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The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets
- Source :
- AAPS PharmSciTech. 4:129-140
- Publication Year :
- 2003
- Publisher :
- Springer Science and Business Media LLC, 2003.
-
Abstract
- The objective of this work was to apply artificial neural networks (ANNs) to examine the relative importance of various factors, both formulation and process, governing the in-vitro dissolution from enteric-coated sustained release (SR) minitablets. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of the various formulation and processing variables in determining minitablet dissolution rate. Both forward and backward stepwise algorithms were used as well as genetic algorithms. Networks were subsequently trained using the back propagation algorithm in order to check whether or not the IFS process had correctly located any unimportant inputs. IFS gave consistent rankings for the importance of the various formulation and processing variables in determining the release of drug from minitablets. Consistent ranking was achieved for both indices of the release process; ie, the time taken for release to commence through the enteric coat (T(lag)) and that for the drug to diffuse through the SR matrix of the minitablet into the dissolution medium (T9(0-10)). In the case of the T(lag) phase, the main coating parameters, along with the original batch blend size and the blend time with lubricant, were found to have most influence. By contrast, with the T(90-10 phase), the amounts of matrix forming polymer and direct compression filler were most important. In the subsequent training of the ANNs, removal of inputs regarded as less important led to improved network performance. ANNs were capable of ranking the relative importance of the various formulations and processing variables that influenced the release rate of the drug from minitablets. This could be done for all main stages of the release process. Subsequent training of the ANN verified that removal of less relevant inputs from the training process led to an improved performance from the ANN.
- Subjects :
- In vitro dissolution
Chemistry, Pharmaceutical
Pharmaceutical Science
Feature selection
Aquatic Science
Article
Artificial Intelligence
Drug Discovery
Computer Simulation
Ecology, Evolution, Behavior and Systematics
Selection (genetic algorithm)
Mathematics
Ecology
Artificial neural network
Process (computing)
General Medicine
Blend time
Improved performance
Models, Chemical
Ranking
Delayed-Action Preparations
Neural Networks, Computer
Biological system
Agronomy and Crop Science
Software
Tablets
Subjects
Details
- ISSN :
- 15309932
- Volume :
- 4
- Database :
- OpenAIRE
- Journal :
- AAPS PharmSciTech
- Accession number :
- edsair.doi.dedup.....a91f823a58964d5fbf48d27e8d75d64b