4.7 Article

Enhanced oral bioavailability of valsartan using a polymer-based supersaturable selfmicroemulsifying drug delivery system

期刊

INTERNATIONAL JOURNAL OF NANOMEDICINE
卷 12, 期 -, 页码 3533-3545

出版社

DOVE MEDICAL PRESS LTD
DOI: 10.2147/IJN.S136599

关键词

valsartan; SMEDDS; supersaturation; factorial design; optimization; bioavailability

资金

  1. Advanced Technology Center program - Ministry of Trade, Industry & Energy (MI, Republic of Korea) [10051950]
  2. Korea Evaluation Institute of Industrial Technology (KEIT) [10051950] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

A novel, supersaturable self-microemulsifying drug delivery system (S-SMEDDS) was successfully formulated to enhance the dissolution and oral absorption of valsartan (VST), a poorly water-soluble drug, while reducing the total quantity for administration. Poloxamer 407 is a selectable, supersaturating agent for VST-containing SMEDDS composed of 10% Capmul (R) MCM, 45% Tween (R) 20, and 45% Transcutol (R) P. The amounts of SMEDDS and Poloxamer 407 were chosen as formulation variables for a 3-level factorial design. Further optimization was established by weighting different levels of importance on response variables for dissolution and total quantity, resulting in an optimal S-SMEDDS in large quantity (S-SMEDDS_LQ; 352 mg in total) and S-SMEDDS in reduced quantity (S-SMEDDS_RQ; 144.6 mg in total). Good agreement was observed between predicted and experimental values for response variables. Consequently, compared with VST powder or suspension and SMEDDS, both S-SMEDDS_LQ and S-SMEDDS_RQ showed excellent in vitro dissolution and in vivo oral bioavailability in rats. The magnitude of dissolution and absorption-enhancing capacities using quantity-based comparisons was in the order S-SMEDDS_RQ > S-SMEDDS_LQ > SMEDDS > VST powder or suspension. Thus, we concluded that, in terms of developing an effective SMEDDS preparation with minimal total quantity, S-SMEDDS_RQ is a promising candidate.

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