Research Articles

2017  |  Vol: 2(6)  |  Issue: 6 (November-December)
Implementation of statistical tools and novel approaches to develop and validate the method for the estimation of pazopanib in bulk, tablets and fabricated Nano-sized formulation

Sameer J. Nadaf1,2, Suresh G. Killedar1*

1Bharati Vidyapeeth College of Pharmacy, Near Chitranagari, Kolhapur-416013, Maharashtra, India.

2Adarsh College of Pharmacy, Bhavaninagar, Vita-415311, Maharashtra, India.

*Address for correspondence

Dr. Suresh G. Killedar

Bharati Vidyapeeth College of Pharmacy, Near Chitranagari, Kolhapur-416013, Maharashtra, India.

Phone: +91-231-2637286


Background: Spectrophotometry states the material properties (reflection or transmission) as function of wavelength. Objectives: Aim of current work is to develop a simple, rapid, and cost effective spectrophotometric method for the determination of pazopanib in fabricated nano-formulation and marketed formulation. Materials and methods: Methanol was optimized as solvent for pazopanib and further spectrophotometric detection was carried at analytical wavelength i.e. 214 nm. The method was further validated as per International conference on Harmonisation (ICH) guidelines for linearity, specificity, accuracy, precision, ruggedness and robustness. Results: The concentration of pazopanib over range of 1-08 µg/ml obeys Beers law (R2= 0.9996). Percent Recovery estimated was found to be 99.29 ± 0.23. The limit of detection (LOD) and limit of quantification (LOQ) was found to be 0.139 μg/ml and 0.464 μg/ml respectively. Statistical analysis showed high accuracy and good precision of proposed method. Shapiro-Wilk test (P=0.5473) and Kolmogorov-Smirnov test (D=0.1212; P>0.10) accepted the normality of data. Bland-Altman plot demonstrated a satisfactory repeatability coefficient. Youden Plot demonstrated the exceptional inter-laboratory reproducibility and it was used to identify random and total errors. Control charts like Levey-Jennings chart, X- chart showed that method is under statistical control. CUSUM chart revealed about targetability of the method. Capability analysis demonstrated greater values of Cp (1.93) and Cpk (1.75) than 1, indicating the capability of method to analyze the samples accurately and consistently with minimum variation. Conclusion: Validation report assured that common excipients from commercial formulations don’t interfere with the proposed method, hence can be applied in regular laboratory analysis.

Keywords: Pazopanib, Accuracy, Precision, UV-method development, Validation


Pazopanib, (figure 1) a Kinase Inhibitor is chemically designated as known as 5-[[4-[2, 3-dimethyl-2H-indazol-6yl) methylamino]-2-pyrimidinyl] amino]-2-methyl benzene sulfonamide monochloride (Sharada and Babu, 2016). It is orally administered multi-target drug and inhibits vascular endothelial growth factor receptors (VEGFR)-1, -2 and -3, and platelet derived growth factor receptor (PDGF-R) that inhibits angiogenesis and cell proliferation leading to potential antineoplastic activity (Pulla et al., 2015; Gorja et al., 2015). It has been recommended for renal cell carcinoma soft tissue sarcoma, and Solitary Fibrous Tumour (Cella and Beaumont, 2016; FDA, 2009; Sleijfer et al., 2009; Kawai et al., 2017; Maruzzo et al., 2015; Manasa et al., 2013).

Figure 1. Structure of Pazopanib


Literature survey reveals some spectrophotometric methods (Manasa et al., 2013; Minocha et al., 2012; Chaitanya and Pawar, 2015), chromatographic methods like HPLC (Sharada and Babu, 2016; Pulla et al., 2015; Gorja et al., 2015) for the estimation of pazopanib in bulk, pharmaceutical dosage form and biological fluids (Yang et al., 2010; Pratap et al., 2013). Even afterwards countless developments and recent novel technologies, spectrophotometry remains to be very fecund, due to its ease, specificity and little cost amongst all sophisticated techniques available for the determination of Active Pharmaceutical Ingredients (APIs) in pharmaceutical dosage forms and biological fluids (Iftequar et al., 2012).Still ample researchers are working on estimation of APIs in bulk as well as various dosage forms using different appropriate spectroscopic or chromatographic techniques. Although there is advancement in the field of statistics, very few researchers and academic professionals are validating and analyzing the data statistically. In this study an attempt has been made to implement the various statistical tools and approaches during development and validation of method, to compare inter-laboratory data using novel statistical techniques like, Bland-Altman plot, Youden plot and different control charts.

