Artificial Neural Network as a Novel Method to Optimize Pharmaceutical Formulations View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1999-01

AUTHORS

Kozo Takayama, Mikito Fujikawa, Tsuneji Nagai

ABSTRACT

One of the difficulties in the quantitative approach to designing pharmaceutical formulations is the difficulty in understanding the relationship between causal factors and individual pharmaceutical responses. Another difficulty is desirable formulation for one property is not always desirable for the other characteristics. This is called a multi-objective simultaneous optimization problem. A response surface method (RSM) has proven to be a useful approach for selecting pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The aim of this review is to describe the basic concept of the multi-objective simultaneous optimization technique in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the non-linear relationship between causal factors and response variables. The usefulness and reliability of this ANN approach is demonstrated by the optimization for ketoprofen hydrogel ointment as a typical numerical example, in comparison with the results obtained with a classical RSM approach. More... »

PAGES

1-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1011986823850

DOI

http://dx.doi.org/10.1023/a:1011986823850

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007767460

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/9950271


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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": "Chemistry, Pharmaceutical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Hydrogels", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ketoprofen", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neural Networks, Computer", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Nonlinear Dynamics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ointments", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.412239.f", 
          "name": [
            "Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Takayama", 
        "givenName": "Kozo", 
        "id": "sg:person.0774557773.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774557773.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.412239.f", 
          "name": [
            "Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fujikawa", 
        "givenName": "Mikito", 
        "id": "sg:person.01027115253.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027115253.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.412239.f", 
          "name": [
            "Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nagai", 
        "givenName": "Tsuneji", 
        "id": "sg:person.01236353151.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01236353151.72"
        ], 
        "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: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:1016064930502", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021003482", 
          "https://doi.org/10.1023/a:1016064930502"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1018966222807", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042859520", 
          "https://doi.org/10.1023/a:1018966222807"
        ], 
        "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:1016260720218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000414328", 
          "https://doi.org/10.1023/a:1016260720218"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1999-01", 
    "datePublishedReg": "1999-01-01", 
    "description": "One of the difficulties in the quantitative approach to designing pharmaceutical formulations is the difficulty in understanding the relationship between causal factors and individual pharmaceutical responses. Another difficulty is desirable formulation for one property is not always desirable for the other characteristics. This is called a multi-objective simultaneous optimization problem. A response surface method (RSM) has proven to be a useful approach for selecting pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The aim of this review is to describe the basic concept of the multi-objective simultaneous optimization technique in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the non-linear relationship between causal factors and response variables. The usefulness and reliability of this ANN approach is demonstrated by the optimization for ketoprofen hydrogel ointment as a typical numerical example, in comparison with the results obtained with a classical RSM approach.", 
    "genre": "article", 
    "id": "sg:pub.10.1023/a:1011986823850", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1094644", 
        "issn": [
          "0724-8741", 
          "1573-904X"
        ], 
        "name": "Pharmaceutical Research", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "16"
      }
    ], 
    "keywords": [
      "response surface method", 
      "typical numerical examples", 
      "simultaneous optimization problem", 
      "optimization problem", 
      "artificial neural network", 
      "numerical examples", 
      "optimization techniques", 
      "simultaneous optimization technique", 
      "polynomial equation", 
      "second-order polynomial equation", 
      "neural network", 
      "response variables", 
      "surface method", 
      "poor estimation", 
      "formulation", 
      "desirable formulation", 
      "basic concepts", 
      "multi-objective simultaneous optimization technique", 
      "equations", 
      "RSM approach", 
      "ANN approach", 
      "non-linear relationship", 
      "optimization", 
      "approach", 
      "estimation", 
      "network", 
      "problem", 
      "pharmaceutical formulations", 
      "novel method", 
      "prediction", 
      "variables", 
      "properties", 
      "difficulties", 
      "optimal formulation", 
      "quantitative approach", 
      "useful approach", 
      "technique", 
      "reliability", 
      "concept", 
      "results", 
      "comparison", 
      "pharmaceutical research", 
      "usefulness", 
      "characteristics", 
      "pharmaceutical responses", 
      "relationship", 
      "method", 
      "response", 
      "research", 
      "factors", 
      "aim", 
      "levels", 
      "example", 
      "review", 
      "causal factors", 
      "low levels", 
      "ointment"
    ], 
    "name": "Artificial Neural Network as a Novel Method to Optimize Pharmaceutical Formulations", 
    "pagination": "1-6", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1007767460"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/a:1011986823850"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "9950271"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/a:1011986823850", 
      "https://app.dimensions.ai/details/publication/pub.1007767460"
    ], 
    "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_347.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1023/a:1011986823850"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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:1011986823850'

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:1011986823850'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/a:1011986823850'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/a:1011986823850'


 

This table displays all metadata directly associated to this object as RDF triples.

