Development And Validation Of A Stability-indicating RP-HPLC Method Of .
Journal of Applied Pharmaceutical Science Vol. 9(06), pp 021-032, June, 2019Available online at http://www.japsonline.comDOI: 10.7324/JAPS.2019.90604ISSN 2231-3354Development and validation of a stability-indicating RP-HPLCmethod of cholecalciferol in bulk and pharmaceutical formulations:Analytical quality by design approachDilipkumar Suryawanshi*, Durgesh Kumar Jha, Umesh Shinde, Purnima D. AminDepartment of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, UGC-CAS (Elite Status), Mumbai, India.ARTICLE INFOABSTRACTReceived on: 06/11/2018Accepted on: 06/03/2019Available online: 05/06/2019The present article utilized analytical quality by design (AQbD) methodology to optimize chromatographic conditionsfor the routine analysis of Cholecalciferol (CHL). Taguchi orthogonal array design and Box–Behnken designwere employed to screen and optimize critical method parameters for augmenting the method performance. Theoptimal chromatographic separation was attained on Eurosphere 100-5, C8 (250 4.6 mm i.d., 5 μm) column in anisocratic elution mode using methanol:acetonitrile (50:50, % v/v) as mobile phase at a flow rate of 1.0 ml/minutesand photodiode array detection at 265 nm. The optimized chromatographic method was successfully validated asper International Council for Harmonisation Q2 (R1) guidelines. The method was found to be linear (r2 0.9993)in the range of 20–100 IU/ml. Limit of detection and limit of quantitation were found to be 10 and 20 IU/ml. Theprecision, robustness, and ruggedness values were within the acceptance limits (relative standard deviation 2). Thepercent recovery of in-house developed 400 IU mouth dissolving tablets and marketed Tayo 60k tablets were foundto be 99.89% and 101.46%, respectively. The forced degradation products were well resolved from the main peaksuggesting the stability-indicating the power of the method. In conclusion, the AQbD-driven method is highly suitablefor analysis of CHL in bulk and pharmaceutical formulations.Key words:Cholecalciferol, analyticalquality by design, Taguchiorthogonal array design,Box–Behnken design, methodvalidation, forced degradationstudies.INTRODUCTIONDuring product development, quality assurance ofpharmaceutical molecules is a matter of great concern in thepharmaceutical industry. Analytical methods are critical elementsin product development due to their roles in assisting with processdevelopment and product quality control. Poor analytical methodscan lead to inaccurate results, resulting in misleading informationthat may be detrimental to the drug development program. Inan endeavor to address such plausible crucial issues, differentPharma regulatory agencies, such as International Council forHarmonisation (ICH) and U.S. Food and Drug Administration,have been transforming by adopting quality by design (QbD)Corresponding AuthorDilipkumar Suryawanshi, Department of Pharmaceutical Sciences andTechnology, Institute of Chemical Technology, UGC-CAS (Elite Status),Mumbai, India. E-mail: dilipraj14 @ gmail.com*principles to circumvent these quality crises. Recently, ICHhas announced new guideline ICH Q14 on analytical proceduredevelopment and revision of Q2 (R1) analytical Validation Q2(R2)/Q14 (ICH Assembly, Kobe, Japan, June 2018).The traditional liquid chromatographic methoddevelopment for any drug molecule was performed by a trial anderror approach, for example, by varying one-factor-at-a-time andexamines the resolution of the result until the best method wasfound. It is a time-consuming process and required a large amountof manual data interpretation. This approach requires typically someexperimental trials, and in some circumstances, the establishedmethod requires further modification in method or a supplementarypurification stage when scaled up, consequently slackening thedrug development process (Monks et al., 2011; Peraman et al.,2015). Moreover, this type of method development provides alimited understanding of a method’s capabilities and robustness.This can be overcome by applying QbD principles to the analyticalmethod development as it uses a statistical experimental design to 2019 Dilipkumar Suryawanshi et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International 0/).