Materials and methods


Pazopanib was obtained as gift sample. Methanol used was of analytical grade and purchased from Merk Chemicals, India. All other chemicals and reagents used were of analytical grade. Water used for the study was of double distilled grade.

Method development


A Shimadzu UV–visible spectrophotometer (UV mini-1700, Shimadzu Corporation, Kyoto, Japan) was used for all absorbance measurements with matched quartz cells.

Selection and optimization of solvent

Different solvents like DMSO, Acetone and Methanol were screened for solubility of pazopanib. From all the conditions based on peak quality and non-interference at the specified wavelength methanol was optimized as solvent.

Preparation of standard stock solution

Standard stock solution containing 100 μg/ml of pazopanib was prepared by initially dissolving accurately weighed 10 mg of pazopanib in 50 ml of methanol, followed by sonication for 10 minutes and the final volume of solution was made up to 100 ml with methanol.

Selection of wavelength

The wavelength at which maximum absorption takes place is selected for further analysis. Selection of wavelength was carried out by transferring appropriate volume of 1 ml of standard stock solution into 10 ml volumetric flask, diluted to mark with methanol to give concentration of 10 μg/ml. The resulting Solution was scanned in range of 200-400 nm. The wavelength showing maximum absorption is selected for further analysis. In spectrum pazopanib showed absorbance maximum at 214 nm (Figure 2).

Validation of method

The method was validated in terms of linearity, accuracy, precision and ruggedness as per ICH guidelines.

Linearity study

Aliquots of 1–8 ml portion of stock solutions were transferred into series of 10 ml volumetric flasks and further volume was made up to mark with methanol, to get concentrations 1- 8 μg/ml respectively. All the Solutions were scanned in the range of 200–400 nm against blank. The absorption maxima were found to be at 214 nm against blank. Further calibration curve was plotted using the data.


To the preanalyzed 4 μg/ml pazopanib solutions, a known amount of standard pazopanib was added at different levels, i.e. 80%, 100% and 120%. Solutions were reanalysed by the proposed method. Three samples were prepared for each recovery level. The accuracy was reported as % recovery.


Precision of the method was studied as intra-day (Repeatability) and inter-day (Intermediate Precision) variations. Intra-day precision was determined by analyzing 3, 5, and 7 μg/ml of pazopanib solution for three times in the same day. Inter-day precision was determined by analyzing 3, 5, and 7 μg/ml of pazopanib solution for three days.


The sensitivity of the proposed method was estimated in terms of the limit of detection (LOD) and limit of quantitation (LOQ). The LOD and LOQ were calculated using equations,

LOD= 3Sa/b                                         Eq. 1

LOQ= 10Sa/                                     Eq. 2

Where, Sa is the standard deviation of the response and b is the slope of the corresponding calibration curve (Shrivastava and Gupta, 2011). Percent relative standard deviation, standard deviation was reported for each set of data.


Ruggedness of the proposed method was determined by analyzing 4 µg/ml concentration of pazopanib by two different analysts under similar operational and environmental conditions (Jadhav et al., 2014).


Robustness of the proposed method was also determined by changing the λ max of the analysis by ± 2.0 nm. Percent mean recovery as well as percent relative error was reported (Navgire et al., 2016).

