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Quality by Design-Based Development of a Stability-Indicating RP-HPLC Method for the Simultaneous Determination of Methylparaben, Propylparaben, Diethylamino Hydroxybenzoyl Hexyl Benzoate, and Octinoxate in Topical Pharmaceutical Formulation.

Roy C, Chakrabarty J - Sci Pharm (2014)

Bottom Line: Recovery for all the components was found to be in the range of 98-102%.The design expert with ANOVA software with the linear model was applied and a 2(4) full factorial design was employed to estimate the model coefficients and also to check the robustness of the method.Results of the two-level full factorial design, 2(4) with 20 runs including four centrepoint analysis based on the variance analysis (ANOVA), demonstrated that all four factors, as well as the interactions of resolution between DAHHB and OCT are statistically significant.

View Article: PubMed Central - PubMed

Affiliation: Analytical Research and Development, Integrated Product Development, Dr. Reddy's Laboratories Ltd., Bachupally, Hyderabad-500090, Andhra Pradesh, India. ; Department of Chemistry, National Institute of Technology, Durgapur-713209, West Bengal, India.

ABSTRACT
A stability-indicating RP-HPLC method has been developed and validated for the simultaneous determination of methylparaben (MP), propylparaben (PP), diethylamino hydroxybenzoyl hexyl benzoate (DAHHB), and octinoxate (OCT) in topical pharmaceutical formulation. The desired chromatographic separation was achieved on the Kinetex(TM) C18 (250 × 4.6 mm, 5 μm) column using gradient elution at 257 nm detection wavelength. The optimized mobile phase consisted of a buffer : acetonitrile : tetrahydrofuran (60 : 30 : 10, v/v/v) as solvent A and acetonitrile : tetrahydrofuran (70 : 30, v/v) as solvent B. The method showed linearity over the range of 0.19-148.4 μg/mL, 0.23-15.3 μg/mL, 1.97-600.5 μg/mL, and 1.85-451.5 μg/mL for MP, PP, DAHHB, and OCT, respectively. Recovery for all the components was found to be in the range of 98-102%. The stability-indicating capability of the developed method was established by analysing the forced degradation samples in which the spectral purity of MP, PP, DAHHB, and OCT, along with the separation of the degradation products from the analyte peaks, was achieved. The proposed method was successfully applied for the quantitative determination of MP, PP, DAHHB, and OCT in the lotion sample. The design expert with ANOVA software with the linear model was applied and a 2(4) full factorial design was employed to estimate the model coefficients and also to check the robustness of the method. Results of the two-level full factorial design, 2(4) with 20 runs including four centrepoint analysis based on the variance analysis (ANOVA), demonstrated that all four factors, as well as the interactions of resolution between DAHHB and OCT are statistically significant.

No MeSH data available.


Related in: MedlinePlus

Three-dimensional plot of the full factorial for the predicted response resolution between the DAHHB and OCT peaks plotted on the y–axis as a function of factor A (flow rate) and B (column temperature); fixed factor: C (mobile phase A % tetrahydrofuran 10.0%) and D (mobile phase B % tetrahydrofuran 30.0%)
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Figure 6: Three-dimensional plot of the full factorial for the predicted response resolution between the DAHHB and OCT peaks plotted on the y–axis as a function of factor A (flow rate) and B (column temperature); fixed factor: C (mobile phase A % tetrahydrofuran 10.0%) and D (mobile phase B % tetrahydrofuran 30.0%)

Mentions: R2 refers to the proportion of the variation in the dependent variable accounted for by the independent variable. When R2 equals 1, the relationship is perfect and all values of the dependent and independent variables lie on a straight line. Adjusted R2 is the change of R2 that adjusts the number of terms in a model. It calculates the proportion of the variation in the dependent variable accounted by the explanatory variables. R2 always increases when a new term is added to a model, but adjusted R2 increases only if the new term improves the model. The adjusted R2 can be negative, and will always be less than or equal to R2. The predicted R2 indicates how well a regression model predicts responses for new observations. This statistic helps to determine when the model fits the original data but is less capable of providing valid predictions for new observations. Like adjusted R2, predicted R2 can be negative and it is always lower than R2. Table 3 shows the R2 value (0.9), which indicates that 90 % of the data variability was successfully explained by the model. This means that with a slight change in temperature, flow rate, and % tetrahydrofuran composition in the mobile phase during analysis, the resolution between the DAHHB and OCT peaks will not be affected. Hence, from this data generated by the model, it can be expounded that the resolution between the DAHHB and OCT peaks decreases with an increase in column temperature and increases with increasing % tetrahydrofuran composition in the mobile phase as shown in Figure 6.


