Advanced process monitoring and feedback control to enhance cell culture process production and robustness
ABSTRACT It is a common practice in biotherapeutic manufacturing to define a fixed‐volume feed strategy for nutrient feeds, based on historical cell demand. However, once the feed volumes are defined, they are inflexible to batch‐to‐batch variations in cell growth and physiology and can lead to inco...
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Veröffentlicht in: | Biotechnology and bioengineering 2015-12, Vol.112 (12), p.2495-2504 |
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description | ABSTRACT
It is a common practice in biotherapeutic manufacturing to define a fixed‐volume feed strategy for nutrient feeds, based on historical cell demand. However, once the feed volumes are defined, they are inflexible to batch‐to‐batch variations in cell growth and physiology and can lead to inconsistent productivity and product quality. In an effort to control critical quality attributes and to apply process analytical technology (PAT), a fully automated cell culture feedback control system has been explored in three different applications. The first study illustrates that frequent monitoring and automatically controlling the complex feed based on a surrogate (glutamate) level improved protein production. More importantly, the resulting feed strategy was translated into a manufacturing‐friendly manual feed strategy without impact on product quality. The second study demonstrates the improved process robustness of an automated feed strategy based on online bio‐capacitance measurements for cell growth. In the third study, glucose and lactate concentrations were measured online and were used to automatically control the glucose feed, which in turn changed lactate metabolism. These studies suggest that the auto‐feedback control system has the potential to significantly increase productivity and improve robustness in manufacturing, with the goal of ensuring process performance and product quality consistency. Biotechnol. Bioeng. 2015;112: 2495–2504. © 2015 Wiley Periodicals, Inc.
The comparison between cIBC based auto feed and fixed feed at low seed density. The cIBC auto‐feedback control low seed process showed similar performance as the control regular seed process. However, the fixed feed low seed process crashed earlier due to over‐feeding. |
doi_str_mv | 10.1002/bit.25684 |
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It is a common practice in biotherapeutic manufacturing to define a fixed‐volume feed strategy for nutrient feeds, based on historical cell demand. However, once the feed volumes are defined, they are inflexible to batch‐to‐batch variations in cell growth and physiology and can lead to inconsistent productivity and product quality. In an effort to control critical quality attributes and to apply process analytical technology (PAT), a fully automated cell culture feedback control system has been explored in three different applications. The first study illustrates that frequent monitoring and automatically controlling the complex feed based on a surrogate (glutamate) level improved protein production. More importantly, the resulting feed strategy was translated into a manufacturing‐friendly manual feed strategy without impact on product quality. The second study demonstrates the improved process robustness of an automated feed strategy based on online bio‐capacitance measurements for cell growth. In the third study, glucose and lactate concentrations were measured online and were used to automatically control the glucose feed, which in turn changed lactate metabolism. These studies suggest that the auto‐feedback control system has the potential to significantly increase productivity and improve robustness in manufacturing, with the goal of ensuring process performance and product quality consistency. Biotechnol. Bioeng. 2015;112: 2495–2504. © 2015 Wiley Periodicals, Inc.
The comparison between cIBC based auto feed and fixed feed at low seed density. The cIBC auto‐feedback control low seed process showed similar performance as the control regular seed process. However, the fixed feed low seed process crashed earlier due to over‐feeding.</description><identifier>ISSN: 0006-3592</identifier><identifier>EISSN: 1097-0290</identifier><identifier>DOI: 10.1002/bit.25684</identifier><identifier>PMID: 26108810</identifier><identifier>CODEN: BIBIAU</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Animals ; auto-feedback control ; Bioreactors ; Biotechnology ; capacitance ; Cell culture ; Cell Culture Techniques - methods ; Cell growth ; Cell Proliferation ; Chinese hamster ovary (CHO) cell culture ; CHO Cells - physiology ; Control systems ; Cricetulus ; Culture Media - chemistry ; fed-batch ; Feedback control ; Feedback control systems ; Feeds ; Glucose ; Glucose - metabolism ; glucose/lactate control ; human embryonic kidney (HEK) cell culture ; Lactic Acid - metabolism ; Metabolism ; Product quality ; Productivity ; Robustness ; Seeds ; Strategy</subject><ispartof>Biotechnology and bioengineering, 2015-12, Vol.112 (12), p.2495-2504</ispartof><rights>2015 Wiley Periodicals, Inc.