Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Paclawski:2015:ijnm,
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author = "Adam Paclawski and Jakub Szlek and Raymond Lau and
Renata Jachowicz and Aleksander Mendyk",
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title = "Empirical modeling of the fine particle fraction for
carrier-based pulmonary delivery formulations",
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journal = "International Journal of Nanomedicine",
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year = "2015",
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volume = "10",
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pages = "801--810",
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month = "21 " # jan,
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keywords = "genetic algorithms, genetic programming, fine particle
fraction, pulmonary delivery, deposition modelling,
feature selection, empirical modelling",
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publisher = "Dove Press",
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ISSN = "1178-2013",
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bibsource = "OAI-PMH server at doaj.org",
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language = "English",
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oai = "oai:doaj.org/article:7c7d32d7b5f843b2a79d467cff77f634",
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URL = "http://www.dovepress.com/empirical-modeling-of-the-fine-particle-fraction-fornbspcarrier-based--peer-reviewed-article-IJN",
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DOI = "doi:10.2147/IJN.S75758",
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size = "10 pages",
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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.",
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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