||Combination||% of aqueous phase (X1)||Ph of aqueous phase (X2)||Flow rate (X3)||Retention time (Y1)||No of theoretical plates (Y2)|
|8||X1 x2 x3||40||6.5||1.1||4.94||1183|
CCD: Central composite design
TABLE 3: CENTRAL COMPOSITE ANALYSIS FOR RESPONSE (Y1; RETENTION TIME)
|Coefficient||Name of variable and interaction||Value of coefficient||SS %||F-test||P-value|
|B2||% aqueous phase||0.063||0.2076||1.3686||<0.01|
|B3||pH and % aqueous phase||0.0955||0.477||3.1449||<0.01|
|B12||Flow rate and mobile phase||-0.5512||15.8911||104.766||<0.01|
|B13||pH and flow rate||-0.0587||0.1802||1.1882||<0.01|
|B23||% aqueous phase and flow rate||-0.2337||2.8566||18.833||<0.01|
|B123||pH, % aqueous phase and flow rate||0.0237||0.0294||0.1937||<0.01|
P value <0.01 indicates that interaction levels among variables are significant
TABLE 4: CENTRAL COMPOSITE ANALYSIS FOR RESPONSE (Y2; PLATE COUNT)
|Combination||Name of variable and interaction||Coefficient||% SS ratio||F-Value||P-value|
|B2||% aqueous phase||-190.5||15.4739||19.5644||<0.01|
|B3||pH and % aqueous phase||-26.5||0.2993||0.3786||<0.01|
|B12||Flow rate and mobile phase||-167.25||11.9273||15.0802||<0.01|
|B13||pH and flow rate||-230.75||22.7036||28.7051||<0.01|
|B23||% aqueous phase and flow rate||111.5||5.301||6.7023||<0.01|
|B123||pH, % aqueous phase and flow rate||283.0||34.1495||43.1766||<0.01|
Statistical Analysis: The behavior of the system was explained by the following polynomial equation.
Where, Y1 and Y2 are the responses. B0 is the intercept, b1, b2, b3 are the regression coefficients of variables for X1, X2, and X3 respectively.b12, b23, b13 are the regression coefficients for two factor interactions between X1X2X3. Sigma Tech software was used for the statistical analysis of the experimental observations and the analysis is given in Table 3 and 4. The optimized method was found to be at 20% aqueous phase with a pH 5. The understanding are as follows so that co efficient of % aqueous phase was found to be negative (-190.5) with % SS ratio of 15 which was a maximum of effect was found to be among all variables. So, hence the attempt was made to reduce the % aqueous phase as low as possible. The effect of pH was found to be (coeffient) +154 with a % ratio of 10.144 indicating the positive effect on theoretical plate.
The Interaction Effect among Variables as Follows:
Two Variable Interactions: Flow rate and mobile phase it was found to be 11.9%, pH and flow rate it was found to be 22.7% and both above interactions were found to be negative co efficient value with significant effect.
The three variable interactions also found to be positive coefficient with % SS of 34% and F value was found to be 43 indicated the interactions of three variables effect in method performance is highly significant. ANOVA indicated that the process model with X1, X2, X3 along with interactions is highly significant at 99% Confidence level (P<0.1). Since, the Curvature effect is significant and says it has nonlinear relationship between Y and Xs, it requires to go for CCD i.e. central composite design and accordingly the CCD plan and observed data are given below in Table 6. The following Quadratic model was obtained on application of Sigma Tech software,
Y1=b0+b1X1+b2X2+b3 X3+b12X1X2+b23X2X3+b13X1X3+b123X1 X2X3….Eqn. 1.
Y2=b0+b1X1+b2X2+b3 X3+b12X1X2+b23X2X3+b13X1X3+b123X1 X2X3….Eqn. 2
The coefficient of determination (r2) for the above process model was 0.9812 and 0.995. Hence, the Process model is well valid to predict the behavior of the process and can be used for simulation of the process model. The design space or MODR region for robustness was achieved from contours Fig. 3. These regions offer robust processes parameters.
