Effect of an Experimental Design for Evaluating the Nonlinear Optimal Formulation of Theophylline Tablets Using a Bootstrap Resampling Technique

Arai, Hiroaki; Suzuki, Tatsuya; Kaseda, Chosei; Takayama, K.

doi:10.1248/cpb.57.572

Cited by 18 publications

(17 citation statements)

References 18 publications

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“…The standard deviation of the response surfaces, which represented the reliability of the models, was evaluated via bootstrap resampling and SOM clustering using dataNESIA™ and Viscovery ® (Eudapics Software Gmbh, Austria), respectively, according to a previously reported method. 17,18) Bootstrap resampling was performed 200 times.…”

Section: Construction Of Response Surfaces and Design Space Of 100 Mgmentioning

confidence: 99%

“…15) RSM-S is an effective tool for obtaining reliable response surfaces of nonlinear phenomena and calculating optimal solutions. A bootstrap resampling technique and self-organizing map (SOM) clustering have been adopted to qualify the reliability of an optimal solution [16][17][18] and response surfaces. 19) In general, the components of commercial tablets containing the same active pharmaceutical ingredient are similar, regardless of the dose strength, because different strengths of tablets were developed on the basis of the same formulation development concept.…”

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confidence: 99%

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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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Section: Construction Of Response Surfaces and Design Space Of 100 Mgmentioning

confidence: 99%

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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“…SLN was prepared according to previous articles with some modification 8,18 by the following melt-emulsion ultrasonication and low temperature-solidification methods. In brief, aqueous and oil phases were separately prepared in glass vials.…”

Section: Preparation Of Slnmentioning

confidence: 99%

“…In particular, it is necessary to establish a science-based rationale and a design space to identify multidimensional combinations of the many causal factors that determine target quality. 8 However, it is difficult (and inefficient) to obtain an optimized formulation with rapid and complete dissolution using a traditional formulation screening and optimization process (one-factor-at-a-time). The one-factor-at-a-time optimization also ignores interaction between factors and may call for an unnecessarily large number of runs.…”

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confidence: 99%

Development and optimization of solid lipid nanoparticle formulation for ophthalmic delivery of chloramphenicol using a Box-Behnken design

Hao

Fang²,

Zhou³

et al. 2011

IJN

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The purpose of the present study was to optimize a solid lipid nanoparticle (SLN) of chloramphenicol by investigating the relationship between design factors and experimental data using response surface methodology. A Box-Behnken design was constructed using solid lipid ( X 1 ), surfactant ( X 2 ), and drug/lipid ratio ( X 3 ) level as independent factors. SLN was successfully prepared by a modified method of melt-emulsion ultrasonication and low temperature-solidification technique using glyceryl monostearate as the solid lipid, and poloxamer 188 as the surfactant. The dependent variables were entrapment efficiency (EE), drug loading (DL), and turbidity. Properties of SLN such as the morphology, particle size, zeta potential, EE, DL, and drug release behavior were investigated, respectively. As a result, the nanoparticle designed showed nearly spherical particles with a mean particle size of 248 nm. The polydispersity index of particle size was 0.277 ± 0.058 and zeta potential was −8.74 mV. The EE (%) and DL (%) could reach up to 83.29% ± 1.23% and 10.11% ± 2.02%, respectively. In vitro release studies showed a burst release at the initial stage followed by a prolonged release of chloramphenicol from SLN up to 48 hours. The release kinetics of the optimized formulation best fitted the Peppas–Korsmeyer model. These results indicated that the chloramphenicol-loaded SLN could potentially be exploited as a delivery system with improved drug entrapment efficiency and controlled drug release.

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“…16) Moreover, we confirmed that self-organizing map (SOM) clustering 17) reinforced the applicability of the novel evaluation method. 18) Therefore, we quantitatively evaluated the reliability of the response surface and the design space predicted by RSM S using the BS and SOM techniques. In this study, we used an experimental dataset of mefenamic acid tablets prepared by a high-shear granulation method.…”

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confidence: 99%

Reliability Evaluation of the Design Space of the Granulation Process of Mefenamic Acid Tablets Using a Bootstrap Resampling Technique

Arai

Suzuki

Yada

et al. 2011

CHEMICAL & PHARMACEUTICAL BULLETIN

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The design space of the granulation process of mefenamic acid tablets, based on Box and Behnken design datasets, was described by a response surface method incorporating multivariate spline interpolation. The reliability of the optimal solutions and the acceptance ranges were evaluated by a bootstrap (BS) resampling technique. The distribution of the BS optimal solutions was almost symmetrical; however, several solutions, which were quite different from the original solution, were mixed. The reason for this problem was considered to be the mixing of the global and the local optima. Therefore, we applied self-organizing map (SOM) clustering for dividing data into several clusters and identified the cluster containing the global optima. The accuracy and reproducibility of the optimal solution in the cluster containing the optimal solution were quantitatively evaluated. In addition, the response surfaces modeled from all the BS datasets contained in the cluster were plotted into the same coordinates with the original response surface. The plots of BS optimal solutions were distributed around the original solution. Moreover, the average of all the BS response surfaces sufficiently corresponded with the original response surface. The conservative limits of the 95% confidence intervals of the acceptance ranges in three response variables could be calculated using the standard deviations of the BS response surfaces. Consequently, it was considered that a novel evaluation method based on BS resampling and SOM could be used for quantitatively evaluating the precision of the nonlinear response surface model.

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Effect of an Experimental Design for Evaluating the Nonlinear Optimal Formulation of Theophylline Tablets Using a Bootstrap Resampling Technique

Cited by 18 publications

References 18 publications

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

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

Development and optimization of solid lipid nanoparticle formulation for ophthalmic delivery of chloramphenicol using a Box-Behnken design

Reliability Evaluation of the Design Space of the Granulation Process of Mefenamic Acid Tablets Using a Bootstrap Resampling Technique

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