</rights><rights>Copyright Wiley Subscription Services, Inc. Dec 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5604-911f9e92b9169120b5ca7815317bacbd2052b87f98d25573232429d8fa5d48c33</citedby><cites>FETCH-LOGICAL-c5604-911f9e92b9169120b5ca7815317bacbd2052b87f98d25573232429d8fa5d48c33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fbit.25684$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fbit.25684$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26108810$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, An</creatorcontrib><creatorcontrib>Tsang, Valerie Liu</creatorcontrib><creatorcontrib>Moore, Brandon</creatorcontrib><creatorcontrib>Shen, Vivian</creatorcontrib><creatorcontrib>Huang, Yao-Ming</creatorcontrib><creatorcontrib>Kshirsagar, Rashmi</creatorcontrib><creatorcontrib>Ryll, Thomas</creatorcontrib><title>Advanced process monitoring and feedback control to enhance cell culture process production and robustness</title><title>Biotechnology and bioengineering</title><addtitle>Biotechnol. Bioeng</addtitle><description>ABSTRACT
It is a common practice in biotherapeutic manufacturing to define a fixed‐volume feed strategy for nutrient feeds, based on historical cell demand. However, once the feed volumes are defined, they are inflexible to batch‐to‐batch variations in cell growth and physiology and can lead to inconsistent productivity and product quality. In an effort to control critical quality attributes and to apply process analytical technology (PAT), a fully automated cell culture feedback control system has been explored in three different applications. The first study illustrates that frequent monitoring and automatically controlling the complex feed based on a surrogate (glutamate) level improved protein production. More importantly, the resulting feed strategy was translated into a manufacturing‐friendly manual feed strategy without impact on product quality. The second study demonstrates the improved process robustness of an automated feed strategy based on online bio‐capacitance measurements for cell growth. In the third study, glucose and lactate concentrations were measured online and were used to automatically control the glucose feed, which in turn changed lactate metabolism. These studies suggest that the auto‐feedback control system has the potential to significantly increase productivity and improve robustness in manufacturing, with the goal of ensuring process performance and product quality consistency. Biotechnol. Bioeng. 2015;112: 2495–2504. © 2015 Wiley Periodicals, Inc.
The comparison between cIBC based auto feed and fixed feed at low seed density. The cIBC auto‐feedback control low seed process showed similar performance as the control regular seed process. However, the fixed feed low seed process crashed earlier due to over‐feeding.</description><subject>Animals</subject><subject>auto-feedback control</subject><subject>Bioreactors</subject><subject>Biotechnology</subject><subject>capacitance</subject><subject>Cell culture</subject><subject>Cell Culture Techniques - methods</subject><subject>Cell growth</subject><subject>Cell Proliferation</subject><subject>Chinese hamster ovary (CHO) cell culture</subject><subject>CHO Cells - physiology</subject><subject>Control systems</subject><subject>Cricetulus</subject><subject>Culture Media - chemistry</subject><subject>fed-batch</subject><subject>Feedback control</subject><subject>Feedback control systems</subject><subject>Feeds</subject><subject>Glucose</subject><subject>Glucose - metabolism</subject><subject>glucose/lactate control</subject><subject>human embryonic kidney (HEK) cell culture</subject><subject>Lactic Acid - metabolism</subject><subject>Metabolism</subject><subject>Product quality</subject><subject>Productivity</subject><subject>Robustness</subject><subject>Seeds</subject><subject>Strategy</subject><issn>0006-3592</issn><issn>1097-0290</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1PVDEUxRujkQFZ-A-YJm5k8aAfr19LIIpEIkiGmLhp-to-fcObFts-lf_ezgzMwsSE1c3N_Z2Tc3MAeI3RIUaIHHVDOSSMy_YZmGGkRIOIQs_BDCHEG8oU2QG7OS_qKiTnL8EO4RhJidEMLI7dLxOsd_AuRetzhssYhhLTEL5DExzsvXedsbfQxlBSHGGJ0IcfKw20fhyhncYyJb_V1-kmW4YY1voUuymXUC-vwIvejNnvP8w9cPPh_fz0Y3NxeXZ-enzRWMZR2yiMe-UV6RTmChPUMWuExIxiUXN0jiBGOil6JR1hTFBCSUuUk71hrpWW0j3wbuNbk_ycfC56OeRVVBN8nLLGQiBKWybbJ6CUyMrzp6BEEiZawir69h90EacU6s9rw1oQa2WlDjaUTTHn5Ht9l4alSfcaI72qVdda9brWyr55cJy6pXdb8rHHChxtgN_D6O__76RPzuePls1GMeTi_2wVJt1qLqhg-uvnM_3l-opfi09z_Y3-BcHOuhc</recordid><startdate>201512</startdate><enddate>201512</enddate><creator>Zhang, An</creator><creator>Tsang, Valerie Liu</creator><creator>Moore, Brandon</creator><creator>Shen, Vivian</creator><creator>Huang, Yao-Ming</creator><creator>Kshirsagar, Rashmi</creator><creator>Ryll, Thomas</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>201512</creationdate><title>Advanced process monitoring