From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Mendyk:2015:CMMM,
  author =       "Aleksander Mendyk and Sinan Gures and 
                 Renata Jachowicz and Jakub Szlek and Sebastian Polak and 
                 Barbara Wisniowska and Peter Kleinebudde",
  title =        "From Heuristic to Mathematical Modeling of Drugs
                 Dissolution Profiles: Application of Artificial Neural
                 Networks and Genetic Programming",
  journal =      "Computational and Mathematical Methods in Medicine",
  year =         "2015",
  pages =        "Article ID 863874",
  keywords =     "genetic algorithms, genetic programming",
  bibsource =    "OAI-PMH server at www.ncbi.nlm.nih.gov",
  language =     "en",
  oai =          "oai:pubmedcentral.nih.gov:4460208",
  rights =       "Copyright 2015 Aleksander Mendyk et al.; This is an
                 open access article distributed under the Creative
                 Commons Attribution License, which permits unrestricted
                 use, distribution, and reproduction in any medium,
                 provided the original work is properly cited.",
  publisher =    "Hindawi Publishing Corporation",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460208/",
  URL =          "http://www.ncbi.nlm.nih.gov/pubmed/26101544",
  URL =          "http://dx.doi.org/10.1155/2015/863874",
  URL =          "http://downloads.hindawi.com/journals/cmmm/2015/863874.pdf",
  size =         "9 pages",
  abstract =     "The purpose of this work was to develop a mathematical
                 model of the drug dissolution (Q) from the solid lipid
                 extrudates based on the empirical approach. Artificial
                 neural networks (ANNs) and genetic programming (GP)
                 tools were used. Sensitivity analysis of ANNs provided
                 reduction of the original input vector. GP allowed
                 creation of the mathematical equation in two major
                 approaches: (1) direct modelling of Q versus extrudate
                 diameter (d) and the time variable (t) and (2) indirect
                 modelling through Weibull equation. ANNs provided also
                 information about minimum achievable generalisation
                 error and the way to enhance the original dataset used
                 for adjustment of the equations' parameters. Two inputs
                 were found important for the drug dissolution: d and t.
                 The extrudates length (L) was found not important. Both
                 GP modelling approaches allowed creation of relatively
                 simple equations with their predictive performance
                 comparable to the ANNs (root mean squared error (RMSE)
                 from 2.19 to 2.33). The direct mode of GP modelling of
                 Q versus d and t resulted in the most robust model. The
                 idea of how to combine ANNs and GP in order to escape
                 ANNs' black-box drawback without losing their superior
                 predictive performance was demonstrated. Open Source
                 software was used to deliver the state-of-the-art
                 models and modelling strategies.",
}

Genetic Programming entries for Aleksander Mendyk Sinan Gures Renata Jachowicz Jakub Szlek Sebastian Polak Barbara Wisniowska Peter Kleinebudde

Citations