title: Analysis of pellet properties with use of artificial neural networks.
abstract: The objective was to prepare neural models identifying relationships between formulation characteristics and pellet properties based on algorithmic approach of crucial variables selection and neuro-fuzzy systems application. The database consisted of information about 227 pellet formulations prepared by extrusion/spheronization method, with various model drugs and excipients. Cheminformatic description of excipients and model drugs was employed for numerical description of pellet formulations. Initial numbers of neural model inputs were up to around 3000. The inputs reduction procedure based on sensitivity analysis allowed to obtain less than 40 inputs for each model. The reduced models were subjects of fuzzy logic implementation resulting in logical rules tables providing human-readable rule sets applicable in future development of pellet formulations. Neural modeling enhanced knowledge about pelletization process and provided means for future computer-guided search for the optimal formulation.
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University, Medical College, Faculty of Pharmacy, 30-688 Kraków, Poland. mfmendyk@cyf-kr.edu.pl
authors.forename: Aleksander
authors.initials: A
authors.lastname: Mendyk
authors.forename: Peter
authors.initials: P
authors.lastname: Kleinebudde
authors.forename: Markus
authors.initials: M
authors.lastname: Thommes
authors.forename: Angelina
authors.initials: A
authors.lastname: Yoo
authors.forename: Jakub
authors.initials: J
authors.lastname: Szlęk
authors.forename: Renata
authors.initials: R
authors.lastname: Jachowicz
mesh_terms: D000465:Algorithms; D000470:Alkaloids; D016208:Databases, Factual; D004343:Drug Implants / Q000737:chemistry*; D017143:Fuzzy Logic; D007546:Isoquinolines; D008956:Models, Chemical; D016571:Neural Networks, Computer
chemical_list: D000470:Alkaloids; D004343:Drug Implants; D007546:Isoquinolines; C067639:chondocurine