QUALITY BY DESIGN (QbD) APPROACH TO DEVELOPMENT OF … · 2019. 12. 10. · Vol 7, Issue 11, 2018....

www.wjpps.com Vol 7, Issue 11, 2018.

1151

Shaha et al. World Journal of Pharmacy and Pharmaceutical Sciences

QUALITY BY DESIGN (QbD) APPROACH TO DEVELOPMENT OF

ANALYTICAL RP-HPLC METHOD FOR REGADENOSON AND

BALOFLOXACIN

Parvin I. Shaha*1, Mirza Shahed Baig

2 and Shaikh Sayeed-Ur-Reheman

3

1,2

Y. B. Chavan College of Pharmacy, Dr. Rafiq Zakaria Campus, Rauza Bagh, Aurangabad-

431001.

3Allana College of Pharmacy, Azam Campus, Hidayatulla Road, Pune-411001.

ABSTRACT

Quality by design (QbD) refers to the achievement of certain

predictable quality with desired and predetermined specifications. A

very useful component of the QbD is the understanding of factors and

their interaction effects by a desired set of experiments. The present

study describes the development of a comprehensive science and risk

based RP-HPLC method and subsequent validation for the analysis of

Regadenoson and Balofloxacin using a quality by design approach.

Experimental designs were applied for multivariate optimization of the

experimental conditions of RP-HPLC method. Interaction of

independent factors on the depended factor such as tailing factor was

studied for both drug. Box Behenken Experimental Design was used to study response

surface technique and to study in depth the effects of these independent factors. The

optimized chromatographic conditions of HPLC method for regadenoson were water (0.1%

o-phosphoric acid): methanol (60:40) as mobile phase, flow rate 1.2 ml/min, wavelength 247.

And for Balofloxacin were flow rate 1ml/min, pH 5.7 and Phosphate buffer: Acetonitrile

(70:30) as mobile phase. The optimized method condition was validated according to ICH

guidelines to confirm LOD and LOQ, linearity, accuracy and precision. The proposed method

can be used for routine analysis of Regadenosone and Balofloxacin in quality control

laboratories.

KEYWORDS: Quality by design, Regadenoson, Balofloxacin, HPLC.

WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES

SJIF Impact Factor 7.421

Volume 7, Issue 11, 1151-1164 Research Article ISSN 2278 – 4357

*Corresponding Author

Parvin I. Shaha

Y. B. Chavan College of

Pharmacy, Dr. Rafiq Zakaria

Campus, Rauza Bagh,

Aurangabad-431001.

Article Received on

31 August 2018,

Revised on 21 Sept. 2018,

Accepted on 12 Oct. 2018,

DOI: 10.20959/wjpps201811-12581


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1. INTRODUCTION

Chemically regadenoson (REGA) is 1-[6-amino-9-[(2R,3R,4S,5R)-3,4 dihydroxy-5

(hydroxymethyl) oxolan-2-yl] purin-2-yl]-N-methylpyrazole-4-carboxamide. REGA is an

adenosine derivative and selective A2A adenosine receptor agonist with coronary

vasodilating activity. It is used as Coronary Vasodilator, Pharmacologic Stress Agent.

whereas Balofloxacin (BALO) is Chemically1-cyclopropyl-6-fluoro-1,4-dihydro-8-methoxy-

7-(3-methyl amino) piperidin-1-yl)-4-oxoquinoline-3-carboxylic acid. BALO is used as

antibacterial. The bactericidal action of BALO results from interference with the enzyme

DNA gyrase (Topoisomerase II & IV enzyme) which is required for the synthesis of bacterial

DNA. Balofloxacin is effective against Gram-negative bacteria.

N

NN

N

NH2

O

OHOH

HO

N

N

O

HN CH3

Figure 1: Chemical structure of REGA.

N

HN

N

F

O

OH

O

O

Figure 2: Chemical Structure of Balofloxacin.

2. MATERIAL AND METHOD

2.1.Reagent and chemicals

A pure drug sample of REGA was obtained as gift sample from Wokhhradt pharmaceutical

Ltd, Aurangabad (India). And drug sample of BALO was obtained as gift sample from Lupin

pharmaceutical, Aurangabad(India). All chemicals used were HPLC grade purchased from E.