022Suryawanshi et al. / Journal of Applied Pharmaceutical Science 9 (06); 2019: 021-032Figure 1. A complete flow layout of AQbD scheme.generate a “method operable design space” of a robust analyticalmethod (Peraman et al., 2015). The method operable design spaceoutlines the experimental operable region in which variations tomethod parameters will not considerably influence the quality andresults of the method. Therefore, it will be technically essential tounderstand if a method operable design space for variations in highperformance liquid chromatography (HPLC) method parameterscan be obtained to assist the development of a robust and ruggedanalytical method (Rozet et al. 2013). Various research scientistshave started to adopt the QbD principles and methodology tochromatographic analysis (Awotwe-Otoo et al., 2012; Bossuniaet al., 2017; Garg et al., 2015; Ganorkar et al., 2017; Panda et al.,2017; Thakur et al., 2017).Rational and systematic adoption of quality by design(QbD) elements to analytical method development to achieveoptimal method performance is termed as analytical QbD(AQbD) (Jayagopal and Shivashankar, 2017; Reid et al., 2013b).This approach guarantees the high quality and reliability ofthe analytical method and diminishes the risk of failure in thevalidation phase and routine practice. It is a scientific and riskbased approach for the understanding of the critical analyticalattributes (CAAs) and influential independent factors impactingthe method performance. Instituted on the doctrines of risksassessment and design of experiments, AQbD offers the in-depthknowledge about the possible risks and connected interactionsbetween the method variables (Borman et al., 2010; Jayagopaland Shivashankar, 2017; Karmarkar et al., 2011; Reid et al.,2013a; 2013b). Besides, AQbD helps in reducing and controllingthe source of variability to gain in-process information for takingcontrol decisions promptly. Figure 1 portrays a complete flowlayout of AQbD scheme.Cholecalciferol (CHL), renowned as vitamin D3, is themost widely prescribed drug for vitamin D3 deficiency. VitaminD3 deficiency is associated with osteoporosis and osteomalacia inadults and rickets in children (Holick and Chen, 2008). CHL playsa critical role in calcium and phosphorus homeostasis and skeletalmineralization (Gueli et al., 2012). In common medical practice,vitamin D3 deficiency is normally treated with CHL rangingfrom 400 to 1,000 IU/day. Recent studies of the physiologiceffects of vitamin D3 suggest its role in autoimmune diseases likecancers, type 1 diabetes mellitus, hypertension, multiple sclerosis,Alzheimer’s disease, and cognitive impairment (Marques et al.,2010). Chemically, it is (3β, 5Z, 7E)-9, 10-secocholesta-5,7,10(19)trien-3-ol. CHL exists as a white, odorless needle-like crystallinepowder, soluble in ethanol, benzene, acetone, chloroform, andfatty oils but practically insoluble in water. The log P of the drugsubstance is 10.24 at 20oC and pH 7. The molecular weight ofCHL is 384.64 g/mol and formula is C27H44O. Figure 2 depicts thechemical structure of the CHL.The USP analytical method is the only reliable methodfor CHL estimation, but suffers from various disadvantages ofhaving complicated, tedious, multiple extraction steps that makeit as time-consuming method. Also, the majority of publishedHPLC-UV methods of vitamin D3 have limited application as theyhave complex mobile phase composition, no stability indicatingcapability, longer retention time, i.e., more than 10 minutes andmostly followed by time consuming and complicated sampleFigure 2. Chemical structure of Cholecalciferol.
