, Volume 32, Issue 1, pp 69–81 | Cite as

Codon Optimization in the Production of Recombinant Biotherapeutics: Potential Risks and Considerations

  • Vincent P. Mauro
Review Article


Biotherapeutics are increasingly becoming the mainstay in the treatment of a variety of human conditions, particularly in oncology and hematology. The production of therapeutic antibodies, cytokines, and fusion proteins have markedly accelerated these fields over the past decade and are probably the major contributor to improved patient outcomes. Today, most protein therapeutics are expressed as recombinant proteins in mammalian cell lines. An expression technology commonly used to increase protein levels involves codon optimization. This approach is possible because degeneracy of the genetic code enables most amino acids to be encoded by more than one synonymous codon and because codon usage can have a pronounced influence on levels of protein expression. Indeed, codon optimization has been reported to increase protein expression by >  1000-fold. The primary tactic of codon optimization is to increase the rate of translation elongation by overcoming limitations associated with species-specific differences in codon usage and transfer RNA (tRNA) abundance. However, in mammalian cells, assumptions underlying codon optimization appear to be poorly supported or unfounded. Moreover, because not all synonymous codon mutations are neutral, codon optimization can lead to alterations in protein conformation and function. This review discusses codon optimization for therapeutic protein production in mammalian cells.



I would like to thank Stephen Chappell and Daiki Matsuda for critical reading of the manuscript and valuable comments, Kathryn Crossin for helpful discussions on how codon optimization might lead to lost opportunities during therapeutic protein development, and Daniel Ivansson for a series of thoughtful discussions that sparked my interest in this topic.

Compliance with Ethical Standards


No funding has been received for the conduct of this study and/or preparation of this manuscript.

Conflict of interest

Vincent Mauro declares no conflict of interest.


