Nanoparticle Design Characterized by In Silico Preparation Parameter Prediction Using Ensemble Models

Nanoparticles (NPs) are now widely applied in new drug delivery and targeting strategies. A predictive tool for the carrier design would allow for reducing the number of experiments to determine the optimal formulation. Here we investigated the performance of two different statistical approaches to predicting NP properties. NPs were prepared by an oil/water emulsification method using Eudragit RS or poly(lactide-co-glycolide) (PLGA) as matrix polymer and dichloromethane (DCM) or ethyl acetate (EA) as organic solvent while ibuprofen was entrapped as model drug. Statistical analysis on the impact of the various formulations and process on the particle properties was performed using response surface methodology, and linear and nonlinear ensemble models. Particle size diminished with EA and the use of Eudragit RS (RS + EA: 50-100 nm; RS + DCM: 200-400 nm; PLGA + EA: 100-800 nm; PLGA + DCM: 200 1000 nm). Zeta potential was around zero for PLGA and positive with Eudragit RS. Encapsulation rates were generally higher than 80% with the tendency to increase with larger particles. Values predicted using response surface modeling or nonlinear ensemble models exhibited a high correlation with experimental values. Especially the more recent nonlinear ensemble models may be a valuable approach to facilitate and speed up the otherwise very time-consuming process of NP design for drug delivery. (C) 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:1982-1996, 2010