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www.wjpr.net Vol 7, Issue 9, 2018. 360

QUALITY RISK MANAGEMENT (QRM) SUPPORTED SYSTEMATIC

DEVELOPMENT AND VALIDATION OF AN UFLC METHOD FOR

DETERMINATION OF A NOVEL ANTI-PAH AGENT IN

PHARMACEUTICALS

Sagar Suman Panda*, Bera Venkata Varaha Ravi Kumar, Biswajit Sahu Department of Pharmaceutical Analysis & Quality Assurance, Roland Institute of

Pharmaceutical Sciences, Khodashingi, Berhampur-10, Odisha, India.

ABSTRACT

A liquid chromatographic method was optimized and developed for

determining a novel anti-PAH agent, bosentan (BSN) in bulk and

pharmaceutical formulation. A novel approach of quality risk

management (QRM) was followed to develop a robust and reliable

chromatographic method. The QRM consists of steps such as

assessment, control and review of risks and its management through

design of experiments (DoE) and control strategies. Scouted method

variables such as % acetonitrile, pH and flow rate were optimized

using DoE and their effect on critical quality attributes viz. retention

time, plate number and asymmetry was studied. The method linearity

was observed over a range of 5-200µg/ml of BSN. The developed

method was also subjected to validation studies such as specificity,

accuracy (97.52-97.95%), precision (0.006-0.13%), stability,

sensitivity (LOD=2.5 µg/ml, LOQ= 5µg/ml), selectivity etc. Utilizing QRM approach

ensured development of an analytical method devoid of any quality risks. The developed

method was found suitable for determining analyte in both bulk as well as in a in-house novel

drug formulation. Overall the method was reliable, robust and possesses the potential of

application in routine and bio-analytical purposes.

KEYWORDS: Bosentan, QRM, UFLC, validation, robustness.

Volume 7, Issue 9, 360-375. Conference Article ISSN 2277– 7105

Article Received on 19 March 2018,

Revised on 09 April 2018, Accepted on 29 April 2018,

DOI: 10.20959/wjpr20189-12115

8533

*Corresponding Author

Sagar Suman Panda

Department of

Pharmaceutical Analysis &

Quality Assurance, Roland

Institute of Pharmaceutical

Sciences, Khodashingi,

Berhampur-10, Odisha,

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www.wjpr.net Vol 7, Issue 9, 2018. 361 INTRODUCTION

Quality risk management (QRM) is a novel approach which effectively performs risk

assessment and its management, using different statistical and experimental design tools to

produce quality end product.[1] In the current scenario, the analytical scientists have already

started incorporating the principles of quality by design (QbD) for developing highly efficient

and quality analytical methods.[2-4] However, the principle of QRM are still to be explored in

analytical sciences.

According to ICH Q9 guidance, QRM is a systematized controlled approach (Figure 1) for

evaluating, regulating, conveying and reviewing the risks to quality of a drug product during

its lifecycle. Scientific evaluation of various risks and the associated efforts to control it

through necessary formalities and documentation process is the basis of QRM. It has the

[image:2.595.156.436.347.564.2]

potential to provide the most significant information for obtaining a quality product.

Figure 1: Various steps involved in QRM process.

Liquid chromatography is the most versatile analytical tool for drug analysis and testing,

being capable of analyzing drugs in diverse samples. However, the recent development to

liquid chromatography technique such as ultrafast liquid chromatography (UFLC) is finding

its widespread applications in the field of pharmaceuticals.[5-7] The various merits like lower

mobile phase usage, rapid analysis, and high sensitivity, thus advocates for utilization of

UFLC method over traditional HPLC for analysis of drugs in different samples during routine

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www.wjpr.net Vol 7, Issue 9, 2018. 362 Bosentan (BSN), i.e.