184 TRIPLES      21 PREDICATES      96 URIs      82 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/a:1011986823850 schema:about N0df90b458f2047489ee5f1fc966b42e4
2 N38771237b2f54b75a5ed4a46190d74dd
3 N51c878b2737143ae8af9502baa850381
4 N9513f5ec3da94532b2d06124eb243ec7
5 Nb29211b57fb244a588a5654991147687
6 Nb2ed3b4c231c4c94a99afe8b6d9f300a
7 Nc09d8e9f5de04bf8980a54f2c930e0e5
8 anzsrc-for:11
9 anzsrc-for:1115
10 schema:author N3782c91e1e754f25b5d35258f2e5d710
11 schema:citation sg:pub.10.1007/bf02551274
12 sg:pub.10.1023/a:1015843527138
13 sg:pub.10.1023/a:1016064930502
14 sg:pub.10.1023/a:1016260720218
15 sg:pub.10.1023/a:1018917128684
16 sg:pub.10.1023/a:1018966222807
17 schema:datePublished 1999-01
18 schema:datePublishedReg 1999-01-01
19 schema:description One of the difficulties in the quantitative approach to designing pharmaceutical formulations is the difficulty in understanding the relationship between causal factors and individual pharmaceutical responses. Another difficulty is desirable formulation for one property is not always desirable for the other characteristics. This is called a multi-objective simultaneous optimization problem. A response surface method (RSM) has proven to be a useful approach for selecting pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The aim of this review is to describe the basic concept of the multi-objective simultaneous optimization technique in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the non-linear relationship between causal factors and response variables. The usefulness and reliability of this ANN approach is demonstrated by the optimization for ketoprofen hydrogel ointment as a typical numerical example, in comparison with the results obtained with a classical RSM approach.
20 schema:genre article
21 schema:isAccessibleForFree false
22 schema:isPartOf N8d83f75a8e0744a8b4987a63a85dd471
23 Nb7d55c121eb140e3b93d76466a9047f9
24 sg:journal.1094644
25 schema:keywords ANN approach
26 RSM approach
27 aim
28 approach
29 artificial neural network
30 basic concepts
31 causal factors
32 characteristics
33 comparison
34 concept
35 desirable formulation
36 difficulties
37 equations
38 estimation
39 example
40 factors
41 formulation
42 levels
43 low levels
44 method
45 multi-objective simultaneous optimization technique
46 network
47 neural network
48 non-linear relationship
49 novel method
50 numerical examples
51 ointment
52 optimal formulation
53 optimization
54 optimization problem
55 optimization techniques
56 pharmaceutical formulations
57 pharmaceutical research
58 pharmaceutical responses
59 polynomial equation
60 poor estimation
61 prediction
62 problem
63 properties
64 quantitative approach
65 relationship
66 reliability
67 research
68 response
69 response surface method
70 response variables
71 results
72 review
73 second-order polynomial equation
74 simultaneous optimization problem
75 simultaneous optimization technique
76 surface method
77 technique
78 typical numerical examples
79 useful approach
80 usefulness
81 variables
82 schema:name Artificial Neural Network as a Novel Method to Optimize Pharmaceutical Formulations
83 schema:pagination 1-6
84 schema:productId N748a395b5b8a4da39c0e9489ce56d034
85 Ne45ebd7d458c40c19968a5af6031ba5f
86 Ne6934baee6104a00be69a78bc0acc5a0
87 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007767460
88 https://doi.org/10.1023/a:1011986823850
89 schema:sdDatePublished 2022-08-04T16:54
90 schema:sdLicense https://scigraph.springernature.com/explorer/license/
91 schema:sdPublisher Ne174dc253b8b44b8973b854d01510b89
92 schema:url https://doi.org/10.1023/a:1011986823850
93 sgo:license sg:explorer/license/
94 sgo:sdDataset articles
95 rdf:type schema:ScholarlyArticle
96 N0df90b458f2047489ee5f1fc966b42e4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Chemistry, Pharmaceutical
98 rdf:type schema:DefinedTerm
99 N375fde951f31471080a3d170fe641685 rdf:first sg:person.01236353151.72
100 rdf:rest rdf:nil
101 N3782c91e1e754f25b5d35258f2e5d710 rdf:first sg:person.0774557773.42
102 rdf:rest N83c930b3d0fb4fe69bfc7555c327522f
