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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Bioeng. Biotechnol.</journal-id>
<journal-title>Frontiers in Bioengineering and Biotechnology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Bioeng. Biotechnol.</abbrev-journal-title>
<issn pub-type="epub">2296-4185</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">856049</article-id>
<article-id pub-id-type="doi">10.3389/fbioe.2022.856049</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Bioengineering and Biotechnology</subject>
<subj-group>
<subject>Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Strategies and Considerations for Improving Recombinant Antibody Production and Quality in Chinese Hamster Ovary Cells</article-title>
<alt-title alt-title-type="left-running-head">Zhang et&#x20;al.</alt-title>
<alt-title alt-title-type="right-running-head">Strategies for Improving Recombinant Antibody</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhang</surname>
<given-names>Jun-He</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1395174/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Shan</surname>
<given-names>Lin-Lin</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liang</surname>
<given-names>Fan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Du</surname>
<given-names>Chen-Yang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Jing-Jing</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Institutes of Health Central Plains</institution>, <institution>Xinxiang Medical University</institution>, <addr-line>Xinxiang</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Biochemistry and Molecular Biology</institution>, <institution>Xinxiang Medical University</institution>, <addr-line>Xinxiang</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Henan International Joint Laboratory of Recombinant Pharmaceutical Protein Expression System</institution>, <institution>Xinxiang Medical University</institution>, <addr-line>Xinxiang</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/505076/overview">Zivko Nikolov</ext-link>, Texas A and M University, United&#x20;States</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1067590/overview">Qiong Wang</ext-link>, Johns Hopkins University, United&#x20;States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1585758/overview">Laura Soto Sierra</ext-link>, Molecular Templates, Inc. (MTEM), United&#x20;States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1591492/overview">Ayswarya Ravi</ext-link>, Corteva Agriscience&#x2122;, United&#x20;States</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Jun-He Zhang, <email>zjh@xxmu.edu.cn</email>
</corresp>
<fn fn-type="other">
<p>This article was submitted to Industrial Biotechnology, a section of the journal Frontiers in Bioengineering and Biotechnology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>04</day>
<month>03</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>10</volume>
<elocation-id>856049</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>16</day>
<month>02</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Zhang, Shan, Liang, Du and Li.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Zhang, Shan, Liang, Du and Li</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these&#x20;terms.</p>
</license>
</permissions>
<abstract>
<p>Recombinant antibodies are rapidly developing therapeutic agents; approximately 40 novel antibody molecules enter clinical trials each year, most of which are produced from Chinese hamster ovary (CHO) cells. However, one of the major bottlenecks restricting the development of antibody drugs is how to perform high-level expression and production of recombinant antibodies. The high-efficiency expression and quality of recombinant antibodies in CHO cells is determined by multiple factors. This review provides a comprehensive overview of several state-of-the-art approaches, such as optimization of gene sequence of antibody, construction and optimization of high-efficiency expression vector, using antibody expression system, transformation of host cell lines, and glycosylation modification. Finally, the authors discuss the potential of large-scale production of recombinant antibodies and development of culture processes for biopharmaceutical manufacturing in the future.</p>
</abstract>
<kwd-group>
<kwd>recombinant antibody</kwd>
<kwd>Chinese hamster ovary cells</kwd>
<kwd>expression vector</kwd>
<kwd>glycosylation</kwd>
<kwd>genetic engineering</kwd>
</kwd-group>
<contract-num rid="cn001">U1604193</contract-num>
<contract-sponsor id="cn001">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content>
</contract-sponsor>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>In recent years, recombinant antibody drugs are emerging with the rapid development of modern molecular biology technology as well as in-depth exploration on the three-dimensional structure and mechanism of action of antibody molecules. Recombinant antibody drugs undergo the stages of mouse monoclonal antibody, human-mouse chimeric antibody, humanized antibody, and fully human antibody (<xref ref-type="fig" rid="F1">Figure&#x20;1</xref>), which have been applied in many fields, such as anti-tumor, anti-autoimmune diseases, and biosensor (<xref ref-type="bibr" rid="B24">DeLuca et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B32">Galvani et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B63">Liao et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B90">Rudenko et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B91">Rybchenko et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B114">Yang G et&#x20;al., 2021</xref>). Improving the affinity of antibodies and reducing their immunogenicity are two basic principles of genetic engineering of antibody drugs. The development of antibody drugs is relatively rapid. According to industry statistics, the global annual sales of antibody drugs in 1997 were only $300 million, more than $60 billion in 2012, exceeded $100 billion for the first time in 2017, and reached $123.2 billion in 2018. At present, antibody drug development has become one of the fastest growing, most profitable, and most feasible biopharmaceutical fields in the pharmaceutical industry; in particular, monoclonal antibodies have become one of the most important therapeutic recombinant antibodies in the global pharmaceutical market (<xref ref-type="bibr" rid="B70">Masuda et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B93">Savizi et&#x20;al., 2021</xref>). Up to now, more than 300 biological drugs have been approved by the U.S. Food and Drug Administration (FDA), among which monoclonal antibodies are developing rapidly (<xref ref-type="bibr" rid="B101">Tihanyi and Nyitray, 2020</xref>). The production of recombinant antibody drugs is mostly achieved by constructing expression vectors <italic>in&#x20;vitro</italic> through genetic engineering technology. Given that recombinant antibodies often need to undergo a series of post-translational modifications (such as glycosylation modification), folding, and correct cleavage, antibody drugs with biological activity and low immunogenicity can be produced, therefore, mammalian cells have become the dominant system for the production of recombinant antibodies, especially for full-length monoclonal antibodies (<xref ref-type="bibr" rid="B47">Hussain et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B53">Kim et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B37">Ha et&#x20;al., 2022</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Development process of recombinant antibody drugs. Mouse monoclonal antibodies are all mouse-derived components, with large side effects and high immunogenicity; human-mouse chimeric antibodies are 65% human-derived components, with lower side effects than mouse monoclonal antibody; human-derived components in humanized antibodies are over 90%, with mild side effects and very low immunogenicity; fully human antibodies are all human-derived components, which can obviously remove the immunogenicity and side effects, and with good therapeutic effect.</p>
