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Computational Predictability of Microsponge Properties Using Different Multivariate Models
The capabilities of principal component regression (PCR) and multiple linear regression (MLR) were evaluated to decipher and predict the impact of formulation and process parameters on the modeled metronidazole benzoate (MB)–ethyl cellulose (EC) microsponge (MBECM) properties. MBECM were prepared by...
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Published in: | AAPS PharmSciTech 2019-07, Vol.20 (5), p.172-172, Article 172 |
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description | The capabilities of principal component regression (PCR) and multiple linear regression (MLR) were evaluated to decipher and predict the impact of formulation and process parameters on the modeled metronidazole benzoate (MB)–ethyl cellulose (EC) microsponge (MBECM) properties. MBECM were prepared by a quasi-emulsion solvent diffusion method. A minimum experimentation was designed using Box-Behnken approach with one center point after initial screening experiments. Data was modeled by principal component analysis (PCA), PCR, and MLR. Two distinct groupings of developed MBECM was observed in initial qualitative PCA as a function of their respective formulation and processing parameters. Group A formulations with low dichloromethane, high PVA, and low stirring speed exhibited larger particle size, lower entrapment efficiency (EE), and lower actual drug content (ADC) than Group B formulations. Optimized quantitative PCR and MLR models demonstrated a linear dependence of particle size and quadratic dependence of EE and ADC on the studied formulation and process parameters. Interestingly, MLR models showed relatively better predictability of the selected MBECM formulation properties when compared with PCR. MBECM were amorphous in nature and spherical shaped. Carbopol® 940 NF based hydrogel of selected MBECM formulation exhibited a prolonged MB release than the commercial MB gel (Metrogyl®), showing no signs of necrosis in the goat mucosa. Thus, a properly designed minimum experimentation coupled with multivariate modeling generated a knowledge-rich target space, which enabled to understand and predict the performance of developed MBECM within a prescribed design space. |
doi_str_mv | 10.1208/s12249-019-1383-2 |
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MBECM were prepared by a quasi-emulsion solvent diffusion method. A minimum experimentation was designed using Box-Behnken approach with one center point after initial screening experiments. Data was modeled by principal component analysis (PCA), PCR, and MLR. Two distinct groupings of developed MBECM was observed in initial qualitative PCA as a function of their respective formulation and processing parameters. Group A formulations with low dichloromethane, high PVA, and low stirring speed exhibited larger particle size, lower entrapment efficiency (EE), and lower actual drug content (ADC) than Group B formulations. Optimized quantitative PCR and MLR models demonstrated a linear dependence of particle size and quadratic dependence of EE and ADC on the studied formulation and process parameters. Interestingly, MLR models showed relatively better predictability of the selected MBECM formulation properties when compared with PCR. MBECM were amorphous in nature and spherical shaped. Carbopol® 940 NF based hydrogel of selected MBECM formulation exhibited a prolonged MB release than the commercial MB gel (Metrogyl®), showing no signs of necrosis in the goat mucosa. Thus, a properly designed minimum experimentation coupled with multivariate modeling generated a knowledge-rich target space, which enabled to understand and predict the performance of developed MBECM within a prescribed design space.</description><identifier>ISSN: 1530-9932</identifier><identifier>EISSN: 1530-9932</identifier><identifier>DOI: 10.1208/s12249-019-1383-2</identifier><identifier>PMID: 31016473</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Acrylic Resins ; Animals ; Biochemistry ; Biomedical and Life Sciences ; Biomedicine ; Biotechnology ; Cellulose - analogs & derivatives ; Cellulose - chemistry ; Diffusion ; Drug Compounding ; Emulsions ; Goats ; Metronidazole - chemistry ; Microscopy, Electron, Scanning ; Models, Theoretical ; Particle Size ; Pharmacology/Toxicology ; Pharmacy ; Principal Component Analysis ; Research Article ; Theme: Translational Multi-Disciplinary Approach for the Drug and Gene Delivery Systems</subject><ispartof>AAPS PharmSciTech, 2019-07, Vol.20 (5), p.172-172, Article 172</ispartof><rights>American Association of Pharmaceutical Scientists 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c344t-a97f5de3d87718054f629862a81ac6a0397731316294185f0919d8b87b6474013</citedby><cites>FETCH-LOGICAL-c344t-a97f5de3d87718054f629862a81ac6a0397731316294185f0919d8b87b6474013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31016473$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mahaparale, Paresh R.