Equipment characterization to mitigate risks during transfers of cell culture manufacturing processes
The production of monoclonal antibodies by mammalian cell culture in bioreactors up to 25,000 L is state of the art technology in the biotech industry. During the lifecycle of a product, several scale up activities and technology transfers are typically executed to enable the supply chain strategy o...
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Veröffentlicht in: | Cytotechnology (Dordrecht) 2016-08, Vol.68 (4), p.1381-1401 |
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description | The production of monoclonal antibodies by mammalian cell culture in bioreactors up to 25,000 L is state of the art technology in the biotech industry. During the lifecycle of a product, several scale up activities and technology transfers are typically executed to enable the supply chain strategy of a global pharmaceutical company. Given the sensitivity of mammalian cells to physicochemical culture conditions, process and equipment knowledge are critical to avoid impacts on timelines, product quantity and quality. Especially, the fluid dynamics of large scale bioreactors versus small scale models need to be described, and similarity demonstrated, in light of the Quality by Design approach promoted by the
FDA
. This approach comprises an associated design space which is established during process characterization and validation in bench scale bioreactors. Therefore the establishment of predictive models and simulation tools for major operating conditions of stirred vessels (mixing, mass transfer, and shear force.), based on fundamental engineering principles, have experienced a renaissance in the recent years. This work illustrates the systematic characterization of a large variety of bioreactor designs deployed in a global manufacturing network ranging from small bench scale equipment to large scale production equipment (25,000 L). Several traditional methods to determine power input, mixing, mass transfer and shear force have been used to create a data base and identify differences for various impeller types and configurations in operating ranges typically applied in cell culture processes at manufacturing scale. In addition, extrapolation of different empirical models, e.g. Cooke et al. (Paper presented at the proceedings of the 2nd international conference of bioreactor fluid dynamics, Cranfield, UK,
1988
), have been assessed for their validity in these operational ranges. Results for selected designs are shown and serve as examples of structured characterization to enable fast and agile process transfers, scale up and troubleshooting. |
doi_str_mv | 10.1007/s10616-015-9899-0 |
format | Article |
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FDA
. This approach comprises an associated design space which is established during process characterization and validation in bench scale bioreactors. Therefore the establishment of predictive models and simulation tools for major operating conditions of stirred vessels (mixing, mass transfer, and shear force.), based on fundamental engineering principles, have experienced a renaissance in the recent years. This work illustrates the systematic characterization of a large variety of bioreactor designs deployed in a global manufacturing network ranging from small bench scale equipment to large scale production equipment (25,000 L). Several traditional methods to determine power input, mixing, mass transfer and shear force have been used to create a data base and identify differences for various impeller types and configurations in operating ranges typically applied in cell culture processes at manufacturing scale. In addition, extrapolation of different empirical models, e.g. Cooke et al. (Paper presented at the proceedings of the 2nd international conference of bioreactor fluid dynamics, Cranfield, UK,
1988