Statistical analysis of proposed method

Normality of the data and outlier detection

Most of the statistical tests mainly parametric tests rest upon the assumption of normality (Ghasemi and Zahediasl, 2012). Hence it is vital to see whether data is normal or non-normal. Normality of data is assayed by normal quantile-quantile plot (Q-Q plot) in which normal score of the observations plotted against expected normal score. Shapiro-Wilk test and Shapiro-Francia test for normal distribution is also applied (Ghasemi and Zahediasl, 2012; Elliott and Woodward, 2007).

Shape of the distribution, its central value, its variability and outlier detection is determined by box and whisker plot (Doane and Lori, 2011). Variability in a data set is given by the interquartile range (Q3 – Q1), i.e. the difference between the 75th percentile and the 25th percentile.

Coefficient of Repeatability by Bland-Altman plot

The Bland-Altman plot is a graphical technique used to investigating the agreement between two measurements techniques intended to measure the same parameter (Bland and Altman, 1986a, 1999b). It is used to look for any systematic bias and to identify possible outliers. 

In this study we have used Bland-Altman plot to compare repeated measurements obtained using one single method on a series of subjects in order to evaluate the repeatability or precision of a method. Therefore the Coefficient of Repeatability (CR) can be calculated as 2 times the standard deviation of the differences between the two measurements (D2 and D1) (Bland and Altman, 1986). Study was performed using sample of known concentration (7 µg/ml).

 .......................................................Eq. 3

Reproducibility using the Youden plot

The Youden plots can be used to study and compare inter-laboratories data obtained using an analytical method (Heath et al., 2016). In this work two samples, comparable and reasonably close in the magnitude are analyzed in four different laboratories using proposed method. From the data obtained youden plot is constructed. To perform this study two samples of known concentrations (7µg/ml) was prepared. Youden plot and other analysis are performed using MedCalc Statistical Software version 17.8 (MedCalc Software bvba, Ostend, Belgium)

Statistical control of proposed method

Quality control of data is studied using control charts.  In this study control charts are used to check the ability of the analytical method to meet the set requirements. It is useful to detect variations from statistical control (Masson, 2007; Schafer et al., 2011).

Zone test

Zone test is performed in order to determine whether process in influencing by variables or not. For this study control charts are divided into Zone A, B and C (figure 8F). Each zone is one standard deviation in width (

Capability analysis of proposed method

Capability analysis is used to assess whether a method is statistically able to meet a set of predetermined specifications/requirements or not (Koppel and Chang, 2016). In order to perform capability analysis sample of known concentration is prepared and tested using proposed method. Lower specification limit (LSL), Nominal value and upper specification limit (USL) was set at 6.97, 6.99 and 7.01 respectively.

Process capability (Cp) is calculated by,

 ...............................................................Eq. 4

Process capability index (Cpk) is calculated by,

  .........................Eq. 5

Cp and Cpk should be greater than 1. Capability analysis should be accomplished only after it has been brought under statistical control. It is performed using SPC for excel (Version 5;BPI Consulting, LLC).

Application of the proposed method for pharmaceutical formulation

From pazopanib tablet, powder equivalent to 10 mg of pazopanib was weighed accurately and transferred into 100 ml volumetric flask. Further 50 ml of methanol was added and resulting solution was sonicated for 15 min to facilitate extraction of the drug from the powder. Subsequently volume was made up to 100 ml and solution was filtered through the Whattman filter paper No. 41. The resulting filtrate was further diluted to get the solution of 10 μg/ml concentration and analyzed for drug content determination against blank using proposed method. The drug content of the preparation was calculated using standard calibration curve (Navgire et al., 2016).

Application of the method to the fabricated Nano-formulation

Amount of pazopanib encapsulated in prepared nanoparticles was estimated by measuring the free pazopanib in the nanoformulations. Powdered nanoformulation was extracted using methanol and sonicated for 15 min and volume was made up to 100 ml. The resulting solution was centrifuged at 2500 rpm for10 min and supernatant was analyzed for drug content (Hazra et al., 2015).