Suryawanshi et al. / Journal of Applied Pharmaceutical Science 9 (06); 2019: 021-032preparation for instance solid phase extraction or supercriticalfluid extraction (Al-Qadi et al., 2010; Gamiz-Gracia et al., 2000;Kienen et al., 2008; Klejdus et al., 2004; Kucukkolbasti et al.,2013; Luque-Garcia and de Castro, 2001; Moreno and Salvado,2000; Sarioglu et al., 2001). Moreover, scientific and risk-basedAQbD-oriented approach to reversed-phase HPLC (RP-HPLC)method development of CHL has not been widely discussed tilldate. Therefore, there is an unmet need for the development ofrobust, simple, and highly sensitive HPLC method of CHL usingAQbD principles to overcome the problems as mentioned aboveand to ensure the quality of the method throughout the materiallifecycle.In this research article, the ultimate goal of present workwas to develop simple, rapid, sensitive, robust, effective, andreliable stability-indicating HPLC method by applying AQbDprinciples and methodology for assessment of CHL in bulk drug andpharmaceutical drug products, i.e., CHL 400 IU mouth dissolvingtablets and marketed 60000 IU chewable (Tayo 60k) tablets.MATERIALS AND METHODSExperimentalChemicals and reagentsCHL (purity 99.7%, 40,000 IU/mg) was obtained asa gift sample from Fermenta Biotech Ltd, Mumbai, India; usedas a reference standard. HPLC grade acetonitrile (ACN) andmethanol (MeOH) were purchased from Avantor PerformanceMaterials India Ltd, Thane, India. The mobile phase was filteredusing 0.45-μm nylon membrane filters made by Pall India Pvt Ltd,Mumbai, India, was sonicated, and degassed using sonicator. Inhouse 400 IU vitamin D3 mouth dissolving tablets (MDTs) andmarketed cholecalciferol 60,000 IU chewable tablets (Tayo 60kmanufactured by Eris Lifesciences Pvt. Ltd.) were analyzed forassay by use of the established RP-HPLC method.Instrumentation and chromatographic conditionsThe HPLC method development of CHL was performedon Jasco AS-2055 plus (Tokyo, Japan) containing a systemcontroller, quaternary gradient pump, mobile phase degasser,autoinjector (injection volume ranging between 5 and 100 µl) andphotodiode array (PDA) detector. Chromatographic separation023was achieved on a reversed-phase C8 column, Eurosphere 1005 C8 (M/S KNAUER Wissenschaftliche Gerate GmbH, Berlin,Germany) with a dimension of 250 mm 4.6 mm and particle size5 µm, at the room temperature. Isocratic elution was employedwith ACN and MeOH (50:50, % v/v) as mobile phase and PDAdetection was carried out at 265 nm. Before the chromatographicanalysis of test solutions, the column was saturated with themobile phase for 60 minutes. The 50 µl of sample was injected forthe quantification of CHL. The run time of all the test samples waskept 10 minutes. The run time for forced degradation test sampleswas extended up to 20 minutes to estimate probable co-elutingdegradation products. The data acquisition, analysis, and storagewere performed by using Jasco ChromNAV software.Preparation of standard stock solutionThe stock solution of CHL was prepared by dissolvingaccurately weighed 25 mg of the drug in 50 ml of MeOH. Thedrug solution was sonicated to dissolve the drug, and then 1 mlof this stock solution transferred into 100 ml of amber coloredvolumetric flask, and it was diluted up to 100 ml with HPLCgrade MeOH (final concentration, 200 IU/ml, knowing that 1 IUof vitamin D3 0.025 µg). It was used for both screening andoptimization experiments. The working standard solutions of CHLwere prepared by subsequent dilutions of the stock solution. Theseries of dilutions were done using HPLC grade MeOH and filteredusing a 0.45 syringe filter. Then, these dilutions were transferred tovials before chromatographic analysis.Defining the method goals, i.e., analytical target profile (ATP)The AQbD-based methodology defines and proposesvital elements of ATP for the stepwise, scientific developmentof the analytical method. The method goals cover a possiblesummary of the quality features of the analytical method. Table 1depicts vital