Non-destructive methods of characterization of risperidone solid lipid nanoparticles
We developed and optimized solid lipid nanoparticle (SLN) formulation of risperidone using compritol 888 ATO as a matrix by design of experiment strategy. The components of SLN were estimated by non destructive near infrared (NIR) and near infrared chemical imaging (NIR-CI). The objective of this in...
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| Veröffentlicht in: | European journal of pharmaceutics and biopharmaceutics 2010-09, Vol.76 (1), p.127-137 |
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| creator | Rahman, Ziyaur Zidan, Ahmed S. Khan, Mansoor A. |
| description | We developed and optimized solid lipid nanoparticle (SLN) formulation of risperidone using compritol 888 ATO as a matrix by design of experiment strategy. The components of SLN were estimated by non destructive near infrared (NIR) and near infrared chemical imaging (NIR-CI).
The objective of this investigation is to evaluate compositional variations and their interaction of the solid lipid nanoparticle (SLN) formulation of risperidone using response surface methodology of design of experiment (DOE) and subsequently, characterize the SLN by non-destructive methods of analysis. Box–Behnken DOE was constructed using drug (
X
1), lipid (
X
2) and surfactant (
X
3) level as independent factors. Compritol 888 ATO and sodium lauryl sulphate were used as lipid and surfactant, respectively. The SLN was prepared by solvent evaporation method and characterized by transmission electron microscopy (TEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRD), fourier infrared spectroscopy (FTIR), near infrared spectroscopy (NIR) and NIR-chemical imaging (NIR-CI). Responses measured were entrapment efficiency (
Y
1),
D
90 (
Y
2), zeta potential (
Y
3), burst effect (
Y
4) and cumulative release in 8
h (
Y
5). Statistically significant (
p
<
0.05) effect of
X
1 on the
Y
1,
Y
2,
Y
3 and
Y
4 were seen. FTIR revealed no interaction between risperidone and compritol 888 ATO. TEM showed spherical and smooth surface SLN. Compritol retained its crystalline nature in the SLN formulation revealed by DSC and XRD studies. Homogenous distribution of risperidone and compritol 888 ATO was revealed by NIR-CI. Principal component analysis (PCA) and partial least square (PLS) were carried out on NIR data of SLN formulation. PLS showed correlation coefficient
>
0.996 for prediction and calibration model of both risperidone and compritol 888 ATO. The accuracy of models in predicting risperidone and compritol 888 ATO were 1.60% and 11.27%, respectively. In conclusion, the DOE reveals significant effect of drug loading on SLN characteristics, and chemometric models based on NIR and NIR-CI data provided non-destructive method of estimation of components of SLN. |
| doi_str_mv | 10.1016/j.ejpb.2010.05.003 |
| format | Article |
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The objective of this investigation is to evaluate compositional variations and their interaction of the solid lipid nanoparticle (SLN) formulation of risperidone using response surface methodology of design of experiment (DOE) and subsequently, characterize the SLN by non-destructive methods of analysis. Box–Behnken DOE was constructed using drug (
X
1), lipid (
X
2) and surfactant (
X
3) level as independent factors. Compritol 888 ATO and sodium lauryl sulphate were used as lipid and surfactant, respectively. The SLN was prepared by solvent evaporation method and characterized by transmission electron microscopy (TEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRD), fourier infrared spectroscopy (FTIR), near infrared spectroscopy (NIR) and NIR-chemical imaging (NIR-CI). Responses measured were entrapment efficiency (
Y
1),
D
90 (
Y
2), zeta potential (
Y
3), burst effect (
Y
4) and cumulative release in 8
h (
Y
5). Statistically significant (
p
<
0.05) effect of
X
1 on the
Y
1,
Y
2,
Y
3 and
Y
4 were seen. FTIR revealed no interaction between risperidone and compritol 888 ATO. TEM showed spherical and smooth surface SLN. Compritol retained its crystalline nature in the SLN formulation revealed by DSC and XRD studies. Homogenous distribution of risperidone and compritol 888 ATO was revealed by NIR-CI. Principal component analysis (PCA) and partial least square (PLS) were carried out on NIR data of SLN formulation. PLS showed correlation coefficient
