Quality by Design based pareto charts responses Evaluation approach for a Validated Stability-indicating RP-HPLC assay Method for sunitinib and its process related impurities in Oral Dosage Forms

 

Kasturi Rajashekhar¹, Challa Gangu Naidu^1,2*, Chebolu Naga Sesha Sai Pavan Kumar¹*,

Eegala Bheema Shankar¹

¹Division of Chemistry, Department of Science and Humanities, Vignan's Foundation for Science, Technology and Research (VFSTRU), (Deemed to be University) Vadlamudi, Guntur, Andhra Pradesh 522213, India.

²Vignan’s institute of Information Technology (VIIT), VSEZ, Duvvada, Visakhapatnam-530046, India.

 

ABSTRACT:

For sunitinib (SUN) and its associated impurities, a simple and rapid stability-indicating liquid chromatographic assay technique was developed. The SUN related impurities in the completed oral dose forms were detected and assessed utilizing the high-pressure liquid chromatography with help of analytical quality by design (AQbD) approach. Impurity quantification necessitates a more comprehensive approach to analytical technique generation. The pareto charts evaluation technique, that is dependent on quality by design, enables for the evaluation of many analytical aspects and their consequences with a small series of investigations. A spiked sample mixture was separated to six specified known SUN impurities as well as undefined degradation products with significant chromatographic resolution. The separation was undertaken on a column of C18 (150 mm x 4.6 mm, 3.5 µm) with a mobile phase flow volume of 1.0 ml in a minute in a gradient elution manner. The mobile phase component A was composed of 20 mM KH₂PO₄ (pH 7.0), while the mobile phase component B was acetonitrile. Compound detection was conducted out at 268 nm, with the column temperature kept fixed at 40 ^oC. Stress degradation samples were subjected to oxidation, acid, base, thermal, and photolysis consistent with the endorsements of “International Conference on Harmonization” (Q2) methodology. The established method for SUN and its associated impurities assessment was validated as stability indicating, precise, robust, specific, rugged, and accurate.

 

 

 

INTRODUCTION:

Sunitinib (SUN) is an indolinone-based tyrosine kinase blocker that is taken orally and has anti-neoplastic action¹. SUN inhibits angiogenesis as well as cell multiplication by suppressing the tyrosine kinase actions of vascular endothelial growth factor receptor two, platelet-derivative growth factor receptor b, including c-kit².

 

The FDA authorized SUN for the therapy of kidney cell carcinoma and imatinib- resistive gastrointestinal stromal tumor, and also therapy of locally established or metastatic pancreatic neuroendocrine carcinoma that are progressive and well-differentiated^3,4.

 

A comprehensive review of the literature works revealed that just a few quantitation methods were disclosed, notably SUN in human plasma applying the HPLC-UV method for the requisite threshold of sensitivity in order to assess pharmacologically appropriate concentration levels of SUN in the plasma concentration scope of 20–200 ng/ml⁵. Also, another study reports quantification of SUN using RP-HPLC with UV sensing detector, controlling the structural conversion (Z to E isomerization) using different matrixes and effect of temperature on the formation of isomers⁶. This study reported the impurity analysis where the authors tried to resolve the Z and E isomers, related compounds (N-oxide and Impurity-B) using RP-HPLC-UV method⁶. Another study states a simple HPTLC for determining SUN and probable impurities⁷.

 

No previous reports were found so far for simultaneous evaluation of SUN and associated impurities by using pareto charts responses evaluation approach through quality by design of experiments. Figure 1 shows structures of SUN and associated impurities (SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN). The current study established and validated a stability indicating assay technique according to ICH norms, as well as measurement of SUN and associated impurities that are part of the production process and degradation in the dose forms utilizing QbD principles.

 

MATRIALS AND METHODS:

Availability of data and Materials:

The active pharmaceutical ingredient, SUN (purity, 99.5%) and its related impurities (SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN) were obtained from SynZeal Research Pvt Ltd, Hyderabad, India. The SUN capsules “Sutent” (Aspar pharmaceuticals, Gurgaon) were bought from the local market. Acetonitrile (HPLC grade), potassium dihydrogen orthophosphate and di-potassium hydrogen phosphate, hydrogen peroxide, hydrochloric acid, sodium hydroxide had been procured at Merck, Darmstadt, in Germany. Water purification is performed using Millipore (Bedford, MA, USA) Milli-Q water-purification system and sent via 0.45µm membrane filter (Durapore, Millipore, Ireland) prior using.