), have been assessed for their validity in these operational ranges. Results for selected designs are shown and serve as examples of structured characterization to enable fast and agile process transfers, scale up and troubleshooting.</description><identifier>ISSN: 0920-9069</identifier><identifier>EISSN: 1573-0778</identifier><identifier>DOI: 10.1007/s10616-015-9899-0</identifier><identifier>PMID: 26231834</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Biochemistry ; Biomedicine ; Bioreactors ; Biotechnology ; Biotechnology industry ; Carbon dioxide ; Cell culture ; Chemistry ; Chemistry and Materials Science ; Fermentation ; Fluid dynamics ; Friction ; Gases ; Mammalian cells ; Manufacturing ; Mass transfer ; Monoclonal antibodies ; Original ; Original Article ; Oxygen saturation ; Power ; Prediction models ; Product quality</subject><ispartof>Cytotechnology (Dordrecht), 2016-08, Vol.68 (4), p.1381-1401</ispartof><rights>Springer Science+Business Media Dordrecht 2015</rights><rights>Springer Science+Business Media Dordrecht 2015.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c507t-ad6a5ac57a72f2fa7dec66d82058ae15a0d0511039ddfdbd6810a06b341706803</citedby><cites>FETCH-LOGICAL-c507t-ad6a5ac57a72f2fa7dec66d82058ae15a0d0511039ddfdbd6810a06b341706803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4960186/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918253216?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,21367,27901,27902,33721,33722,41464,42533,43781,51294,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26231834$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sieblist, Christian</creatorcontrib><creatorcontrib>Jenzsch, Marco</creatorcontrib><creatorcontrib>Pohlscheidt, Michael</creatorcontrib><title>Equipment characterization to mitigate risks during transfers of cell culture manufacturing processes</title><title>Cytotechnology (Dordrecht)</title><addtitle>Cytotechnology</addtitle><addtitle>Cytotechnology</addtitle><description>The production of monoclonal antibodies by mammalian cell culture in bioreactors up to 25,000 L is state of the art technology in the biotech industry. During the lifecycle of a product, several scale up activities and technology transfers are typically executed to enable the supply chain strategy of a global pharmaceutical company. Given the sensitivity of mammalian cells to physicochemical culture conditions, process and equipment knowledge are critical to avoid impacts on timelines, product quantity and quality. Especially, the fluid dynamics of large scale bioreactors versus small scale models need to be described, and similarity demonstrated, in light of the Quality by Design approach promoted by the
FDA
. This approach comprises an associated design space which is established during process characterization and validation in bench scale bioreactors. Therefore the establishment of predictive models and simulation tools for major operating conditions of stirred vessels (mixing, mass transfer, and shear force.), based on fundamental engineering principles, have experienced a renaissance in the recent years. This work illustrates the systematic characterization of a large variety of bioreactor designs deployed in a global manufacturing network ranging from small bench scale equipment to large scale production equipment (25,000 L). Several traditional methods to determine power input, mixing, mass transfer and shear force have been used to create a data base and identify differences for various impeller types and configurations in operating ranges typically applied in cell culture processes at manufacturing scale. In addition, extrapolation of different empirical models, e.g. Cooke et al. (Paper presented at the proceedings of the 2nd international conference of bioreactor fluid dynamics, Cranfield, UK,
1988
), have been assessed for their validity in these operational ranges. Results for selected designs are shown and serve as examples of structured characterization to enable fast and agile process transfers, scale up and troubleshooting.</description><subject>Biochemistry</subject><subject>Biomedicine</subject><subject>Bioreactors</subject><subject>Biotechnology</subject><subject>Biotechnology industry</subject><subject>Carbon dioxide</subject><subject>Cell culture</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Fermentation</subject><subject>Fluid dynamics</subject><subject>Friction</subject><subject>Gases</subject><subject>Mammalian cells</subject><subject>Manufacturing</subject><subject>Mass transfer</subject><subject>Monoclonal antibodies</subject><subject>Original</subject><subject>Original Article</subject><subject>Oxygen saturation</subject><subject>Power</subject><subject>Prediction models</subject><subject>Product