Predicting Drug Substances Autoxidation
Purpose Chemical degradation and stability in formulation is a recurrent issue in pharmaceutical development of drugs. The objective of the present study was to develop an in silico risk assessment of active pharmaceutical ingredients (APIs) stability with respect to autoxidation. Methods The chemic...
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| Veröffentlicht in: | Pharmaceutical research 2015, Vol.32 (1), p.300-310 |
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| creator | Lienard, P. Gavartin, J. Boccardi, G. Meunier, M. |
| description | Purpose
Chemical degradation and stability in formulation is a recurrent issue in pharmaceutical development of drugs. The objective of the present study was to develop an in silico risk assessment of active pharmaceutical ingredients (APIs) stability with respect to autoxidation.
Methods
The chemical degradation by autoxidation of a diverse series of APIs has been investigated with molecular modelling tools. A set of 45 organic compounds was used to test and validate the various computational settings. Aiming to devise a methodology that could reliably perform a risk assessment for potential sensibility to autoxidation, different types of APIs, known for their autoxidation history were inspected. To define the level of approximation needed, various density functional theory (DFT) functionals and settings were employed and their accuracy and speed were compared.
Results
The Local Density Approximation (LDA) gave the fastest results but with a substantial deviation (systematic over-estimation) to known experimental values. The Perdew-Burke-Ernzerhof (PBE) settings appeared to be a good compromise between speed and accuracy.
Conclusions
The present methodology can now be confidently deployed in pharmaceutical development for systematic risk assessment of drug stability. |
| doi_str_mv | 10.1007/s11095-014-1463-7 |
| format | Article |
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Chemical degradation and stability in formulation is a recurrent issue in pharmaceutical development of drugs. The objective of the present study was to develop an in silico risk assessment of active pharmaceutical ingredients (APIs) stability with respect to autoxidation.
Methods
The chemical degradation by autoxidation of a diverse series of APIs has been investigated with molecular modelling tools. A set of 45 organic compounds was used to test and validate the various computational settings. Aiming to devise a methodology that could reliably perform a risk assessment for potential sensibility to autoxidation, different types of APIs, known for their autoxidation history were inspected. To define the level of approximation needed, various density functional theory (DFT) functionals and settings were employed and their accuracy and speed were compared.
Results
The Local Density Approximation (LDA) gave the fastest results but with a substantial deviation (systematic over-estimation) to known experimental values. The Perdew-Burke-Ernzerhof (PBE) settings appeared to be a good compromise between speed and accuracy.
Conclusions
The present methodology can now be confidently deployed in pharmaceutical development for systematic risk assessment of drug stability.</description><identifier>ISSN: 0724-8741</identifier><identifier>EISSN: 1573-904X</identifier><identifier>DOI: 10.1007/s11095-014-1463-7</identifier><identifier>PMID: 25115828</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Biochemistry ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Chemical compounds ; Computer Simulation ; Drug Stability ; Hydrogen - chemistry ; Medical Law ; Models, Chemical ; Oxidation ; Oxidation-Reduction ; Pharmaceutical Preparations - chemistry ; Pharmaceutical Preparations - standards ; Pharmaceutical sciences ; Pharmacology/Toxicology ; Pharmacy ; Research Paper ; Risk Assessment ; Thermodynamics</subject><ispartof>Pharmaceutical research, 2015, Vol.32 (1), p.300-310</ispartof><rights>Springer Science+Business Media New York 2014</rights><rights>Springer Science+Business Media New York 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-9b77f60d3c0476012731816111ea196d022264df9db7260fa435dafe7d0099b63</citedby><cites>FETCH-LOGICAL-c372t-9b77f60d3c0476012731816111ea196d022264df9db7260fa435dafe7d0099b63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11095-014-1463-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11095-014-1463-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25115828$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lienard, P.</creatorcontrib><creatorcontrib>Gavartin, J.</creatorcontrib><creatorcontrib>Boccardi, G.</creatorcontrib><creatorcontrib>Meunier, M.</creatorcontrib><title>Predicting Drug Substances Autoxidation</title><title>Pharmaceutical research</title><addtitle>Pharm Res</addtitle><addtitle>Pharm Res</addtitle><description>Purpose
Chemical degradation and stability in formulation is a recurrent issue in pharmaceutical development of drugs. The objective of the present study was to develop an in silico risk assessment of active pharmaceutical ingredients (APIs) stability with respect to autoxidation.
Methods
The chemical degradation by autoxidation of a diverse series of APIs has been investigated with molecular modelling tools. A set of 45 organic compounds was used to test and validate the various computational settings. Aiming to devise a methodology that could reliably perform a risk assessment for potential sensibility to autoxidation, different types of APIs, known for their autoxidation history were inspected. To define the level of approximation needed, various density functional theory (DFT) functionals and settings were employed and their accuracy and speed were compared.
Results
The Local Density Approximation (LDA) gave the fastest results but with a substantial deviation (systematic over-estimation) to known experimental values. The Perdew-Burke-Ernzerhof (PBE) settings appeared to be a good compromise between speed and accuracy.
