Optimization of Sustained-Release Propranolol Dosage form Using Factorial Design and Response Surface Methodology

Huang, Yaw‐Bin; Tsai, Yi‐Hung; Yang, Wan-Chiech; Chang, Jyh-Jong; Wu, Pao‐Chu

doi:10.1248/bpb.27.1626

Cited by 34 publications

(19 citation statements)

References 20 publications

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“…Several workers have applied optimization techniques based response surface methodology (RSM) utilizing polynomial equation or composite design for the design of controlled release formulations. [18][19][20] The optimization procedure involves systematic formulations designs to minimize the number of trials, and analyze the response surfaces in order to realize the eŠect of causal factors and to achieve appropriate formulations with target goals.…”

Section: Celecoxib (Ccx) (4-[5-(4-methylphenyl)-3-mentioning

confidence: 99%

Development and <i>In vitro</i> Evaluation of Oral Controlled Release Formulations of Celecoxib Using Optimization Techniques

Chandran¹,

Ravi²,

Saha³

2006

YAKUGAKU ZASSHI

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The objective of this study was to develop controlled release matrix embedded formulations of celecoxib (CCX) as candidate drug using hydroxy propyl methyl cellulose (HPMC) and ethyl cellulose (EC), either alone or in combination, using optimization techniques like polynomial method and composite design. This would enable development of controlled release formulations with predictable and better release characteristics in lesser number of trials. Controlled release matrix tablets of CCX were prepared by wet granulation method. The in vitro release rate studies were carried out in USP dissolution apparatus (paddle method) in 900 ml of sodium phosphate buŠer (pH 7.4) with 1％ v/v tween-80. The in vitro drug release data was suitably transformed and used to develop mathematical models usingˆrst order polynomial equation and composite design techniques of optimization. In the formulations prepared using HPMC alone, the release rate decreased as the polymer proportion in the matrix base was increased. Whereas in case of formulations prepared using EC alone, only marginal diŠerence was observed in the release rate upon increasing the polymer proportion. In case of formulations containing combination of HPMC and EC, the release of the drug was found to be dependent on the relative proportions of HPMC and EC used in the tablet matrix. The release of the drug from these formulations was extended up to 21 h indicating they can serve as once daily controlled release formulations for CCX. Mathematical analysis of the release kinetics indicates a near approximate Fickian release character for most of the designed formulations. Mathematical equation developed by transforming the in vitro release data using composite design model showed better correlation between observed and predicted t 50％ (time required for 50％ of the drug release) when compared toˆrst order polynomial equation model. The equation thus developed can be used to predict the release characteristics of the drug from matrix embedded formulations depending upon the proportion of HPMC and EC used in the formulation.

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Section: Celecoxib (Ccx) (4-[5-(4-methylphenyl)-3-mentioning

confidence: 99%

Development and <i>In vitro</i> Evaluation of Oral Controlled Release Formulations of Celecoxib Using Optimization Techniques

Chandran¹,

Ravi²,

Saha³

2006

YAKUGAKU ZASSHI

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“…A typical design space is established as a superposition of the response surfaces for each quality attribute generated by the response surface method (RSM) using the DoE results. [5][6][7][8][9][10][11] RSM includes statistical analyses such as multiple linear regression analysis 12) and artificial neural networks. 13,14) Takayama et al developed RSM-S, a novel RSM that incorporates multivariate spline interpolation.…”

mentioning

confidence: 99%

Design Space Construction of Multiple Dose-Strength Tablets Utilizing Bayesian Estimation Based on One Set of Design-of-Experiments