</caption>
<graphic xlink:href="fbioe-10-856049-g001.tif"/>
</fig>
<p>Compared with <italic>Escherichia coli</italic> and other expression systems, the molecular structure and glycosylation type of recombinant antibodies produced by mammalian cell expression systems are similar to natural antibodies, and mammalian cells can be cultured in suspension or serum-free medium on a large scale (<xref ref-type="bibr" rid="B57">Kunert and Reinhart, 2016</xref>). Chinese hamster ovary (CHO) cells are the preferred system for the production of recombinant antibodies in mammalian cell expression systems (<xref ref-type="bibr" rid="B88">Ritacco et&#x20;al., 2018</xref>; <xref ref-type="bibr" rid="B33">Gupta et&#x20;al., 2021</xref>). In 1986, the first recombinant therapeutic protein, tissue plasminogen activator (tPA), was approved for marketing (<xref ref-type="bibr" rid="B51">Kaufman et&#x20;al., 1985</xref>), its expression titer was less than 50&#xa0;mg/L. After 30&#x20;years of rapidly development and technological breakthroughs, progress in therapeutic antibody field has been accelerated, the antibody expression titers have increased more than 100&#x2013;200 times, even the yield of therapeutic antibody has exceeded 10&#xa0;g/L (<xref ref-type="bibr" rid="B60">Li et&#x20;al., 2010</xref>; <xref ref-type="bibr" rid="B111">Weng et&#x20;al., 2020</xref>). The approved therapeutic monoclonal antibodies produced in CHO cells until 2020 are listed in <xref ref-type="table" rid="T1">Table&#x20;1</xref>. However, the low expression level of the CHO cells system and high investments during large-scale cell culture lead to high costs for producing recombinant antibody drugs (<xref ref-type="bibr" rid="B26">Donaldson et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B102">Torres and Dickson, 2021</xref>). Therefore, to improve the productivity and quality of recombinant antibodies, researchers need to optimize and design the gene sequences of antibody and expression vectors, to optimize antibody expression system and transform the host cell lines of antibodies, and to control glycosylation modification, for upgrading key parameters during industrial production and promoting the healthy development of recombinant antibody drugs and even the biomedical industry. A workflow for optimizing recombinant antibody production and quality in CHO cells is listed in <xref ref-type="fig" rid="F2">Figure&#x20;2</xref>.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>List of approved therapeutic monoclonal antibodies produced in CHO&#x20;cells.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Trade name</th>
<th align="center">Active&#x2002;ingredient</th>
<th align="center">Developer/Manufacturer</th>
<th align="center">Year of first approval</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Rituxan</td>
<td align="left">Rituximab</td>
<td align="left">Genentech</td>
<td align="char" char=".">1997</td>
</tr>
<tr>
<td align="left">Enbrel</td>
<td align="left">Etanercep</td>
<td align="left">Immunex, now Amgen</td>
<td align="char" char=".">1998</td>
</tr>
<tr>
<td align="left">Herceptin</td>
<td align="left">Trastuzumab</td>
<td align="left">Genetech</td>
<td align="char" char=".">1998</td>
</tr>
<tr>
<td align="left">Campath</td>
<td align="left">Alemtuzumab</td>
<td align="left">Genzyme</td>
<td align="char" char=".">2001</td>
</tr>
<tr>
<td align="left">Humira</td>
<td align="left">Adalimumab</td>
<td align="left">Abbott</td>
<td align="char" char=".">2002</td>
</tr>
<tr>
<td align="left">Zevalin</td>
<td align="left">Ibritumomab tiuxetan</td>
<td align="left">Biogen-Idec&#xa0;Pharmaceuticals</td>
<td align="char" char=".">2002</td>
</tr>
<tr>
<td align="left">Raptiva</td>
<td align="left">Efalizumab</td>
<td align="left">Genzyme</td>
<td align="char" char=".">2003</td>
</tr>
<tr>
<td align="left">Xolair</td>
<td align="left">Omalizumab</td>
<td align="left">Genetech</td>
<td align="char" char=".">2003</td>
</tr>
<tr>
<td align="left">Avastin</td>
<td align="left">Bevacizumab</td>
<td align="left">Genentech</td>
<td align="char" char=".">2004</td>
</tr>
<tr>
<td align="left">Vectibix</td>
<td align="left">Panitumumab</td>
<td align="left">Amgen</td>
<td align="char" char=".">2006</td>
</tr>
<tr>
<td align="left">Actemra</td>
<td align="left">Tocilizumab</td>
<td align="left">Genentech</td>
<td align="char" char=".">2009</td>
</tr>
<tr>
<td align="left">Prolia</td>
<td align="left">Denosumab</td>
<td align="left">Amgen</td>
<td align="char" char=".">2010</td>
</tr>
<tr>
<td align="left">Adcetris</td>
<td align="left">Brentuximab vedotin</td>
<td align="left">Seattle Genetics</td>
<td align="char" char=".">2011</td>
</tr>
<tr>
<td align="left">Yervoy</td>
<td align="left">Ipilimumab</td>
<td align="left">Bristol-Myers Squibb</td>
<td align="char" char=".">2011</td>
</tr>
<tr>
<td align="left">Perjeta</td>
<td align="left">Pertuzumab</td>
<td align="left">Genentech</td>
<td align="char" char=".">2012</td>
</tr>
<tr>
<td align="left">Gazyvaro</td>
<td align="left">Obinutuzumab</td>
<td align="left">Roche</td>
<td align="char" char=".">2013</td>
</tr>
<tr>
<td align="left">Kadcyla</td>
<td align="left">Trastuzumab emtansine</td>
<td align="left">Roche</td>
<td align="char" char=".">2013</td>
</tr>
<tr>
<td align="left">Blincyto</td>
<td align="left">Blinatumomab</td>
<td align="left">Amgen</td>
<td align="char" char=".">2014</td>
</tr>
<tr>
<td align="left">Entyvio</td>
<td align="left">Vedolizumab</td>
<td align="left">Takeda</td>
<td align="char" char=".">2014</td>
</tr>
<tr>
<td align="left">Keytruda</td>
<td align="left">Pembrolizumab</td>
<td align="left">Merck and Co.</td>
<td align="char" char=".">2014</td>
</tr>
<tr>
<td align="left">Lemtrada</td>
<td align="left">Alemtuzumab</td>
<td align="left">Sanofi</td>
<td align="char" char=".">2014</td>
</tr>
<tr>
<td align="left">Opdivo</td>
<td align="left">Nivolumab</td>
<td align="left">Bristol-Myers Squibb</td>
<td align="char" char=".">2014</td>
</tr>
<tr>
<td align="left">Sylvant</td>
<td align="left">Siltuximab</td>
<td align="left">Janssen</td>
<td align="char" char=".">2014</td>
</tr>
<tr>
<td align="left">Cosentyx</td>
<td align="left">Secukinumab</td>
<td align="left">Novartis</td>
<td align="char" char=".">2015</td>
</tr>
<tr>
<td align="left">Darzalex</td>
<td align="left">Daratumumab</td>
<td align="left">Janssen</td>
<td align="char" char=".">2015</td>
</tr>
<tr>
<td align="left">Nucala</td>
<td align="left">Mepolizumab</td>
<td align="left">GlaxoSmithKline</td>
<td align="char" char=".">2015</td>
</tr>
<tr>
<td align="left">Praluent</td>
<td align="left">Alirocumab</td>
<td align="left">Sanofi, Regeneron</td>
<td align="char" char=".">2015</td>
</tr>
<tr>
<td align="left">Praxbind</td>
<td align="left">Idarucizumab</td>
<td align="left">Boehringer-Ingelheim</td>
<td align="char" char=".">2015</td>
</tr>
<tr>
<td align="left">Repatha</td>
<td align="left">Evolocumab</td>
<td align="left">Amgen</td>
<td align="char" char=".">2015</td>
</tr>
<tr>
<td align="left">Lartruvo</td>
<td align="left">Olaratuumab</td>
<td align="left">Eli Lilly</td>
<td align="char" char=".">2016</td>
</tr>
<tr>
<td align="left">Taltz</td>
<td align="left">Ixekizumab</td>
<td align="left">Eli Lilly</td>
