The prediction of the palatability of orally disintegrating tablets by an electronic gustatory system

被引:43
作者
Nakamura, Hideshi [1 ,2 ]
Uchida, Shinya [1 ]
Sugiura, Takeshi [2 ]
Namiki, Noriyuki [1 ]
机构
[1] Univ Shizuoka, Sch Pharmaceut Sci, Dept Pharm Practice & Sci, Suruga Ku, Shizuoka 4228526, Japan
[2] Towa Pharmaceut Co Ltd, Cent Labs, Pharmaceut Res & Technol Div, Kadoma, Osaka 5710033, Japan
关键词
Orally disintegrating tablet; Famotidine; Amlodipine; Visual analogue scale; Taste-masking; Electronic gustatory system; TASTE; FAMOTIDINE; BITTERNESS; SWEETENERS;
D O I
10.1016/j.ijpharm.2015.07.056
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
In this study, the human gustatory palatability sensation of taste-masked famotidine and amlodipine orally disintegrating tablets (ODTs) was quantitatively predicted by an electronic gustatory system (alpha-Astree e-Tongue). Furthermore, its use in formulation design was evaluated. The famotidine- and amlodipine-containing ODTs, which were bitter-and highly bitter-tasting, respectively, were prepared using a physical (granules spray-coated with ethyl cellulose) or organoleptic (the addition of a sweetener and a flavor) masking method and combinations thereof. The taste-masking effects of different masking methods on the ODTs were investigated in a human gustatory sensation test. In the test, volunteers scored the overall palatability using a 100 mm visual analog scale (VAS). The electronic gustatory system was evaluated using the Euclidean distance (the distance between each drug-containing ODT and its corresponding placebo) and partial least squares (PLS) regression analysis of the sensor response values. A good linear relationship was observed between each ODT's Euclidean distance analysis, PLS regression analysis, and clinical VAS scores. Cross-validation verification of each analysis confirmed the model's predictive power. This study suggests that the alpha-Astree can quantitatively evaluate physical and organoleptic taste masking and that the palatability of unknown formulations can be predicted by Euclidean distance and PLS regression data analysis. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:305 / 312
页数:8
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