103 N38771237b2f54b75a5ed4a46190d74dd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Nonlinear Dynamics
105 rdf:type schema:DefinedTerm
106 N51c878b2737143ae8af9502baa850381 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
107 schema:name Neural Networks, Computer
108 rdf:type schema:DefinedTerm
109 N748a395b5b8a4da39c0e9489ce56d034 schema:name pubmed_id
110 schema:value 9950271
111 rdf:type schema:PropertyValue
112 N83c930b3d0fb4fe69bfc7555c327522f rdf:first sg:person.01027115253.63
113 rdf:rest N375fde951f31471080a3d170fe641685
114 N8d83f75a8e0744a8b4987a63a85dd471 schema:volumeNumber 16
115 rdf:type schema:PublicationVolume
116 N9513f5ec3da94532b2d06124eb243ec7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Reproducibility of Results
118 rdf:type schema:DefinedTerm
119 Nb29211b57fb244a588a5654991147687 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Ointments
121 rdf:type schema:DefinedTerm
122 Nb2ed3b4c231c4c94a99afe8b6d9f300a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Ketoprofen
124 rdf:type schema:DefinedTerm
125 Nb7d55c121eb140e3b93d76466a9047f9 schema:issueNumber 1
126 rdf:type schema:PublicationIssue
127 Nc09d8e9f5de04bf8980a54f2c930e0e5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Hydrogels
129 rdf:type schema:DefinedTerm
130 Ne174dc253b8b44b8973b854d01510b89 schema:name Springer Nature - SN SciGraph project
131 rdf:type schema:Organization
132 Ne45ebd7d458c40c19968a5af6031ba5f schema:name dimensions_id
133 schema:value pub.1007767460
134 rdf:type schema:PropertyValue
135 Ne6934baee6104a00be69a78bc0acc5a0 schema:name doi
136 schema:value 10.1023/a:1011986823850
137 rdf:type schema:PropertyValue
138 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
139 schema:name Medical and Health Sciences
140 rdf:type schema:DefinedTerm
141 anzsrc-for:1115 schema:inDefinedTermSet anzsrc-for:
142 schema:name Pharmacology and Pharmaceutical Sciences
143 rdf:type schema:DefinedTerm
144 sg:journal.1094644 schema:issn 0724-8741
145 1573-904X
146 schema:name Pharmaceutical Research
147 schema:publisher Springer Nature
148 rdf:type schema:Periodical
149 sg:person.01027115253.63 schema:affiliation grid-institutes:grid.412239.f
150 schema:familyName Fujikawa
151 schema:givenName Mikito
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027115253.63
153 rdf:type schema:Person
154 sg:person.01236353151.72 schema:affiliation grid-institutes:grid.412239.f
155 schema:familyName Nagai
156 schema:givenName Tsuneji
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01236353151.72
158 rdf:type schema:Person
159 sg:person.0774557773.42 schema:affiliation grid-institutes:grid.412239.f
160 schema:familyName Takayama
161 schema:givenName Kozo
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774557773.42
163 rdf:type schema:Person
164 sg:pub.10.1007/bf02551274 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023250347
165 https://doi.org/10.1007/bf02551274
166 rdf:type schema:CreativeWork
167 sg:pub.10.1023/a:1015843527138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002628904
168 https://doi.org/10.1023/a:1015843527138
169 rdf:type schema:CreativeWork
170 sg:pub.10.1023/a:1016064930502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021003482
171 https://doi.org/10.1023/a:1016064930502
172 rdf:type schema:CreativeWork
173 sg:pub.10.1023/a:1016260720218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000414328
174 https://doi.org/10.1023/a:1016260720218
175 rdf:type schema:CreativeWork
176 sg:pub.10.1023/a:1018917128684 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028417432
177 https://doi.org/10.1023/a:1018917128684
178 rdf:type schema:CreativeWork
179 sg:pub.10.1023/a:1018966222807 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042859520
180 https://doi.org/10.1023/a:1018966222807
181 rdf:type schema:CreativeWork
182 grid-institutes:grid.412239.f schema:alternateName Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan
183 schema:name Department of Pharmaceutics, Hoshi University, Ebara 2-4-41, 142-8501, Shina-gawa-ku, Tokyo, Japan
184 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...