elements of ATP framework for obtaining an efficientHPLC method for CHL.Critical analytical attributes (CAAs)To achieve the anticipated ATP, various CAAs wererecognized and explored. These are peak area, retention time,theoretical plates, and peak tailing factor.Table 1. Vital elements of ATP for HPLC method of CHL.ATP elementsTargetJustificationTarget AnalyteCHLHPLC method development of CHL is needed for drug content analysis in pharmaceutical drugproducts and stability samples.Chromatographic modeRP-HPLCCommonly, RP-HPLC has the advantage of good retention of the lipophilic drug molecules asit consists of hydrophobic stationary phase. CHL exhibits high lipophilicity (log P 10.24). Thus,reverse-phase method would be more accurate and reliable for this purpose.Instrument necessityQuaternary pumpQuaternary Pump delivers a precise and efficient mixing of solvents ofmobile phase as compared to the binary pump.Sample stateLiquidIn RP-HPLC, analyte should be in a liquid state for its miscibility with mobile phaseStandard preparationStandard dilutions of CHLStandard dilution of the drug is generally prepared in MeOH for proper separationSample preparationHandling, weighing, sampling, admixing with solventsSample preparation is generally carried out by weighing the accurate quantity of CHL, mixingwith Sample solution to get a stock solution, followed by sonication and appropriate dilutions.Method applicationFor assay of CHLThe method has applicability for assay of CHL and its degradation product in bulk drugs andpharmaceutical drug products.
Suryawanshi et al. / Journal of Applied Pharmaceutical Science 9 (06); 2019: 021-032024Risk assessment studiesRisk assessment studies were performed to identifythe critical method parameters (CMPs), which are high-riskfactors and have a critical impact on the CAAs. In the riskassessment plan, Ishikawa fishbone diagram was constructed toidentify potential risk factors that may have an effect on methodperformance and corresponding causes. This could be methodfactors like extraction method, extraction time, extractionsolvent, etc. and instrumental settings such as chromatographicmode, mobile phase ratio, flow rate, injection volume, etc.From this, high-risk method variables were shortlisted basedon criticality and impact on the method CAAs and exposed tofurther analysis by applying suitable screening and experimentaloptimization design.Taguchi orthogonal array screening study designTaguchi orthogonal array (TOA) design is a multifactorialtwo-level design that can be applied for identification and controlof the main effect independent variables with a minimum numberof experiment runs from various suspected independent factors(Dash et al., 2016; Sahu et al., 2017). Therefore, this experimentaldesign was generally employed for identification of independentfactors that could be fixed or eliminated in further study.The TOA design was employed in this study forscreening studies to recognize the CMPs censoriously influencingthe method CAAs using the following polynomial model as shownin the following equation:.Y A0 A1X1 A2X2 A3X3 A4X4 A5X5 A6X6 A7X7 (1)where, Y is the response variable, A0 is the constant, andA1, A2, A3, A4, A5, A6, and A7 are the regression coefficients of theindependent factors.Table 2 represents the TOA design layout enlisting thedifferent factors with respective low ( 1) and high levels ( 1) andtheir studied responses (theoretical plates and peak tailing factor).Standard Pareto charts were drawn to illustrate the effect of eachindependent factor on the specified responses. Then, the criticalfactors were recognized and further employed for Box–Behnkendesign.Box–Behnken optimization study designThe optimization of chromatographic conditions wasperformed by employing three factors, three levels Box–Behnkendesign to estimate the main, interaction and quadratic effectsof critical factors on the specified response variables (Ahmadet al., 2016; Beg et al., 2012; Ferreira et al., 2007; Sahu et al.,2015; 