<td align="char" char=".">2016</td>
</tr>
<tr>
<td align="left">Tecentriq</td>
<td align="left">Atezolizumab</td>
<td align="left">Roche</td>
<td align="char" char=".">2016</td>
</tr>
<tr>
<td align="left">Zinplava</td>
<td align="left">Bezlotoxumab</td>
<td align="left">Merck and Co.</td>
<td align="char" char=".">2016</td>
</tr>
<tr>
<td align="left">Bavencio</td>
<td align="left">Avelumab</td>
<td align="left">Merck KGaA, Pfizer</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Dupixent</td>
<td align="left">Dupilumab</td>
<td align="left">Regeneron, Sanofi</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Fasenra</td>
<td align="left">Benralizumab</td>
<td align="left">AstraZeneca</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Hemlibra</td>
<td align="left">Emicizumab</td>
<td align="left">Genentech</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Imfinzi</td>
<td align="left">Durvalumab</td>
<td align="left">AstraZeneca</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Kevzara</td>
<td align="left">Sarilumab</td>
<td align="left">Sanofi, Regeneron</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Ocrevus</td>
<td align="left">Ocrelizumab</td>
<td align="left">Roche</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Siliq</td>
<td align="left">Brodalumab</td>
<td align="left">Valeant</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Tremfya</td>
<td align="left">Guselkumab</td>
<td align="left">Janssen</td>
<td align="char" char=".">2017</td>
</tr>
<tr>
<td align="left">Aimovig</td>
<td align="left">Erenumab</td>
<td align="left">Amgen</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Ajovy</td>
<td align="left">Fremanezumab</td>
<td align="left">Teva</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Cablivi</td>
<td align="left">Caplacizumab</td>
<td align="left">Sanofi</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Crysvita</td>
<td align="left">Burosumab</td>
<td align="left">Ultragenyx</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Emgality</td>
<td align="left">Galcanezumab</td>
<td align="left">Eli Lilly</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Ilumya</td>
<td align="left">Tildrakizumab</td>
<td align="left">Sun Pharma</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Libtayo</td>
<td align="left">Cemiplimab</td>
<td align="left">Sanofi, Regeneron</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Lumoxiti</td>
<td align="left">Moxetumomab pasudotox</td>
<td align="left">Innate Pharma</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Takhzyro</td>
<td align="left">Lanadelumab</td>
<td align="left">Shire</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Trogarzo</td>
<td align="left">Ibalizumab</td>
<td align="left">TaiMed Biologics</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Ultomiris&#xa0;</td>
<td align="left">Ravulizumab</td>
<td align="left">Alexion</td>
<td align="char" char=".">2018</td>
</tr>
<tr>
<td align="left">Adakveo</td>
<td align="left">Crizanlizumab</td>
<td align="left">Novartis</td>
<td align="char" char=".">2019</td>
</tr>
<tr>
<td align="left">Beovu</td>
<td align="left">Brolucizumab</td>
<td align="left">Novartis</td>
<td align="char" char=".">2019</td>
</tr>
<tr>
<td align="left">Evenity</td>
<td align="left">Romosozumab</td>
<td align="left">Amgen</td>
<td align="char" char=".">2019</td>
</tr>
<tr>
<td align="left">Polivy</td>
<td align="left">Polatuzumab</td>
<td align="left">Genentech</td>
<td align="char" char=".">2019</td>
</tr>
<tr>
<td align="left">Skyrizi</td>
<td align="left">Risankizumab</td>
<td align="left">AbbVie</td>
<td align="char" char=".">2019</td>
</tr>
<tr>
<td align="left">Blenrep</td>
<td align="left">Belantamab</td>
<td align="left">GlaxoSmithKline</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Danyelza</td>
<td align="left">Naxitamab</td>
<td align="left">Y-mAbs Therapeutics</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Ebanga</td>
<td align="left">Ansuvimab</td>
<td align="left">Ridgeback Biotherapeutics</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Enspryng</td>
<td align="left">Satralizumab</td>
<td align="left">Roche</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Margenza</td>
<td align="left">Margetuximab</td>
<td align="left">MacroGenics</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Monjuvi</td>
<td align="left">Tafasitamab</td>
<td align="left">MorphoSys, Incyte</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Sarclisa</td>
<td align="left">Isatuximab</td>
<td align="left">Sanofi</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Tepezza</td>
<td align="left">Teprotumumab</td>
<td align="left">Horizon</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Trodelvy</td>
<td align="left">Sacituzumab</td>
<td align="left">Gilead</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Uplizna</td>
<td align="left">Inebilizumab</td>
<td align="left">Viela Bio</td>
<td align="char" char=".">2020</td>
</tr>
<tr>
<td align="left">Vyepti</td>
<td align="left">Eptinezumab</td>
<td align="left">Lundbeck</td>
<td align="char" char=".">2020</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>A workflow for optimizing recombinant antibody production and quality in CHO cells. Too many factors can impact recombinant antibody production and quality in CHO cells, therefore, it is important to follow a specific workflow when dealing with recombinant antibody production and quality.</p>
</caption>
<graphic xlink:href="fbioe-10-856049-g002.tif"/>
</fig>
</sec>
<sec id="s2">
<title>Optimization of Gene Sequences of Antibody</title>
<sec id="s2-1">
<title>Control the Proportion of Light and Heavy Chains</title>
<p>Most IgG molecules have a symmetrical structure consisting of four polypeptide chains including two heavy chains (HC) and two light chains (LC) (<xref ref-type="fig" rid="F3">Figure&#x20;3</xref>). In the endoplasmic reticulum, the antibody binding protein (BiP) briefly binds to the heavy-chain polypeptide of the antibody, and the light chain is secreted out of the cell as polymerized dimers. Before assembling into a tetramer, BiP maintains a binding state with the heavy-chain polypeptide. Once the light-chain polypeptide is lacking, the heavy chain will not be secreted outside the cell, causing the proteasome in the cell to be unable to degrade the heavy-chain polypeptides secreted to extracellularly; thus, the unit productivity of antibody is significantly reduced (<xref ref-type="bibr" rid="B106">Vanhove et&#x20;al., 2001</xref>; <xref ref-type="bibr" rid="B36">Ha et&#x20;al., 2019</xref>). Therefore, excess light-chain polypeptides should be ensured to improve the expression of antibodies. In general, in the traditional process of antibody preparation, researchers often construct light- and heavy-chain genes of antibodies into two vectors to further express the antibody molecules. The disadvantage of this method is that the ratio of light and heavy chains is not controllable. In addition, the ratio of light and heavy chains affects the quality of monoclonal antibodies, such as the formation of aggregates and glycosylation modification. Chung et&#x20;al. confirmed that excessive heavy-chain polypeptides may be the main cause of polymerization (<xref ref-type="bibr" rid="B21">Chung et&#x20;al., 2018</xref>). In transient expression, the ratio of polypeptides can be controlled by adjusting the relative amount of each vector. However, this method is difficult to operate because the integration of plasmids is often random, and the number of gene copies and integration sites cannot be controlled artificially (<xref ref-type="bibr" rid="B95">Schlatter et&#x20;al., 2005</xref>). Therefore, the industrial production of antibody drugs requires stable transfection of cell lines and proper control of the ratio of light and heavy chains, which are particularly important to achieve high expression, low polymerization, and consistent N-glycosylation profile of antibodies.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Basic structure of antibody. Each IgG antibody molecule consists of four polypeptide chains (two identical light chains and two identical heavy chains joined by disulfide bonds) and has two antigen-binding sites. Each light chain and each heavy chain consists of a variable region and a constant region. Each heavy chain consists of a variable domain (VH) and three or four constant domains (CH); each light chain consists of a variable domain (VL) and a single constant domain (CL). Human IgG structure with glycans attached at Asn297&#xa0;N-glycosylation site in the CH2.</p>