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Merck, Mumbai (India). All aqueous solutions were prepared with HPLC grade water

obtained in- house, Milli Q water purification system.

2.2.Instrument and software

HPLC analysis was carried out using a Jasco HPLC 2080 model chromatograph (Japan)with

a PU-2080 isocratic delivery system (pump), Jasco UV-2075 plus detector, the analytical

column used is Grace reverse phase C-18 column (4.6 × 250 mm, 5 μm particle size). Data

acquisition and processing was obtained using JASCO BORWIN software. The wavelength

of maximum absorbance was detected by UV-Visible spectrophotometer (Double Beam),

Shimadzu UV 1800 model and wavelength scanning range 200-400nm was exercised using

UV probe software. For applying QbD software used was Design of expert® version 10.0.2.

2.3. Preparation of standard stock solution

Standard stock solution of REGA and BALO for optimization of experiments was prepared

separately by accurately weighing 10mg of drug and dissolving in 100ml Methanol to get a

final concentration of 100µg/ml. From above stock solution further dilutions was made to

prepare 10µg/ml for analysis.

2.4. Preparation of mobile phase

Mobile phase for REGA was prepared by mixing of 60ml of Water containing 0.1% o-

phosphoric acid and 40ml of Methanol then sonicate it for 30 minutes and vaccume filter

through 0.45μ Millipore filter. Phosphate buffer prepared by dissolving 1gm of potassium

dihydrogen ortho phosphate in 500ml HPLC grade water and pH was adjusted to 5.5 by o-

phosphoric acid. For BALO mobile phase was prepared by mixing 70ml phosphate buffer

with 30 ml Acetonitrile then sonicated for 30min and vaccume filter through 0.45μ Millipore

filter.

2.5. Selection of wavelength for analysis

Appropriate dilution of standard solution of REGA and BALO were prepared separately by

using methanol and it was scanned in UV spectrophotometer for entire wavelength region

200-400 nm in spectrum mode for maximum absorbance. The wavelength of maximum

absorbance for REGA and BALO was selected as 247nm and 293 respectively.


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2.6. Analytical target profile

QbD is systematic approach to product, process design and development. Hence it begins

with determination of goal or method intent. An emphasis is given on the product and process

understanding. Here method intent was to develop HPLC method for REGA and BALO

which is robust, accurate, precise and USP tailing less than 1.25 and 1.2 respectively. number

of theoretical plates as per requirement and short analysis time i.e. less than 10 min. as per

QbD norms a robust method should be developed with help of visualized a design space.

2.7. Initial Chromatographic condition

For REGA Chromatographic separation was carried on Grace C-18 column (4.6×250 mm, 5-

µm particle size) by using Water (0.1%o-phosphoric acid):methanol (60:40) as mobile phase

and Peak was obtained at 247nm with retention time of 4.3 min at flow rate of 1.1 ml/min. for

BALO separation was carried out using Phosphate buffer (pH 5.5): Acetonitrile (70:30) as

mobile phase. Peak was obtained at 293nm with retention time of 2.4 min at flow rate of 1

ml/min, prior to the injection of drug solution column was equilibrated with mobile phase.

Further changes were done according to optimization model.

2.8. Critical Quality Attribute (CQA)

By screening critical factors which affect the tailing were determined. Factor such as flow

rate, pH , wavelength, methanol and Acetonitrile concentration in mobile phase were found to

be critical. Selection of stationary phase was also critical parameter.

2.9. Design of Experiment (DoE)

Optimization was done by response surface methodology, applying a three level Box

Behnken design with three centre points (Table 1.).

Table 1: Chromatographic Factors and Response Variables For Box Behnken

Experimental Design.

Drug Sr.No. Chromatographic Condition

Low Level used

Centre High

REGA

1. Flow rate (X1) 1 1.1 1.2

2. Methanol.(X2) 30 40 50

3. Wavelength (X3) 245 247 249

BALO

1 Flow rate (X1) 0.8 1 1.2

2 pH (X3) 5 5.5 6

3 Acetonitrile (X2) 20 30 40


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Three factor for REGA such as flow rate, wavelength and methanol concentration in mobile

phase were selected as independent variables and in case of BALO independed factor

selected was flow rate, pH and Acetonitrile concentration in mobile phase. USP tailing were

selected as dependent variables. The resulting data were fitted into Design Expert® software

and analysed statistically using analysis of variance (ANOVA). The data were also subjected

to 3-D response surface methodology to determine the influence of independed on dependent

variables. The probable trial runs using Box behnken designs are as shown in Table 2.