Conclusions
The present methodology can now be confidently deployed in pharmaceutical development for systematic risk assessment of drug stability.</description><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Chemical compounds</subject><subject>Computer Simulation</subject><subject>Drug Stability</subject><subject>Hydrogen - chemistry</subject><subject>Medical Law</subject><subject>Models, Chemical</subject><subject>Oxidation</subject><subject>Oxidation-Reduction</subject><subject>Pharmaceutical Preparations - chemistry</subject><subject>Pharmaceutical Preparations - standards</subject><subject>Pharmaceutical sciences</subject><subject>Pharmacology/Toxicology</subject><subject>Pharmacy</subject><subject>Research Paper</subject><subject>Risk Assessment</subject><subject>Thermodynamics</subject><issn>0724-8741</issn><issn>1573-904X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp1kE1LxDAQhoMo7vrxA7zIgge9RGeSNGmOy_oJCwoqeAtpky5ddts1aUH_vS1dRQRPc5jnfWd4CDlBuEQAdRURQScUUFAUklO1Q8aYKE41iLddMgbFBE2VwBE5iHEJAClqsU9GLEFMUpaOyflT8K7Mm7JaTK5Du5g8t1lsbJX7OJm2Tf1ROtuUdXVE9gq7iv54Ow_J6-3Ny-yezh_vHmbTOc25Yg3VmVKFBMdzEEoCMsUxRYmI3qKWDhhjUrhCu0wxCYUVPHG28MoBaJ1Jfkguht5NqN9bHxuzLmPuVytb-bqNBqXgKBKlevTsD7qs21B13_UUQwEySTsKByoPdYzBF2YTyrUNnwbB9BbNYNF0Fk1v0aguc7ptbrO1dz-Jb20dwAYgdqtq4cOv0_-2fgHvenmh</recordid><startdate>2015</startdate><enddate>2015</enddate><creator>Lienard, P.</creator><creator>Gavartin, J.</creator><creator>Boccardi, G.</creator><creator>Meunier, M.</creator><general>Springer US</general><general>Springer Nature B.V</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>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>PRINS</scope><scope>7X8</scope></search><sort><creationdate>2015</creationdate><title>Predicting Drug Substances Autoxidation</title><author>Lienard, P. ; Gavartin, J. ; Boccardi, G. ; Meunier, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-9b77f60d3c0476012731816111ea196d022264df9db7260fa435dafe7d0099b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Chemical compounds</topic><topic>Computer Simulation</topic><topic>Drug Stability</topic><topic>Hydrogen - chemistry</topic><topic>Medical Law</topic><topic>Models, Chemical</topic><topic>Oxidation</topic><topic>Oxidation-Reduction</topic><topic>Pharmaceutical Preparations - chemistry</topic><topic>Pharmaceutical Preparations - standards</topic><topic>Pharmaceutical sciences</topic><topic>Pharmacology/Toxicology</topic><topic>Pharmacy</topic><topic>Research Paper</topic><topic>Risk Assessment</topic><topic>Thermodynamics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lienard, P.</creatorcontrib><creatorcontrib>Gavartin, J.</creatorcontrib><creatorcontrib>Boccardi, G.</creatorcontrib><creatorcontrib>Meunier, M.</creatorcontrib><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>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Pharmaceutical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lienard, P.</au><au>Gavartin, J.</au><au>Boccardi, G.</au><au>Meunier, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting Drug Substances Autoxidation</atitle><jtitle>Pharmaceutical research</jtitle><stitle>Pharm Res</stitle><addtitle>Pharm Res</addtitle><date>2015</date><risdate>2015</risdate><volume>32</volume><issue>1</issue><spage>300</spage><epage>310</epage><pages>300-310</pages><issn>0724-8741</issn><eissn>1573-904X</eissn><abstract>Purpose
Chemical degradation and stability in formulation is a recurrent issue in pharmaceutical development of drugs. The objective of the present study was to develop an in silico risk assessment of active pharmaceutical ingredients (APIs) stability with respect to autoxidation.
Methods
The chemical degradation by autoxidation of a diverse series of APIs has been investigated with molecular modelling tools. A set of 45 organic compounds was used to test and validate the various computational settings. Aiming to devise a methodology that could reliably perform a risk assessment for potential sensibility to autoxidation, different types of APIs, known for their autoxidation history were inspected. To define the level of approximation needed, various density functional theory (DFT) functionals and settings were employed and their accuracy and speed were compared.
Results
The Local Density Approximation (LDA) gave the fastest results but with a substantial deviation (systematic over-estimation) to known experimental values. The Perdew-Burke-Ernzerhof (PBE) settings appeared to be a good compromise between speed and accuracy.
Conclusions
The present methodology can now be confidently deployed in pharmaceutical development for systematic risk assessment of drug stability.</abstract><cop>Boston</cop><pub>Springer US</pub><pmid>25115828</pmid><doi>10.1007/s11095-014-1463-7</doi><tpages>11</tpages></addata></record> |
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| subjects | Biochemistry Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Chemical compounds Computer Simulation Drug Stability Hydrogen - chemistry Medical Law Models, Chemical Oxidation Oxidation-Reduction Pharmaceutical Preparations - chemistry Pharmaceutical Preparations - standards Pharmaceutical sciences Pharmacology/Toxicology Pharmacy Research Paper Risk Assessment Thermodynamics |
| title | Predicting Drug Substances Autoxidation |
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