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:1.8051

@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