Application of multivariate regression analysis resulted in a fitted full quadrate mode for the

average responses for USP peak tailing given by the equation 1.

Y= β0+β1X1+β2X2+ β3X3+ β11X12+ β22X2

2+ β33 X3

2 + β12 X1 X2+ β13 X1X3+ β23X2X3.

Where Y is the response, β0 is the arithmetic mean response. β1 β2 and β3 are regression

coefficients of the factor X1, X2 and X3 respectively. β11, β22 β33 are squared coefficients β12,

β13 and β23 are interaction coefficients.

Table 2: Box Behnken method used for REGA and BALO in HPLC method for

optimization.

Run

REGA BALO

Coded

(X 1,X2, X3)

Flow Rate

(ml/min)

Wavelength

(nm)

Methanol

Conc.(%)

Flow Rate

(ml/min) pH

Acetonitrile

Conc. (%)

1. +0+ 1.2 247 50 1.2 5.5 40

2. -0+ 1 247 50 0.8 5.5 40

3. 000 1 247 40 1 5.5 30

4. 000 1 247 40 1 5.5 30

5. ++0 1.2 .249 40 1.2 6 30

6. 0++ 1.1 249 50 1 6 40

7. -0- 1 247 30 0.8 5.5 20

8. +0- 1.2 247 30 1.2 5.5 20

9. 0+- 1.1 249 30 1 6 20

10. +-0 1.2 245 40 1.2 5 30

11. 000 1.1 247 40 1 5.5 30

12. -+0 1 249 40 0.8 6 30

13. 0-+ 1.1 245 50 1 5 40

14. 000 1.1 247 40 1 5.5 30

15. --0 1 245 40 0.8 5 20

16. 0-- 1.1 245 30 1 5 20

17. 000 1.1 247 40 1 5.5 30


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2.10. Method Validation

a. Linearity

Standard calibration curves was prepared saparatly with five different conc. of REGA and

BALO by making serial volume to volume dilution of stock solution with methanol, over the

range of 5,10,15,20,25 µg/ml. Linear concentration curves of peak area versus drug

concentration were plotted using linear least squares regression and evaluated for linearity.

b. Accuracy

Accuracy was obtained by performing recovery studies by the standard addition method at

different levels of standard drug i.e., 80%, 100% and 120% of REGA and BALO to

previously analyzed tablet powder sample and mixtures were reanalyzed by the proposed

method. From the amount of drug found percentage recovery was calculated.

c. Precision

Precision of the method was performed by intra-day and inter-day studies. For intra-day

studies, triplicate of prepared samples were analyzed within same day. For inter -day

validation, same concentrations were determined on three separate days. The % RSD values

obtained from peak area should be less than 2.

d. Limits of Detection and Limit of Quantification

LOD and LOQ were estimated experimentally and mathematically using formulae:

LOD = 3.3 standard deviation of Y intercept/ slope of the calibration curve.

LOQ = 10 standard deviation of y intercept/slope of the calibration curve.

LOD and LOQ values were experimentally verified by diluting known concentrations of

sample solution.

e. Robustness

Robustness of the method was determined by carrying out the analysis under conditions

during which flow rate (±0.1ml/min), Wavelength (±0.1 units), mobile phase composition,

were altered and the effects on the area were noted.