</caption>
<graphic xlink:href="fbioe-10-856049-g003.tif"/>
</fig>
</sec>
<sec id="s2-2">
<title>Change of Gene Arrangement</title>
<p>The heavy- and light-chain genes of antibodies use different vectors and then enter the host cells by co-transfection. This method has very low requirements on the vector, but the transfection efficiency is high, which is a common strategy for recombinant antibody expression (<xref ref-type="bibr" rid="B14">Carrara et&#x20;al., 2021</xref>). However, the main defect of this strategy lies in the random integration of light- and heavy-chain genes into chromosomes, and the insertion positions and numbers are different, resulting in uncontrollable expression levels of light and heavy chains (<xref ref-type="bibr" rid="B1">Ahmadi et&#x20;al., 2016</xref>). The second arrangement is to construct an independent expression unit of light and heavy chains on the same vector; the light and heavy chain genes use separate promoters and polyadenylic acid (Poly A) tail sequences of different lengths. This strategy can theoretically achieve equal mole expression levels of light- and heavy-chain genes. However, such adjacent reading frames may cause transcriptional interference, resulting in the imbalanced transcription of light and heavy chain and ultimately inhibiting the expression of recombinant antibodies (<xref ref-type="bibr" rid="B23">Davies et&#x20;al., 2011</xref>). Based on the advantages and disadvantages of the two gene arrangements, researchers adopted a third arrangement, in which an internal ribosome enter site (IRES) or 2A peptide in the same open reading frame to connect light-chain genes, namely, triscistronic expression vector. This strategy can effectively avoid the occurrence of transcription interference and control the proportion of light and heavy chain expression. When expressing monoclonal antibodies using IRES-mediated tricistronic vector, the IRES connects heavy-chain genes, light-chain genes, and resistance genes for expression on the same vector. This strategy can maintain over 70% productivity of positive clones and 2-fold increase in the yield of recombinant antibodies (<xref ref-type="bibr" rid="B61">Li et&#x20;al., 2007</xref>; <xref ref-type="bibr" rid="B117">Yeo et&#x20;al., 2018</xref>). Ho et&#x20;al. designed four IRES-mediated tricistronic vectors to control the ratio of light and heavy chains, depending on the different positions of the light and heavy chains and selection marker genes on the vector; they also compared the expression level and quality of antibodies, including aggregate formation, N-glycosylation, and conformational stability. The results show that excess light chain is essential for high expression of antibody and also reduces the occurrence of polymerization. In addition, the main cause of polymerization is that the excessive heavy chains cannot effectively fold with the limited light chains and can change the N-glycosylation and reduce the conformational stability (<xref ref-type="bibr" rid="B43">Ho et&#x20;al., 2013b</xref>). Controlling the ratio of light and heavy chains is crucial for the expression and quality of monoclonal antibodies under stable transfection conditions (<xref ref-type="bibr" rid="B41">Ho et&#x20;al., 2012</xref>). In particular, excessive light chains cannot only achieve high antibody expression but also reduce polymerization and maintain low fragmentation levels, which are very beneficial for obtaining high-yield antibodies (<xref ref-type="bibr" rid="B43">Ho et&#x20;al., 2013b</xref>). However, the fragment length of IRES itself is relatively large, which occupies more space, and the expression of IRES upstream and downstream antibody molecules is seriously imbalanced (<xref ref-type="bibr" rid="B8">Bayat et&#x20;al., 2018</xref>). In addition, the activity of IRES is not easy to adjust, which will affect the biological activities of other expressed proteins. In recent years, a prominent approach is based on self-cleavage 2A peptide-mediated multi-gene construction method (<xref ref-type="bibr" rid="B18">Chng et&#x20;al., 2015</xref>; <xref ref-type="bibr" rid="B105">Van der Weken et&#x20;al., 2019</xref>). This strategy effectively avoids the disadvantage of low expression of downstream genes caused by the simultaneous expression of multiple genes and is commonly used in multi-gene expression (<xref ref-type="bibr" rid="B69">Luke and Ryan, 2018</xref>; <xref ref-type="bibr" rid="B62">Li et&#x20;al., 2020</xref>). Ho et&#x20;al. compared the effects of F2A and IRES on the production of a monoclonal antibody in CHO-DG44 cells. The expression level of the monoclonal antibody produced by F2A-mediated tricistronic vector was significantly higher than that of IRES-mediated vector under transient or stable transfection conditions; the expression level of the recombinant antibody was affected by the position of light- and heavy-chain cistrons (<xref ref-type="bibr" rid="B42">Ho et&#x20;al., 2013a</xref>).</p>
</sec>
<sec id="s2-3">
<title>Codon Optimization</title>
<p>Antibody molecules usually have the molecular structure of tetramer glycoprotein. Light- and heavy-chain genes in antibody molecules need to have coordinated expression to biosynthesize tetramer IgG. However, when expressing recombinant antibodies, the special secondary structure in the gene sequence and other factors often lead to low expression of antibody, which can be remedied by codon optimization strategy. Codon optimization involves gene synthesis, gene transcription, mRNA translation, etc., with the ultimate goal of efficiently expressing recombinant antibodies (<xref ref-type="bibr" rid="B7">Ayyar et&#x20;al., 2017</xref>; <xref ref-type="bibr" rid="B72">Mauro and Chappell, 2018</xref>; <xref ref-type="bibr" rid="B120">You et&#x20;al., 2018</xref>). Before expressing a gene, the rare codon in the gene should be searched first. If there are too many rare codons, then the translation rate of antibody will be affected. The expression of the target gene in transgenic hosts can be increased by selecting the codon preferred by the receptor without changing the amino acid sequence (<xref ref-type="bibr" rid="B73">Mauro, 2018</xref>). When the codon of the variable region of an antibody is replaced with the preferred codon of the natural human antibody gene, the antibody expression levels in mammalian cells are significantly increased by two- to 3-fold (<xref ref-type="bibr" rid="B15">Carton et&#x20;al., 2007</xref>).</p>
</sec>
<sec id="s2-4">
<title>Signal Peptide Optimization</title>