4-tert-butyl-N-[6-(2-hydroxyethoxy)-5-(2-methoxyphenoxy)-2-(pyrimidin-2-yl)pyrinidine-4-yl] benzenesulfonamide is an anti-pulmonary artery

hypertension agent.[8] Literature reveals few chromatographic methods are reported for

quantifying BSN in pharmaceuticals, which include, HPLC and LC-MS. [9-16] Most of these

reported methods lack significantly in terms of method robustness effecting the reliability to

the results produced. In order to confirm the method reliability and superiority than the

reported methods a novel approach of QRM was followed for liquid chromatographic

[image:3.595.197.401.258.392.2]

determination of BSN present in API as well as in the in-house prepared tablet formulation.

Figure 2: Chemical structure of bosentan.

In this study, a reliable and quality UFLC method was developed for determining BSN in

bulk drug and samples of in-house tablets based on QRM approach. QRM tools such as

Ishikawa fish-bone diagram, Failure Mode and Effect Analysis (FMEA) etc., and a

Box-Behnken Design (BBD) was used for systematic optimization of the critical method variables

(CMVs). Subsequently, the analytical control space (ACS) was demarcated, and control

strategies were defined for future improvement of method performance. Validation study for

the newly optimized chromatographic method was performed as ICH guidance.[17] Further,

the amount of BSN present in the in-house tablet formulation was determined using UFLC.

MATERIALS AND METHODS Materials

Bosentan (purity > 98%) was obtained from MSN Laboratories Ltd., India. HPLC grade

acetonitrile, potassium di-hydrogen phosphate and disodium hydrogen phosphate Merck Ltd.,

Mumbai, India was used. HPLC grade water prepared by using TKA GenPure

Ultra-Purification System, Germany was used for preparing a buffer. The in-house tablet

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www.wjpr.net Vol 7, Issue 9, 2018. 363 Instrumentation

A SHIMADZU Prominence UFLC with binary pumps and a PDA detector with LC solution

software were used to the chromatographic purpose. A SHIMADZU Shim-pack GWS C-18

column, (250×4.6 mm, 5 µm) was used as stationary phase. A mobile phase of acetonitrile:

buffer (55:45, v/v) flowing at 1 mL/min was utilized for 10min. BSN was detected at 223nm.

About 14.1g of di-sodium hydrogen phosphate along with 5.725g of potassium dihydrogen

phosphate were added upto 500mL of water.

Methods

Quality risk management

QRM initiates with understanding the causal effect relation between the prospective method

variables and CQAs; a typical fish-bone diagram was prepared. In the present studies, a

typical traffic-light risk analysis followed by a Failure Mode and Effect Analysis (FMEA)

approach was employed for discovering the highest risk variables controlling CQAs.

Variables with risk priority number (RPN) scores more than 100 were considered as CMVs

requiring response surface investigation. Response surface methodology provided dual

benefit of method optimization as well as risk management option through generating robust

control space.

Three CMVs viz. % acetonitrile, pH and flow rate scored scores more than 100. Further, these

CMVs were studied and optimized using response surface technique to create a robust

analytical control space.

Method development and optimization studies

DoE approach was followed to evaluate the effect of CMVs on method performance. A

robust analytical control space was established by employing a randomized BBD domain (15

experiments, 3 centers). A 20µg/mL concentration of BSN was tested for all the runs.

Different effects among the CMVs were unearthed using a selected mathematical model. The

analysis of variance (ANOVA), lack of fit, the coefficient of correlation (R2), predicted

residual sum of squares (PRESS) etc. were the various parameters which were evaluated for

data analysis purpose. Also polynomial equations, 2-D and 3-D plots were evaluated to assess

model suitability. Further, the optimized chromatographic conditions were established by

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www.wjpr.net Vol 7, Issue 9, 2018. 364 Analytical control strategy

Control strategy was derived depending on the results of CQAs viz. retention time, plate

number and asymmetry within the study period. Method performance was assessed by using

control charts with respective upper and lower control limits, for considering the data

generated for six days. It reinforced the objective of continuous method improvement while

working within the limits of CMVs.

