PMID- 24348037
OWN - NLM
STAT- MEDLINE
DCOM- 20140609
LR  - 20211021
IS  - 1178-2013 (Electronic)
IS  - 1176-9114 (Print)
IS  - 1176-9114 (Linking)
VI  - 8
DP  - 2013
TI  - Heuristic modeling of macromolecule release from PLGA microspheres.
PG  - 4601-11
LID - 10.2147/IJN.S53364 [doi]
AB  - 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.
FAU - Szlek, Jakub
AU  - Szlek J
AD  - Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian 
      University Medical College, Krakow, Poland.
FAU - Paclawski, Adam
AU  - Paclawski A
AD  - Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian 
      University Medical College, Krakow, Poland.
FAU - Lau, Raymond
AU  - Lau R
AD  - School of Chemical and Biomedical Engineering, Nanyang Technological University 
      (NTU), Singapore.
FAU - Jachowicz, Renata
AU  - Jachowicz R
AD  - Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian 
      University Medical College, Krakow, Poland.
FAU - Mendyk, Aleksander
AU  - Mendyk A
AD  - Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian 
      University Medical College, Krakow, Poland.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20131203
PL  - New Zealand
TA  - Int J Nanomedicine
JT  - International journal of nanomedicine
JID - 101263847
RN  - 0 (Drug Carriers)
RN  - 0 (Proteins)
RN  - 1SIA8062RS (Polylactic Acid-Polyglycolic Acid Copolymer)
RN  - 26009-03-0 (Polyglycolic Acid)
RN  - 33X04XA5AT (Lactic Acid)
SB  - IM
MH  - Artificial Intelligence
MH  - Drug Carriers/*chemistry/metabolism
MH  - Lactic Acid/*chemistry/metabolism
MH  - Microspheres
MH  - *Models, Molecular
MH  - *Models, Statistical
MH  - Nanoparticles
MH  - Polyglycolic Acid/*chemistry/metabolism
MH  - Polylactic Acid-Polyglycolic Acid Copolymer
MH  - Proteins/*chemistry/metabolism
PMC - PMC3857266
OTO - NOTNLM
OT  - artificial neural networks
OT  - feature selection
OT  - genetic programming
OT  - molecular descriptors
OT  - poly(lactic-co-glycolic acid) (PLGA) microparticles
EDAT- 2013/12/19 06:00
MHDA- 2014/06/10 06:00
PMCR- 2013/12/03
CRDT- 2013/12/19 06:00
PHST- 2013/12/19 06:00 [entrez]
PHST- 2013/12/19 06:00 [pubmed]
PHST- 2014/06/10 06:00 [medline]
PHST- 2013/12/03 00:00 [pmc-release]
AID - ijn-8-4601 [pii]
AID - 10.2147/IJN.S53364 [doi]
PST - ppublish
SO  - Int J Nanomedicine. 2013;8:4601-11. doi: 10.2147/IJN.S53364. Epub 2013 Dec 3.