title: Artificial neural networks in the modeling of drugs release profiles from hydrodynamically balanced systems.
abstract: Artificial neural networks (ANNs) were used as modeling tools for prediction of various drugs release patterns from hydrodynamically balanced systems (HBS) composed with Metholose 90SH (hydroxypropylmethylcellulose--HPMC). The objective was to provide predictive and data-mining models of analyzed problem. It was found that ANNs are capable to accurately predict release patterns of different drugs from HBS based on the description of the formulation as well as chemical structure of the drug. Overall generalization error RMSE was 8.7 and after inclusion of pilot study in learning dataset it decreased to ca. 4.5. Sensitivity analysis of ANNs was applied to reduce native input vector from 77 to 7 inputs in order to improve the performance of predictive models. Simultaneously, it revealed crucial variables governing release of drugs from HBS.
authors.affiliation: Dept. of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Collegium Medicum, Jagiellonian University, Kraków, 9 Medyczna Str., 30-688 Kraków, Poland. mfmendyk@cyf-kr.edu.pl
authors.forename: Aleksander
authors.initials: A
authors.lastname: Mendyk
authors.forename: Renata
authors.initials: R
authors.lastname: Jachowicz
authors.forename: Przemysław
authors.initials: P
authors.lastname: Dorozyński
mesh_terms: D000465:Algorithms; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D016000:Cluster Analysis; D016503:Drug Delivery Systems; D005079:Excipients; D065347:Hypromellose Derivatives; D008747:Methylcellulose / Q000031:analogs & derivatives; D016571:Neural Networks, Computer
chemical_list: D005079:Excipients; D065347:Hypromellose Derivatives; D008747:Methylcellulose