Optimization of self-microemulsifying drug delivery system for telmisartan using Box-Behnken design and desirability function
- Authors
- Cho, Hyuk Jun; Lee, Dong Won; Marasini, Nirmal; Poudel, Bijay Kumar; Kim, Jeong Hwan; Ramasamy, Thiruganesh; Yoo, Bong Kyu; Choi, Han-Gon; Yong, Chul Soon; Kim, 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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