</creatorcontrib><creatorcontrib>Vinjamuri, Bhavani Prasad</creatorcontrib><creatorcontrib>Chavan, Mayura S.</creatorcontrib><creatorcontrib>Chougule, Mahavir B.</creatorcontrib><creatorcontrib>Haware, Rahul V.</creatorcontrib><title>Computational Predictability of Microsponge Properties Using Different Multivariate Models</title><title>AAPS PharmSciTech</title><addtitle>AAPS PharmSciTech</addtitle><addtitle>AAPS PharmSciTech</addtitle><description>The capabilities of principal component regression (PCR) and multiple linear regression (MLR) were evaluated to decipher and predict the impact of formulation and process parameters on the modeled metronidazole benzoate (MB)–ethyl cellulose (EC) microsponge (MBECM) properties. MBECM were prepared by a quasi-emulsion solvent diffusion method. A minimum experimentation was designed using Box-Behnken approach with one center point after initial screening experiments. Data was modeled by principal component analysis (PCA), PCR, and MLR. Two distinct groupings of developed MBECM was observed in initial qualitative PCA as a function of their respective formulation and processing parameters. Group A formulations with low dichloromethane, high PVA, and low stirring speed exhibited larger particle size, lower entrapment efficiency (EE), and lower actual drug content (ADC) than Group B formulations. Optimized quantitative PCR and MLR models demonstrated a linear dependence of particle size and quadratic dependence of EE and ADC on the studied formulation and process parameters. Interestingly, MLR models showed relatively better predictability of the selected MBECM formulation properties when compared with PCR. MBECM were amorphous in nature and spherical shaped. Carbopol® 940 NF based hydrogel of selected MBECM formulation exhibited a prolonged MB release than the commercial MB gel (Metrogyl®), showing no signs of necrosis in the goat mucosa. Thus, a properly designed minimum experimentation coupled with multivariate modeling generated a knowledge-rich target space, which enabled to understand and predict the performance of developed MBECM within a prescribed design space.</description><subject>Acrylic Resins</subject><subject>Animals</subject><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Biotechnology</subject><subject>Cellulose - analogs & derivatives</subject><subject>Cellulose - chemistry</subject><subject>Diffusion</subject><subject>Drug Compounding</subject><subject>Emulsions</subject><subject>Goats</subject><subject>Metronidazole - chemistry</subject><subject>Microscopy, Electron, Scanning</subject><subject>Models, Theoretical</subject><subject>Particle Size</subject><subject>Pharmacology/Toxicology</subject><subject>Pharmacy</subject><subject>Principal Component Analysis</subject><subject>Research Article</subject><subject>Theme: Translational Multi-Disciplinary Approach for the Drug and Gene Delivery Systems</subject><issn>1530-9932</issn><issn>1530-9932</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kD9PwzAQxS0EoqXwAVhQRpaAz3YSe0Tlr9QKBrqwWE7iVK7SONgOUr89rlIQE9Od7r170vshdAn4Bgjmtx4IYSLFIFKgnKbkCE0hozgVgpLjP_sEnXm_wZhQEPQUTShgyFlBp-hjbrf9EFQwtlNt8uZ0baqgStOasEtskyxN5azvbbfWUbW9dsFon6y86dbJvWka7XQXkuXQBvOlnFFBJ0tb69afo5NGtV5fHOYMrR4f3ufP6eL16WV-t0grylhIlSiarNa05kUBHGesyYngOVEcVJUrTEVRUKAQrwx41mABouYlL8rYgGGgM3Q95vbOfg7aB7k1vtJtqzptBy8JASooywWLVhit-07e6Ub2zmyV20nAco9UjkhlRCr3SCWJP1eH-KHc6vr344dhNJDR4KMUMTm5sYOLNP0_qd9-c4DD</recordid><startdate>20190701</startdate><enddate>20190701</enddate><creator>Mahaparale, Paresh R.</creator><creator>Vinjamuri, Bhavani Prasad</creator><creator>Chavan, Mayura S.</creator><creator>Chougule, Mahavir B.</creator><creator>Haware, Rahul V.