  1. 1.
    Ladisch MR, Kohlmann KL. Recombinant human insulin. Biotechnol Prog. 1992;8(6):469–78.PubMedCrossRefGoogle Scholar
  2. 2.
    Lieuw K. Many factor VIII products available in the treatment of hemophilia A: an embarrassment of riches? J Blood Med. 2017;8:67–73.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Andersen DC, Krummen L. Recombinant protein expression for therapeutic applications. Curr Opin Biotechnol. 2002;13:117–23.PubMedCrossRefGoogle Scholar
  4. 4.
    Dumont J, Euwart D, Mei B, Estes S, Kshirsagar R. Human cell lines for biopharmaceutical manufacturing: history, status, and future perspectives. Crit Rev Biotechnol. 2016;36(6):1110–22.PubMedCrossRefGoogle Scholar
  5. 5.
    Lagasse HA, Alexaki A, Simhadri VL, Katagiri NH, Jankowski W, Sauna ZE, et al. Recent advances in (therapeutic protein) drug development. F1000Res. 2017;6:113.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Kim JY, Kim YG, Lee GM. CHO cells in biotechnology for production of recombinant proteins: current state and further potential. Appl Microbiol Biotechnol. 2012;93(3):917–30.PubMedCrossRefGoogle Scholar
  7. 7.
    Davami F, Eghbalpour F, Barkhordari F, Mahboudi F. Effect of peptone feeding on transient gene expression process in CHO DG44. Avicenna J Med Biotechnol. 2014;6(3):147–55.PubMedPubMedCentralGoogle Scholar
  8. 8.
    Delafosse L, Xu P, Durocher Y. Comparative study of polyethylenimines for transient gene expression in mammalian HEK293 and CHO cells. J Biotechnol. 2016;10(227):103–11.CrossRefGoogle Scholar
  9. 9.
    Lattenmayer C, Loeschel M, Schriebl K, Steinfellner W, Sterovsky T, Trummer E, et al. Protein-free transfection of CHO host cells with an IgG-fusion protein: selection and characterization of stable high producers and comparison to conventionally transfected clones. Biotechnol Bioeng. 2007;96(6):1118–26.PubMedCrossRefGoogle Scholar
  10. 10.
    Kramer O, Klausing S, Noll T. Methods in mammalian cell line engineering: from random mutagenesis to sequence-specific approaches. Appl Microbiol Biotechnol. 2010;88(2):425–36.PubMedCrossRefGoogle Scholar
  11. 11.
    Harrison RG. Observations on the living developing nerve fiber. Proc Soc Exptl Biol Med. 1907;4:140–3.CrossRefGoogle Scholar
  12. 12.
    Chain E, Florey HW, Adelaide MB, Gardner AD, Oxfd DM, Heatley NG, et al. Penicillin as a chemotherapeutic agent. Lancet. 1940;236:226–8.CrossRefGoogle Scholar
  13. 13.
    Schatz A, Bugie E, Waksman SA. Streptomycin, a substance exhibiting antibiotic activity against gram-positive and gram-negative bacteria. Proc Soc Exp Biol Med. 1944;55:66–9.CrossRefGoogle Scholar
  14. 14.
    Eagle H. Nutrition needs of mammalian cells in tissue culture. Science. 1955;122:501–14.PubMedCrossRefGoogle Scholar
  15. 15.
    Thyagarajan B, Calos MP. Site-specific integration for high-level protein production in mammalian cells. Methods Mol Biol. 2005;308:99–106.PubMedGoogle Scholar
  16. 16.
    Wirth D, Gama-Norton L, Riemer P, Sandhu U, Schucht R, Hauser H. Road to precision: recombinase-based targeting technologies for genome engineering. Curr Opin Biotechnol. 2007;18(5):411–9.PubMedCrossRefGoogle Scholar
  17. 17.
    Campbell M, Corisdeo S, McGee C, Kraichely D. Utilization of site-specific recombination for generating therapeutic protein producing cell lines. Mol Biotechnol. 2010;45(3):199–202.PubMedCrossRefGoogle Scholar
  18. 18.
    Suzuki T, Kazuki Y, Oshimura M, Hara T. A novel system for simultaneous or sequential integration of multiple gene-loading vectors into a defined site of a human artificial chromosome. PLoS One. 2014;9(10):e110404.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Ahmadi M, Damavandi N, Akbari Eidgahi MR, Davami F. Utilization of site-specific recombination in biopharmaceutical production. Iran Biomed J. 2016;20(2):68–76.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Nakamura T, Omasa T. Optimization of cell line development in the GS-CHO expression system using a high-throughput, single cell-based clone selection system. J Biosci Bioeng. 2015;120(3):323–9.PubMedCrossRefGoogle Scholar
  21. 21.
    Priola JJ, Calzadilla N, Baumann M, Borth N, Tate CG, Betenbaugh MJ. High-throughput screening and selection of mammalian cells for enhanced protein production. Biotechnol J. 2016;11(7):853–65.PubMedCrossRefGoogle Scholar
  22. 22.
    Kim M, O’Callaghan PM, Droms KA, James DC. A mechanistic understanding of production instability in CHO cell lines expressing recombinant monoclonal antibodies. Biotechnol Bioeng. 2011;108(10):2434–46.PubMedCrossRefGoogle Scholar
  23. 23.