3. RESULT AND DISCUSSION

3.1. Method Design

Analysis of variance(ANOVA) was perform for findings are ‘statistically significant’ by

convention, it is p<0.05.A value of Probe > F was found to be less than 0.05. Entire model


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was fitted well for optimization. A Significant factors found for REGA were flow rate (p-

value= 0.0001), methanol concentration (p-value=<0.0001), interaction of flow rate x flow

rate (p-value= 0.0034), interaction of wavelength x wavelength (p-value= 0.0035), methanol

concentration x methanol concentration (p-value=<0.0063) and interaction of flow rate x

methanol conc. (p-value= 0002).Significant factors found for BALO was flow rate (p-value=

0.0003), pH (p-value= <0.0001), interaction pH x pH (p-value= 0.0001), interaction of ACN

conc. x ACN conc. (p-value= 0.0045), flow rate x ACN conc. (p-value=0.0020), interaction

of flow rate x pH (p-value= 0.0416) and interaction of ACN conc. x pH (p-value= <0.0001).

Regression analysis and p-values obtained from software generated report for REGA and

BALO are given in Table 3 and Table 4 respectively.

Table 3: Regression coefficients and associated probability values (p-values) for USP

tailing of REGA.

Table 4: Regression coefficients and associated probability values (p-values) for USP

tailing of BALO.

Sr no. Term Coefficient p-value

1 Intercept 1.24 <0.0001

2 flow rate(A) -0.11 0.0001

3 Wavelength(B) 0.018 0.1766

4 methanol conc.(C) 0.091 <0.0001

5 flow rate x wavelength(AB) 0.03 0.7703

6 flow rate x methanol conc.(AC) 0.12 0.0002

7 wavelength x methanol conc.(BC) -0.015 0.3927

8 flow rate x flow rate(A2) 0.042 0.0034

9 wavelength x wavelength(B2) 0.069 0.0035

10 methanol conc. x methanol conc.(C2) 0.062 0.0063

Sr no. Term Coefficient p-value

1. Intercept 1.21 0.0001

2. flow rate(A) -0.046 0.0003

3. pH(B) -0.090 < 0.0001

4. methanol conc.(C) 3.750 0.7352

5. flow rate x pH(AB) 0.038 0.0416

6. flow rate x ACN conc.(AC) 0.004 0.0020

7. pH xACN conc.(BC) 0.14 < 0.0001

8. flow rate x flow rate(A2) 0.031 0.0727

9. pH x pH (B2) -0.11 0.0001

10. ACN conc. x ACN conc.(C2) 0.036 0.0045


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3.2. Response surface plot

Response surface and contour plot were studied to visualize effect of factor and their

interaction so as to get optimized method. In case of REGA, At constant methanol conc. 40%

it can be observed that between wavelength 248-249nm tailing was found to be more than

1.25, tailing was in specified limit below 247nm (Fig.3). At constant wavelength 247nm

methanol conc. is not showing much effect but when flow rate decreases result in increases in

peak tailing above 1.25 (Fig.4). At constant flow rate 1.1ml/min wavelength around 249nm

and methanol conc. at 45-50% tailing factor exceeded the limit (Fig.5).

In case of BALO, At constant ACN conc. at 30% it was found that when pH in range of 5.4-

5.6 tailing was more than 1.2 and tailing was in specified limit at higher pH 5.7-5.9. Flow rate

is not showing much effect but when flow rate decreased throughout pH range tailing was

increases (Fig.6). At constant pH 5.7 when ACN conc. not showing much effect but when

flow rate decreases’ results in higher peak tailing (Fig.7). At constant flow rate at 1ml/min it

was found that at ACN conc. around 25% and pH between 5.4 -5.6 tailing factor exceeded

the limit hence at higher ACN conc. response was optimum though pH is varied (Fig.8).

Design-Expert® SoftwareFactor Coding: ActualUSP tailing ( )

Design points above predicted valueDesign points below predicted value1.48

1.02

X1 = A: flow rateX2 = B: wavelenght

Actual FactorC: methanol conc. = 40

245

246

247

248

249

1

1.05

1.1

1.15

1.2

1

1.1

1.2

1.3

1.4

1.5

US

P t

ailin

g (

)

A: flow rate (ml/imn)

B: wavelenght (nm)

Fig. 3: 3D Response Plot of tailing factor against flow rate and wavelength.


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1.02

X1 = A: flow rateX2 = C: methanol conc.

Actual FactorB: wavelenght = 247

30

35

40

45

50

1

1.05

1.1

1.15

1.2

1

1.1

1.2

1.3

1.4

1.5

US

P t

ailin

g (

)

A: flow rate (ml/imn)C: methanol conc. (ml)

Fig. 4: 3D Response Plot of tailing factor against flow rate and methanol conc.