>
0.996 for prediction and calibration model of both risperidone and compritol 888 ATO. The accuracy of models in predicting risperidone and compritol 888 ATO were 1.60% and 11.27%, respectively. In conclusion, the DOE reveals significant effect of drug loading on SLN characteristics, and chemometric models based on NIR and NIR-CI data provided non-destructive method of estimation of components of SLN.</description><identifier>ISSN: 0939-6411</identifier><identifier>EISSN: 1873-3441</identifier><identifier>DOI: 10.1016/j.ejpb.2010.05.003</identifier><identifier>PMID: 20470882</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Antipsychotic Agents - chemistry ; Biological and medical sciences ; Calorimetry, Differential Scanning ; Chemistry, Pharmaceutical ; Compritol 888 ATO ; Crystallography, X-Ray ; Drug Compounding ; Fatty Acids - chemistry ; General pharmacology ; Kinetics ; Least-Squares Analysis ; Medical sciences ; Microscopy, Electron, Transmission ; Models, Chemical ; Nanoparticles ; Nanotechnology ; NIR ; NIR-CI ; Particle Size ; Pharmaceutical technology. Pharmaceutical industry ; Pharmacology. Drug treatments ; Powder Diffraction ; Principal Component Analysis ; Risperidone ; Risperidone - chemistry ; SLN ; Sodium Dodecyl Sulfate - chemistry ; Solubility ; Spectroscopy, Fourier Transform Infrared ; Spectroscopy, Near-Infrared ; Surface-Active Agents - chemistry ; Technology, Pharmaceutical - methods</subject><ispartof>European journal of pharmaceutics and biopharmaceutics, 2010-09, Vol.76 (1), p.127-137</ispartof><rights>2010</rights><rights>2015 INIST-CNRS</rights><rights>Published by Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-2da88ea5bf0557fc21caa19bf400e98742d45a94a50718bcd03d35388e67b6223</citedby><cites>FETCH-LOGICAL-c451t-2da88ea5bf0557fc21caa19bf400e98742d45a94a50718bcd03d35388e67b6223</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejpb.2010.05.003$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23217777$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20470882$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rahman, Ziyaur</creatorcontrib><creatorcontrib>Zidan, Ahmed S.</creatorcontrib><creatorcontrib>Khan, Mansoor A.</creatorcontrib><title>Non-destructive methods of characterization of risperidone solid lipid nanoparticles</title><title>European journal of pharmaceutics and biopharmaceutics</title><addtitle>Eur J Pharm Biopharm</addtitle><description>We developed and optimized solid lipid nanoparticle (SLN) formulation of risperidone using compritol 888 ATO as a matrix by design of experiment strategy. The components of SLN were estimated by non destructive near infrared (NIR) and near infrared chemical imaging (NIR-CI).
The objective of this investigation is to evaluate compositional variations and their interaction of the solid lipid nanoparticle (SLN) formulation of risperidone using response surface methodology of design of experiment (DOE) and subsequently, characterize the SLN by non-destructive methods of analysis. Box–Behnken DOE was constructed using drug (
X
1), lipid (
X
2) and surfactant (
X
3) level as independent factors. Compritol 888 ATO and sodium lauryl sulphate were used as lipid and surfactant, respectively. The SLN was prepared by solvent evaporation method and characterized by transmission electron microscopy (TEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRD), fourier infrared spectroscopy (FTIR), near infrared spectroscopy (NIR) and NIR-chemical imaging (NIR-CI). Responses measured were entrapment efficiency (
Y
1),
D
90 (
Y
2), zeta potential (
Y
3), burst effect (
Y
4) and cumulative release in 8
h (
Y
5). Statistically significant (
p
<
0.05) effect of
X
1 on the
Y
1,
Y
2,
Y
3 and
Y
4 were seen. FTIR revealed no interaction between risperidone and compritol 888 ATO. TEM showed spherical and smooth surface SLN. Compritol retained its crystalline nature in the SLN formulation revealed by DSC and XRD studies. Homogenous distribution of risperidone and compritol 888 ATO was revealed by NIR-CI. Principal component analysis (PCA) and partial least square (PLS) were carried out on NIR data of SLN formulation. PLS showed correlation coefficient