Method Validation Studies Specificity

To evaluate specificity of the method analyte was added to known placebo and visual

inspection was carried out for chromatographic interferences. The additives used in

formulation were added to the standard solutions and analyzed.

Preparation of Calibration Curve

Around 50 mg of BSN was transferred into a 50 mL volumetric flask having 25 mL of

mobile phase and dissolved in it. Finally, the volumes were made up to produce 1000µg/mL

standard stock solution. From this solution, calibration concentrations of 5-200 µg/mL were

prepared and chromatography was performed for each concentration (n=3). The calibration

plot was generated taking concentration (µg/mL) on x-axis and peak area on the y-axis.

Regression analysis (including ANOVA) of calibration data was performed to detect the

regression statistics.

Accuracy

Recovery studies were performed in triplicate by spiking the known excipients solutions with

standard BSN at 80,100 and 120% of the test concentration. Further, the recovery of the

spiked concentration of standard BSN was calculated.

Precision

The precision study in terms of system, interday and intraday was conducted. System

precision was determined by six injections of a selected concentration (20µg/mL) of analyte.

The intraday (same day) and inter-day (different day) precision were calculated by injecting

six solutions of a fixed concentration (20µg/mL) of analyte. A % relative standard deviations

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www.wjpr.net Vol 7, Issue 9, 2018. 365 Limit of detection (LOD) & Limit of quantitation (LOQ)

Signal to Noise (S/N) ratio of 3:1 and 10:1 were considered vital for visual detection of LOD

and LOQ, respectively.

Assay Procedure

In house tablet formulation powder equivalent to 50 mg of BSN was transferred into a 50 mL

volumetric flask, containing 25 mL of mobile phase. The contents were vortexed for 10min

followed by 30 min of ultrasonication. Finally, volume was made up and filtration was done

through a 0.45μm filter. Further, the filtered solution was diluted for UFLC analysis. These

solutions were stored at 2-8ºC till further use.

RESULTS AND DISCUSSION Method development studies

The early chromatographic method development using QRM approach demands sufficient

preparatory knowledge about different chromatographic variables and physicochemical

properties of analyte. Variables such as, mobile phase ratio, stationary phase, flow rate, etc.

were scouted initially. A SHIMADZU ODS C-18 column was used for chromatography of

the compound based on its suitability towards the analyte. Trials were performed using

mobile phase composition of acetonitrile: buffer at varying ratios (i.e., 45:55, 50:50, 55;45,

60:40, 70:30, v/v) and flow rates (0.9, 1.0 and 1.1 mL/min) at room conditions. Among these,

acetonitrile: buffer (55:45, v/v) flowing at 1 mL/min produced symmetrical peak shape.

Thereafter, principles QRM were implemented to discern the CMVs.

Risk assessment

Being an early risk assessment tool, evaluation of the fish-bone diagram (not shown in

figures) was found worthy as it depicted primary causes and secondary sub causes producing

variation in method performance. Few prospective method variables were chosen and

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[image:7.595.59.539.71.422.2]

www.wjpr.net Vol 7, Issue 9, 2018. 366 Table 1: Traffic light risk analysis matrix for initial scrutiny.

CQAs Method Variables Mobi le Phase pH Inje cti on volum e S olvent gr ade S ampl e P ur it y R ea ge nt P ur it y Humidi ty Te mp . P ea k int egr ati on P ea kP ur it y

UFLC Flow r

ate S onica tor C alcula ti on Er ror Dilut ion Err or Gla sswa re e rror Equil ibra ti on Tim e S tationar y P ha se R etention Ti me P late Numbe r As ymm etry

In the next step to refine and identify the risky method variables FMEA approach with RPN

(Table 2) was followed. This helped finding out the CMVs as per the RPN score. Further,

[image:7.595.95.498.517.756.2]

response surface optimization was carried out to develop robust analytical control space.

Table 2: Different failure modes and their effect on method performance.