Results and discussion

Method validation

The proposed method was validated as per the ICH guidelines (Q2 (R1)). In spectrum pazopanib showed absorbance maximum at 214 nm (Figure 2).

Figure 2. UV Spectra of pazopanib showing lmax at 214 nm



Linearity study

The linear regression data for the calibration curves has shown linear relationship over the concentration range of 01- 08 μg/ml (Figure 3). Linear regression equation was found to be Y = 0.1945x + 0.056 (R² = 0.9996). Absorbance of the solutions of different concentration is depicted in table 1 and result of regression analysis in table 2.

Table 1. Linearity Study of Pazopanib

Sr. No.

Concentration (µg/ml)


























Table 2. Regression Analysis of the data

Dependent variable –Y


Independent variable – X

Concentration (µg/ml)

Least squares regression

Sample size


Coefficient of determination R2


Residual standard deviation


Regression Equation

y = 0.05602 + 0.1945x



Std. Error

95% CI






0.03590 to 0.07614






0.1905 to 0.1985



Analysis of Variance



Sum of Squares

Mean Square











Significance level



Shapiro-Wilk test for Normal distribution

accept Normality (P=0.5473)

Figure 3. Calibration Curve of pazopanib at different concentrations



Accuracy of an analytical method is the closeness of test results to true value and studied by recovery experiments (Jadhav et al., 2014; Navgire et al., 2016). As reported in table 3, the % recovery for the analysis and all the three concentration levels ranged from 99.13% to 99.24% with % RSD from 0.35 to 0.78. This specifies that any minute change in the drug concentration can be correctly determined with high accuracy. Recovery studies demonstrated the reliability of proposed method in routine analytical application.

Table 3. Summary of Recovery Study

Reanalyzed sample

Level of Recovery (%)

Amount of drug added (μg/ml)

Amount of drug detected (μg/ml)

% Recovery


4 μg/ml



3.172 ± 0.018





3.973 ± 0.020





4.763 ±0.037



* Indicates ± SD (n=3)


The precision of an analytical method expresses the degree of scatter between a series of measurements obtained from multiple sampling of the same homogeneous sample under prescribed conditions (Navgire et al., 2016). Intra-day precision refers to the use of analytical procedure within a laboratory over a short period of time using the same operator with the same equipment whereas Inter-day precision involves estimation of variations in analysis when a method is used within a laboratory by different analysts on different days. Repeatability (intraday) was assessed by analyzing these three different Concentrations (3.0, 5.0, 7.0 μg/ml), three times a day. Intermediate precision (Interday) was established by analyzing these three different concentrations (3.0, 5.0, 7.0 μg/ml), three times a day for at least three different days. Intraday and Interday precision showed >99% recovery. Detailed result is reported in table 4 and table 5.

Table 4. Summary of Intra -Day Precision Study

Conc. (μg/ml)

Absorbance 1

Absorbance 2

Absorbance 3

Absorbance (Mean ± SD)

Conc. Found (μg/ml)


% Recovery





0.591 ± 0.0030








0.989 ± 0.0061








1.358 ± 0.0081




* Indicates ± SD (n=3)

Table 5. Summary of Inter-Day Precision Study*



Amount of drug detected (μg/ml)


% Recovery

Day 1

Day 2

Day 3

(Mean ± SD)





2.979 ± 0.0060


99.298± 0.2314





5.004 ± 0.0303


100.08 ± 0.7442





6.936 ± 0.0139


99.085± 0.1999

*Indicates ± SD (n=3)


LOD is the lowest analyte concentration that can be detected at a specified level of confidence, so it is greater than limit of blank. Whereas, the LOQ is the limit at which the difference between two distinct values can be reasonably distinguished (Armbruster and Pry, 2008). The LOD and LOQ for pazopanib were found to be 0.139 μg/ml and 0.464 μg/ml.


Amount of pazopanib recuperated by two analysts applying proposed method and working on same instrument are depicted in Table 6. The result showed that the % R.S.D. was less than 2.

