2010Zhongguo xin yao zazhiRequires access

Formulation optimization of Sinisan osmotic pump tablets by central composite design-response surface method

Weisan Pan

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Abstract

Objective: To optimize the formulation of Sinisan osmotic pump tablets by a central composite design-response surface method.Methods: Independent variables were the N750 and NaCl contents in drug layer,PEG 4000 content in coating solution and weight gain.Dependent variables were percentages of in vitro cumulative releases at 12 h and correlation coefficient of drug release profile.Multilinear,quadratic and third-order quadraric models were used to estimate the relationship between the dependent and the independent variables.The cubic polynomial response surface model and contour map were delineated,the formulation was optimized by determination of the overlap region,and the experimental data were compared with the predicted values.Results: The cubic polynomial was the best fitting model,and the experimental data from optimized formulation were very close to the predictive values of indicators.The ultimate cumulative release was 97.86%,and correlation coefficient of drug release profile was 0.995 6.Conclusion: The central composite design-response surface method based on the cubic polynomial model optimizes the formulation of Sinisan osmotic pump tablets.The optimized formulation displays a complete drug delivery and zero-order release rate.

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Objective: To optimize the formulation of Sinisan osmotic pump tablets by a central composite design-response surface method.Methods: Independent variables were the N750 and NaCl contents in drug layer,PEG 4000 content in coating solution and weight gain.Dependent variables were percentages of in vitro cumulative releases at 12 h and correlation coefficient of drug release profile.Multilinear,quadratic and third-order quadraric models were used to estimate the relationship between the dependent and the independent variables.The cubic polynomial response surface model and contour map were delineated,the formulation was optimized by determination of the overlap region,and the experimental data were compared with the predicted values.Results: The cubic polynomial was the best fitting model,and the experimental data from optimized formulation were very close to the predictive values of indicators.The ultimate cumulative release was 97.86%,and correlation coefficient of drug release profile was 0.995 6.Conclusion: The central composite design-response surface method based on the cubic polynomial model optimizes the formulation of Sinisan osmotic pump tablets.The optimized formulation displays a complete drug delivery and zero-order release rate.

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Available abstract

Objective: To optimize the formulation of Sinisan osmotic pump tablets by a central composite design-response surface method.Methods: Independent variables were the N750 and NaCl contents in drug layer,PEG 4000 content in coating solution and weight gain.Dependent variables were percentages of in vitro cumulative releases at 12 h and correlation coefficient of drug release profile.Multilinear,quadratic and third-order quadraric models were used to estimate the relationship between the dependent and the independent variables.The cubic polynomial response surface model and contour map were delineated,the formulation was optimized by determination of the overlap region,and the experimental data were compared with the predicted values.Results: The cubic polynomial was the best fitting model,and the experimental data from optimized formulation were very close to the predictive values of indicators.The ultimate cumulative release was 97.86%,and correlation coefficient of drug release profile was 0.995 6.Conclusion: The central composite design-response surface method based on the cubic polynomial model optimizes the formulation of Sinisan osmotic pump tablets.The optimized formulation displays a complete drug delivery and zero-order release rate.

Key concepts: Response surface methodology, Central composite design, Quadratic function, Polynomial and rational function modeling, Composite number, Coefficient of determination, Correlation coefficient, Polynomial

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