>
0.996 for prediction and calibration model of both risperidone and compritol 888 ATO. The accuracy of models in predicting risperidone and compritol 888 ATO were 1.60% and 11.27%, respectively. In conclusion, the DOE reveals significant effect of drug loading on SLN characteristics, and chemometric models based on NIR and NIR-CI data provided non-destructive method of estimation of components of SLN.</description><subject>Antipsychotic Agents - chemistry</subject><subject>Biological and medical sciences</subject><subject>Calorimetry, Differential Scanning</subject><subject>Chemistry, Pharmaceutical</subject><subject>Compritol 888 ATO</subject><subject>Crystallography, X-Ray</subject><subject>Drug Compounding</subject><subject>Fatty Acids - chemistry</subject><subject>General pharmacology</subject><subject>Kinetics</subject><subject>Least-Squares Analysis</subject><subject>Medical sciences</subject><subject>Microscopy, Electron, Transmission</subject><subject>Models, Chemical</subject><subject>Nanoparticles</subject><subject>Nanotechnology</subject><subject>NIR</subject><subject>NIR-CI</subject><subject>Particle Size</subject><subject>Pharmaceutical technology. Pharmaceutical industry</subject><subject>Pharmacology. Drug treatments</subject><subject>Powder Diffraction</subject><subject>Principal Component Analysis</subject><subject>Risperidone</subject><subject>Risperidone - chemistry</subject><subject>SLN</subject><subject>Sodium Dodecyl Sulfate - chemistry</subject><subject>Solubility</subject><subject>Spectroscopy, Fourier Transform Infrared</subject><subject>Spectroscopy, Near-Infrared</subject><subject>Surface-Active Agents - chemistry</subject><subject>Technology, Pharmaceutical - methods</subject><issn>0939-6411</issn><issn>1873-3441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kM1q3DAURkVpaCZpX6CL4E3pypOrv7EM3ZTQJoEh2aRrcS1dEw0ey5E8gfTpo2Em7S5aSOLjfBfpMPaVw5IDX11ulrSZuqWAEoBeAsgPbMFNI2upFP_IFtDKtl4pzk_ZWc4bAFCNNp_YqSgXMEYs2MNdHGtPeU47N4dnqrY0P0afq9hX7hETuplS-ItziOM-SyFPJfBxpCrHIfhqCFPZRxzjhGkObqD8mZ30OGT6cjzP2Z_fvx6ubur1_fXt1c917ZTmcy08GkOoux60bnonuEPkbdcrAGpNo4RXGluFGhpuOudBeqll6ayabiWEPGffD3OnFJ925RN2G7KjYcCR4i7bRpm2dJUspDiQLsWcE_V2SmGL6cVysHuZdmP3Mu1epgVti8xSujiO33Vb8v8qb_YK8O0IYHY49AlHF_J_TgrelFW4HweOioznQMlmF2h05EMiN1sfw3vveAXHrJNw</recordid><startdate>20100901</startdate><enddate>20100901</enddate><creator>Rahman, Ziyaur</creator><creator>Zidan, Ahmed S.</creator><creator>Khan, Mansoor A.</creator><general>Elsevier B.V</general><general>Elsevier</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>7X8</scope></search><sort><creationdate>20100901</creationdate><title>Non-destructive methods of characterization of risperidone solid lipid nanoparticles</title><author>Rahman, Ziyaur ; Zidan, Ahmed S. ; Khan, Mansoor A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-2da88ea5bf0557fc21caa19bf400e98742d45a94a50718bcd03d35388e67b6223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Antipsychotic Agents - chemistry</topic><topic>Biological and medical sciences</topic><topic>Calorimetry, Differential Scanning</topic><topic>Chemistry, Pharmaceutical</topic><topic>Compritol 888 ATO</topic><topic>Crystallography, X-Ray</topic><topic>Drug Compounding</topic><topic>Fatty Acids - chemistry</topic><topic>General pharmacology</topic><topic>Kinetics</topic><topic>Least-Squares Analysis</topic><topic>Medical sciences</topic><topic>Microscopy, Electron, Transmission</topic><topic>Models, Chemical</topic><topic>Nanoparticles</topic><topic>Nanotechnology</topic><topic>NIR</topic><topic>NIR-CI</topic><topic>Particle Size</topic><topic>Pharmaceutical technology. Pharmaceutical industry</topic><topic>Pharmacology. Drug treatments</topic><topic>Powder Diffraction</topic><topic>Principal Component Analysis</topic><topic>Risperidone</topic><topic>Risperidone - chemistry</topic><topic>SLN</topic><topic>Sodium Dodecyl Sulfate - chemistry</topic><topic>Solubility</topic><topic>Spectroscopy, Fourier Transform Infrared</topic><topic>Spectroscopy, Near-Infrared</topic><topic>Surface-Active Agents - chemistry</topic><topic>Technology, Pharmaceutical - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rahman, Ziyaur</creatorcontrib><creatorcontrib>Zidan, Ahmed S.