</creator><general>Springer International Publishing</general><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>20190701</creationdate><title>Computational Predictability of Microsponge Properties Using Different Multivariate Models</title><author>Mahaparale, Paresh R. ; Vinjamuri, Bhavani Prasad ; Chavan, Mayura S. ; Chougule, Mahavir B. ; Haware, Rahul V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c344t-a97f5de3d87718054f629862a81ac6a0397731316294185f0919d8b87b6474013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Acrylic Resins</topic><topic>Animals</topic><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Biotechnology</topic><topic>Cellulose - analogs & derivatives</topic><topic>Cellulose - chemistry</topic><topic>Diffusion</topic><topic>Drug Compounding</topic><topic>Emulsions</topic><topic>Goats</topic><topic>Metronidazole - chemistry</topic><topic>Microscopy, Electron, Scanning</topic><topic>Models, Theoretical</topic><topic>Particle Size</topic><topic>Pharmacology/Toxicology</topic><topic>Pharmacy</topic><topic>Principal Component Analysis</topic><topic>Research Article</topic><topic>Theme: Translational Multi-Disciplinary Approach for the Drug and Gene Delivery Systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mahaparale, Paresh R.</creatorcontrib><creatorcontrib>Vinjamuri, Bhavani Prasad</creatorcontrib><creatorcontrib>Chavan, Mayura S.</creatorcontrib><creatorcontrib>Chougule, Mahavir B.</creatorcontrib><creatorcontrib>Haware, Rahul V.</creatorcontrib><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>AAPS PharmSciTech</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mahaparale, Paresh R.</au><au>Vinjamuri, Bhavani Prasad</au><au>Chavan, Mayura S.</au><au>Chougule, Mahavir B.</au><au>Haware, Rahul V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computational Predictability of Microsponge Properties Using Different Multivariate Models</atitle><jtitle>AAPS PharmSciTech</jtitle><stitle>AAPS PharmSciTech</stitle><addtitle>AAPS PharmSciTech</addtitle><date>2019-07-01</date><risdate>2019</risdate><volume>20</volume><issue>5</issue><spage>172</spage><epage>172</epage><pages>172-172</pages><artnum>172</artnum><issn>1530-9932</issn><eissn>1530-9932</eissn><abstract>The capabilities of principal component regression (PCR) and multiple linear regression (MLR) were evaluated to decipher and predict the impact of formulation and process parameters on the modeled metronidazole benzoate (MB)–ethyl cellulose (EC) microsponge (MBECM) properties. MBECM were prepared by a quasi-emulsion solvent diffusion method. A minimum experimentation was designed using Box-Behnken approach with one center point after initial screening experiments. Data was modeled by principal component analysis (PCA), PCR, and MLR. Two distinct groupings of developed MBECM was observed in initial qualitative PCA as a function of their respective formulation and processing parameters. Group A formulations with low dichloromethane, high PVA, and low stirring speed exhibited larger particle size, lower entrapment efficiency (EE), and lower actual drug content (ADC) than Group B formulations. Optimized quantitative PCR and MLR models demonstrated a linear dependence of particle size and quadratic dependence of EE and ADC on the studied formulation and process parameters. Interestingly, MLR models showed relatively better predictability of the selected MBECM formulation properties when compared with PCR. MBECM were amorphous in nature and spherical shaped. Carbopol® 940 NF based hydrogel of selected MBECM formulation exhibited a prolonged MB release than the commercial MB gel (Metrogyl®), showing no signs of necrosis in the goat mucosa. Thus, a properly designed minimum experimentation coupled with multivariate modeling generated a knowledge-rich target space, which enabled to understand and predict the performance of developed MBECM within a prescribed design space.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>31016473</pmid><doi>10.1208/s12249-019-1383-2</doi><tpages>1</tpages></addata></record> |
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subjects | Acrylic Resins Animals Biochemistry Biomedical and Life Sciences Biomedicine Biotechnology Cellulose - analogs & derivatives Cellulose - chemistry Diffusion Drug Compounding Emulsions Goats Metronidazole - chemistry Microscopy, Electron, Scanning Models, Theoretical Particle Size Pharmacology/Toxicology Pharmacy Principal Component Analysis Research Article Theme: Translational Multi-Disciplinary Approach for the Drug and Gene Delivery Systems |
title | Computational Predictability of Microsponge Properties Using Different Multivariate Models |
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