Contribution of the physicochemical properties of active pharmaceutical ingredients to tablet properties identified by ensemble artificial neural networks and Kohonen's self-organizing maps.

The aim of this study was to create a tablet database for use in designing tablet formulations. We focused on the contribution of active pharmaceutical ingredients (APIs) to tablet properties such as hardness and disintegration time (DT). Before we investigated the effects of the APIs, we optimized the tablet base formulation (placebo tablet) according to an expanded simplex search. The optimal placebo tablet showed sufficient hardness and rapid disintegration. We then tested 14 kinds of compounds as the model APIs. The APIs were characterized in terms of their physicochemical properties using Kohonen's self-organizing maps. We also prepared model tablets by incorporating the APIs into the optimal placebo tablet, and then examined the tablet properties, including tensile strength and DT. On the basis of the experimental data, an ensemble artificial neural network incorporating general regression analysis was conducted. A reliable model of the correlation between the physicochemical properties of the APIs and the tablet properties was thus constructed. From the correlation model, we clarified the detailed contributions of each physicochemical property to the tablet attributes.

[1]  H. Sakamoto,et al.  Study of standard tablet formulation based on fluidized-bed granulation. , 1998, Drug development and industrial pharmacy.

[2]  Andrew Hunter,et al.  Application of neural networks and sensitivity analysis to improved prediction of trauma survival , 2000, Comput. Methods Programs Biomed..

[3]  S. Deming,et al.  Simplex optimization of analytical chemical methods , 1974 .

[4]  Peter Wolschann,et al.  Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes , 2009 .

[5]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[6]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[7]  E. Shek,et al.  Simplex search in optimization of capsule formulation. , 1980, Journal of pharmaceutical sciences.

[8]  Simultaneous optimization of response variables in protein mixture formulation: constrained simplex method approach , 2003 .

[9]  K. Zuurman,et al.  Porosity expansion of tablets as a result of bonding and deformation of particulate solids , 1996 .

[10]  G. R. Hext,et al.  Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .

[11]  H. Sunada,et al.  Granulation of Acetaminophen by a Rotating Fluidized-Bed Granulator , 2000, Pharmaceutical development and technology.

[12]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[13]  Hiroaki Arai,et al.  Creation of a tablet database containing several active ingredients and prediction of their pharmaceutical characteristics based on ensemble artificial neural networks. , 2010, Journal of pharmaceutical sciences.

[14]  M. C. Bonferoni,et al.  Characteristics of hydroxypropyl methylcellulose influencing compactibility and prediction of particle and tablet properties by infrared spectroscopy. , 2003, Journal of pharmaceutical sciences.

[15]  M S De Saavedra,et al.  Application of a Mixed Optimization Strategy in the Design of a Pharmaceutical Solid Formulation at Laboratory Scale , 2001, Drug development and industrial pharmacy.

[16]  L. Bailey,et al.  Simplex Optimization of the Blue Tetrazolium Assay Procedure for α-Ketol Steroids , 1985 .

[17]  Pierre Bruneau,et al.  Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets , 2001, J. Chem. Inf. Comput. Sci..