Quality by Design-Based Development of a Stability-Indicating RP-HPLC Method for the Simultaneous Determination of Methylparaben, Propylparaben, Diethylamino Hydroxybenzoyl Hexyl Benzoate, and Octinoxate in Topical Pharmaceutical Formulation.

Roy C, Chakrabarty J - Sci Pharm (2014)

Three-dimensional plot of the full factorial for the predicted response resolution between the DAHHB and OCT peaks plotted on the y–axis as a function of factor A (flow rate) and B (column temperature); fixed factor: C (mobile phase A % tetrahydrofuran 10.0%) and D (mobile phase B % tetrahydrofuran 30.0%)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4318166&req=5

Figure 6: Three-dimensional plot of the full factorial for the predicted response resolution between the DAHHB and OCT peaks plotted on the y–axis as a function of factor A (flow rate) and B (column temperature); fixed factor: C (mobile phase A % tetrahydrofuran 10.0%) and D (mobile phase B % tetrahydrofuran 30.0%)
Mentions: R2 refers to the proportion of the variation in the dependent variable accounted for by the independent variable. When R2 equals 1, the relationship is perfect and all values of the dependent and independent variables lie on a straight line. Adjusted R2 is the change of R2 that adjusts the number of terms in a model. It calculates the proportion of the variation in the dependent variable accounted by the explanatory variables. R2 always increases when a new term is added to a model, but adjusted R2 increases only if the new term improves the model. The adjusted R2 can be negative, and will always be less than or equal to R2. The predicted R2 indicates how well a regression model predicts responses for new observations. This statistic helps to determine when the model fits the original data but is less capable of providing valid predictions for new observations. Like adjusted R2, predicted R2 can be negative and it is always lower than R2. Table 3 shows the R2 value (0.9), which indicates that 90 % of the data variability was successfully explained by the model. This means that with a slight change in temperature, flow rate, and % tetrahydrofuran composition in the mobile phase during analysis, the resolution between the DAHHB and OCT peaks will not be affected. Hence, from this data generated by the model, it can be expounded that the resolution between the DAHHB and OCT peaks decreases with an increase in column temperature and increases with increasing % tetrahydrofuran composition in the mobile phase as shown in Figure 6.

Bottom Line: Recovery for all the components was found to be in the range of 98-102%.The design expert with ANOVA software with the linear model was applied and a 2(4) full factorial design was employed to estimate the model coefficients and also to check the robustness of the method.Results of the two-level full factorial design, 2(4) with 20 runs including four centrepoint analysis based on the variance analysis (ANOVA), demonstrated that all four factors, as well as the interactions of resolution between DAHHB and OCT are statistically significant.

View Article: PubMed Central - PubMed

Affiliation: Analytical Research and Development, Integrated Product Development, Dr. Reddy's Laboratories Ltd., Bachupally, Hyderabad-500090, Andhra Pradesh, India. ; Department of Chemistry, National Institute of Technology, Durgapur-713209, West Bengal, India.

ABSTRACT
A stability-indicating RP-HPLC method has been developed and validated for the simultaneous determination of methylparaben (MP), propylparaben (PP), diethylamino hydroxybenzoyl hexyl benzoate (DAHHB), and octinoxate (OCT) in topical pharmaceutical formulation. The desired chromatographic separation was achieved on the Kinetex(TM) C18 (250 × 4.6 mm, 5 μm) column using gradient elution at 257 nm detection wavelength. The optimized mobile phase consisted of a buffer : acetonitrile : tetrahydrofuran (60 : 30 : 10, v/v/v) as solvent A and acetonitrile : tetrahydrofuran (70 : 30, v/v) as solvent B. The method showed linearity over the range of 0.19-148.4 μg/mL, 0.23-15.3 μg/mL, 1.97-600.5 μg/mL, and 1.85-451.5 μg/mL for MP, PP, DAHHB, and OCT, respectively. Recovery for all the components was found to be in the range of 98-102%. The stability-indicating capability of the developed method was established by analysing the forced degradation samples in which the spectral purity of MP, PP, DAHHB, and OCT, along with the separation of the degradation products from the analyte peaks, was achieved. The proposed method was successfully applied for the quantitative determination of MP, PP, DAHHB, and OCT in the lotion sample. The design expert with ANOVA software with the linear model was applied and a 2(4) full factorial design was employed to estimate the model coefficients and also to check the robustness of the method. Results of the two-level full factorial design, 2(4) with 20 runs including four centrepoint analysis based on the variance analysis (ANOVA), demonstrated that all four factors, as well as the interactions of resolution between DAHHB and OCT are statistically significant.

No MeSH data available.


Related in: MedlinePlus