Heuristic modeling of macromolecule release from PLGA microspheres

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@Article{Szlek:2013:IJNM,
  author =       "Jakub Szlek and Adam Paclawski and Raymond Lau and 
                 Renata Jachowicz and Aleksander Mendyk",
  title =        "Heuristic modeling of macromolecule release from
                 {PLGA} microspheres",
  year =         "2013",
  journal =      "International Journal of Nanomedicine",
  volume =       "8",
  number =       "1",
  pages =        "4601--4611",
  month =        dec # "~03",
  keywords =     "genetic algorithms, genetic programming, poly
                 lactic-co-glycolic acid (PLGA) microparticles, feature
                 selection, artificial neural networks, molecular
                 descriptors",
  bibsource =    "OAI-PMH server at www.ncbi.nlm.nih.gov",
  language =     "en",
  oai =          "oai:pubmedcentral.nih.gov:3857266",
  publisher =    "Dove Medical Press",
  ISSN =         "1178-2013",
  DOI =          "doi:10.2147/IJN.S53364",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857266",
  URL =          "http://www.dovepress.com/getfile.php?fileID=18330",
  URL =          "http://dx.doi.org/10.2147/IJN.S53364",
  size =         "11 pages",
  abstract =     "Dissolution of protein macromolecules from
                 poly(lactic-co-glycolic acid) (PLGA) particles is a
                 complex process and still not fully understood. As
                 such, there are difficulties in obtaining a predictive
                 model that could be of fundamental significance in
                 design, development, and optimisation for medical
                 applications and toxicity evaluation of PLGA-based
                 multiparticulate dosage form. In the present study, two
                 models with comparable goodness of fit were proposed
                 for the prediction of the macromolecule dissolution
                 profile from PLGA micro- and nanoparticles. In both
                 cases, heuristic techniques, such as artificial neural
                 networks (ANNs), feature selection, and genetic
                 programming were employed. Feature selection provided
                 by fscaret package and sensitivity analysis performed
                 by ANNs reduced the original input vector from a total
                 of 300 input variables to 21, 17, 16, and eleven; to
                 achieve a better insight into generalisation error, two
                 cut-off points for every method was proposed. The best
                 ANNs model results were obtained by monotone
                 multi-layer perceptron neural network (MON-MLP)
                 networks with a root-mean-square error (RMSE) of 15.4,
                 and the input vector consisted of eleven inputs. The
                 complicated classical equation derived from a database
                 consisting of 17 inputs was able to yield a better
                 generalisation error (RMSE) of 14.3. The equation was
                 characterised by four parameters, thus feasible
                 (applicable) to standard nonlinear regression
                 techniques. Heuristic modelling led to the ANN model
                 describing macromolecules release profiles from PLGA
                 microspheres with good predictive efficiency. Moreover
                 genetic programming technique resulted in classical
                 equation with comparable predictability to the ANN
                 model.",
}

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

Citations