Keisuke Takagaki
Hoshi University
ChemistryOrally disintegrating tabletActive ingredientArtificial neural networkExperimental data
What is this?
Publications 3
#1Keisuke Takagaki (Hoshi University)H-Index: 2
#2Terushi Ito (University of Toyama)
Last. Yoshinori Onuki (University of Toyama)H-Index: 15
view all 6 authors...
1 CitationsSource
#1Yoshinori Onuki (Hoshi University)H-Index: 15
#2Shota Kawai (Hoshi University)H-Index: 1
Last. Kozo Takayama (Hoshi University)H-Index: 47
view all 6 authors...
Abstract: 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 ...
10 CitationsSource
#1Keisuke Takagaki (Hoshi University)H-Index: 2
#2Hiroaki Arai (Daiichi Sankyo)H-Index: 6
Last. Kozo Takayama (Hoshi University)H-Index: 47
view all 3 authors...
ABSTRACT A tablet database containing several active ingredients for a standard tablet formulation was created. Tablet tensile strength (TS) and disintegration time (DT) were measured before and after storage for 30 days at 40 °C and 75% relative humidity. An ensemble artificial neural network (EANN) was used to predict responses to differences in quantities of excipients and physical-chemical properties of active ingredients in tablets. Most classical neural networks involve a tedious trial and...
14 CitationsSource