Modeling weight variability in a pan coating process using Monte Carlo simulations
Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation tim...
Ausführliche Beschreibung
Autor*in: |
Pandey, Preetanshu [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2006 |
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Schlagwörter: |
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Anmerkung: |
© American Association of Pharmaceutical Scientists 2006 |
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Übergeordnetes Werk: |
Enthalten in: AAPS PharmSciTech - New York, NY : Springer, 2000, 7(2006), 4 vom: Dez., Seite E2-E11 |
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Übergeordnetes Werk: |
volume:7 ; year:2006 ; number:4 ; month:12 ; pages:E2-E11 |
Links: |
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DOI / URN: |
10.1208/pt070483 |
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Katalog-ID: |
SPR024826723 |
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100 | 1 | |a Pandey, Preetanshu |e verfasserin |4 aut | |
245 | 1 | 0 | |a Modeling weight variability in a pan coating process using Monte Carlo simulations |
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520 | |a Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. | ||
650 | 4 | |a Pan coating |7 (dpeaa)DE-He213 | |
650 | 4 | |a video imaging |7 (dpeaa)DE-He213 | |
650 | 4 | |a mass coating variability |7 (dpeaa)DE-He213 | |
650 | 4 | |a Monte Carlo |7 (dpeaa)DE-He213 | |
650 | 4 | |a spray shape |7 (dpeaa)DE-He213 | |
700 | 1 | |a Katakdaunde, Manoj |4 aut | |
700 | 1 | |a Turton, Richard |4 aut | |
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10.1208/pt070483 doi (DE-627)SPR024826723 (SPR)pt070483-e DE-627 ger DE-627 rakwb eng Pandey, Preetanshu verfasserin aut Modeling weight variability in a pan coating process using Monte Carlo simulations 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Association of Pharmaceutical Scientists 2006 Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. Pan coating (dpeaa)DE-He213 video imaging (dpeaa)DE-He213 mass coating variability (dpeaa)DE-He213 Monte Carlo (dpeaa)DE-He213 spray shape (dpeaa)DE-He213 Katakdaunde, Manoj aut Turton, Richard aut Enthalten in AAPS PharmSciTech New York, NY : Springer, 2000 7(2006), 4 vom: Dez., Seite E2-E11 (DE-627)331742357 (DE-600)2052070-0 1530-9932 nnns volume:7 year:2006 number:4 month:12 pages:E2-E11 https://dx.doi.org/10.1208/pt070483 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2006 4 12 E2-E11 |
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10.1208/pt070483 doi (DE-627)SPR024826723 (SPR)pt070483-e DE-627 ger DE-627 rakwb eng Pandey, Preetanshu verfasserin aut Modeling weight variability in a pan coating process using Monte Carlo simulations 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Association of Pharmaceutical Scientists 2006 Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. Pan coating (dpeaa)DE-He213 video imaging (dpeaa)DE-He213 mass coating variability (dpeaa)DE-He213 Monte Carlo (dpeaa)DE-He213 spray shape (dpeaa)DE-He213 Katakdaunde, Manoj aut Turton, Richard aut Enthalten in AAPS PharmSciTech New York, NY : Springer, 2000 7(2006), 4 vom: Dez., Seite E2-E11 (DE-627)331742357 (DE-600)2052070-0 1530-9932 nnns volume:7 year:2006 number:4 month:12 pages:E2-E11 https://dx.doi.org/10.1208/pt070483 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2006 4 12 E2-E11 |
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10.1208/pt070483 doi (DE-627)SPR024826723 (SPR)pt070483-e DE-627 ger DE-627 rakwb eng Pandey, Preetanshu verfasserin aut Modeling weight variability in a pan coating process using Monte Carlo simulations 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Association of Pharmaceutical Scientists 2006 Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. Pan coating (dpeaa)DE-He213 video imaging (dpeaa)DE-He213 mass coating variability (dpeaa)DE-He213 Monte Carlo (dpeaa)DE-He213 spray shape (dpeaa)DE-He213 Katakdaunde, Manoj aut Turton, Richard aut Enthalten in AAPS PharmSciTech New York, NY : Springer, 2000 7(2006), 4 vom: Dez., Seite E2-E11 (DE-627)331742357 (DE-600)2052070-0 1530-9932 nnns volume:7 year:2006 number:4 month:12 pages:E2-E11 https://dx.doi.org/10.1208/pt070483 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2006 4 12 E2-E11 |
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10.1208/pt070483 doi (DE-627)SPR024826723 (SPR)pt070483-e DE-627 ger DE-627 rakwb eng Pandey, Preetanshu verfasserin aut Modeling weight variability in a pan coating process using Monte Carlo simulations 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Association of Pharmaceutical Scientists 2006 Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. Pan coating (dpeaa)DE-He213 video imaging (dpeaa)DE-He213 mass coating variability (dpeaa)DE-He213 Monte Carlo (dpeaa)DE-He213 spray shape (dpeaa)DE-He213 Katakdaunde, Manoj aut Turton, Richard aut Enthalten in AAPS PharmSciTech New York, NY : Springer, 2000 7(2006), 4 vom: Dez., Seite E2-E11 (DE-627)331742357 (DE-600)2052070-0 1530-9932 nnns volume:7 year:2006 number:4 month:12 pages:E2-E11 https://dx.doi.org/10.1208/pt070483 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2006 4 12 E2-E11 |
