Modeling of the burst release from PLGA micro- and nanoparticles as function of physicochemical parameters and formulation characteristics.

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.

A principal components method constrained by elementary flux modes: analysis of flux data sets.

BACKGROUND: Non-negative linear combinations of elementary flux modes (EMs) describe all feasible reaction flux distributions for a given metabolic network under the quasi steady state assumption. However, only a small subset of EMs contribute to the physiological state of a given cell. RESULTS: In this paper, a method is proposed that identifies the subset of EMs that best explain the physiological state captured in reaction flux data, referred to as principal EMs (PEMs), given a pre-specified universe of EM candidates. The method avoids the evaluation of all possible combinations of EMs by using a branch and bound approach which is computationally very efficient. The performance of the method is assessed using simulated and experimental data of Pichia pastoris and experimental fluxome data of Saccharomyces cerevisiae. The proposed method is benchmarked against principal component analysis (PCA), commonly used to study the structure of metabolic flux data sets. CONCLUSIONS: The overall results show that the proposed method is computationally very effective in identifying the subset of PEMs within a large set of EM candidates (cases with ~100 and ~1000 EMs were studied). In contrast to the principal components in PCA, the identified PEMs have a biological meaning enabling identification of the key active pathways in a cell as well as the conditions under which the pathways are activated. This method clearly outperforms PCA in the interpretability of flux data providing additional insights into the underlying regulatory mechanisms.