 

Instrumentation:

A “Waters Alliance” HPLC analyzing system containing a photodiode array detector was utilized for SUN and its related impurities (SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN) HPLC analysis. “Waters Empower” software had been used to control the system. A 150mm, 4.6mm, 3.5µm specification YMC pack pro C18 column (YMC Co., Ltd., Tokyo) employed intended for chromatographic parting. Thermo Scientific's multifuge equipment was used to centrifuge all of the samples. The heating oven, photo stability chamber, as well as heating mantle were used during the specificity investigation. The statistics graphs were created using Design Expert (Ver.12) statistical software (Stat-Ease Inc., USA).

 

Figure 1: Chemical structures of (a) SUN (b) D-SUN (c) SUN-NO (d) SUN-EI (e) DF-SUN (f) SUN-AI (g) SUN-QS

 

Chromatographic conditions:

The analytes (SUN and its related impurities: SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN) separation was accomplished on YMC Pack pro C₁₈ (150mm × 4.6mm, 3.5µm) column maintained with 40˚C temperature employing gradient run mode. The mobile phase flow volume of 1.0ml per min consisting 0.02M K₂HPO₄ buffer was used as the aqueous constituent in mobile phase component A and organic constituent utilized was acetonitrile as mobile phase component B. The HPLC gradient program was set as per the following specification (T/% component B): 0.01/20, 40/35, 41/20, 45/20. A 0.45µm Millipore filter was employed to filter the mobile phase, prior to usage. The detection had been found by PDA detector set with 268nm wavelength, with 10 µL of sample injection volume.

 

Standard and sample preparation:

Every impurity (SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN) stock solutions were formulated by dissolving exactly weighed quantity of impurities in diluent, leading to a concentration 0.1mg/ml of every impurity through a well-mixed and degassed solution of water: acetonitrile in 20:80 (v/v) ratio was optimized by way of diluents and was used for the preparation of standard and test samples. The system suitability solution contained sunitinib (1mg/ml) and the specified impurities with 1.5µg/ml concentration.

 

Moving 0.2ml of standard stock solution into a 20ml capacity volumetric flask and filling volume up with diluent yielded the standard solution (0.01mg/ml). Solution containing an appropriately weighed amount of the capsule pellets corresponding to 100mg SUN within a 100ml volumetric flask comprising 70ml diluent yielded the test solution of 1 mg/ml. Chromatographic analysis for SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN was performed with supernatant liquid.

 

RESULTS AND DISCUSSION:

Method development followed by its optimization:

Structural features of SUN with its impurities (SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN) show the presence of groups with acceptable chromophoric ability and thus a HPLC connected with UV detector was chosen as the analytical technique in determining them in bulk and dosage forms.

 

Analytical Target Profile (Atp)

The ATP is a comprehensive summary of measuring systems which are necessary, and so when completed, will ensure an accurate evaluation of product quality throughout the product's lifespan. For the quantitative/ qualitative measurement of SUN (assay and related substances) in capsules dose form, an instance of ATP is given below.

 

Related substances:

The procedure must be capable enough in quantifying the itemized besides unspecified impurities of API in the existence of drug substance in addition to excipients which in most cases account to the majority of the matrix. Specificity, accuracy, linearity, and precision of the method should be well defined so that readings fall within ±20% of the true value for contaminant levels less than or equal to 0.15 percent, of 80% probability, and also within 15% of true value for impurity levels greater than 0.15 percent, with 90% probability.

 

Selection of Initial Method Conditions:

Screening Experiments:

Three different stationary phases were selected based on orthogonality and covering wide range of chemistries such as octadecylsilane (C₁₈), decyl silane (C₈) and Phenyl (4.6mm, 150mm, and 3.5µm). The experiments suggested were based on all permutation and combinations and the same were performed. Based on the screening experiments, it was observed that at pH 3 and 5, D-SUN and SUN-NO impurities were not well separated whereas at pH 7 best separations was observed among all the experiments. Therefore, the conditions with the best results were selected and taken further for optimization.

