Creation of a Tablet Database Containing Several Active Ingredients and Prediction of Their Pharmaceutical Characteristics Based on Ensemble Artificial Neural Networks

Takagaki, Keisuke; Arai, Hiroaki; Takayama, K.

doi:10.1002/jps.22135

Cited by 31 publications

(18 citation statements)

References 15 publications

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“…[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. 15) RSM-S is an effective tool for obtaining reliable response surfaces of nonlinear phenomena and calculating optimal solutions.…”

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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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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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“…In different research fi elds, it has been proposed that the performance of a well-designed MLP network is comparable with that achieved using classical statistical techniques (Rowe and Roberts, 1998), and therefore is suitable for a wide range of applications including the selection of raw materials and process variables in the development and optimization of formulations as tablets (Takagaki et al, 2010), microspheres (Labouta et al, 2009), microparticles (Leonardi et al, 2009), nanoparticles (Ali et al, 2009), emulsions (Agatonovic-Kustrin et al, 2003Glass et al, 2005;Wei et al, 2008;Gasperlin et al, 2008), hard capsules (Guo et al, 2002) and gels (Lee et al, 2008); the development of analytical procedures and/or interpretation of analytical data (Agatonovic-Kustrin et al, 1998;; establishment of in vivo-in vitro correlations (De Matas 2007;Fatouros et al, 2008); establishment of a quantitative structureproperty relationship (Andrea and Kalayeh, 1991); and establishment of the relationship between a complex formulation and its effi ciency (Qiao et al, 2010;Zhou et al, 2010).…”

Section: Artifi Cial Neural Network Fundamentalsmentioning

confidence: 98%

“…Most classical neural networks involve a trial and error approach to fi nd the optimal architecture, transfer function and learning paradigm (Takagaki et al, 2010). The network architecture is an aspect to study.…”

Section: Limitations Of Artifi Cial Neural Networkmentioning

confidence: 99%

Artificial neural networks technology to model, understand, and optimize drug formulations

Landín

Rowe

2013

Formulation Tools for Pharmaceutical Development

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“…In recent years, there has been increasing interest in these systems with regard to formulation (4) or process optimization (5,6), often in association with a design space approach (2,7), as these examples from the field of solid dose forms supports. Systems of great interest are those in which the physicochemical properties of the raw materials are taken into account in the prediction of the product quality attributes (8)(9)(10). In these cases, however, considerable care must be taken concerning the selection of the appropriate inputs and learning parameters of the ANNs: an inappropriate (small, narrow range, etc.)…”

Section: Introductionmentioning

confidence: 99%

Application of Physicochemical Properties and Process Parameters in the Development of a Neural Network Model for Prediction of Tablet Characteristics

et al. 2013

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Abstract. The importance of in silico modeling in the pharmaceutical industry is continuously increasing. The aim of the present study was the development of a neural network model for prediction of the postcompressional properties of scored tablets based on the application of existing data sets from our previous studies. Some important process parameters and physicochemical characteristics of the powder mixtures were used as training factors to achieve the best applicability in a wide range of possible compositions. The results demonstrated that, after some pre-processing of the factors, an appropriate prediction performance could be achieved. However, because of the poor extrapolation capacity, broadening of the training data range appears necessary.

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Creation of a Tablet Database Containing Several Active Ingredients and Prediction of Their Pharmaceutical Characteristics Based on Ensemble Artificial Neural Networks

Cited by 31 publications

References 15 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

Artificial neural networks technology to model, understand, and optimize drug formulations

Application of Physicochemical Properties and Process Parameters in the Development of a Neural Network Model for Prediction of Tablet Characteristics

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