Propagation of uncertainty in nasal spray in vitro performance models using Monte Carlo simulation: part I. Model prediction using Monte Carlo simulation.

摘要: Design of experiment (DOE) methodology can provide a complete evaluation the influences nasal spray activation and formulation properties on delivery performance which makes it powerful tool for product design purposes. Product models are computed from complex expressions containing multiple factor terms response terms. Uncertainty in regression model be propagated using Monte Carlo simulation. In this study, four input factors, actuation stroke length, velocity, concentration gelling agent, surfactant were investigated their measured responses pattern, plume width, droplet size distribution (DSD), impaction force. Quadratic calculated optimized Box–Behnken experimental to describe relationship between factors responses. Assuming that perfectly represent variables responses, propagation uncertainty both measurements prediction was performed simulations. The simulations presented article illustrate predictions. most influential variable variances variance identified, could help prioritize importance during continuous improvement design. This work extends recent process realm development models. © 2009 Wiley-Liss, Inc. American Pharmacists Association J Pharm Sci 99: 2114–2122, 2010