 

Evaluation of multiple factors in experimental design:

Optimized conditions for Design of Experiments (DOE):

For obtaining a method with desirable properties, the optimized method after screening should be evaluated for various parameters and also certain changes/ modifications need to be made (if necessary). In the present study, the following method conditions were selected as optimized: The initial column chosen was YMC Pack Pro C18, (150mm × 4.6mm, 3.5µm) activate at column oven of 40˚C with 1.0ml/min flow volume, K₂HPO₄ buffer at a concentration of 0.02 M was used as mobile phase component A also mobile phase component B was acetonitrile. The HPLC gradient manner program was maintained at: (T/%component B) 0.01/20, 40/35, 41/20, 45/20. And 0.45µm Millipore filter was used to filter the mobile phase prior use. PDA detector at 268nm detection wavelength was employed with 10µL injection quantity volume.

 

Factor - Response Election:

From screening and optimization tests, the crucial method parameters (CMP) and related method characteristics (responses) were discovered, and low and high factor limitations for testing, as well as acceptability limits for assessing the attributes, were defined. R1 represents the resolution of SUN-EI and SUN-AI, R2 represents the resolution across D-SUN and SUN-NO, R3 represents the resolution among SUN-DF and SUN, and R4 represents the tailing factor of SUN at test concentration. The responses R1, R2 and R3 characterize RS system performance strategy where all the system suitability criteria are evaluated for the impurities whereas R4 represents assay method performance design which is mainly related to the main peak (SUN). The present study possesses the unique advantage where in one DOE is capable of establishing together assay along with RS design space. Table 1 provides the factors (parameters) in addition to attributes (responses) along with explored experimental ranges. The buffer concentrations were fixed and therefore not assessed experimentally in the design.

 

Table 1: Factor - Response election findings

Factors including levels of study
S. No.	Factors		Units		Levels of study
					−1		0	+1
1	Flow rate		ml/min		0.8		1.0	1.2
2	Temperature		˚C		35		40	45
3	pH		-		6.9		7.0	7.1
Experiments designing and found responses
	Factor 1	Factor 2	Factor 3	Response 1		Response 2	Response 3	Response 4
Run	A: Temperature	B: Flow	Mobile phase A pH	Rs SUN-EI and SUN-AI		Rs D- SUN and SUN-NO	Rs SUN-DF and SUN	AY SUN TF
1	40	1	7	4.75		4.15	8.75	1.22
2	35	0.8	7.1	6.79		4.2	8.2	1.42
3	45	0.8	6.9	3.85		4.1	9.1	1.11
4	35	1.2	7.1	6.75		4.3	8.1	1.40
5	45	1.2	6.9	3.72		3.6	9	1.01
6	35	0.8	6.9	6.2		3.8	8.3	1.42
7	40	1	7	4.72		4.16	8.73	1.23
8	45	1.2	7.1	3.51		4.1	9.1	1.02
9	35	1.2	6.9	6.1		3.9	8.2	1.40
10	45	0.8	7.1	3.55		3.5	9.3	1.12
11	40	1	7	4.73		4.14	8.74	1.22

 

Selecting doe and design draught:

We chose and developed a statistical layout for factors that enables us to determine relevant main effects including two-factor interactions by using Design-Expert statistical software application. The number of components for this investigation was set at three, and a complete factorial design was recommended as a result. A complete factorial design is a straightforward method for gathering thorough information about the variables and associated interactions with the answers in as little as eight experimental runs and three center points. By changing the design to a higher order design, more factorial research may be done.

 

Doe statistical analysis and inferences:

All the responses were analyzed using the Design Expert 12 software. For each outcome, Supplementary Table 4 provides the p values of the appropriate ANOVA (adjusted for curvature). The p values were used to assess the degree of significance. A response term having a p value of less than 0.05 was found important, whereas one with a p value of larger than 0.05 was not.

 

Doe augmentation:

Within the examined range, the R1 pareto chart (Figure 2 (a)) shows that resolution among SUN-EI and SUN-AI impurities increases as column temperature decreases, although pH of mobile phase component A has limited influence while flow rate has minimal or even no effect. According to the R2 pareto chart (Figure 2 (b)), resolution among D-SUN and SUN-NO improves as the pH of mobile phase component A with flow rate rise, however column temperature has a negative influence within the investigated range. Within the examined range, the R3 pareto (Figure 2 (c)) chart shows that resolution among SUN-DF and SUN increases as column temperature rises, although pH of mobile phase component A has had no influence while flow rate has a slight negative effect. As per R4 pareto (Figure 2 (d)) chart, the tailing factor of SUN peak diminishes as column temperature rises, although flow rate has a little negative influence and mobile phase component A pH has no effect within the examined range.

