Modeling of the burst release from PLGA micro- and nanoparticles as function of physicochemical parameters and formulation characteristics
| Autor: | Fabienne Danhier, M. Margarida Cardoso, Mariana Sousa Costa, Véronique Préat, Cristiana Rodrigues de Azevedo, Moritz von Stosch, Rui Oliveira, Ana M. Ramos |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Drug
Drug Liberation media_common.quotation_subject Drug Compounding Kinetics Pharmaceutical Science Nanoparticle Nanotechnology 02 engineering and technology 010402 general chemistry 01 natural sciences chemistry.chemical_compound Polylactic Acid-Polyglycolic Acid Copolymer Partial least squares regression Lactic Acid media_common Models Theoretical 021001 nanoscience & nanotechnology 0104 chemical sciences PLGA chemistry Pharmaceutical Preparations Drug delivery Drug release Biophysics Nanoparticles Regression Analysis 0210 nano-technology Polyglycolic Acid |
| Zdroj: | International journal of pharmaceutics. 532(1) |
| ISSN: | 1873-3476 |
| Popis: | A substantial drug release from poly(lactic-co-glycolic) acid (PLGA) micro- and nanoparticles can occur in the first hours of immersion, which is referred to as burst release. A strong burst release (when not intentional) is to be avoided as it decreases the efficacy of the treatment and could be dangerous to the host. In this work we analyze the total amount of drug released during burst and respective kinetics in relation to formulations characteristics, experimental conditions and drug molecular properties in 154 drug release experiments with 41 different drugs by partial least squares (PLS) and decision tree regression. The model created enables to quantify to which degree the physicochemical parameters control the burst release from PLGA particles. Our analysis shows that the amount of drug released during burst is mostly influenced by the formulation characteristics and the synthesis parameters, whereas the drug release kinetics is also influenced by the molecular properties of the drug. The variables that significantly influence the amount and kinetics of the burst release are discussed in detail and compared with findings from other researchers. The final regression models are shown to predict the release profile of a new drug, opening the possibility to be applied to systematically manipulate the burst release by means of designing an optimized drug delivery system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect