Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor

Pharmaceutical Research

Volumn 17, Issue 11, 2000, Pages 1384-1388

  Peh, Kok Khiang ^a   Lim, Chee Peng ^b   Quek, Siow San ^b   Khoh, Kean Hock ^b  

^a SCHOOL OF PHARMACEUTICAL SCIENCES   (Malaysia)

^b Universiti Sains Malaysia   (Malaysia)

Author keywords

Artificial neural networks; Drug dissolution profiles; Multilayered perceptron; Similarity factor

Indexed keywords

GLYCEROL STEARATE; MICROCRYSTALLINE CELLULOSE; THEOPHYLLINE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED RELEASE FORMULATION; DRUG PELLET; DRUG RELEASE; DRUG SOLUBILITY; PERFORMANCE; PREDICTION; PRIORITY JOURNAL;

CELLULOSE; CHEMISTRY, PHARMACEUTICAL; DELAYED-ACTION PREPARATIONS; EXCIPIENTS; GLYCERIDES; KINETICS; MODELS, CHEMICAL; NEURAL NETWORKS (COMPUTER); SOLUBILITY; THEOPHYLLINE;

EID: 0034495109     PISSN: 07248741     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007578321803     Document Type: Article

References (15)

1. Multi-objective simultaneous optimization based on artificial neural network in sustained released formulations
2. Multi-objective simultaneous optimization based on artificial neural network in a ketoprofen hydrogel formula containing O-ethylmenthol as a percutaneous absorption enhancer
3. Application of neural computing in pharmaceutical product formulation
4. Feasibility of developing a neural network for prediction of human pharmacokinetic parameters from animal data
5. Neural network predicted peak and trough of gentamicin concentrations
6. Learning internal representation by error propagation
7. Approximation by superposition of a sigmoidal function
8. Development and in vitro evaluation of a novel multi-particulate matrix controlled release formulation of theophylline