 

Method validation:

The method optimized by using the DOE tool was found to be promising and was capable in separating SUN and its impurities. Interference peaks from the blank were not observed and the peaks corresponding to SUN with its impurities (SUN-EI, SUN-AI, D-SUN, SUN-NO, SUN-QS and DF-SUN) had been well resolved thru good peak shapes. To verify its fitness for its designated use, the improved method was subjected to technique validation according to the “ICH Q2 (R1)” standards⁸ and as same followed in the related study^9,10. The characteristics of system suitability were in line with the acceptance requirements. The relative standard deviation for standard regions of replicate injections was less than 5.0 percent, that the resolution between D-SUN and SUN-NO was better than 2.0 (Table 2), and that the relative retention duration of the impurity peak was comparable. The supplementary Figure 3 shows the system suitability solution, RS standard, and assay standard.

 

Specificity:

The methodology's specificity was evaluated by infusing the analyte using all of the documented constituents that are predicted to be contained within drug product. Separate solutions of diluents (blank), SUN sample, known impurities (SUN-AI, SUN-NO, D-SUN, SUN-QA, SUN-EI and SUN-DF), and even a spiked mixture comprising all of these components was injected into an HPLC at a concentration of 0.15 percent. Table 2 shows the retention time, RRTs, Resolution and peak purities for each component in the spiked solution.

 

Table 2: Selectivity data

Compound	tR (min)	RRT	RRF	Rs	Tailing factor
SUN-EI	11.952	0.376	1.00	-	1.1
SUN-AI	14.260	0.452	0.76	7.96	1.2
D-SUN	18.395	0.583	0.90	4.50	1.2
SUN-NO	19.901	0.631	1.09	5.96	1.2
SUN-QA	22.006	0.698	1.21	15.33	1.1
SUN-DF	27.177	0.862	0.80	10.37	1.1
SUN	31.536	1.000	1.00	5.27	1.1

t_R – Retention time; RRT – Relative retention time; RRF - Relative response factor; Rs- Resolution

 

Forced degradation studies:

The stress tests performed on the drug substance as well as drug product offer an insight of the molecule's intrinsic stability. It also mandates indispensable requirement of an analytical assessment processes employed with stability samples as stability indicating. The forced degradation study was performed as per “ICH Q1A (R2)” standards¹¹, “ICH Q1B” standards¹² and “ICH Q1E” standards^13,14. The various stress conditions which was used in the study were mentioned Supplementary Table 5. It also summarizes the mass balance, % total degradation, and peak purity data for stress trials. As indicated in the supplementary Figure 3 depicts the overlay chromatogram.

 

Precision:

Six samples of SUN capsule trial preparation actually prepared by spiking at limit of quantification level (LOQ) density of target concentration (1000 µg/ml) of impurities, blending solution to acquire a LOQ concentration (refer Table 3) of every impurity as well as analysing per the testing method, and then a standard solution made at 1.5µg/ml of all impurities (SUN-AI, SUN-NO, D-SUN, SUN-QA, SUN-EI and SUN-DF) linked to SUN. Intermediate precision also was investigated utilizing different columns and different days of analysis. Data from precision studies are provided in Table 3.

 

Accuracy:

A number of recovery experiments were worked out using the traditional standard addition approach to confirm the accuracy of the suggested method. Spiking impurities (SUN-AI, SUN-NO, D-SUN, SUN-QA, SUN-EI and SUN-DF) in test preparation there at limit of quantification (LOQ) level, 50 percent, 100 percent, 120 percent, and 150 percent (a notional concentration of approximately LOQ to 2.25µg/ml) of the standard concentration resulted in duplicate samples with results shown in Table 3.

 

Sensitivity:

Employing signal to noise ratio assessment, the approach was found to have a sensitive limit of detection (LOD) of contaminants of roughly 0.02%. At a signal-to-noise ratio of 10:1, the limits of quantitation (LOQs) for SUN and its impurities (SUN-AI, SUN-NO, D-SUN, SUN-QA, SUN-EI and SUN-DF) were established by inoculating a variety of dilute solutions having specified concentrations Table 3.

