Prediction of a stable microemulsion formulation for the oral delivery of a combination of antitubercular drugs using ANN methodology
The aim of this project was to develop a colloidal dosage form for the oral delivery of rifampicin and isoniazid in combination with the aid of artificial neural network (ANN) data modeling. Data from the 20 pseudoternary phase triangles containing miglyol 812 as the oil component and a mixture of s...
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| Veröffentlicht in: | Pharmaceutical research 2003-11, Vol.20 (11), p.1760-1765 |
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| creator | AGATONOVIC-KUSTRIN, Snezana GLASS, Beverley D WISCH, Michael H ALANY, Raid G |
| description | The aim of this project was to develop a colloidal dosage form for the oral delivery of rifampicin and isoniazid in combination with the aid of artificial neural network (ANN) data modeling.
Data from the 20 pseudoternary phase triangles containing miglyol 812 as the oil component and a mixture of surfactants or a surfactant/cosurfactant blend were used to train, test, and validate the ANN model. The weight ratios of individual components were correlated with the observed phase behavior using radial basis function (RBF) network architecture. The criterion for judging the best model was the percentage success of the model prediction.
The best model successfully predicted the microemulsion region as well as the coarse emulsion region but failed to predict the multiphase liquid crystalline phase for cosurfactant-free systems indicating the difference in microemulsion behavior on dilution with water.
A novel microemulsion formulation capable of delivering rifampicin and isoniazid in combination was created to allow for their differences in solubility and potential for chemical reaction. The developed model allowed better understanding of the process of microemulsion formation and stability within pseudoternary colloidal systems. |
| doi_str_mv | 10.1023/B:PHAM.0000003372.56993.39 |
| format | Article |
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Data from the 20 pseudoternary phase triangles containing miglyol 812 as the oil component and a mixture of surfactants or a surfactant/cosurfactant blend were used to train, test, and validate the ANN model. The weight ratios of individual components were correlated with the observed phase behavior using radial basis function (RBF) network architecture. The criterion for judging the best model was the percentage success of the model prediction.
The best model successfully predicted the microemulsion region as well as the coarse emulsion region but failed to predict the multiphase liquid crystalline phase for cosurfactant-free systems indicating the difference in microemulsion behavior on dilution with water.
A novel microemulsion formulation capable of delivering rifampicin and isoniazid in combination was created to allow for their differences in solubility and potential for chemical reaction. The developed model allowed better understanding of the process of microemulsion formation and stability within pseudoternary colloidal systems.</description><identifier>ISSN: 0724-8741</identifier><identifier>EISSN: 1573-904X</identifier><identifier>DOI: 10.1023/B:PHAM.0000003372.56993.39</identifier><identifier>PMID: 14661919</identifier><identifier>CODEN: PHREEB</identifier><language>eng</language><publisher>New York, NY: Springer</publisher><subject>Administration, Oral ; Antibacterial agents ; Antibiotics. Antiinfectious agents. Antiparasitic agents ; Antitubercular Agents - administration & dosage ; Biological and medical sciences ; Chemistry, Pharmaceutical ; Child ; Drug Delivery Systems - methods ; Drug dosages ; Drug Stability ; Emulsions - administration & dosage ; General pharmacology ; Humans ; Lipids ; Medical sciences ; Microemulsions ; Models, Chemical ; Neural networks ; Neural Networks (Computer) ; Pharmaceutical technology. Pharmaceutical industry ; Pharmacology. Drug treatments ; Predictive Value of Tests ; Surfactants ; Tuberculosis</subject><ispartof>Pharmaceutical research, 2003-11, Vol.20 (11), p.1760-1765</ispartof><rights>2004 INIST-CNRS</rights><rights>Copyright Kluwer Academic Publishers Nov 2003</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-939b32f63d1170c72e8164cac61975be4264da87d3d9a8bc2326abc74b23de3f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15313563$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/14661919$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>AGATONOVIC-KUSTRIN, Snezana</creatorcontrib><creatorcontrib>GLASS, Beverley D</creatorcontrib><creatorcontrib>WISCH, Michael H</creatorcontrib><creatorcontrib>ALANY, Raid G</creatorcontrib><title>Prediction of a stable microemulsion formulation for the oral delivery of a combination of antitubercular drugs using ANN methodology</title><title>Pharmaceutical research</title><addtitle>Pharm Res</addtitle><description>The aim of this project was to develop a colloidal dosage form for the oral delivery of rifampicin and isoniazid in combination with the aid of artificial neural network (ANN) data modeling.
