Back to Search
Start Over
Statistical design for formulation optimization of hydrocortisone butyrate-loaded PLGA nanoparticles.
- Source :
-
AAPS PharmSciTech [AAPS PharmSciTech] 2014 Jun; Vol. 15 (3), pp. 569-87. Date of Electronic Publication: 2014 Feb 07. - Publication Year :
- 2014
-
Abstract
- The aim of this investigation was to develop hydrocortisone butyrate (HB)-loaded poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles (NP) with ideal encapsulation efficiency (EE), particle size, and drug loading (DL) under emulsion solvent evaporation technique utilizing various experimental statistical design modules. Experimental designs were used to investigate specific effects of independent variables during preparation of HB-loaded PLGA NP and corresponding responses in optimizing the formulation. Plackett-Burman design for independent variables was first conducted to prescreen various formulation and process variables during the development of NP. Selected primary variables were further optimized by central composite design. This process leads to an optimum formulation with desired EE, particle size, and DL. Contour plots and response surface curves display visual diagrammatic relationships between the experimental responses and input variables. The concentration of PLGA, drug, and polyvinyl alcohol and sonication time were the critical factors influencing the responses analyzed. Optimized formulation showed EE of 90.6%, particle size of 164.3 nm, and DL of 64.35%. This study demonstrates that statistical experimental design methodology can optimize the formulation and process variables to achieve favorable responses for HB-loaded NP.
- Subjects :
- Chemistry, Pharmaceutical
Hydrocortisone chemistry
Particle Size
Polylactic Acid-Polyglycolic Acid Copolymer
Polyvinyl Alcohol
Solvents chemistry
Sonication
Time Factors
Anti-Inflammatory Agents chemistry
Drug Carriers
Hydrocortisone analogs & derivatives
Lactic Acid chemistry
Models, Statistical
Nanoparticles
Polyglycolic Acid chemistry
Technology, Pharmaceutical methods
Subjects
Details
- Language :
- English
- ISSN :
- 1530-9932
- Volume :
- 15
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- AAPS PharmSciTech
- Publication Type :
- Academic Journal
- Accession number :
- 24504495
- Full Text :
- https://doi.org/10.1208/s12249-014-0072-4