1.02

X1 = B: wavelenghtX2 = C: methanol conc.

Actual FactorA: flow rate = 1.1

30

35

40

45

50

245 246

247 248

249

1

1.1

1.2

1.3

1.4

1.5

US

P t

ailin

g (

)

B: wavelenght (nm)C: methanol conc. (ml)

Fig. 5: 3D Response Plot of tailing factor against wavelength and methanol conc.

Design-Expert® SoftwareFactor Coding: Actualtailing


1

X1 = A: flow rateX2 = B: pH

Actual FactorC: ACN conc. = 30

5.4

5.5

5.6

5.7

5.8

5.9

6

0.8

0.9

1

1.1

1.2

0.8

0.9

1

1.1

1.2

1.3

1.4

taili

ng

A: flow rate (ml/min)

B: pH

Fig. 6: 3D Response Plot of tailing factor against flow rate and pH.


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1

tailing = 1.24Std # 16 Run # 14X1 = A: flow rate = 1X2 = C: ACN conc. = 30

Actual FactorB: pH = 5.7

20

25

30

35

40

0.8

0.9

1

1.1

1.2

0.8

0.9

1

1.1

1.2

1.3

1.4

taili

ng

A: flow rate (ml/min)C: ACN conc. (ml)

Fig. 7: 3D Response Plot of tailing factor against flow rate and ACN conc.



1

X1 = B: pHX2 = C: ACN conc.

Actual FactorA: flow rate = 1

20

25

30

35

40

5.4 5.5

5.6 5.7

5.8 5.9

6

0.8

0.9

1

1.1

1.2

1.3

1.4

taili

ng

B: pH

C: ACN conc. (ml)

Fig. 8: 3D Response Plot of tailing factor against pH and ACN conc.

3.3. Optimized Method

To obtain optimum set of condition to achieve desired goal composite desirability parameters

were applied. For REGA and BALO Response was set to minimum tailing below target value

of 1.25 and 1.2 respectively. Optimum condition having desirability was chosen from

obtained runs. The optimized condition for REGA was found to be flow rate of 1.2 ml/min,

Methanol concentration of 40% and wavelength at 247 nm which give sharp peak at retention

time 4.1 min (Fig.9). And for BALO flow rate of 1 ml/min, ACN concentration of 40% and

pH 5.7 which give sharp peak at retention time 2.1 min (Fig.10).


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Fig. 9: HPLC Chromatogram of REGA.

Fig. 10: HPLC Chromatogram of BALO.

3.4. Control strategy

Set of conditions were analyzed to compare predicted response with actual response. Six

Replicate of 20µg/ml of solution at above specified conditions were taken. Difference in the

response was not more than 3%.


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Table no. 5: validation study for REGA and BALO.

Sr.no Parameter Data for REGA Data for BALO

1. Linearity 5-25 µg/Ml 5-25 µg/Ml

2. Regressions equation y = 99125x – 25985 y = 23556x – 8212

3. Correlation coefficient (r2) 0.999 0.999

4. Retention time (mins) 4.1 2.1

5.

Accuracy

80 %

100 %

120 %

99.30

98.75

99.94

99.40

99.43

101.1

6. LOD(µg/mL) 1.13 8.3

7. LOQ(µg/mL) 3.44 7.1

8.

Precision (RSD, %)

Intraday (n=3)

Interday (n=3)

1.43%

1.36%

1.24

1.66

9. Robustness Robust Robust

4. CONCLUSION

A systematic and practical approach was utilized to develop an efficient and robust HPLC

method for RAGA and BALO. The application of quality by design resulted in a

methodology that was simple in implementation, chromatographically efficient. Multivariate

regression analysis was successfully employed to effectively screen the main effects of

factors that significantly affected the resolution and tailing. Three factors for REGA and

BALO were determined to significantly affect the peaks were then analyzed to determine

their interactions and quadratic effects with the least number of runs as possible using a Box–

Behnken design in conjunction with response surface methodology. A desirability function

was applied to determine the optimum conditions. The optimum conditions were validated

according to ICH Q2R1 guidelines.

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