Maeda

Suzuki

Takayama

2012

CHEMICAL & PHARMACEUTICAL BULLETIN

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Design spaces for multiple dose strengths of tablets were constructed using a Bayesian estimation method with one set of design of experiments (DoE) of only the highest dose-strength tablet. The lubricant blending process for theophylline tablets with dose strengths of 100, 50, and 25 mg is used as a model manufacturing process in order to construct design spaces. The DoE was conducted using various Froude numbers (X 1 ) and blending times (X 2 ) for theophylline 100-mg tablet. The response surfaces, design space, and their reliability of the compression rate of the powder mixture (Y 1 ), tablet hardness (Y 2 ), and dissolution rate (Y 3 ) of the 100-mg tablet were calculated using multivariate spline interpolation, a bootstrap resampling technique, and self-organizing map clustering. Three experiments under an optimal condition and two experiments under other conditions were performed using 50-and 25-mg tablets, respectively. The response surfaces of the highest-strength tablet were corrected to those of the lower-strength tablets by Bayesian estimation using the manufacturing data of the lower-strength tablets. Experiments under three additional sets of conditions of lower-strength tablets showed that the corrected design space made it possible to predict the quality of lower-strength tablets more precisely than the design space of the highest-strength tablet. This approach is useful for constructing design spaces of tablets with multiple strengths.Key words quality by design; design of experiments; multivariate regression; modelingThe International Conference on Harmonization (ICH) 1) has outlined Quality by Design (QbD) as a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management. According to the QbD principle, pharmaceutical quality should not be tested via day-to-day release testing, instead, it should be elaborated by design in advance. One of the most significant aspects in the QbD concept is the establishment of a design space based on a multidimensional combination of input formulation parameters, process parameters, or material attributes that have been shown to provide assurance of quality attributes 2) . A design of experiments (DoE) study has been effectively used in order to construct a design space.3,4) DoE is a useful method for systematically understanding the relationship between input parameters and output quality attributes. A typical design space is established as a superposition of the response surfaces for each quality attribute generated by the response surface method (RSM) using the DoE results.5-11) RSM includes statistical analyses such as multiple linear regression analysis 12) and artificial neural networks. 13,14) Takayama et al. developed RSM-S, a novel RSM that incorporates multivariate spline interpolation.15) RSM-S is an effective tool for obtaining reliable response surfaces of nonlinear phenomena and calculating optimal solutions. A boots...

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“…2,3) A response surface method (RSM) is useful for visual understanding of the derived multidimensional relationships. [4][5][6][7] However, the multidimensional relationships that are observed in pharmaceutical development studies are often nonlinear, and therefore predictions based on the linear response surface model using polynomial equations often exhibit poor estimation. 8) To overcome this problem, we have developed a nonlinear RSM incorporating multivariate spline interpolation (RSM-S) that enables us to understand nonlinear multidimensional relationships among causal factors and response variables and to estimate the high-integrity design space.…”

Section: )mentioning

confidence: 99%

A Novel Approach to Establishing the Design Space for the Oral Formulation Manufacturing Process

Norioka

Hayashi

Onuki

et al. 2013

CHEMICAL & PHARMACEUTICAL BULLETIN

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A novel approach to establishing the design space for the oral formulation manufacturing process was investigated. A response surface method incorporating multivariate spline interpolation was applied to overcome the nonlinear problem, which is always problematic in pharmaceutical development studies, and a bootstrap resampling technique, polynomial approximation technique, and 95% confidence intervals based on a nonparametric approach were applied to estimate the reliability of the established design space derived from the nonlinear response surface model. The critical quality attributes (CQAs) of intermediate material rather than the critical process parameters (CPPs) were chosen as the causal factors for the response variables, which were CQAs of the final product to avoid scale-gap and equipment-gap. This enabled the effective use of data sets accumulated during all pharmaceutical development studies. It was confirmed that a conservative border as well as an optimistic border of the design space for practical use was obtained considering the variability of the border of the design spaces on nonlinear response surfaces. Furthermore, the nonlinear response surface model using CQAs of intermediate material derived from data sets of a laboratory scale study and pilot scale studies could predict the CQA of the final product (2.5 h dissolution of commercial-scale study) with high accuracy. Consequently, the proposed novel approach overcame all of the difficulties for the manufacturing process development of oral formulations and this is the first study to demonstrate the effectiveness of the design space using CQA of intermediate material for the oral formulation manufacturing process.Key words design space; experimental design; response surface method; multivariate statistical analysis; confidence interval; critical quality attribute In recent years, the "quality by design" (QbD) concept has been introduced by the International Conference on Harmonization (ICH) Q8 guideline. This guideline has recommended establishing a science-based rationale in pharmaceutical development studies for both formulation development and manufacturing process development. The guideline also noted that the multidimensional relationships of causal factors that have been demonstrated to provide specified target values of response variables are defined as the design space, and the establishment of the design space based on scientific understanding gained from pharmaceutical development studies and manufacturing experience provides the regulatory flexibility. 1)Therefore, the establishment of the design space is important not only to achieve a higher level of scientific understanding, but also to gain regulatory flexibility.To establish the design space, a design-of-experiments (DoE) approach was used effectively to determine the multidimensional relationships among causal factors and response variables.2,3) A response surface method (RSM) is useful for visual understanding of the derived multidimensional relationships.4-7) However, the m...

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Optimization of Sustained-Release Propranolol Dosage form Using Factorial Design and Response Surface Methodology

Cited by 34 publications

References 20 publications

Development and <i>In vitro</i> Evaluation of Oral Controlled Release Formulations of Celecoxib Using Optimization Techniques

Development and <i>In vitro</i> Evaluation of Oral Controlled Release Formulations of Celecoxib Using Optimization Techniques

Design Space Construction of Multiple Dose-Strength Tablets Utilizing Bayesian Estimation Based on One Set of Design-of-Experiments

A Novel Approach to Establishing the Design Space for the Oral Formulation Manufacturing Process

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