Ontology type: schema:ScholarlyArticle
2000-11
AUTHORSKok Khiang Peh, Chee Peng Lim, Siow San Quek, Kean Hock Khoh
ABSTRACTPurpose. To use artificial neural networks for predicting dissolution profiles of matrix-controlled release theophylline pellet preparation, and to evaluate the network performance by comparing the predicted dissolution profiles with those obtained from physical experiments using similarity factor.Methods. The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).Results. The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.Conclusion. The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development. More... »
PAGES1384-1389
http://scigraph.springernature.com/pub.10.1023/a:1007578321803
DOIhttp://dx.doi.org/10.1023/a:1007578321803
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1000308030
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/11205731
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1115",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Pharmacology and Pharmaceutical Sciences",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Cellulose",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Chemistry, Pharmaceutical",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Delayed-Action Preparations",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Excipients",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Glycerides",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Kinetics",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Chemical",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Neural Networks, Computer",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Solubility",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Theophylline",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "School of Pharmaceutical Sciences, University of Science Malaysia, 11800, Penang, Malaysia",
"id": "http://www.grid.ac/institutes/grid.11875.3a",
"name": [
"School of Pharmaceutical Sciences, University of Science Malaysia, 11800, Penang, Malaysia"
],
"type": "Organization"
},
"familyName": "Peh",
"givenName": "Kok Khiang",
"id": "sg:person.0610230334.11",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610230334.11"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "School of Industrial Technology, University of Science Malaysia, 11800, Penang, Malaysia",
"id": "http://www.grid.ac/institutes/grid.11875.3a",
"name": [
"School of Industrial Technology, University of Science Malaysia, 11800, Penang, Malaysia"
],
"type": "Organization"
},
"familyName": "Lim",
"givenName": "Chee Peng",
"id": "sg:person.014644360675.43",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014644360675.43"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "School of Industrial Technology, University of Science Malaysia, 11800, Penang, Malaysia",
"id": "http://www.grid.ac/institutes/grid.11875.3a",
"name": [
"School of Industrial Technology, University of Science Malaysia, 11800, Penang, Malaysia"
],
"type": "Organization"
},
"familyName": "Quek",
"givenName": "Siow San",
"id": "sg:person.01175124131.23",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01175124131.23"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "School of Industrial Technology, University of Science Malaysia, 11800, Penang, Malaysia",
"id": "http://www.grid.ac/institutes/grid.11875.3a",
"name": [
"School of Industrial Technology, University of Science Malaysia, 11800, Penang, Malaysia"
],
"type": "Organization"
},
"familyName": "Khoh",
"givenName": "Kean Hock",
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/bf02551274",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023250347",
"https://doi.org/10.1007/bf02551274"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1018917128684",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028417432",
"https://doi.org/10.1023/a:1018917128684"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1015843527138",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002628904",
"https://doi.org/10.1023/a:1015843527138"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1016260720218",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000414328",
"https://doi.org/10.1023/a:1016260720218"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4684-6036-0_25",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011433912",
"https://doi.org/10.1007/978-1-4684-6036-0_25"
],
"type": "CreativeWork"
}
],
"datePublished": "2000-11",
"datePublishedReg": "2000-11-01",
"description": "Purpose. To use artificial neural networks for predicting dissolution profiles of matrix-controlled release theophylline pellet preparation, and to evaluate the network performance by comparing the predicted dissolution profiles with those obtained from physical experiments using similarity factor.Methods. The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).Results. The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.Conclusion. The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.",
"genre": "article",
"id": "sg:pub.10.1023/a:1007578321803",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1094644",
"issn": [
"0724-8741",
"1573-904X"
],
"name": "Pharmaceutical Research",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "11",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "17"
}
],
"keywords": [
"artificial neural network",
"neural network",
"multi-layered perceptron neural network",
"perceptron neural network",
"network performance",
"MLP network",
"network",
"similarity factor",
"physical experiments",
"product development",
"one-out",
"estimation basis",
"set",
"performance",
"experiments",
"concept",
"training",
"output",
"model",
"drug dissolution profiles",
"data",
"closeness",
"evaluation",
"use",
"development",
"dissolution profiles",
"basis",
"glyceryl monostearate",
"pellet preparation",
"microcrystalline cellulose",
"drug release",
"theophylline pellets",
"values",
"profile",
"factors",
"rate",
"different ratios",
"preparation",
"F2 values",
"ratio",
"monostearate",
"cellulose",
"matrix ratio",
"release",
"pellets"
],
"name": "Use of Artificial Neural Networks to Predict Drug Dissolution Profiles and Evaluation of Network Performance Using Similarity Factor",
"pagination": "1384-1389",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1000308030"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1023/a:1007578321803"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"11205731"
]
}
],
"sameAs": [
"https://doi.org/10.1023/a:1007578321803",
"https://app.dimensions.ai/details/publication/pub.1000308030"
],
"sdDataset": "articles",
"sdDatePublished": "2022-08-04T16:54",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_319.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1023/a:1007578321803"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1023/a:1007578321803'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/a:1007578321803'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/a:1007578321803'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/a:1007578321803'
This table displays all metadata directly associated to this object as RDF triples.
188 TRIPLES
21 PREDICATES
86 URIs
73 LITERALS
17 BLANK NODES