Table 6. Summary of Ruggedness Studies*

Conc. (μg/ml)


Amount detected (μg/ml)

Amount detected (%)



Analyst 1

3.971 ± 0.0051



Analyst 2

3.980 ± 0.0047



*Indicates ± SD (n=3)


Robustness of projected method was studied by checking the influence of small deviations of wavelength. The outcomes achieved after alteration of wavelength were not dissimilar (Table 7). These alterations of wavelength do not affect the assay of pazopanib, henceforward the proposed method could be considered robust.

Table 7. Summary of Robustness Studies

Conc. (μg/ml)

Wavelength (nm)

Amount detected (μg/ml)

Amount detected (%)











Statistical analysis of proposed method

Normality of the data and outlier detection

The normal Q-Q plot is shown in figure 4, demonstrating the normality of data. Values of Coefficient of Skewness and Coefficient of Kurtosis was found to be -0.2994 (P=0.5042) and -0.8636 (P=0.2598) respectively. Remarkably, Shapiro-Wilk test (W=0.9508; P=0.2818) and Shapiro-Francia test (W'=0.9688; P=0.5494) accept the normality of data.

Box and Whisker plot (Figure 5) showed that the concentration determined is skewed little right. The part of the box to the left of the median is slightly longer than the part to the right of the median. Figure reveals the descriptive statistics of the data and confirms the right skewness of the data. The median concentration (6.9940) is closer to mean concentration (7.00). Interquartile range was found to be 0.008 indicating extremely less variability in the data set. This means that, from group of samples whose concentrations were closest to the median, half of them were within 0.008 µg of each other when they analyzed using proposed method. No outlier was detected, confirmed by Generalized ESD test (α = 0.05) and Grubbs - left-sided test (α = 0.05).

Figure 4. Q-Q Plot showing Goodness of fit



Figure 5. Box and Whisker Plot with descriptive statistics



Repeatability coefficient by Bland-Altman plot

The graph displays a scatter diagram of the differences plotted against the averages of the two measurements. Horizontal lines are drawn at the mean difference, and at the limits of agreement (LOA) which is defined as the mean difference ± 1.96 SD of differences.  Coefficient of Repeatability was found to be 59.411. Noteworthy, 95 % confidence intervals of LOA do not exceed the maximum allowed difference between runs, demonstrating the closeness of the results (Figure 6). Henceforth the developed method can be used to perform the routine analysis of samples repeatedly (Vaz et al., 2013; Fujimura et al., 2013).

Figure 6. The Bland-Altman plot for repetitive measurements for same method



Reproducibility using the Youden plot

Youden plot, depicted in figure 7A and 7B is constructed by plotting response variable 1 on vertical axis: (i.e., run 1) or response variable 2 on horizontal axis: (i.e., run 2). Each point in the graph corresponds to result of run 1 and run 2 of one laboratory. Points that lie near the 45-degree reference line but far from the Manhattan median (intersecting of two medians) indicate large systematic error. Points that lie far from the 45-degree line indicate large random error (Karkalousos and Evangelopoulos, 2011).

Figure 7A. Youden plot for inter-laboratories data. Rectangles represent 1, 2 and 3 SD.



Figure 7B. Youden plot for inter-laboratories data. 1, 2, 3 and 4 indicated different laboratories.



Statistical control of proposed method

The control charts are the statistical approach which shows picture of a process over time and can be used to study the analytical process for improving its precision. Different quality control chart are depicted in figure 8.

In Levey-Jennings chart the distance from the mean is measured in standard deviations (SD). Upper control limit (UCL) and lower control limits (LCL) along with target value which is generated in a graph helps to determine the outliers and give a visual indication whether a laboratory method is working well or not. As shown in figure 8C the Levey-Jennings charts for the process shows that most points are near the average and no points are beyond the control limits, indicative of absence of special cause variation in the process. Hence the process is under statistical control (Karkalousos and Evangelopoulos, 2011; Levey and Jennings, 1950). XmR (individual and moving range) charts also support this, see figure 8A and 8B (Spath et al., 2001).