 

Linearity:

SUN impurities ((SUN-AI, SUN-NO, D-SUN, SUN-QA, SUN-EI and SUN-DF) series solution was generated and put into the HPLC system in concentrations varying from LOQ to 150 percent (2.25 µg/ml) of the reference concentration. Table 3 shows the results of the regression analysis of calibration curves.

 

Table 3: Regression, precision as well as accuracy data

Parameter	SUN	SUN-EI	SUN-AI	D-SUN	SUN-NO	SUN-QS	SUN-DF
Sensitivity Data
LOD (μg/ml)	0.13	0.12	0.10	0.10	0.10	0.11	0.12
LOQ (μg/ml)	0.39	0.39	0.30	0.30	0.31	0.35	0.37
Regression Data
Correlation coefficient	1.000	0.9999	0.9992	0.9996	0.9998	0.9990	0.9991
% of Y-intercept	-0.05	-0.05	-1.3	-1.5	-1.5	-1.8	-1.7
Precision data
Standard solution Precision (% RSD)	0.5	0.6	0.7	0.6	0.8	0.9	0.7
Intermediate precision (%RSD)	0.8	0.5	1.8	1.2	1.5	0.9	0.7
Precision at LOQ (%RSD)	2.5	2.1	2.2	1.7	2.3	2.4	1.8
Accuracy data
Spiked level	Recovery of impurities (%)
LOQ	-	99.5 ± 1.6	99.6±1.8	97.0±0.9	98.5±1.4	100.9±0.8	102.1±1.5
50%	-	100.6 ± 1.3	99.2 ± 0.6	100.9 ± 0.7	101.7 ± 0.4	100.1 ± 1.1	98.3±1.9
100%	-	99.2 ± 1.1	98.3 ± 0.8	99.5 ± 1.1	101.8 ± 1.5	99.1 ± 1.2	100.6±1.0
120%	-	98.4 ± 0.9	97.4 ± 0.7	98.4 ± 1.3	97.3 ± 0.6	97.9 ± 1.5	101.7±1.2
150%	-	99.9 ± 0.7	98.9 ± 0.5	98.9 ± 0.7	95.8 ± 0.1	100.4 ± 0.2	102.8±0.5

 

Figure 2: Pareto chart for responses (a). Resolution between SUN-EI and SUN-AI (b). Resolution between D-SUN AND SUN-NO (c). Resolution between SUN-DI and SUN (d). SUN assay Tailing factor

 

CONCLUSION:

A reversed-phase LC technique development methodology was devised and shown using work flow oriented QbD (quality by design) principles. Comprehending the method needs/goals (ATP-Analytical target profile) was the first step in the experiment approach, continued by method design, technique selection, risk appraisals, and DOE (Design of experiments) to mitigate experimental risk factors. The method is primarily dependent on sample structural knowledge with software-based decision assistance. This is a quick and effective strategy that reduces the quantity of time an analytical scientist invests developing methods and analysing data while examining a wide range of experimental factors. As a consequence, a chromatographic technique with a well comprehended MODR (Method operable design region) and control approach has been developed. When compared to traditional techniques, the resources spent establishing a method using an AQbD philosophy are much less across the lifespan of the method, notably when technique transfer difficulties are considered. The existing workflow has a gap in terms of data processing parameter refinement and simplifying, as well as interfacing with the DOE statistic package to enable an automated QbD workflow of samples to MODR. The simple HPLC approach described in this work is ideal for separation also quantification of contaminants in pharmaceutical matrices as well as interference by excipients and other related chemicals. The approach is trustworthy and sufficiently robust, as evidenced by its analytical performance and also the findings acquired from formulation analysis. Finally, the HPLC approach proposed in this work is ideal for quality control analysis of complex pharmaceutical preparations including sunitinib and related contaminants due to its high sensitivity, superior selectivity, accuracy, and reproducibility.

 

ACKNOWLEDGEMENTS:

The Author’s express their gratitude to Vignan's University for their inspiration and for sharing the article for publication.

 

CONFLICT OF INTEREST:

There are no conflicts of interest declared by the authors.

 

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Accepted on 27.07.2022           © RJPT All right reserved

Research J. Pharm. and Tech 2023; 16(4):1999-2005.