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Cited 30 time in webofscience Cited 35 time in scopus
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Optimization of self-microemulsifying drug delivery system for telmisartan using Box-Behnken design and desirability function

Authors
Cho, Hyuk JunLee, Dong WonMarasini, NirmalPoudel, Bijay KumarKim, Jeong HwanRamasamy, ThiruganeshYoo, Bong KyuChoi, Han-GonYong, Chul SoonKim, Jong Oh
Issue Date
Oct-2013
Publisher
WILEY-BLACKWELL
Keywords
bioavailability; Box-Behnken design; optimization; self-microemulsifying drug delivery system; telmisartan
Citation
JOURNAL OF PHARMACY AND PHARMACOLOGY, v.65, no.10, pp.1440 - 1450
Journal Title
JOURNAL OF PHARMACY AND PHARMACOLOGY
Volume
65
Number
10
Start Page
1440
End Page
1450
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14256
DOI
10.1111/jphp.12115
ISSN
0022-3573
Abstract
Objectives To develop and optimize the novel self-microemulsifying drug delivery system (SMEDDS) formulation for enhanced water solubility and bioavailability of telmisartan (TMS) using the Box-Behnken design (BBD) and desirability function. Method TMS-SMEDDS formulation consisted of the mixture of oil (Peceol), surfactant (Labrasol), co-surfactant (Transcutol), TMS and triethanolamine. A three-level BBD was applied to explore the main effect, interaction effect and quadratic effect of three independent variables, including the amount of Peceol (X-1), Labrasol (X-2) and Transcutol (X-3). Determined conditions were 20 < X-1 < 40, 50 < X-2 < 80 and 5 < X-3 < 30. The response variables were droplet size (Y-1), polydispersity index (Y-2) and dissolution percentage of TMS after 15 min (Y-3). Key findings The optimized conditions were 28.93, 80 and 28.08 (mg) for X-1, X-2 and X-3, respectively, and the response variables were predicted to be 159.8 nm, 0.241 and 85.8% for Y-1, Y-2 and Y-3, respectively. The actual values from the optimized formulation showed good agreement with predicted values. The optimized TMS-SMEDDS formulation showed faster drug dissolution rate and higher bioavailability than TMS powder. Conclusions Our results suggest that response surface methodology using BBD and desirability function is a promising approach to understand the effect of SMEDDS variables and to optimize the formulation.
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