Source Failure Cause Effect S O D RPN

Method

Organic phase (%) Multiple 7 5 7 245

Flow rate Multiple 7 5 6 210

pH Multiple 6 6 5 180

Stationary Phase Longer retention 5 3 4 60

Material

Solvent grade Extraneous peaks 5 4 4 80

Sample purity Extraneous peaks 4 5 3 60

Reagent purity Extraneous peaks 4 4 3 48

Milieu Humidity Inaccurate weighing 3 4 4 48

Temperature Varying resolution 3 3 3 27

Measurement Peak Integration Varied response 4 3 3 36

Peak Purity Co-eluting peaks 3 3 4 36

Machine UFLC Decreased performance 3 2 3 18

Sonicator Varying pressure 2 2 3 12

Men Calculation Error Incorrect purity 4 3 3 36

Dilution Error Incorrect purity 3 2 2 12

a

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www.wjpr.net Vol 7, Issue 9, 2018. 367 Risk management and method optimization

The risk assessment studies revealed that, three CMVs were affecting the CQAs. Percentage

acetonitrile, flow rate and buffer pH were the CMVs, which required further investigation to

assess the method robustness. Responses from the fifteen experimental runs obtained as per

BBD model (Table 3) were performed randomly and analysed to establish optimum

[image:8.595.135.463.227.522.2]

chromatographic conditions.

Table 3: Experimental design matrix for robustness study.

Run No Acetonitrile (%) pH Flow rate(mL/min)

1 57 6.8 1.1

2 55 6.8 1.0

3 53 6.8 1.1

4 53 7.0 1.0

5 57 6.8 0.9

6 53 6.8 0.9

7 55 6.6 1.1

8 55 6.8 1.0

9 57 7.0 1.0

10 55 7.0 1.1

11 55 6.8 1.0

12 53 6.6 1.0

13 55 7.0 0.9

14 55 6.6 0.9

15 57 6.6 1.0

Levels Studied Acetonitrile (%) pH Flow rate(mL/min)

Low 53 6.6 0.9

Nominal 55 6.8 1.0

High 57 7.0 1.1

Optimization data analysis

The optimization data was subjected to appropriate mathematical models for analysis.

Polynomial equations (Eq.1, 2 and 3) consisting of model terms for both main effects and

interaction effects were generated for the CQAs. It helped to unearth the connection among

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www.wjpr.net Vol 7, Issue 9, 2018. 368 Where A= Acetonitrile (%), B= pH and C=Flow rate (mL/min)

Assessment of ANOVA (P<0.05) along with satisfactory values of r2 (r2>0.9) advocated for

the adequacy of the selected mathematical model for obtaining optimum values of CQAs.

High degree of interaction among both the CMVs was noticed for CQAs viz. resolution and

plate number, as the factor lines were intersecting each other. Response surface evaluation

was performed employing 3-D plots (Figure 3-(a-i). Figure 3(a) depicts a declining trend in

retention time with increasing levels of % acetonitrile. pH was found to have no significant

effect on the retention of BSN. Slightly high value of retention time was found at low levels

of %acetonitrile and flow rate (Figure 3(b)) which was decreasing gradually with increase in

levels of both the CMVs. In case of CMVs pH and flow rate the retention of BSN was found

slightly decreasing with increase in flow rate. However, no change in retention time was

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www.wjpr.net Vol 7, Issue 9, 2018. 369 Figure 3: 3-D response surface obtained for responses (a) retention time, (b) plate number and (c) asymmetry.

A decreasing trend was noticed for plate number with gradual increase in levels of pH at all

levels of %acetonitrile indicating critical influence of pH on separation efficiency (Figure

3(d)). A complex interaction among %acetonitrile and flow rate was noticed producing a

typical “saddle system” with contours approaching towards each other (Figure 3(e)). A

maximal response was observed at high levels of flow rate whereas no significant change in

[image:10.595.88.498.69.577.2]
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www.wjpr.net Vol 7, Issue 9, 2018. 370 In case of asymmetry, a complex interaction was found with CMVs pH and %acetonitrile.