<p>Signal peptide is a key factor for the secretion of recombinant antibodies, and the high-efficiency expression of these antibodies is closely related to the signal peptide (<xref ref-type="bibr" rid="B39">Haryadi et&#x20;al., 2015</xref>; <xref ref-type="bibr" rid="B109">Wang et&#x20;al., 2016</xref>). When expressing recombinant antibodies, in addition to the use of their own signal peptides, the following strategies are often applied: &#x2460; replace the signal peptide sequence of the efficiently expressed secreted proteins (<xref ref-type="bibr" rid="B6">Attallah et&#x20;al., 2017</xref>); &#x2461; modifying the primary structure of the signal peptide sequence in the original antibody; &#x2462; replace the protein signal peptide sequence in some viruses; however, viral vectors are generally not recommended when considering the safety of the expressed recombinant antibodies; and &#x2463; select the preferred host codon. When selecting a suitable signal peptide, the preferred codons of the expression host should be fully considered, and the signal peptide should be further optimized. Ramezani et&#x20;al. showed a 2-fold increase of the pertuzumab production in CHO cells by optimizing codons and selecting appropriate signal peptide strategies (<xref ref-type="bibr" rid="B86">Ramezani et&#x20;al., 2017</xref>).</p>
</sec>
</sec>
<sec id="s3">
<title>Construction and Optimization of Expression Vectors</title>
<p>The construction of high-efficiency expression vectors is considered an important strategy to improve the expression level of recombinant antibodies. The expression level of the target genes in mammalian cells is mainly affected by the state of chromosome region integrated by the gene of interest, the copy number of the target gene, and its transcription and translation efficiency (<xref ref-type="bibr" rid="B46">Hung et&#x20;al., 2010</xref>; <xref ref-type="bibr" rid="B34">Gupta et&#x20;al., 2019</xref>; <xref ref-type="bibr" rid="B16">Carver et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B44">Hoseinpoor et&#x20;al., 2020</xref>). Therefore, vector construction strategy should consider optimizing the integration site and position effect on the chromosome and improving the transcription and translation efficiency to effectively improve the expression level of recombinant antibodies.</p>
<sec id="s3-1">
<title>Optimization of Integration Site and Position Effect</title>
<p>The integration site status of target gene on the mammalian cell chromosome plays a decisive role in its expression level and its stability in host cells. Only clones formed by cells whose integration sites are in transcription active region of chromosomes can express the target gene at a high level. However, transgene silencing often occurs due to the random integration of the target gene after transfer into cells (<xref ref-type="bibr" rid="B40">Hilliard and Lee, 2021</xref>). During the expansion of cell culture, the promoter methylation will lead to the attenuation of expression. Therefore, selecting an appropriate promoter and optimizing the combination of promoters and different regulatory elements can improve the expression of recombinant antibodies and increase the stability of expression. Some chromatin-modifying elements can prevent transgene silencing, including matrix attachment regions (MAR) (<xref ref-type="bibr" rid="B11">Buceta et&#x20;al., 2011</xref>; <xref ref-type="bibr" rid="B74">Mohammadian et&#x20;al., 2019</xref>; <xref ref-type="bibr" rid="B121">Zhang et&#x20;al., 2020</xref>), locus control regions (LCR) (<xref ref-type="bibr" rid="B97">Sharma et&#x20;al., 2019</xref>; <xref ref-type="bibr" rid="B75">Morgan et&#x20;al., 2020</xref>), ubiquitous chromatin opening elements (UCOEs) (<xref ref-type="bibr" rid="B84">Pfaff et&#x20;al., 2013</xref>; <xref ref-type="bibr" rid="B38">Harraghy et&#x20;al., 2015</xref>; <xref ref-type="bibr" rid="B77">Nematpour et&#x20;al., 2017</xref>; <xref ref-type="bibr" rid="B89">Rocha-Piza&#xf1;a et&#x20;al., 2017</xref>), stabilizing anti-repressor elements (STAR) (<xref ref-type="bibr" rid="B58">Kwaks et&#x20;al., 2003</xref>; <xref ref-type="bibr" rid="B81">Otte et&#x20;al., 2007</xref>; <xref ref-type="bibr" rid="B104">Van Blokland et&#x20;al., 2007</xref>), and insulators (<xref ref-type="bibr" rid="B9">Benabdellah et&#x20;al., 2014</xref>; <xref ref-type="bibr" rid="B17">Chetverina et&#x20;al., 2014</xref>; <xref ref-type="bibr" rid="B76">Naderi et&#x20;al., 2018</xref>; <xref ref-type="bibr" rid="B83">P&#xe9;rez-Gonz&#xe1;lez and Caro, 2019</xref>). Moreover, artificial chromosome expression (ACE) and targeted integration technology, including Flp-In and recombinase-mediated cassette exchange system (RMCE), can overcome the shortcomings of random integration (<xref ref-type="bibr" rid="B52">Kennard et&#x20;al., 2009</xref>; <xref ref-type="bibr" rid="B98">Soler et&#x20;al., 2018</xref>; <xref ref-type="bibr" rid="B87">Reinhart et&#x20;al., 2019</xref>; <xref ref-type="bibr" rid="B78">Ng et&#x20;al., 2021</xref>). The introduction of these functional elements into the vector construction can greatly increase the proportion of high-expression clones and shorten the construction cycle of engineered host cells (<xref ref-type="table" rid="T2">Table&#x20;2</xref>).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Optimization strategies of integration site and position effect for improving recombinant antibody expression.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Element</th>
<th align="center">Mechanism of action</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">MAR</td>
<td align="left">A boundary element, which acts as an insulator and overcomes the position effect</td>
</tr>
<tr>
<td align="left">LCR</td>
<td align="left">A DNA sequence composed of many regulatory elements such as enhancers or isolators, which has the function of stabilizing the loose structure of chromatin and controlling the sequential expression of individual genes at the locus</td>
</tr>
<tr>
<td align="left">UCOE</td>
<td align="left">A class of non-methylated CpG islands, combined with the promoter to prevent the formation of heterochromatin and gene silencing</td>
</tr>
<tr>
<td align="left">STAR</td>
<td align="left">Regulatory factors that can block the repressor protein, increase transgene expression and expression stability</td>
</tr>
<tr>
<td align="left">Insulator</td>
<td align="left">A class of enhancer blocking elements prevent the heterochromatin marker from spreading to the euchromatin region</td>
</tr>
<tr>
<td align="left">ACE</td>
<td align="left">Pre-engineered artificial chromosomes, which allows for the targeted transfection of single or multiple genes and eliminates the need for random integration into native host chromosomes</td>
</tr>
<tr>
<td align="left">Flp-In</td>
<td align="left">Site-directed integration of target genes into transcriptionally active regions</td>
</tr>
<tr>
<td align="left">RMCE</td>
<td align="left">Recombinase-mediated site-directed integration technology</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-2">
<title>Improvement of Transcription and Translation Efficiency</title>
<p>Transcription is the initial step of gene expression, among which promoters, enhancers, and transcription termination signals have very important impacts on transcription efficiency and mRNA stability. CMV promoters are considered to be one of the strongest viral promoters, and hEF-1&#x3b1; promoters have the stronger transcription initiation efficiency and are more suitable for large-scale production of recombinant antibodies (<xref ref-type="bibr" rid="B28">Ebadat et&#x20;al., 2017</xref>). In recent years, the construction of synthetic or heterozygous promoters will become an effective strategy to improve the transcription efficiency (<xref ref-type="bibr" rid="B82">Patel et&#x20;al., 2021</xref>).</p>