2017; Wani and Patil, 2017). In the present study, the Box–Behnken experimental design, comprising 15 experiment runswith 12 factorial points and three center points, was employed toget design space for attaining the desired ATP. The polynomialquadratic equation is generated by this design as shown in thefollowing equation:Y B0 B1X1 B2X2 B3X3 B12X1X2 B23X2X3 B13X1X3 B11X12 B22X22 B33X32 (2)where Y is the response variable, B0 is the constant, andB1, B2, and B3 are the regression coefficients of the linear termsof X1, X2, and X3, respectively. B12, B23, and B13 are the regressioncoefficients for the interaction terms of X1X2, X2X3, and X1X3,Table 2. Chromatographic factors and response variables for Taguchi experimental design.Low level ( 1)High level ( 1)40:6060:40X2: Flow rate (ml/minutes)0.81.2X3: Injection volume (µl)1030IsocraticGradientIndependent factorsX1: Mobile Phase ratio (% v/v)X4: Mode of FlowX5: Column typeC8C18X6: Column length (mm)150250X7: Column temperature ( C)2530Dependent factors (responses)Y1: Theoretical platesY2: Peak tailing factorTaguchi design layout (seven-factor eight-run)RunsX1X2X3X4X5X6X71 1 1 1 1 1 1 12 1 1 1 1 1 1 13 1 1 1 1 1 1 14 1 1 1 1 1 1 15 1 1 1 1 1 1 16 1 1 1 1 1 1 17 1 1 1 1 1 1 18 1 1 1 1 1 1 1
Suryawanshi et al. / Journal of Applied Pharmaceutical Science 9 (06); 2019: 021-032respectively. B11, B22, and B33 are the regression coefficients for thesquared terms of X12, X22, and X32, respectively.The independent variables selected were mobile phase[ACN:MeOH] ratio (X1), flow rate (X2), and injection volume(X3), whereas peak area (Y1), retention time (Y2), theoretical plates(Y3), and peak tailing factor (Y4) were selected as the dependentresponses. The Box–Behnken optimization study design layout isshown in Table 3.Analytical method validationThe optimized chromatographic method wasvalidated as per the ICH Q2 (R1) guidelines for specificity,linearity, accuracy, precision, limit of detection (LOD), limitof quantitation (LOQ), robustness, and ruggedness (GuidelineICH, 2005).Forced degradation studiesForced degradation of CHL was executed to deliver asign of the stability indicating properties and specificity of theestablished method (Blessy et al., 2014; Krishna et al., 2016).PDA detection was employed to analyze the purity of degradedtest samples. The stress conditions used for the degradationstudy included acid hydrolysis (1 N HCl), base hydrolysis(1 N NaOH), All the test samples were filtered using a 0.45 μm025nylon membrane filter and analyzed to estimate the percentdegradation of CHL.Application of the analytical method for analysis of CHL intablet dosage formThe established and validated analytical method for CHLwas applied for its determination in in-house CHL 400 IU mouthdissolving tablets (50% overages) and marketed 60,000 IU (Tayo60k) chewable tablets. Vitamin D3 MDTs (50% overages) weremanufactured by blending stabilized CHL (100 IU/mg) with otherexcipients by direct compression technique. To determine thecontent of CHL in the developed MDTs and marketed chewabletablets, 20 tablets were weighed and finely powdered withthe help of mortar and pestle. The required quantity of powderwas accurately weighed and transferred to a volumetric flaskcontaining HPLC grade MeOH and sonicated for 30 minutes, forcomplete extraction of the drug to take place.Finally, this preparedtest sample was filtered through 0.45 μm nylon membrane beforeusing it for analysis. The analysis was performed in five replicates.RESULT AND DISCUSSIONPreliminary method development studiesThe preliminary studies were performed accordingto previously reported literature for the development of HPLCTable 3. Chromatographic factors and response variables for Box–Behnken optimization design.Low level ( 1)Medium level (0)High level ( 1)Independent factorsX1: Mobile Phase ratio (% v/v)40:6050:5060:40X2: Flow rate (ml/minutes)0.81.01.2X3: Injection volume (µl)102030Dependent factors (responses)Y1: Peak areaY2: Retention times (minutes)Y3: Theoretical platesY4: Peak tailing factorBox–Behnken optimization design layoutRunCoded level pattern (X1 X2 X3)X1: Mobile phaseratio (% v/v)X2: Flow rate(ml/minutes)X3: InjectionVolume (µl)10 1 150:500.8302 1 1060:401.220300050:501204 1 1040:601.2205 10 140:601106 10 160:401307 1 1040:600.8208 1 1060:400.820900050:5012010 10 160:40110110 1 150:501.23012 10 140:601301300050:50120140 1 150:500.810150 1 150:501.210
