Optimization of self-microemulsifying drug delivery system for telmisartan using Box-Behnken design and desirability function

被引:36
作者
Cho, Hyuk Jun [1 ]
Lee, Dong Won [1 ]
Marasini, Nirmal [1 ]
Poudel, Bijay Kumar [1 ]
Kim, Jeong Hwan [1 ]
Ramasamy, Thiruganesh [1 ]
Yoo, Bong Kyu [2 ]
Choi, Han-Gon [3 ]
Yong, Chul Soon [1 ]
Kim, Jong Oh [1 ]
机构
[1] Yeungnam Univ, Coll Pharm, Gyongsan 712749, Gyungbuk, South Korea
[2] Gachon Univ, Coll Pharm, Inchon, South Korea
[3] Hanyang Univ, Coll Pharm, Ansan, South Korea
基金
新加坡国家研究基金会;
关键词
bioavailability; Box-Behnken design; optimization; self-microemulsifying drug delivery system; telmisartan; EXCIPIENTS; SMEDDS;
D O I
10.1111/jphp.12115
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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.
引用
收藏
页码:1440 / 1450
页数:11
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