<p>Enhancers can improve the transcription efficiency and can function over long distances. Constructing heterozygous enhancers is a good strategy to improve transcription efficiency and obtain high transcriptional activity. Xu et&#x20;al. used the CMV promoter and CA hybrid promoter to insert the SV40 enhancers downstream of SV40 Poly A; compared with the CMV promoter, cells driven by the CA hybrid promoter can increase the production of target proteins by two times (<xref ref-type="bibr" rid="B113">Xu et&#x20;al., 2001</xref>).</p>
<p>Introns can increase the expression of foreign genes. Xu et&#x20;al. compared the effects of five different introns on transgene expression in CHO cells. Under transient and stable transfection conditions, the SV40 intron can obtain the highest transgene expression level among five introns, which can also obtain high-level of recombinant protein production in CHO cells (<xref ref-type="bibr" rid="B112">Xu et&#x20;al., 2018</xref>).</p>
<p>The regulation of translation level and processing efficiency of translation products will also significantly affect the expression of target gene. Eisenhut et&#x20;al. developed the 5&#x2032;-untranslated region (UTR) RNA-structures to impact translation efficiency, further systematically tune protein expression levels in mammalian cells and eventually help to optimize recombinant protein expression (<xref ref-type="bibr" rid="B29">Eisenhut et&#x20;al., 2020</xref>). Vivirius et&#x20;al. found a universal translation enhancer element in the 5&#x2032;-end non-translation area of Hsp70 mRNA, which can enhance the translation efficiency of the cap-dependent structure (<xref ref-type="bibr" rid="B107">Vivinus et&#x20;al., 2001</xref>).</p>
</sec>
</sec>
<sec id="s4">
<title>Optimzation of Antibody Expression System</title>
<sec id="s4-1">
<title>Gene Amplification Screening System</title>
<p>At present, the two common gene amplification screening systems are dihydrofolate reductase (DHFR) and glutamine synthetase (GS) (<xref ref-type="bibr" rid="B12">Budge et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B45">Huhn et&#x20;al., 2021</xref>). The CHO-DHFR amplification system mainly uses the CHO cell line of DHFR defect to obtain high yields of recombinant antibodies in the presence of methotrexate (MTX). Akbarzade-sharbaf et&#x20;al. used DHFR system to express a therapeutic antibody (Trastuzumab); after seven rounds of MTX, the total antibody valence can be as high as 50&#x2013;60&#xa0;mg/L/day (<xref ref-type="bibr" rid="B2">Akbarzadeh-Sharbaf et&#x20;al., 2013</xref>). As another mature and widely used system, the GS amplification system is an explicit gene amplification selection marker, which usually requires pressure and stable selection of two rounds of methionine sulphoximine (MSX) to obtain high-expression cell lines and reduce the screening time. However, the two systems have some defects, such as the decline in the antibody production after long-term screening; therefore, these systems need to be further optimized. The strategy of using low activity of GS as the screening marker could obtain high-yield clones after MSX removal, further efficiently increase the antibody production (<xref ref-type="bibr" rid="B65">Lin et&#x20;al., 2019</xref>).</p>
</sec>
<sec id="s4-2">
<title>Screening Marker Weakening</title>
<p>Two strategies can be employed to weaken the screening marker. One is to mutate the screening marker and reduce its activity. Neomycin-phosphotransferase (NPT) is one of the commonly used screening markers in eukaryotic expression systems. The second strategy is to reduce the expression of the screening marker. Noguchi et&#x20;al. started the transcription of DHFR through the late promoter of weak promoter SV40 and used the method of IR/MAR-<italic>dhfr</italic> fusion to easily separate the cell line of high-expression recombinant protein, which further improved the production of the recombinant proteins (<xref ref-type="bibr" rid="B80">Noguchi et&#x20;al., 2012</xref>).</p>
</sec>
</sec>
<sec id="s5">
<title>Modification of Antibody Host Cells</title>
<p>Many mammalian cells can produce recombinant antibodies, including CHO, NS0, and SP2/0 cells, which can produce non-human glycosylated modification. Owing to the difference between the growth and metabolic characteristics of animal cells, the expression level and modification ability of recombinant antibody are also different (<xref ref-type="bibr" rid="B25">Dhara et&#x20;al., 2018</xref>). Therefore, we can further transform the host cells, improve the expression level by engineering cell line, and meet the production capacity and quality requirement of antibody drugs as much as possible (<xref ref-type="bibr" rid="B68">Lu et&#x20;al., 2018</xref>; <xref ref-type="bibr" rid="B67">Liu et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B125">Zhou et&#x20;al., 2021</xref>).</p>
<sec id="s5-1">
<title>Glycosylation Engineering</title>
<p>The enzymes responsible for glycosylation can be modified in non-humanized cells, so the expressed recombinant antibody is more similar to the natural humanized protein in terms of glycosylation level and type, thereby improving the biological effect of recombinant antibody. Glycosylation mainly includes genetic and non-genetic modification strategies. The glycosylation of protein can affect the pharmacological activity and pharmacodynamics of the antibody, so researchers can optimize the recombinant antibodies through genetic engineering to achieve the glycosylation modification of cell lines (<xref ref-type="bibr" rid="B115">Yang Y et&#x20;al., 2021</xref>). For example, the therapeutic activity of recombinant IgG3 antibodies was significantly improved after transfecting murine 2, 6-sialyltransferase into CHO cells (<xref ref-type="bibr" rid="B48">Jassal et&#x20;al., 2001</xref>).</p>
</sec>
<sec id="s5-2">
<title>Anti-Apoptosis</title>
<p>Anti-apoptosis is a hot spot in the current host cell transformation strategy, and the overexpression of anti-apoptotic genes is one of the common strategies. Effective anti-apoptotic genes include Bcl-2, Bcl-xL, and so on. Lee et&#x20;al. achieved the overexpression of Bcl-2 and Beclin-1 in CHO-DG44 cells; the results showed that the time of cell culture became longer, the cell survival rate was significantly improved, and the occurrence of apoptosis was suppressed (<xref ref-type="bibr" rid="B59">Lee et&#x20;al., 2013</xref>). Kim et&#x20;al. overexpressed Bcl-xL in recombinant CHO cells, which can improve cell survival rate, prolong culture time, and inhibit apoptosis and autophagy by inhibiting the activation of caspase-3and caspase-7 (<xref ref-type="bibr" rid="B54">Kim et&#x20;al., 2009</xref>).</p>
</sec>
<sec id="s5-3">
<title>Cellular Metabolic Engineering</title>
<p>In mammalian cell culture, the metabolic engineering strategy changes the cell metabolism pathway, which can effectively promote cell growth and product synthesis, discover more novel metabolite additives, and reduce the accumulation of metabolic inhibitors (<xref ref-type="bibr" rid="B103">Toussaint et&#x20;al., 2016</xref>; <xref ref-type="bibr" rid="B35">Gupta et&#x20;al., 2017</xref>; <xref ref-type="bibr" rid="B30">Fouladiha et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B116">Yao et&#x20;al., 2021</xref>). In cells with low lactate/glucose, the expression levels of lactate dehydrogenase and the accumulation of lactate are reduced, resulting in an increase in the synthetic products. Zhou et&#x20;al. used siRNA technology to reduce the expression levels of lactate dehydrogenase A (LDHa) and pyruvate dehydrogenase kinase (PDHK) genes, reducing the lactate levels by 90% and increasing the production of therapeutic monoclonal antibodies (<xref ref-type="bibr" rid="B124">Zhou et&#x20;al., 2011</xref>). In addition, the production of recombinant antibodies can be multiplied by several times by effectively blocking engineered cells in the G<sub>1</sub>&#x20;phase.</p>
