title: Application of neural computing in pharmaceutical product development.
abstract: Neural computing technology is capable of solving problems involving complex pattern recognition. This technology is applied here to pharmaceutical product development. The most commonly used computational algorithm, the delta back-propagation network, was utilized to recognize the complex relationship between the formulation variables and the in vitro drug release parameters for a hydrophilic matrix capsule system. This new computational technique was also compared with the response surface methodology (RSM). Artificial neural network (ANN) analysis was able to predict the response values for a series of validation experiments more precisely than RSM. ANN may offer an alternative to RSM because it allows for the development of a system that can incorporate literature and experimental data to solve common problems in the pharmaceutical industry.
authors.affiliation: Division of Pharmaceutics and Drug Delivery Systems, College of Pharmacy, University of Cincinnati-Medical Center, Ohio 45267-0004.
authors.forename: A S
authors.initials: AS
authors.lastname: Hussain
authors.forename: X Q
authors.initials: XQ
authors.lastname: Yu
authors.forename: R D
authors.initials: RD
authors.lastname: Johnson
mesh_terms: D000465:Algorithms; D002214:Capsules; D002626:Chemistry, Pharmaceutical / Q000295:instrumentation*; D005079:Excipients; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011108:Polymers; D012044:Regression Analysis
chemical_list: D002214:Capsules; D005079:Excipients; D011108:Polymers