Suryawanshi et al. / Journal of Applied Pharmaceutical Science 9 (06); 2019: 021-032026Figure 3. Ishikawa Fish-bone diagram illustrating the influence of possible factors on CAAs of analytical method of CHL.method for the estimation of CHL in pharmaceutical dosage forms.The RP-HPLC method was successfully employed for evaluationof CHL. First, several combinations of mobile phase were triedby using ACN and MeOH at a variable flow rate between 0.8and 1.2 ml/minutes. From preliminary studies, it was found thatthe selection of ACN and MeOH as a mobile phase compositionshowed excellent chromatographic resolution with low peaktailing factor.Risk assessment studiesRisk assessment studies as per ICH Q9 guidelines wereperformed with an objective to get all the possible high impactfactors which will be subjected to the design of experimentstudy to establish method operable design region. Ishikawa fishbone was used for risk identification and risk assessment. Figure3 depicts the effect of possible key factors such as method &material attributes, environmental factors, operator, instrumentrequirements, and measurement & data analysis affecting onmethod performance. It illustrates the cause and effect relationshipbetween method parameters and CAAs of the analytical methodof CHL. Risk assessment studies identified seven high potentialrisk factors such as X1: mobile phase ratio (% v/v), X2: flowrate (ml/minutes), X3: injection volume (µl), X4: mode of flow,X5: column type, X6: column dimension (mm), and X7: columntemperature ( C). These factors have potential impact on criticalanalytical attributes (CAAs), i.e., theoretical plates (Y1) and peaktailing factor (Y2). These seven factors would be used for furtherscreening study to get the major factors affecting selected CAAsby Taguchi orthogonal array (TOA) design.of seven independent factors on selected CAAs. Figure 4 showsthe standard Pareto charts illustrating the impact of methodparameters on the CAAs of the method. The standard Paretoranking analysis presented that the factors, such as mobilephase ratio, flow rate, and injection volume, had a significantimpact on method CAAs. Hence, these factors were selectedas CMPs for further analytical optimization study employingBox–Behnken design.The standard Pareto charts were derivative of multivariateregression analysis and the length of each bar in the Pareto chart isequal to the magnitude of the regression coefficient of that factor.It was observed that little change in mobile phase ratio, flow rate,and injection volume resulted in a pronounced change in CAAs.Henceforth, these factors needed to be strictly controlled while theeffect of mode of flow, column type, column length, and columntemperature were found to be statistically insignificant. Basedon the desirability function of Taguchi screening design, mobilephase ratio, flow rate, and injection volume were optimized furtherby Box–Behnken optimization design to detect main, interactionand quadratic effects of these factors on peak area, retention time,theoretical plates, and peak tailing factor.Taguchi orthogonal array screening study designIdeally, screening designs are applied when numerousindependent factors expected to have an impact on a specificresponse. The objective of this study was to identify the mostsignificant factors influencing the CAAs by using TOA screeningstudy design. TOA design was used to estimate the main effectsFigure 4. Standard Pareto charts showing effects of independent variables onanalytical method CAAs. (A) Theoretical plates and (B) peak tailing factor ofCHL during the screening.
