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

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@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