title: Heuristic modeling of macromolecule release from PLGA microspheres.
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 optimization 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 generalization 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 generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling 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.
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland.
authors.forename: Jakub
authors.initials: J
authors.lastname: Szlęk
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland.
authors.forename: Adam
authors.initials: A
authors.lastname: Pacławski
authors.affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological University (NTU), Singapore.
authors.forename: Raymond
authors.initials: R
authors.lastname: Lau
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland.
authors.forename: Renata
authors.initials: R
authors.lastname: Jachowicz
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland.
authors.forename: Aleksander
authors.initials: A
authors.lastname: Mendyk
mesh_terms: D001185:Artificial Intelligence; D004337:Drug Carriers / Q000737:chemistry* / Q000378:metabolism; D019344:Lactic Acid / Q000737:chemistry* / Q000378:metabolism; D008863:Microspheres; D008958:Models, Molecular; D015233:Models, Statistical; D053758:Nanoparticles; D011100:Polyglycolic Acid / Q000737:chemistry* / Q000378:metabolism; D000077182:Polylactic Acid-Polyglycolic Acid Copolymer; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D004337:Drug Carriers; D011506:Proteins; D000077182:Polylactic Acid-Polyglycolic Acid Copolymer; D011100:Polyglycolic Acid; D019344:Lactic Acid
keywords: artificial neural networks; feature selection; genetic programming; molecular descriptors; poly(lactic-co-glycolic acid) (PLGA) microparticles