</sec>
<sec id="s5-4">
<title>Cellular Cycle Regulation Engineering</title>
<p>In the large-scale cell culture process, with the continuous exploration of the regulatory mechanism of cell cycle, researchers have applied cell cycle regulation genes to cell proliferation control. Fussenegger et&#x20;al. found that p21, p27, and p53 are cell cycle G<sub>l</sub>/S suppressor proteins; after CHO cells express these proteins, they can prevent cells from entering the S phase, keep the growth in a static state, and increase the production of secreted alkaline phosphatase (SEAP) to 10&#x2013;15&#x20;times (<xref ref-type="bibr" rid="B31">Fussenegger et&#x20;al., 1998</xref>).</p>
</sec>
</sec>
<sec id="s6">
<title>Control Glycosylation Modification</title>
<p>Recombinant antibodies are biological macromolecular drugs, and the normal performance of their biological functions is inseparable from the complex post-translational modification process. As the most important type of post-translational modification of recombinant antibodies, glycosylation modification has certain effects on the biological activity of antibody, immunogenicity, <italic>in vivo</italic> metabolism, antibody-dependent cytotoxicity, and complement-dependent cytotoxicity. Therefore, glycosylation modification of antibody molecules has been widely used in the development of novel antibody&#x20;drugs.</p>
<sec id="s6-1">
<title>Type of Antibody Glycosylation Modification</title>
<p>According to different connection modes, antibody glycosylation is usually divided into N-linked glycosylation and O-linked glycosylation. The Fc fragments of two heavy chains in the monoclonal antibody molecule contain an N-glycosylation modification site at the 297th aspartic acid. The N-glycosylation modification of monoclonal antibody drugs usually presents biantennary sugar chains, and sometimes fucosylation and sialylation may occur (<xref ref-type="bibr" rid="B99">Szabo et&#x20;al., 2022</xref>). In addition, some monoclonal antibody drugs undergo O-glycosylation modification. For example, a humanized monoclonal antibody drug produced by CHO cells has O-glycosylation modified by a single glucose molecule (<xref ref-type="bibr" rid="B100">Tanaka et&#x20;al., 2013</xref>). At present, most of the antibody drugs on the market are produced by CHO cells. CHO cells can produce antibodies that are close to the glycotype of human serum antibodies; however, the glycosylation of antibodies produced by most engineered cell lines is different from that of human serum antibodies. For example, antibodies produced from mouse-derived animal cells have a high proportion of fucose modifications and a low proportion of galactosylation modifications (<xref ref-type="bibr" rid="B4">An, 2009</xref>). In contrast to human cells, CHO cells lack the expression of <italic>&#x3b1;</italic>-2,6-sialyltransferase, while only express <italic>&#x3b1;</italic>-2,3-sialyltransferase (<xref ref-type="bibr" rid="B50">Jenkins et&#x20;al., 1996</xref>; <xref ref-type="bibr" rid="B64">Lin et&#x20;al., 2015</xref>; <xref ref-type="bibr" rid="B20">Chung et&#x20;al., 2020</xref>). Consequently, CHO cells inherently cannot produce glycoproteins with similar terminal sialic acid content as compared to human cells (<xref ref-type="bibr" rid="B119">Yin et&#x20;al., 2017</xref>). Furthermore, CHO cells lack N-acetylglucosamine transferase (GnT-III) expressed by human cells, resulting in the differences in the modification of N-acetylglucosamine glycosylation from human cells (<xref ref-type="bibr" rid="B13">Butler, 2005</xref>). These findings indicate that different cell types produce different types of antibody glycosylation (<xref ref-type="bibr" rid="B5">Attallah et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B27">Dyukova et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B123">Zhao et&#x20;al., 2021</xref>). In summary, the type of glycosylation modification of monoclonal antibody drugs is closely related to the production system, selected cell line, and incubation process.</p>
</sec>
<sec id="s6-2">
<title>Control Strategy of Antibody Glycosylation Modification</title>
<p>Although the mass of sugar chains only accounts for 2% of the total molecular weight of antibody, the sugar chains on the Fc and Fab fragments play a very important role in the affinity, structural maintenance, metabolism, and immunogenicity of the antibody molecule (<xref ref-type="bibr" rid="B56">Krapp et&#x20;al., 2003</xref>; <xref ref-type="bibr" rid="B19">Chung et&#x20;al., 2008</xref>; <xref ref-type="bibr" rid="B3">Alessandri et&#x20;al., 2012</xref>). The physiological activity of therapeutic antibodies is mainly mediated by two mechanisms: one is mediated by the affinity between the variable region of the antibody and antigen, causing the neutralization or apoptosis of the target antigen, which is mainly by means of the Fab fragment of antibody to recognize and bind antigenic substances; the other is the immune effect mediated by the Fc fragment of antibody, including antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) (<xref ref-type="bibr" rid="B92">Salinas-Jazm&#xed;n et&#x20;al., 2022</xref>). Metabolic analysis and mathematical model can be used to analyze the production process to control the type and degree of glycosylation modification of recombinant antibodies to reduce the immunogenicity of antibody drugs and optimize their effector function (<xref ref-type="bibr" rid="B96">Sha et&#x20;al., 2016</xref>). The types of glycosylation modification of recombinant antibodies are shown in <xref ref-type="fig" rid="F4">Figure&#x20;4</xref>.</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Types of glycosylation modification of recombinant antibodies. The representative N-glycan structures identified on antibody Fc fragment are G0F, G1F, and G2F. G0F: asialo, agalactose, biantennary complex, core substituted with fucose; G1F: asialo, mono-galactosylated, biantennary complex, core substituted with fucose; G2F: asialo, galactosylated, biantennary complex, core substituted with fucose.</p>
</caption>
<graphic xlink:href="fbioe-10-856049-g004.tif"/>
</fig>
<p>Sialylation modification: Sialylatedglycans are the key components of glycoproteins. According to relevant studies, monoclonal antibody sialylation can inhibit inflammatory response and reduce cell toxicity by means of different receptors in the Fc fragment (<xref ref-type="bibr" rid="B94">Scallon et&#x20;al., 2007</xref>). The anti-inflammatory activity of the over-sialylated monoclonal antibody obtained by affinity chromatography purification will be significantly enhanced. Moreover, the higher degree of monoclonal antibody sialylation is closely related to a decrease in ADCC activity (<xref ref-type="bibr" rid="B122">Zhang et&#x20;al., 2019</xref>; <xref ref-type="bibr" rid="B79">Nguyen et&#x20;al., 2020</xref>).</p>
<p>Core fucosylation modification: Related studies have shown that core fucose is an important glycosylation structure that affects the ADCC activity of recombinant antibodies (<xref ref-type="bibr" rid="B71">Matsumiya et&#x20;al., 2007</xref>). Most recombinant IgG produced by CHO cells have core fucose in their Fc sugar chains (<xref ref-type="bibr" rid="B126">Zimmermann et&#x20;al., 2021</xref>). To improve the binding activity of the low-affinity receptor IIIa (Fc&#x3b3;R3a) of the antibody to the Fc fragment as well as ADCC activity, researchers have adopted a variety of strategies to reduce the level of fucosylation of IgG, including the use of <italic>&#x3b1;</italic>-1,6-fucose cell lines in which <italic>&#x3b1;</italic>-1,6-fucosetransferase gene is knocked out (<xref ref-type="bibr" rid="B22">Davies et&#x20;al., 2001</xref>; <xref ref-type="bibr" rid="B108">Wang et&#x20;al., 2018</xref>).</p>