Suryawanshi et al. / Journal of Applied Pharmaceutical Science 9 (06); 2019: 021-032Box–Behnken optimization study designThis study aimed at detecting the main, interactions andquadratic effects of mobile phase ratio, flow rate, and injectionvolume on peak area (Y1), retention time (Y2), theoretical plates(Y3), and peak tailing factor (Y4). The 15 experimental runs wereperformed, and obtained results were statistically analyzed usingDesign expert software version 7.0.0 (Stat-Ease Inc., Minneapolis,MN). The software performs response surface methodology,which includes the multiple regression analysis (MRA), analysisof variance (ANOVA), and statistical optimization. The classicalpolynomial quadratic equation in terms of coded factors for eachselected CAAs estimating regression coefficients are shown in thefollowing equations:Peak area (Y1) 1 1744.33 319.75X1 14.88X2 226.88X3 44.00X1X2 95.50X2X3 204.25X1X3 3260.04X12 3059.79X22 3375.29X32 (3)Retention time (Y2) 5 .03 0.088X1 0.11X2 0.025X3 0.100X1X2 0.025X2X3 0.025X1X3 0.092X12 0.49X22 0.32X32 (4)Theoretical plates (Y3) 11442.00 500.12X1 34.13X 88.50X3 50.25X1X2 124.00X2X3 70.50X1X3 2164.13X12 1249.62X22 1279.38X32 (5)Peak tailing factor (Y4) 1 .00 0.094X1 0.059X2 0.023X3 0.045X1X2 0.052X2X3 0.052X1X3 0.22X12 0.17X22 0.19X32 (6)The ANOVA results for each CAAs were shown inTable 4. The ANOVA with its significance method for all CAAsproves that the relationship between response and variablesis statistically significant (p 0.05). The value of correlationcoefficient (R2) for all CAAs indicates a perfect fit of the model.This implies that the model is valid. The adjusted R-squared was amore valuable marker of the variation in response variables, whilepredicted R-squared indicated how well the model could predictfuture data, relatively high values of adjusted and predictedR-squared inferred that the applied statistical model effectivelypredicted the response. The main effects (B1, B2, and B3) signifythe average response of varying one factor at a time from its lowto high level. The interaction term (B12, B23, and B13) shows howthe response changes when two factors are concurrently altered.The polynomial quadratic terms (B11, B22, and B33) were addedto examine nonlinearity. The Polynomial quadratic equationswere employed to conclude after considering the magnitude ofcoefficients and the mathematical sign it carries, i.e., positive ornegative.From Equation (3), it was observed that factors X1 andX2 have a negative effect, while X3 has a positive effect on thepeak area. Negative value coefficients of X1 and X2 factor indicatethat peak area increases with a decrease in the mobile phase ratioand flow rate, whereas positive sign of coefficients of X3 termsindicates that low to medium level of injection volume favors theincreased peak area. When the coefficient values of independentkey variables (X1, X2, and X3) compared, the coefficient value ofvariable X3 (226.88) was found to be higher, and hence injectionvolume was considered to be a major contributing factor forincredible effect on peak area (Y1).From Equation (4), variation in mobile phase ratiosignificantly affects the retention time as the coefficient value ofX1 was found to be maximum among all independent factors. Thepolynomial equation for retention time suggests that factor X1 hasa positive effect on Y2, up to a particular concentration. After aparticular concentration factor, X1 has a negative effect on Y2 asindicated by the negative sign of the coefficient of X1.Equation (5) showed that the positive value ofcoefficients of X1 and X3 factor indicates that theoretical platesincrease with the increase in the mobile phase ratio and injectionvolume up to medium level. After a medium level factor, X1 andX3 have a negative effect on Y3 as indicated by the negative signof the coefficient of X1 and X3. Mobile phase ratio was consideredto be an influential factor to have a major impact on theoreticalplates as the coefficient value of variable X1 was found to behigher.From Equation (6), it was observed that the negative signof coefficients of X1, X2, and X3 terms indicates that low to mediumlevel of independent factors favors the lesser tailing factor. Frommedium to high level of factors displays increased peak tailingfactor. As compared to the magnitude of all factor coefficients,injection volume