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10.1208/pt070483 doi (DE-627)SPR024826723 (SPR)pt070483-e DE-627 ger DE-627 rakwb eng Pandey, Preetanshu verfasserin aut Modeling weight variability in a pan coating process using Monte Carlo simulations 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Association of Pharmaceutical Scientists 2006 Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. Pan coating (dpeaa)DE-He213 video imaging (dpeaa)DE-He213 mass coating variability (dpeaa)DE-He213 Monte Carlo (dpeaa)DE-He213 spray shape (dpeaa)DE-He213 Katakdaunde, Manoj aut Turton, Richard aut Enthalten in AAPS PharmSciTech New York, NY : Springer, 2000 7(2006), 4 vom: Dez., Seite E2-E11 (DE-627)331742357 (DE-600)2052070-0 1530-9932 nnns volume:7 year:2006 number:4 month:12 pages:E2-E11 https://dx.doi.org/10.1208/pt070483 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2006 4 12 E2-E11 |
language |
English |
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Enthalten in AAPS PharmSciTech 7(2006), 4 vom: Dez., Seite E2-E11 volume:7 year:2006 number:4 month:12 pages:E2-E11 |
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Enthalten in AAPS PharmSciTech 7(2006), 4 vom: Dez., Seite E2-E11 volume:7 year:2006 number:4 month:12 pages:E2-E11 |
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topic_facet |
Pan coating video imaging mass coating variability Monte Carlo spray shape |
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Pandey, Preetanshu @@aut@@ Katakdaunde, Manoj @@aut@@ Turton, Richard @@aut@@ |
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2006-12-01T00:00:00Z |
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Pandey, Preetanshu |
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Pandey, Preetanshu misc Pan coating misc video imaging misc mass coating variability misc Monte Carlo misc spray shape Modeling weight variability in a pan coating process using Monte Carlo simulations |
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Modeling weight variability in a pan coating process using Monte Carlo simulations Pan coating (dpeaa)DE-He213 video imaging (dpeaa)DE-He213 mass coating variability (dpeaa)DE-He213 Monte Carlo (dpeaa)DE-He213 spray shape (dpeaa)DE-He213 |
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Modeling weight variability in a pan coating process using Monte Carlo simulations |
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Modeling weight variability in a pan coating process using Monte Carlo simulations |
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Pandey, Preetanshu Katakdaunde, Manoj Turton, Richard |
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title_sort |
modeling weight variability in a pan coating process using monte carlo simulations |
title_auth |
Modeling weight variability in a pan coating process using Monte Carlo simulations |
abstract |
Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. © American Association of Pharmaceutical Scientists 2006 |
abstractGer |
Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. © American Association of Pharmaceutical Scientists 2006 |
abstract_unstemmed |
Abstract The primary objective of the current study was to investigate process variables affecting weight gain mass coating variability (CVm) in pan coating devices using novel video-imaging techniques and Monte Carlo simulations. Experimental information such as the tablet location, circulation time distribution, velocity distribution, projected surface area, and spray dynamics was the main input to the simulations. The data on the dynamics of tablet movement were obtained using novel video-imaging methods. The effects of pan speed, pan loading, tablet size, coating time, spray flux distribution, and spray area and shape were investigated. CVm was found to be inversely proportional to the square root of coating time. The spray shape was not found to affect the CVm of the process significantly, but an increase in the spray area led to lower CVms. Coating experiments were conducted to verify the predictions from the Monte Carlo simulations, and the trends predicted from the model were in good agreement. It was observed that the Monte Carlo simulations underpredicted CVms in comparison to the experiments. The model developed can provide a basis for adjustments in process parameters required during scale-up operations and can be useful in predicting the process changes that are needed to achieve the same CVm when a variable is altered. © American Association of Pharmaceutical Scientists 2006 |
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container_issue |
4 |
title_short |
Modeling weight variability in a pan coating process using Monte Carlo simulations |
url |
https://dx.doi.org/10.1208/pt070483 |
remote_bool |
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author2 |
Katakdaunde, Manoj Turton, Richard |
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Katakdaunde, Manoj Turton, Richard |
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doi_str |
10.1208/pt070483 |
up_date |
2024-07-04T02:31:31.642Z |
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score |
7.402237 |