Active ingredient

Biology

Materials science

Computer science

Self-organizing map

Pattern recognition (psychology)

Pharmacology

Artificial intelligence

Medicine

Artificial neural network

Biological system

Journal of Pharmaceutical Sciences

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. 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.

Contribution of the Physicochemical Properties of Active Pharmaceutical Ingredients to Tablet Properties Identified by Ensemble Artificial Neural Networks and Kohonen's Self-Organizing Maps