Data from the 20 pseudoternary phase triangles containing miglyol 812 as the oil component and a mixture of surfactants or a surfactant/cosurfactant blend were used to train, test, and validate the ANN model. The weight ratios of individual components were correlated with the observed phase behavior using radial basis function (RBF) network architecture. The criterion for judging the best model was the percentage success of the model prediction.
The best model successfully predicted the microemulsion region as well as the coarse emulsion region but failed to predict the multiphase liquid crystalline phase for cosurfactant-free systems indicating the difference in microemulsion behavior on dilution with water.
A novel microemulsion formulation capable of delivering rifampicin and isoniazid in combination was created to allow for their differences in solubility and potential for chemical reaction. The developed model allowed better understanding of the process of microemulsion formation and stability within pseudoternary colloidal systems.</description><subject>Administration, Oral</subject><subject>Antibacterial agents</subject><subject>Antibiotics. Antiinfectious agents. Antiparasitic agents</subject><subject>Antitubercular Agents - administration & dosage</subject><subject>Biological and medical sciences</subject><subject>Chemistry, Pharmaceutical</subject><subject>Child</subject><subject>Drug Delivery Systems - methods</subject><subject>Drug dosages</subject><subject>Drug Stability</subject><subject>Emulsions - administration & dosage</subject><subject>General pharmacology</subject><subject>Humans</subject><subject>Lipids</subject><subject>Medical sciences</subject><subject>Microemulsions</subject><subject>Models, Chemical</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Pharmaceutical technology. Pharmaceutical industry</subject><subject>Pharmacology. Drug treatments</subject><subject>Predictive Value of Tests</subject><subject>Surfactants</subject><subject>Tuberculosis</subject><issn>0724-8741</issn><issn>1573-904X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNpdkdFuFCEUhonR2LX6CoY00btZBw4DQ--2jbUmtfZCE-8IA8yWZmaowJjsA_jeZbsTN5EbTsL3Hzh8CJ2Rek1qCp8uzu-uN9_W9fMCEHTdcClhDfIFWpFGQCVr9uslWtWCsqoVjJygNyk9FLolkr1GJ4RxTiSRK_T3LjrrTfZhwqHHGqesu8Hh0ZsY3DgPaX_Sh1hKnZca53uHQ9QDtm7wf1zcHbImjJ2f9L9mU_Z57lw0JRuxjfM24Tn5aYs3t7d4dPk-2DCE7e4tetXrIbl3y36Kfl59_nF5Xd18__L1cnNTmTJlriTIDmjPwRIiaiOoawlnRpsyjGg6xyhnVrfCgpW67QwFynVnBOsoWAc9nKKPh76PMfyeXcpq9Mm4YdCTC3NSgjCgkrMCnv0HPoQ5TuVtilLKpSCcFuj8AJWvSim6Xj1GP-q4U6RWe1PqQu1NqaMp9WxKgSzh98sNczc6e4wuagrwYQF0Mnroo56MT0euAQINB3gCz2ieuQ</recordid><startdate>20031101</startdate><enddate>20031101</enddate><creator>AGATONOVIC-KUSTRIN, Snezana</creator><creator>GLASS, Beverley D</creator><creator>WISCH, Michael H</creator><creator>ALANY, Raid G</creator><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope></search><sort><creationdate>20031101</creationdate><title>Prediction of a stable microemulsion formulation for the oral delivery of a combination of antitubercular drugs using ANN methodology</title><author>AGATONOVIC-KUSTRIN, Snezana ; GLASS, Beverley D ; WISCH, Michael H ; ALANY, Raid G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-939b32f63d1170c72e8164cac61975be4264da87d3d9a8bc2326abc74b23de3f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Administration, Oral</topic><topic>Antibacterial agents</topic><topic>Antibiotics. Antiinfectious agents. Antiparasitic agents</topic><topic>Antitubercular Agents - administration & dosage</topic><topic>Biological and medical sciences</topic><topic>Chemistry, Pharmaceutical</topic><topic>Child</topic><topic>Drug Delivery Systems - methods</topic><topic>Drug dosages</topic><topic>Drug Stability</topic><topic>Emulsions - administration & dosage</topic><topic>General pharmacology</topic><topic>Humans</topic><topic>Lipids</topic><topic>Medical sciences</topic><topic>Microemulsions</topic><topic>Models, Chemical</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Pharmaceutical technology. Pharmaceutical industry</topic><topic>Pharmacology. Drug treatments</topic><topic>Predictive Value of Tests</topic><topic>Surfactants</topic><topic>Tuberculosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>AGATONOVIC-KUSTRIN, Snezana</creatorcontrib><creatorcontrib>GLASS, Beverley D</creatorcontrib><creatorcontrib>WISCH, Michael H</creatorcontrib><creatorcontrib>ALANY, Raid G</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>Pharmaceutical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>AGATONOVIC-KUSTRIN, Snezana</au><au>GLASS, Beverley D</au><au>WISCH, Michael H</au><au>ALANY, Raid G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of a stable microemulsion formulation for the oral delivery of a combination of antitubercular drugs using ANN methodology</atitle><jtitle>Pharmaceutical research</jtitle><addtitle>Pharm Res</addtitle><date>2003-11-01</date><risdate>2003</risdate><volume>20</volume><issue>11</issue><spage>1760</spage><epage>1765</epage><pages>1760-1765</pages><issn>0724-8741</issn><eissn>1573-904X</eissn><coden>PHREEB</coden><abstract>The aim of this project was to develop a colloidal dosage form for the oral delivery of rifampicin and isoniazid in combination with the aid of artificial neural network (ANN) data modeling.
Data from the 20 pseudoternary phase triangles containing miglyol 812 as the oil component and a mixture of surfactants or a surfactant/cosurfactant blend were used to train, test, and validate the ANN model. The weight ratios of individual components were correlated with the observed phase behavior using radial basis function (RBF) network architecture. The criterion for judging the best model was the percentage success of the model prediction.
The best model successfully predicted the microemulsion region as well as the coarse emulsion region but failed to predict the multiphase liquid crystalline phase for cosurfactant-free systems indicating the difference in microemulsion behavior on dilution with water.
A novel microemulsion formulation capable of delivering rifampicin and isoniazid in combination was created to allow for their differences in solubility and potential for chemical reaction. The developed model allowed better understanding of the process of microemulsion formation and stability within pseudoternary colloidal systems.</abstract><cop>New York, NY</cop><pub>Springer</pub><pmid>14661919</pmid><doi>10.1023/B:PHAM.0000003372.56993.39</doi><tpages>6</tpages></addata></record> |
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| subjects | Administration, Oral Antibacterial agents Antibiotics. Antiinfectious agents. Antiparasitic agents Antitubercular Agents - administration & dosage Biological and medical sciences Chemistry, Pharmaceutical Child Drug Delivery Systems - methods Drug dosages Drug Stability Emulsions - administration & dosage General pharmacology Humans Lipids Medical sciences Microemulsions Models, Chemical Neural networks Neural Networks (Computer) Pharmaceutical technology. Pharmaceutical industry Pharmacology. Drug treatments Predictive Value of Tests Surfactants Tuberculosis |
| title | Prediction of a stable microemulsion formulation for the oral delivery of a combination of antitubercular drugs using ANN methodology |
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