Heuristic modeling of macromolecule release from PLGA microspheres

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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