Contours: There could be different combinations, which may give a number of feasible solutions for robust process. X1 vs. X2 with X3 as kept constant, X2 vs. X3 with X1 as kept constant, X1 vs. X3 with X2 as kept constant. Out of these combinations, whichever is the most desirable from the point of retention time that can be selected as a robust process. This contour space is called as design space in products and method operable design region (MODR) in analytical works. The MODR that control the variation in response is obtained from contours a two dimensional plot and it resembles same as Fig. 2 for other variable combinations.
Contour indicated that (at flow rate1, 1.1, 1.2, 0.9) the highest flow rate at 1.1 to 1.2 shows the acceptable plate count from 3000 - 4000 and it was also compared with suitable % aqueous phase, so indicated that the lowest % aqueous phase with highest flow rate i.e. % aqueous phase < -2 level, pH > 0 level and flow rate > 0 level. Could be the most preferred levels to obtain the acceptable method performance hence the method was optimized at % aqueous phase 20, pH 5 and flow rate 1.1 ml/min. These three combinations are shown at Table 7. The three combinations, the contour Fig. 6 gave the best design space covering entire range of variables and retention time of 3 to 4 and were taken for verification purpose.
FIG. 2: pH OF AQUEOUS PHASE VERSUS % OF AQUEOUS PHASE CONTOUR AT 1 ml/min FLOW RATE OF MOBILE PHASE
FIG. 3: CONTOUR OF METHOD OPTIMIZATION. pH OF AQUEOUS PHASE VERSUS % OF AQUEOUS PHASE CONTOUR AT 1.1 ml/min FLOW RATE OF MOBILE PHASE (ANALYTE SHOWS LARGE DESIGN SPACE PITAVASTATIN ONLY WHEN THEORETICAL PLATES IS MORE THAN 2000)
FIG. 4: pH OF AQUEOUS PHASE VERSUS % OF AQUEOUS PHASE CONTOUR AT 0.9 ml/min FLOW RATE OF MOBILE PHASE
FIG. 5: CONTOUR OF METHOD OPTIMIZATION. pH OF AQUEOUS PHASE VERSUS % OF AQUEOUS PHASE CONTOUR AT 1.1 ml/min FLOW RATE OF MOBILE PHASE (ANALYTE SHOWS LARGE DESIGN SPACE PITAVASTATIN ONLY WHEN RETENTION TIME IS MORE THAN 3)
FIG. 6: CONTOUR OF METHOD OPTIMIZATION. pH OF AQUEOUS PHASE VERSUS FLOW RATE CONTOUR AT 20% OF AQUEOUS PHASE (ANALYTE SHOWS LARGE DESIGN SPACE PITAVASTATIN ONLY WHEN THEORETICAL PLATES IS MORE THAN 3000)
FIG. 7: CONTOUR OF METHOD OPTIMIZATION. % OF AQUEOUS PHASE VERSUS FLOW RATE CONTOUR AT PH 5 OF AQUEOUS PHASE (ANALYTE SHOWS LARGE DESIGN SPACE PITAVASTATIN ONLY WHEN THEORETICAL PLATES IS MORE THAN 2000)
It was also noted that the optimized % aqueous of 20% (X1) at flow rate 1.1 ml/min (X3), gives significant results, which are not affected by the pH from 4.5 to 5. Hence, the pH was kept at pH 5, which offered several method advantages like column long life, mobile phase stability of analyte and symmetric elution. The mathematical model the proposed contours were validated by experi-mental verification of predicted retention time (tR) and the results are shown in Table 5.
