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,
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
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
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
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
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
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
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
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
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]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.
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]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
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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