<p>Galactosylation modification: Approximately 95% of recombinant IgG produced by CHO cells contains galactose as the terminal sugar. Terminal galactosylation plays an important role in the conformation of the Fc fragment, and the core fucosylation modification alone has slight effect on the conformation of the Fc fragment (<xref ref-type="bibr" rid="B55">Kotidis et&#x20;al., 2019</xref>). The existing results indicate that galactosylation at the end of the Fc fragment will seriously affect the CDC activity of IgG, and the reduction of its glycosylation level will also weaken the CDC activity (<xref ref-type="bibr" rid="B110">Wei et&#x20;al., 2021</xref>).</p>
<p>Mannosylation modification: The content of a high-mannose structure should be minimized as much as possible during the production of recombinant antibodies due to the high immunogenicity of high-mannose structures. In addition, the content of mannose in the Fc fragment of the antibody varies greatly between different cultured cells and different batches (<xref ref-type="bibr" rid="B10">Brantley et&#x20;al., 2021</xref>). Recombinant antibodies have two clearance pathways in organisms; one is the asialoglycoprotein receptor in the liver that binds and mediates the clearance, and the other is to bind to mannose receptors on the surface of macrophages in the liver. The high-mannose structure antibody molecules in the Fc fragment can be quickly eliminated from the plasma, further reducing the efficacy of recombinant antibodies (<xref ref-type="bibr" rid="B66">Liu, 2015</xref>).</p>
<p>Expression of GnT-III: Studies have shown that the bisected glycosylated epidermal growth factor receptor (EGFR) monoclonal antibody is prepared by introducing the GnT-III gene and highly expresses bisecting acetylglucosamine residues; this strategy can increase the ADCC activity by 3&#x20;times and increase the anti-proliferative activity by 1.36 times, and almost no <italic>&#x3b1;</italic>-Gal was detected. The bisecting EGFR monoclonal antibody prepared by glycosylation engineering contains only a small amount of <italic>&#x3b1;</italic>-Gal, which greatly improves the biological activity <italic>in&#x20;vitro</italic>. At present, this study has not been further verified <italic>in vivo</italic> (<xref ref-type="bibr" rid="B49">Jenkins and Curling, 1994</xref>; <xref ref-type="bibr" rid="B118">Yi et&#x20;al., 2014</xref>).</p>
</sec>
</sec>
<sec id="s7">
<title>Conclusion and Future Perspectives</title>
<p>In recent years, with the development and application of proteomics technology, the development of large-scale culture of animal cells for antibody drug production has devolved from the simple optimization of some process parameters to the recent omics research, e.g., transcriptomics, proteomics, metabolomics, glycomics, and fluxomics. The complex metabolic network of production cells and production mechanism of recombinant antibodies have gradually become clear. Raab et&#x20;al. developed a cell line through genetic engineering by a novel bottom-up microRNA (miRNA) screening approach for optimizing the production and secretion of therapeutic antibodies (<xref ref-type="bibr" rid="B85">Raab et&#x20;al., 2022</xref>). At present, the expression level and quality control of recombinant antibodies has always been one of the important bottlenecks restricting the development of antibody&#x20;drugs.</p>
<p>The high-efficiency expression and quality of recombinant antibodies can be affected by multiple factors, which can be achieved by genetic engineering, including the optimization of antibody gene sequence, construction of efficient expression vector, optimization of antibody expression system, modification of antibody host cells, and glycosylation site modification (<xref ref-type="table" rid="T3">Table&#x20;3</xref>). The application of these optimization strategies can effectively shorten the time of antibody generation and improve the expression of target antibodies. However, different optimization strategies have advantages and disadvantages, and how to effectively integrate these optimization strategies to make it an efficient operation system needs further study. Under different process conditions, the differences in the yield of recombinant antibodies by the production cell lines can be analyzed from the levels of genomics, proteomics, and metabolomics, laying a solid foundation for large-scale cell culture processes. In the future, the large-scale production of recombinant antibodies and development of culture processes will develop rapidly in the direction of stabilizing production capacity and improving quality. Furthermore, big data and multi-omics technologies are also beneficial to provide new research directions, more and more process analytical technologies are applied to cell culture processes, which will provide more ideas for improving the efficiency of recombinant antibodies. Therefore, the focus shifts towards how to control the quality of recombinant antibody drugs scientifically and rationally, researchers should further combine the clinical evaluation and post-market safety monitoring, and continue to explore quality control.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Basic strategies for improving recombinant antibody production and quality in CHO&#x20;cells.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Action stage</th>
<th align="center">Strategy</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Processing assembly</td>
<td align="left">Light and heavy chain expression balance, host cell modification</td>
</tr>
<tr>
<td align="left">Integration site</td>
<td align="left">Screening marker weakening, chromosome location screening</td>
</tr>
<tr>
<td align="left">Gene optimization</td>
<td align="left">Change of gene arrangement, codon optimization</td>
</tr>
<tr>
<td align="left">Gene dose</td>
<td align="left">The amplifiable screening marker gene weakening</td>
</tr>
<tr>
<td align="left">Transcription</td>
<td align="left">The potent promoter, enhancer and intron, appropriate antibody gene structure</td>
</tr>
<tr>
<td align="left">Translation</td>
<td align="left">Translational enhancer and the regulation of translation products</td>
</tr>
<tr>
<td align="left">Post-translational modification</td>
<td align="left">Control of antibody glycosylation modification</td>
</tr>
<tr>
<td align="left">Secretion</td>
<td align="left">The suitable antibody secretion signal peptide</td>
</tr>
<tr>
<td align="left">Other</td>
<td align="left">Choose the suitable host cells, modify host cells and try to remove non-productive clones</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</body>
<back>
<sec id="s8">
<title>Author Contributions</title>
<p>J-HZ: manuscript design, preparation and revision. L-LS, FL, and C-YD: proofreading and manuscript revision. J-JL: manuscript revision.</p>
</sec>
<sec id="s9">
<title>Funding</title>
<p>This work was supported by the grant from the National Natural Science Foundation of China (No. U1604193) and Open Program of International Joint Research Laboratory for Recombinant Pharmaceutical Protein Expression System of Henan (No. KFKTYB202205).</p>
</sec>
<sec sec-type="COI-statement" id="s10">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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