title: Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs.
abstract: OBJECTIVE
  Poly(lactic-co-glycolic acid) (PLGA) has become one of the most promising in design, development, and optimization for medical applications polymers. PLGA-based multiparticulate dosage forms are usually prepared as microspheres where the size is from 5 to 100 µm, depending on the route of administration. The main objectives of the study were to develop a predictive model of mean volumetric particle size and on its basis extract knowledge of PLGA containing proteins forming behaviour.
  
  
  METHODS
  In the present study, a model for the prediction of mean volumetric particle size developed by an rgp package of R environment is presented. Other tools like fscaret, monmlp, fugeR, MARS, SVM, kNNreg, Cubist, randomForest and piecewise linear regression are also applied during the data mining procedure.
  
  
  RESULTS
  The feature selection provided by the fscaret package reduced the original input vector from a total of 295 input variables to 10, 16 and 19. The developed models had good predictive ability, which was confirmed by a normalized root-mean-square error (NRMSE) of 6.8 to 11.1% in 10-fold cross validation training procedure. Moreover, the best models were validated using external experimental data. The superior predictiveness had a model obtained by rgp in the form of a classical equation with a normalized root-mean-squared error (NRMSE) of 6.1%.
  
  
  CONCLUSIONS
  A new approach is proposed for computational modelling of the mean particle size of PLGA microspheres and rules extraction from tree-based models. The feature selection leads to revealing chemical descriptor variables which are important in predicting the size of PLGA microspheres. In order to achieve better understanding in the relationships between particle size and formulation characteristics, the surface analysis method and rules extraction procedures were applied.
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St., 30-688 Cracow, Poland. Electronic address: j.szlek@uj.edu.pl.
authors.forename: Jakub
authors.initials: J
authors.lastname: Szlęk
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St., 30-688 Cracow, Poland.
authors.forename: Adam
authors.initials: A
authors.lastname: Pacławski
authors.affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore.
authors.forename: Raymond
authors.initials: R
authors.lastname: Lau
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St., 30-688 Cracow, Poland.
authors.forename: Renata
authors.initials: R
authors.lastname: Jachowicz
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St., 30-688 Cracow, Poland.
authors.forename: Pezhman
authors.initials: P
authors.lastname: Kazemi
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St., 30-688 Cracow, Poland.
authors.forename: Aleksander
authors.initials: A
authors.lastname: Mendyk
mesh_terms: D036262:Empirical Research; D019344:Lactic Acid / Q000737:chemistry*; D000069550:Machine Learning; D008863:Microspheres; D010316:Particle Size; D011100:Polyglycolic Acid / Q000737:chemistry*; D000077182:Polylactic Acid-Polyglycolic Acid Copolymer; D060388:Support Vector Machine
chemical_list: D000077182:Polylactic Acid-Polyglycolic Acid Copolymer; D011100:Polyglycolic Acid; D019344:Lactic Acid
keywords: Feature selection; Heuristic modelling; Molecular descriptors; Particle size; Poly-lactide-co-glycolide (PLGA) microparticles; Rules extraction