In the present study CUSUM control chart is used to verify process targetability. Target was fixed by analyzing known concentration sample i.e. 7 µg/ml. The CUSUM chart is plotted with center line indicating zero along with both the cumulative sums on the high side (SH) and lower side (SL). From the graph (Figure 8D) it is observed that cumulative sums on the high side decreased for samples analyzed on 4th day. However, process doesn’t get beyond UCL or LCL. Conclusively, although the control charts indicated the process in under statistical control, CUSUM chart depicted the drifting of process off-target for few samples (Adeoti, 2013).

Zone test

Each zone is one standard deviation in width. Region between the average and average plus one standard deviation is denoted as zone C. Region between the average plus one standard deviation and the average plus two standard deviations is denoted as Zone B. Whereas, region between the average plus two standard deviations and the average plus three standard deviations is denoted as Zone A.

From the figure 8F it was observed that two out of three consecutive points does not fall in zone A or beyond, four out five consecutive points does not fall in zone B or beyond and notably seven consecutive points does not fall in zone C or beyond. It means that no special cause variation is present and process in under statistical control (Benneyan, 1998; Roberts 1958).

Figure 8. Control charts; A) X-Individual chart, B) MR (Moving range) chart, C) Levey Jennings chart, D) CUSUM chart, E) EWMA chart and F) Zone test using control chart



Capability analysis of proposed method

Process capability analysis ensures the performance of a process against its pre-determined specifications (Manoj, 2016). In figure 9 the dashed red line represents the normal distribution of the data using the overall standard deviation, while the blue solid line represents the normal distribution of the data using the within standard deviation. Process performance (Pp) uses the overall standard deviation and Cp uses the within standard deviation.

From capability analysis it is clear that 6σ less wider than specification width which indicates the capability of process to continuously provides the test results within the specification limits and near to true value. This is confirmed by greater values of Cp (1.93) and Cpk (1.75) than 1. Higher Cpk value indicates the proposed method meeting the target or true value consistently with minimum variation (Senvar and Tozan, 2010; Wooluru et al., 2014).

Figure 9. Capability analysis indicating Cp and Cpk for the proposed method



Application of the proposed method for pharmaceutical formulation

Marketed formulation was analyzed by the proposed method. The assay value for marketed formulation was found to be 99.03 %.

Table 8. Analysis of the Marketed Formulation

Conc. (μg/ml)

Conc. found (μg/ml)

Conc. found (%)

Conc. found (Mean ± SD)





99.066± 0.5595








Application of the method to the fabricated Nano-formulation

The prepared nano-formulation was analyzed by the proposed method. Notably, the assay value for fabricated formulations was found to be 99.066 %.


UV spectrophotometric method was successfully developed and validated as per ICH guidelines. Developed method was simple, accurate, precise, reproducible, and sensitive. Quality control analysis and estimation of pazopanib from all kind of pharmaceutical formulations can be effortlessly carried out by implementing this method. This is the first report of detail statistical analysis of any analytical method. Statistical analysis showed that process is under statistical control and capable to analyze the samples unceasingly.


Adeoti OA. 2013. Application of Cusum Control Chart for Monitoring HIV/AIDS Patients in Nigeria. International Journal of Statistics and Applications 3(3):77-80.

Armbruster D, Pry T. 2008. Limit of Blank, Limit of Detection and Limit of Quantitation. The Clinical Biochemist Reviews 29 (Suppl 1):S49-S52.

Benneyan J. 1998. Use and interpretation of statistical quality control charts. International Journal of Quality in Health Care 10(1):69-73.

Bland J, Altman D. 1986. Statistical method for assessing agreement between two methods of clinical measurement. The Lancet 307-10.

Bland J, Altman D. 1999. Measuring agreement in method comparison studies. Statistical Methods in Medical Research 8:135-60. 

Cella D,and Beaumont JL. 2016. Pazopanib in the treatment of advanced renal cell carcinoma. Therapeutic Advances in Urology 8(1): 61–69.