TABLE 5: SUMMARY OF ALL CONTOURS FOR DIFFERENT METHOD OPERABLE DESIGN REGION
|Range of coded values of variables X2||Range of absolute values of variables X1||Constant absolute value of variables||Y = retention time||Y2= no. of theoretical plates|
|X1 = -0.8 to 0.2||X1 = 11 to 16||X3=1.1 (flow rate)||3-5||2000-3000|
|X2 = -2 to 0.2||X2 = 5 to 7|
|X1 = -2 to 0. 2||X1 = 5 to 25||X1=20% (aqueous)||3-4||2500-3500|
|X3 = 0 to 2||X3 = 1 to 1.2|
|X2 = -2 to 2||X2 = 5 to 7||X2=5 (pH)||5-6||2000-5000|
|X3 = -1.2 to 2||X3 = 0.88 to 1.2|
*Constant absolute value are used as optimized method conditions
TABLE 6: OPTIMIZED METHOD VARIABLE AND CONDITIONS
|Parameter||Range||Condition adopted||Robust verified|
|% Aqueosu||20-30%||20%||± 3%|
|Flow rate||0.9-1.1||1.1||± 0.1|
|Column||C18 (250 × 4.6 mm, 5µ) Qualisil Gold|
|Buffer||0.3 % (pH 5.0)|
|Detection||PDA @245 nm|
|Injection volume||20 µL (manual injection)|
Chromatographic Conditions after Optimization: After robust process was obtained as at Fig. 1, HPLC analyses were carried out using methanol and 0.1% OPA in water (80:20, % v/v) as mobile phase, pH adjusted to 5 and flow rate at 1.1 ml/min on C18 analytical column, UV-PDA detection wavelength at 245 nm and 20 μl of injection volume, which gave a retention time (Rt) of 3.39 min. These parameters are within MODR and hence this design space has been validated also.
Verification of Method by Method Transfer: The robust method was verified on two instruments in different laboratory and the robustness and other system suitability parameters were compared. The % assay result and its % RSD value were calculated. Accuracy and precision were compared.
Validation of the Robust Method: Method parameters for robust process were obtained from MODR of contour, and verified experimentally. The verified method was validated as per ICH Q2 (R2) guidelines for assay method. Method performance like assay, precision and robustness was considered as target response. The results are given in Table 7.
TABLE 7: VALIDATION OF THE MODEL BY METHOD VERIFICATION
Method Validation: Method validation was performed following ICH Q2 guidelines specifications 18 for specificity, selectivity, linearity and range, accuracy, precisions, robustness, detection limit and quantitation limit.
System Suitability Parameters (SST): Chromatographic conditions were tested for SST in two different laboratories. 50 μg/ml of pitavastatin was injected in replicates through manual rheodyne injector it can be detected at retention time 5.3 min with theoretical plates more than 8000 and tailing factor of 1.12. SST parameters are within the limit in both laboratories I and II,data shown in Table 8.
Linearity: The linearity of peak area responses versus concentrations was studied from 2 to10 µg/ml for pitavastatin. A linear response was observed over the examined concentration range and the regression equation was Y=36344x+33134 (R2=0.945) and it was good against the targeted value.
TABLE 8: SYSTEM SUITABILITY PARAMETERS AND VALIDATION RESULTS ON METHOD TRANSFER
|Parameters||Laboratory I||Laboratory II|
|Chromatographic column||C18 (250 × 4.6 mm i.d, 5μm)||C18 (250 × 4.6 mm i.d, 5μm)|
|Mobile phase||85 % Methanol :15 % Water
(0.2 % TEA) pH: 6.5
|85 % Methanol :15 % Water (0.2 % TEA) pH: 6.5|
|Flow rate||1.1 mL /min||1.1 mL/ min|
|Detection wavelength||286 nm||286 nm|
|Retention time (tR)b||5.3 ± 0.1 min||5.2 ± 0.1 min|
|Theoretical plates||> 8000||> 8000|
|Repeatability (% RSD)||0.45||0.52|
|Assay (%)||100.26 ± 0.89 (% RSD : 0.88 )||99.89 ± 1.02 (% RSD : 1.02)|
|Flow rate (0.2 ml)||% RSD : 0.98||% RSD : 1.08|
|pH (± 0. 2%)||% RSD : 1.08||% RSD : 1.26|
Repeatability: The system repeatability was calculated from five replicate injections of pitavastatin at the analytical concentration about 10 μg/ml and the % RSD was found to be 0.56.
Accuracy: Accuracy was studied using three different solutions, containing 90, 100 and 110 μg/ml of pitavastatin. Recovery data are reported in Table 8. The obtained values were within the range of 99.6 and 101.3%, mean % RSD was 0.19, satisfying the acceptance criteria for the study.
Precision: Both intraday and interday precisions were studied at different levels in linearity levels are reported in Table 8. Its % RSD was within the limit (below 2%). The precision was tested for the optimized method in two different laboratories, the % RSD was below 2%.