showed the influential impact on peak tailingfactor, while the flow rate was found to have relatively less impacton the peak tailing factor. Minimum values were observed at theintermediate level.Table 4. ANOVA results for each CAAs.Y1: Peak areaY2: Retention timeY3: Theoretical platesY4: Peak tailing factorR-squared0.99650.97280.96980.9588Adjusted R-squared0.99010.92370.91560.9145Predicted R-squared0.95410.89780.88550.8986Standard deviationANOVA .97PRESS4.697E 0060.547.301E 0060.33F-value156.9219.8317.8712.92p-value 0.00010.00210.00270.0058R-squared Coefficient of determination.F-value Value on the F distribution.p-value Probability of falsely detecting a significant effect.PRESS Predicted errors sum of squares.C.V.% Percent Coefficients of variance.027
028Suryawanshi et al. / Journal of Applied Pharmaceutical Science 9 (06); 2019: 021-032Three-dimensional surface and 2-D contour plotswere also analyzed to define design space and to visualizethe effect of independent factors and their interactions on theconcerned response variables. Since the proposed model hasmore than two independent factors, one factor was kept constantfor each plot. These plots were found to agree with MRA andANOVA parameters. Figure 5 portrays 3D surface plot, and it’scorresponding 2D contour plot depicting the effects of mobilephase ratio, flow rate and injection volume on peak area,
Development and validation of a stability-indicating RP-HPLC method of cholecalciferol in bulk and pharmaceutical formulations: . understand if a method operable design space for variations in high-performance liquid chromatography (HPLC) method parameters can be obtained to assist the development of a robust and rugged analytical method .
Cleaning validation Process validation Analytical method validation Computer system validation Similarly, the activity of qualifying systems and . Keywords: Process validation, validation protocol, pharmaceutical process control. Nitish Maini*, Saroj Jain, Satish ABSTRACTABSTRACT Sardana Hindu College of Pharmacy, J. Adv. Pharm. Edu. & Res.
Pharmaceutical Engineers (ISPE) GAMP 5. Our validation service is executed in accordance with GxP standards producing a validation library that features the following documents: Validation and Compliance Plan The Validation and Compliance Plan (VCP) defines the methodology, deliverables, and responsibilities for the validation of Qualer.
heard. These goals relate closely to the Validation principles. Validation Principles and Group Work The following eleven axioms are the Validation Principles as revised in 2007. I have tried to find various ways of incorporating the principles into teaching Group Validation and by doing so, anchoring group work to theory. 1.
Validation of standardized methods (ISO 17468) described the rules for validation or re-validation of standardized (ISO or CEN) methods. Based on principles described in ISO 16140-2. -Single lab validation . describes the validation against a reference method or without a reference method using a classical approach or a factorial design approach.
Dipl.-Ing. Becker EN ISO 13849-1 validation EN ISO 13849-2: Validation START Design consideration validation-plan validation-principles documents criteria for fault exclusions faults-lists testing is the testing complete? Validation record end 05/28/13 Seite 4 Analysis category 2,3,4 all
Analytical Method Development and Validation of Bendamustine in Bulk Using RP-HPLC J Pharm Res Analytical Method Development and Validation of Bendamustine in Bulk Using RP-HPLC . Table 3: Variables in HPLC.-Hplc Method Validation is a key process for effective quality assurance. "Validation" is established documented .
contents of the tool box" used for validation. The methods and techniques listed in the report are grouped as - review - models - analysis - dynamic methods - methods regarding formality - development methods The validation methods have to be combined together in a validation plan. The plan shall list requirements and validation methods.
contents of the tool box" used for validation. The methods and techniques listed in the report are grouped as - review - models - analysis - dynamic methods - methods regarding formality - development methods The validation methods have to be combined together in a validation plan. The plan shall list requirements and validation methods.