Chaitanya G. Pawar AKM. 2015. Development and validation of UV spectrophotometric method for the determination of pazopanib hydrochloride in bulk and tablet formulation. Journal of Chemical and Pharmaceutical Research 7(12):219-225.

Doane DP, Lori E. 2011. Measuring Skewness: A Forgotten Statistic? Journal of Statistics Education 19(2): 1-11.

Elliott AC, Woodward WA. 2007. Statistical analysis quick reference guidebook with SPSS examples. 1st ed. London: Sage Publications.

FDA Approves GlaxoSmithKline's Votrient (TM) For Advanced Renal Cell Cancer". Medical News Today. 20 October 2009. Retrieved 14 September 2017.

Fujimura F, Kamiya K, Fujiwara K, Shoji N, Shimizu S. 2013. Repeatability and Reproducibility of Measurements Using a NT-530P Noncontact Tono/Pachymeter and Correlation of Central Corneal Thickness with Intraocular Pressure. BioMed Research International Article ID 370592, 5.

Ghasemi A, Zahediasl S. 2012. Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology Metabolism 10(2):486-489.

Gorja A, Mondal S, Ganapaty S, Bandla J. 2015. Development and validation of stability indicating method for the estimation of pazopanib hydrochloride in pharmaceutical dosage forms by RP-HPLC. Der Pharmacia Lettre 7 (12):234-241.

Hazra K, Kumar R, Sarkar B, Chowdary Y, Devgan M, Ramaiah M. 2015. Uv-Visible Spectrophotometric estimation of curcumin in Nanoformulation. International Journal of Pharmacognosy 2(3):127-130.

Heath E, Česen M, Negreira N, Lopez de Alda M, Ferrando L. Lucie Blahova C. et al. 2016. First inter-laboratory comparison exercise for the determination of anticancer drugs in aqueous samples. Environmental Science and Pollution Research 23(15):14692–14704.

Iftequar S, Swaroop L, Zaheer Z, Shahid M, Imran S, Dehghan M. 2012. UV Spectrophotometric methods for estimation of Ramipril in Pharmaceutical dosage form by absorption maxima method and area under curve. International Journal of Drug Development and Research 4(1):286-90.

Jadhav NR, Kambar RS, Nadaf SJ. 2014. Dual Wavelength Spectrophotometric Method for Simultaneous Estimation of Atorvastatin Calcium and Felodipine from Tablet Dosage Form. Advances in Chemistry Article ID 131974, 6 pages, 2014.

Karkalousos P. and Evangelopoulos A. 2011. Quality Control in Clinical Laboratories, Applications and Experiences of Quality Control, Prof. Ognyan Ivanov (Ed.), ISBN: 978-953-307-236-4, InTech.

Kawai A, Yonemori K,  Takahashi S, Araki N, Ueda T. 2017.Systemic Therapy for Soft Tissue Sarcoma: Proposals for the Optimal Use of Pazopanib, Trabectedin, and Eribulin.  Advances in Therapy 34 (7): 1556–1571.

Koppel S, Chang S. 2016. A Process Capability Analysis Method Using Adjusted Modified Sample Entropy. Procedia Manufacturing 5:122-131.

Levey S, Jennings E. 1950. The use of control charts in the clinical laboratory. American Journal of Clinical Pathology 20:1059-1066.

Manasa E, Ravi Pratap P, Susena S,Umashankar B, Vanitha Prakash K. 2013.New Extractive Method Development of Pazopanib HCl in API and Its Unit Dosage Form By Spectrophotometry. International Journal of Pharmaceutical, Chemical and Biological Sciences 3(3): 533-537.

Manoj K.2016. Verification strategies for establishing reliability and validity in pharmaceutical manufacturing process- An enroute to regulatory compliance. International Journal of Pharmaceutical Sciences and Research 7(11): 4341-4357.