quality</subject><issn>0920-9069</issn><issn>1573-0778</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kU1rFTEUhoMo9rb6A9xIwI2b0XMyN18bQUqtQsGNrkNukrlNnZlMk0xBf70zTK0f4CqLPHlO3vMS8gLhDQLItwVBoGgAeaOV1g08Ijvksm1ASvWY7EAzaDQIfUJOS7kBAC2xfUpOmGAtqna_I-Hido7TEMZK3bXN1tWQ4w9bYxppTXSINR5tDTTH8q1QP-c4HmnNdixdyIWmjrrQ99TNfZ1zoIMd526RbNyUkwulhPKMPOlsX8Lz-_OMfP1w8eX8Y3P1-fLT-furxnGQtbFeWG4dl1ayjnVW-uCE8IoBVzYgt-CBI0Krve_8wQuFYEEc2j1KEAraM_Ju807zYQjeLbGy7c2U42Dzd5NsNH_fjPHaHNOd2WsBqMQieH0vyOl2DqWaIZY1oR1DmotBBZJJ0Sq2oK_-QW_SnMclnmEaFeMtw1WIG-VyKiWH7uEzCGYt0WwlmqVEs5Zo1hQv_0zx8OJXawvANqBM655D_j36_9afer-qfQ</recordid><startdate>20160801</startdate><enddate>20160801</enddate><creator>Sieblist, Christian</creator><creator>Jenzsch, Marco</creator><creator>Pohlscheidt, Michael</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FH</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160801</creationdate><title>Equipment characterization to mitigate risks during transfers of cell culture manufacturing processes</title><author>Sieblist, Christian ; Jenzsch, Marco ; Pohlscheidt, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c507t-ad6a5ac57a72f2fa7dec66d82058ae15a0d0511039ddfdbd6810a06b341706803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Biochemistry</topic><topic>Biomedicine</topic><topic>Bioreactors</topic><topic>Biotechnology</topic><topic>Biotechnology industry</topic><topic>Carbon dioxide</topic><topic>Cell culture</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Fermentation</topic><topic>Fluid dynamics</topic><topic>Friction</topic><topic>Gases</topic><topic>Mammalian cells</topic><topic>Manufacturing</topic><topic>Mass transfer</topic><topic>Monoclonal antibodies</topic><topic>Original</topic><topic>Original Article</topic><topic>Oxygen saturation</topic><topic>Power</topic><topic>Prediction models</topic><topic>Product quality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sieblist, Christian</creatorcontrib><creatorcontrib>Jenzsch, Marco</creatorcontrib><creatorcontrib>Pohlscheidt, Michael</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</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><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cytotechnology (Dordrecht)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sieblist, Christian</au><au>Jenzsch, Marco</au><au>Pohlscheidt, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Equipment characterization to mitigate risks during transfers of cell culture manufacturing processes</atitle><jtitle>Cytotechnology (Dordrecht)</jtitle><stitle>Cytotechnology</stitle><addtitle>Cytotechnology</addtitle><date>2016-08-01</date><risdate>2016</risdate><volume>68</volume><issue>4</issue><spage>1381</spage><epage>1401</epage><pages>1381-1401</pages><issn>0920-9069</issn><eissn>1573-0778</eissn><abstract>The production of monoclonal antibodies by mammalian cell culture in bioreactors up to 25,000 L is state of the art technology in the biotech industry. 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FDA
. This approach comprises an associated design space which is established during process characterization and validation in bench scale bioreactors. Therefore the establishment of predictive models and simulation tools for major operating conditions of stirred vessels (mixing, mass transfer, and shear force.), based on fundamental engineering principles, have experienced a renaissance in the recent years. This work illustrates the systematic characterization of a large variety of bioreactor designs deployed in a global manufacturing network ranging from small bench scale equipment to large scale production equipment (25,000 L). Several traditional methods to determine power input, mixing, mass transfer and shear force have been used to create a data base and identify differences for various impeller types and configurations in operating ranges typically applied in cell culture processes at manufacturing scale. In addition, extrapolation of different empirical models, e.g. Cooke et al. (Paper presented at the proceedings of the 2nd international conference of bioreactor fluid dynamics, Cranfield, UK,
1988
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subjects | Biochemistry Biomedicine Bioreactors Biotechnology Biotechnology industry Carbon dioxide Cell culture Chemistry Chemistry and Materials Science Fermentation Fluid dynamics Friction Gases Mammalian cells Manufacturing Mass transfer Monoclonal antibodies Original Original Article Oxygen saturation Power Prediction models Product quality |
title | Equipment characterization to mitigate risks during transfers of cell culture manufacturing processes |
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