Robustness Verification: Method critical para-meters such as pH, wavelength and mobile phase are considered as robustness parameters and tested on as a part of validation in laboratory I and compared with the results obtained from laboratory II. The deliberate changes in variables (Xs) were made within MODR region in order to assess the robustness of the method in same and different laboratory. % Change of organic phase was tested up to 3%. The % RSD was below 1.75% for 3% change organic phase. The results for all variables are below 2% (RSD), indicated the robustness of the method. In the same way the method was robust for all test parameters. Results are shown in Table 8.
Limit of Detection and Limit of Quantification: LOD and LOQ were determined based on signal to noise ratio. The S/N ratio of 3:1 was taken as LOD and S/N of 10:1 was taken as LOQ. LOD was found to be 1.9 μg/ml, while LOQ was 5.7 μg/ml.
DISCUSSION: There were few works reported on implementation of quality by design in analytical method development. But the sequence of implementation has to be considered as per FDA. Some papers have reported a method based on stability assay by considering resolution, as a method response to support specificity in robustness 39-40. However, method verification in design space, method performance has to be added. The knowledge based QTPP for the product of pitavastatin was constructed with the assessment of criticality for its critical attribute. Analytical target profile (ATP) was derived based on QTPP profile and then objective of this analytical QbD work was considered as assay component of QTPP of product specifications. To initiate the QbD work, the nature of chemical structure, PKa and solubility profile of pitavastatin were considered in the selection of input variables (X1, X2, and X3) for factorial design (23). Mid points were added to find the curvature effect. Once the curvature effect was significant, CCD was adopted to get response surface to optimize design. C18 column was chosen as stationary phase due to wide acceptability pharmaceuticals and high reproducibility. In order to achieve complete scientific understanding between method results (Y1; such as t R and Y2; such as tN) and input variables, a central composite design was designed and performed. The various variables and their levels were shown in Table 1 and 2.
The obtained experimental results was subjected various statistical parameter for better under-standing and was found to be a nonlinear relationship between input variable and response. The statistical data and ANOVA analysis are shown in Table 3 and 4. The curvature effect was significant, so the Quadratic model (Eqn. 2) was obtained using of Sigma Tech software. The above model was validated by coefficient of determination (R2). The value was 1.00 indicated the process model is valid for predicting the behavior of the process and it was used for simulation of the process model and contours were obtained. The design space or MODR region for robustness was achieved from contours. These regions offer robust processes parameters and shown in Table 8. The obtained method conditions and chromatograms are shown in Fig. 1.
The AQbD approach on development of reversed phase high -performance liquid chromatographic method for pitavastatin in pharmaceutical dosage forms. The prediction form MODR has been verified by actual experimental results indicating its robustness. Thus the method developed based on AQbD is more precise, accurate, and robust during method transfer and also cost effective. This method satisfy the design space concept for analytical method (MODR) and suitable for regulatory submission under regulatory flexibility.
CONCLUSION: Chromatographic method was found to be more accurate, precise, robust, and more sensitive. Statistical analysis proves that the developed method can be used for routine analysis of pitavastatin in the pharmaceutical dosage form. Implementation of QbD approach resulted in more robust method which can produce consistent, reliable, and quality data throughout the process and also save time and money.
ACKNOWLEDGEMENT: The authors wish to thank the Spain and Sigma Tech Services private limited, Hyderabad, India for supporting this work. This work was conducted at Research laboratory of Raghavendra Institute of Pharmaceutical Education and Research, Andhra Pradesh, India in collaboration with Sigma Tech. Consultancy, Hyderabad, India.
CONFLICT OF INTEREST: There are no conflicts of interest.
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How to cite this article:
Gundala A, Bharathi K and Prasad KVSRG: Analytical quality by design approach in RP-HPLC method development for the assay of pitavastatin in tablet dosage form. Int J Pharm Sci & Res 2018; 9(11): 4992-01. doi: 10.13040/IJPSR.0975-8232.9(11).4992-01.
All © 2013 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
A. Gundala *, K. Bharathi and K. V. S. R. G. Prasad
Department of Pharmaceutical Analysis & Quality Assurance, Krishna Teja Pharmacy College, Tirupati, Andhra Pradesh, India.
13 March, 2018
22 May, 2018
21 October, 2018
01 November, 2018