High values of asymmetry were obtained at all levels of pH. But the asymmetry was found

gradually decreasing with increase in %acetonitrile (Figure 3(g)). A stationary “minima” was

obtained for flow rate and %acetonitrile(Figure 3(h)).A decreasing trend in asymmetry was

seen with increase in flow rate. But no significant change in asymmetry was observed

throughout all the levels of pH (Figure 3 (i)). Parallel information was drawn by interpreting

[image:11.595.94.499.227.719.2]

the 2-dimensional contours (Figure 4) for all the respective CMVs.

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www.wjpr.net Vol 7, Issue 9, 2018. 371 The desirability as well as overlay plot (Figure 5) represented chromatographic conditions for

obtaining optimum values of all the three CQAs. Based on the above obtained conditions the

[image:12.595.104.503.149.447.2]

method was performed for validation studies.

Figure 5: Analytical control space obtained for the optimized method.

A typical chromatogram (Figure 6) of BSN in tablets revealed optimum peak shape in the

predicted experimental conditions.

[image:12.595.113.484.546.709.2]
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www.wjpr.net Vol 7, Issue 9, 2018. 372 Method validation studies

Specificity

Visual assessment of chromatograms for both analyte and placebo revealed that the method is

specific for determination of BSN, without any interference from placebo content.

Linearity

The method was found linear over concentration range of 5-200µg/mL (r2=0.999). Further,

satisfactory results obtained through regression analysis and ANOVA of linearity data

indicated goodness of fit.

Accuracy

Satisfactory recoveries of BSN between 97.52-97.95%, advocated for optimum method

accuracy and reliability.

Precision

The precision study revealed acceptable values of % RSD (<2%). The values were 0.02%,

0.13% and 0.007% for intraday, inter-day and system precision, respectively.

Limit of detection (LOD) & Limit of quantitation (LOQ) The LOD and LOQ values were 2.5 and 5µg/mL, respectively.

Analytical control strategy

Preparation of control charts (Table 4) helped developing analytical control strategies.

Reproducible results for CQAs were obtained by working within the analytical control space.

The control space was defined to be within limits such as, acetonitrile proportion (±2%), flow

rate (±0.1mL/min) and, pH (± 0.2).

Table 4: Result of Control Charts Obtained for CQAs.

Parameter Retention Time(min) Plate Number Asymmetry

Mean 3.747 4239.55 1.494

S.D. 0.0011 24.58 0.0005

RSD (%) 0.03 0.58 0.03

LCL 3.745 4206.75 1.494

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www.wjpr.net Vol 7, Issue 9, 2018. 373 Assay of in-house formulation

The visual evaluation of chromatograms obtained for in-house tablet formulation indicated

method selectivity due to non-interference of any of the formulation components. The mean

(n= 3) content of BSN was found to be 99.02% (SD = ±0.43).

CONCLUSION

The present research explains optimization and development of an UFLC method for

determining BSN in bulk and tablets. To achieve the objective a systematized novel approach

of QRM was followed. Utilizing QRM approach not only ensured increased method

robustness but also presented an option for continuous improvement in performance of

CQAs. It helped discovering three CMVs and their effects on the CQAs. Based on the results

of QRM control strategies were outlined to obtain desired UFLC method performance.

Overall, the chromatographic method was found suitable and trustworthy for determining

BSN. Results of validation study were found compliant with ICH guidelines. Hence, this

method is acceptable for estimating BSN in bulk and tablet formulation. Further, the above

mentioned method has the potential for determining BSN in biological fluids.

ACKNOWLEDGEMENT

The authors are thankful to MSN Laboratories Ltd., India for providing the gift samples of

bosentan standard drug and Principal, Roland Institute of Pharmaceutical Sciences,

Berhampur for providing the necessary research facilities.

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Figure

Figure 1: Various steps involved in QRM process.
Figure 2: Chemical structure of bosentan.
Table 1: Traffic light risk analysis matrix for initial scrutiny. Method Variables
Table 3: Experimental design matrix for robustness study.
+4

References

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