Maruzzo M, Martin-Liberal J, Messiou C, Miah A, Thway K, Alvarado R, et al., 2015.Pazopanib as first line treatment for solitary fibrous tumours: the Royal Marsden Hospital experience. Clinical Sarcoma Research 5(5):1-7.

 Masson P. 2007. Quality control techniques for routine analysis with liquid chromatography in laboratories. Journal of Chromatography A 1158: 168–173.

MedCalc Statistical Software version 17.8 (MedCalc Software bvba, Ostend, Belgium)

Minocha M, Khurana V, Mitra AK.2012. Determination of pazopanib (GW-786034) in mouse plasma and brain tissue by liquid chromatography-tandem mass spectrometry (LC/MS-MS),Journal of Chromatography B Analytical Technologies in the Biomedical and Life Sciences901:85-92.

National Center for Biotechnology Information. PubChem Compound Database; CID=10113978, (accessed Sept. 14, 2017).

Navgire S, Ghadge A, Gurav S, Killedar S, Nadaf S. 2016. Development and Validation of A Simple UV Spectrophotometric Method For The Determination of Urapidil Hydrochloride Both In Bulk And Pharmaceutical Formulation. CIBTech Journal of Pharmaceutical Sciences 5(3):10-15.

Pulla K, Puspha K, Gowri Sankar D. 2015. Development and validation of stability indicating RP-HPLC method for the determination of pazopanib hydrochloride in bulk drug and its pharmaceutical dosage form. Journal of Chemical and Pharmaceutical Research 7(8):114-120.

Pulla RP, Khan A, Rao JV, Sudam SK, Sujana K. 2013. Estimation of Pazopanib Hydrochloride in Tablet Dosage Forms by RP-HPLC. International Journal of Advances in Pharmaceutical Analysis 3(1) 24-29.

Roberts S. 1958. Properties of Control Chart Zone Tests. Bell Labs Technical Journal 37(1):83–114.

Schafer WD, Coverdale BJ, Luxenberg H. Jin Y. 2011. Quality Control Charts in Large-Scale Assessment Programs. Practical Assessment, Research & Evaluation, 16(15). retrieved on 15 september, 2017.

Senvar O, TozanH. 2010. Process Capability and Six Sigma Methodology Including Fuzzy and Lean Approaches, Products and Services; from RandD to Final Solutions, Igor Fuerstner (Ed.), ISBN: 978-953- 307-211-1, InTech.

Sharada M, Babu RR. 2016. Determination and Characterization of Process Impurities in Pazopanib Hydrochloride Drug Substance. International Journal of Pharmacy and Pharmaceutical Sciences 8(4): 97-102.

Shrivastava A, Gupta VB. 2011. Methods for the determination of limit of detection and limit of quantitation of the analytical methods. Chronicles of Young Scientists 2(1):21-25.

Sleijfer S, Ray-Coquard I,Papai Z, Le Cesne A, Scurr M, Schoffski P, et al. 2009. Pazopanib, a Multikinase Angiogenesis Inhibitor, in Patients with Relapsed or Refractory Advanced Soft Tissue Sarcoma: A Phase II Study from the European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (EORTC Study 62043). Journal of Clinical Oncology 27 (19): 3126.

Spath P.2001. Use XmR (individual and moving range) charts to better understand performance. Part 2.Hospital Peer Review 26(2):23-25.

Vaz S, Falkmer T, Passmore A, Parsons R, Andreou P. 2013. The Case for Using the Repeatability Coefficient When Calculating Test–Retest Reliability. PLoS ONE8(9):e73990.

Wooluru Y, Swamy D, Nagesh P. 2014. The Process Capability Analysis - A Tool For Process Performance Measures And Metrics - A Case Study. International Journal for Quality Research 8(3):399-416.

Yang S, Li, Y, Chen TK, Kord AS.2010. A phase appropriate approach to RP-HPLC method development for impurities analysis in active pharmaceutical ingredients via continuous manufacturing process understanding. Journal of Liquid Chromatography & Related Technologies 34(1). 61- 82.

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