Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Paclawski:2015:ijnm,

• author = "Adam Paclawski and Jakub Szlek and Raymond Lau and Renata Jachowicz and Aleksander Mendyk",
• title = "Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations",
• journal = "International Journal of Nanomedicine",
• year = "2015",
• volume = "10",
• pages = "801--810",
• month = "21 " # jan,
• keywords = "genetic algorithms, genetic programming, fine particle fraction, pulmonary delivery, deposition modelling, feature selection, empirical modelling",
• publisher = "Dove Press",
• ISSN = "1178-2013",
• bibsource = "OAI-PMH server at doaj.org",
• language = "English",
• oai = "oai:doaj.org/article:7c7d32d7b5f843b2a79d467cff77f634",
• URL = "http://www.dovepress.com/empirical-modeling-of-the-fine-particle-fraction-fornbspcarrier-based--peer-reviewed-article-IJN",
• DOI = "doi:10.2147/IJN.S75758",
• size = "10 pages",
• abstract = "In vitro study of the deposition of drug particles is commonly used during development of formulations for pulmonary delivery. The assay is demanding, complex, and depends on: properties of the drug and carrier particles, including size, surface characteristics, and shape; interactions between the drug and carrier particles and assay conditions, including flow rate, type of inhaler, and impactor. The aerodynamic properties of an aerosol are measured in vitro using impactors and in most cases are presented as the fine particle fraction, which is a mass percentage of drug particles with an aerodynamic diameter below 5microns. In the present study, a model in the form of a mathematical equation was developed for prediction of the fine particle fraction. The feature selection was performed using the R-environment package fscaret. The input vector was reduced from a total of 135 independent variables to 28. During the modelling stage, techniques like artificial neural networks, genetic programming, rule-based systems, and fuzzy logic systems were used. The 10-fold cross-validation technique was used to assess the generalisation ability of the models created. The model obtained had good predictive ability, which was confirmed by a root-mean-square error and normalised root-mean-square error of 4.9 and 11percent, respectively. Moreover, validation of the model using external experimental data was performed, and resulted in a root-mean-square error and normalised root-mean-square error of 3.8 and 8.6percent, respectively.",
• notes = "Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland; School of Chemical and Biomedical Engineering, College of Engineering, Nanyang Technological University, Singapore",

}

Genetic Programming entries for Adam Paclawski Jakub Szlek Raymond Lau Renata Jachowicz Aleksander Mendyk

Citations