and feedback control to enhance cell culture process production and robustness</title><author>Zhang, An ; Tsang, Valerie Liu ; Moore, Brandon ; Shen, Vivian ; Huang, Yao-Ming ; Kshirsagar, Rashmi ; Ryll, Thomas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5604-911f9e92b9169120b5ca7815317bacbd2052b87f98d25573232429d8fa5d48c33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Animals</topic><topic>auto-feedback control</topic><topic>Bioreactors</topic><topic>Biotechnology</topic><topic>capacitance</topic><topic>Cell culture</topic><topic>Cell Culture Techniques - methods</topic><topic>Cell growth</topic><topic>Cell Proliferation</topic><topic>Chinese hamster ovary (CHO) cell culture</topic><topic>CHO Cells - physiology</topic><topic>Control systems</topic><topic>Cricetulus</topic><topic>Culture Media - chemistry</topic><topic>fed-batch</topic><topic>Feedback control</topic><topic>Feedback control systems</topic><topic>Feeds</topic><topic>Glucose</topic><topic>Glucose - metabolism</topic><topic>glucose/lactate control</topic><topic>human embryonic kidney (HEK) cell culture</topic><topic>Lactic Acid - metabolism</topic><topic>Metabolism</topic><topic>Product quality</topic><topic>Productivity</topic><topic>Robustness</topic><topic>Seeds</topic><topic>Strategy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, An</creatorcontrib><creatorcontrib>Tsang, Valerie Liu</creatorcontrib><creatorcontrib>Moore, Brandon</creatorcontrib><creatorcontrib>Shen, Vivian</creatorcontrib><creatorcontrib>Huang, Yao-Ming</creatorcontrib><creatorcontrib>Kshirsagar, Rashmi</creatorcontrib><creatorcontrib>Ryll, Thomas</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Biotechnology and bioengineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, An</au><au>Tsang, Valerie Liu</au><au>Moore, Brandon</au><au>Shen, Vivian</au><au>Huang, Yao-Ming</au><au>Kshirsagar, Rashmi</au><au>Ryll, Thomas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Advanced process monitoring and feedback control to enhance cell culture process production and robustness</atitle><jtitle>Biotechnology and bioengineering</jtitle><addtitle>Biotechnol. Bioeng</addtitle><date>2015-12</date><risdate>2015</risdate><volume>112</volume><issue>12</issue><spage>2495</spage><epage>2504</epage><pages>2495-2504</pages><issn>0006-3592</issn><eissn>1097-0290</eissn><coden>BIBIAU</coden><abstract>ABSTRACT
It is a common practice in biotherapeutic manufacturing to define a fixed‐volume feed strategy for nutrient feeds, based on historical cell demand. However, once the feed volumes are defined, they are inflexible to batch‐to‐batch variations in cell growth and physiology and can lead to inconsistent productivity and product quality. In an effort to control critical quality attributes and to apply process analytical technology (PAT), a fully automated cell culture feedback control system has been explored in three different applications. The first study illustrates that frequent monitoring and automatically controlling the complex feed based on a surrogate (glutamate) level improved protein production. More importantly, the resulting feed strategy was translated into a manufacturing‐friendly manual feed strategy without impact on product quality. The second study demonstrates the improved process robustness of an automated feed strategy based on online bio‐capacitance measurements for cell growth. In the third study, glucose and lactate concentrations were measured online and were used to automatically control the glucose feed, which in turn changed lactate metabolism. These studies suggest that the auto‐feedback control system has the potential to significantly increase productivity and improve robustness in manufacturing, with the goal of ensuring process performance and product quality consistency. Biotechnol. Bioeng. 2015;112: 2495–2504. © 2015 Wiley Periodicals, Inc.
The comparison between cIBC based auto feed and fixed feed at low seed density. The cIBC auto‐feedback control low seed process showed similar performance as the control regular seed process. However, the fixed feed low seed process crashed earlier due to over‐feeding.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>26108810</pmid><doi>10.1002/bit.25684</doi><tpages>10</tpages></addata></record> |
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subjects | Animals auto-feedback control Bioreactors Biotechnology capacitance Cell culture Cell Culture Techniques - methods Cell growth Cell Proliferation Chinese hamster ovary (CHO) cell culture CHO Cells - physiology Control systems Cricetulus Culture Media - chemistry fed-batch Feedback control Feedback control systems Feeds Glucose Glucose - metabolism glucose/lactate control human embryonic kidney (HEK) cell culture Lactic Acid - metabolism Metabolism Product quality Productivity Robustness Seeds Strategy |
title | Advanced process monitoring and feedback control to enhance cell culture process production and robustness |
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