</creatorcontrib><creatorcontrib>Khan, Mansoor A.</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>MEDLINE - Academic</collection><jtitle>European journal of pharmaceutics and biopharmaceutics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rahman, Ziyaur</au><au>Zidan, Ahmed S.</au><au>Khan, Mansoor A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Non-destructive methods of characterization of risperidone solid lipid nanoparticles</atitle><jtitle>European journal of pharmaceutics and biopharmaceutics</jtitle><addtitle>Eur J Pharm Biopharm</addtitle><date>2010-09-01</date><risdate>2010</risdate><volume>76</volume><issue>1</issue><spage>127</spage><epage>137</epage><pages>127-137</pages><issn>0939-6411</issn><eissn>1873-3441</eissn><abstract>We developed and optimized solid lipid nanoparticle (SLN) formulation of risperidone using compritol 888 ATO as a matrix by design of experiment strategy. The components of SLN were estimated by non destructive near infrared (NIR) and near infrared chemical imaging (NIR-CI).
The objective of this investigation is to evaluate compositional variations and their interaction of the solid lipid nanoparticle (SLN) formulation of risperidone using response surface methodology of design of experiment (DOE) and subsequently, characterize the SLN by non-destructive methods of analysis. Box–Behnken DOE was constructed using drug (
X
1), lipid (
X
2) and surfactant (
X
3) level as independent factors. Compritol 888 ATO and sodium lauryl sulphate were used as lipid and surfactant, respectively. The SLN was prepared by solvent evaporation method and characterized by transmission electron microscopy (TEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRD), fourier infrared spectroscopy (FTIR), near infrared spectroscopy (NIR) and NIR-chemical imaging (NIR-CI). Responses measured were entrapment efficiency (
Y
1),
D
90 (
Y
2), zeta potential (
Y
3), burst effect (
Y
4) and cumulative release in 8
h (
Y
5). Statistically significant (
p
<
0.05) effect of
X
1 on the
Y
1,
Y
2,
Y
3 and
Y
4 were seen. FTIR revealed no interaction between risperidone and compritol 888 ATO. TEM showed spherical and smooth surface SLN. Compritol retained its crystalline nature in the SLN formulation revealed by DSC and XRD studies. Homogenous distribution of risperidone and compritol 888 ATO was revealed by NIR-CI. Principal component analysis (PCA) and partial least square (PLS) were carried out on NIR data of SLN formulation. PLS showed correlation coefficient
>
0.996 for prediction and calibration model of both risperidone and compritol 888 ATO. The accuracy of models in predicting risperidone and compritol 888 ATO were 1.60% and 11.27%, respectively. In conclusion, the DOE reveals significant effect of drug loading on SLN characteristics, and chemometric models based on NIR and NIR-CI data provided non-destructive method of estimation of components of SLN.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><pmid>20470882</pmid><doi>10.1016/j.ejpb.2010.05.003</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
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| ispartof | European journal of pharmaceutics and biopharmaceutics, 2010-09, Vol.76 (1), p.127-137 |
| issn | 0939-6411 1873-3441 |
| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Antipsychotic Agents - chemistry Biological and medical sciences Calorimetry, Differential Scanning Chemistry, Pharmaceutical Compritol 888 ATO Crystallography, X-Ray Drug Compounding Fatty Acids - chemistry General pharmacology Kinetics Least-Squares Analysis Medical sciences Microscopy, Electron, Transmission Models, Chemical Nanoparticles Nanotechnology NIR NIR-CI Particle Size Pharmaceutical technology. Pharmaceutical industry Pharmacology. Drug treatments Powder Diffraction Principal Component Analysis Risperidone Risperidone - chemistry SLN Sodium Dodecyl Sulfate - chemistry Solubility Spectroscopy, Fourier Transform Infrared Spectroscopy, Near-Infrared Surface-Active Agents - chemistry Technology, Pharmaceutical - methods |
| title | Non-destructive methods of characterization of risperidone solid lipid nanoparticles |
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