    Pilbrough W, Munro TP, Gray P. Intraclonal protein expression heterogeneity in recombinant CHO cells. PLoS One. 2009;4(12):e8432.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Dharshanan S, Chong H, Hung CS, Zamrod Z, Kamal N. Rapid automated selection of mammalian cell line secreting high level of humanized monoclonal antibody using Clone Pix FL system and the correlation between exterior median intensity and antibody productivity. Electron J Biotechnol. 2011;14(2).
  25. 25.
    Tsuruta LR, Lopes Dos Santos M, Yeda FP, Okamoto OK, Moro AM. Genetic analyses of Per. C6 cell clones producing a therapeutic monoclonal antibody regarding productivity and long-term stability. Appl Microbiol Biotechnol. 2016;100(23):10031–41.PubMedCrossRefGoogle Scholar
  26. 26.
    Wurm FM. Production of recombinant protein therapeutics in cultivated mammalian cells. Nat Biotechnol. 2004;22:1393–8.PubMedCrossRefGoogle Scholar
  27. 27.
    Kunert R, Reinhart D. Advances in recombinant antibody manufacturing. Appl Microbiol Biotechnol. 2016;100(8):3451–61.PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Kinch MS. An overview of FDA-approved biologics medicines. Drug Discov Today. 2015;20(4):393–8.PubMedCrossRefGoogle Scholar
  29. 29.
    Jayapal KP, Wlaschin KF, Hu WS, Yap MG. Recombinant protein therapeutics from CHO cells—20 years and counting. CHO Consortium SBE Special Section 2007:40–7.Google Scholar
  30. 30.
    Kretzmer G. Industrial processes with animal cells. Appl Microbiol Biotechnol. 2002;59:135–42.PubMedCrossRefGoogle Scholar
  31. 31.
    Ayyar BV, Arora S, Ravi SS. Optimizing antibody expression: the nuts and bolts. Methods. 2017;01(116):51–62.CrossRefGoogle Scholar
  32. 32.
    Brown AJ, James DC. Precision control of recombinant gene transcription for CHO cell synthetic biology. Biotechnol Adv. 2016;34(5):492–503.PubMedCrossRefGoogle Scholar
  33. 33.
    Wang W, Jia YL, Li YC, Jing CQ, Guo X, Shang XF, et al. Impact of different promoters, promoter mutation, and an enhancer on recombinant protein expression in CHO cells. Sci Rep. 2017;7(1):10416.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Ebadat S, Ahmadi S, Ahmadi M, Nematpour F, Barkhordari F, Mahdian R, et al. Evaluating the efficiency of CHEF and CMV promoter with IRES and Furin/2A linker sequences for monoclonal antibody expression in CHO cells. PLoS One. 2017;12(10):e0185967.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Majocchi S, Aritonovska E, Mermod N. Epigenetic regulatory elements associate with specific histone modifications to prevent silencing of telomeric genes. Nucleic Acids Res. 2014;42(1):193–204.PubMedCrossRefGoogle Scholar
  36. 36.
    Kaufman RJ. Overview of vector design for mammalian gene expression. Methods Mol Biol. 1997;62:287–300.PubMedGoogle Scholar
  37. 37.
    Gu MB, Kern JA, Todd P, Kompala DS. Effect of amplification of dhfr and lac Z genes on growth and beta-galactosidase expression in suspension cultures of recombinant CHO cells. Cytotechnology. 1992;9:237–45.PubMedCrossRefGoogle Scholar
  38. 38.
    Payne SH. The utility of protein and mRNA correlation. Trends Biochem Sci. 2015;40(1):1–3.PubMedCrossRefGoogle Scholar
  39. 39.
    Vogel C. Evolution. Protein expression under pressure. Science. 2013;342(6162):1052–3.PubMedGoogle Scholar
  40. 40.
    Wurm FM, Pallavicini MG, Arathoon R. Integration and stability of CHO amplicons containing plasmid sequences. Dev Biol Stand. 1992;76:69–82.PubMedGoogle Scholar
  41. 41.
    Kim SJ, Lee GM. Cytogenetic analysis of chimeric antibody-producing CHO cells in the course of dihydrofolate reductase-mediated gene amplification and their stability in the absence of selective pressure. Biotechnol Bioeng. 1999;64:741–9.PubMedCrossRefGoogle Scholar
  42. 42.
    Gallegos JE, Rose AB. The enduring mystery of intron-mediated enhancement. Plant Sci. 2015;237:8–15.PubMedCrossRefGoogle Scholar
  43. 43.
    Chappell SA, Edelman GM, Mauro VP. A 9-nt segment of a cellular mRNA can function as an internal ribosome entry site (IRES) and when present in linked multiple copies greatly enhances IRES activity. Proc Natl Acad Sci USA. 2000;97:1536–41.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Chappell SA, Edelman GM, Mauro VP. Ribosomal tethering and clustering as mechanisms for translation initiation. Proc Natl Acad Sci USA. 2006;103(48):18077–82.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Matoulkova E, Michalova E, Vojtesek B, Hrstka R. The role of the 3′ untranslated region in post-transcriptional regulation of protein expression in mammalian cells. RNA Biol. 2012;9(5):563–76.PubMedCrossRefGoogle Scholar
  46. 46.
    Gouse BM, Boehme AK, Monlezun DJ, Siegler JE, George AJ, Brag K, et al. New thrombotic events in ischemic stroke patients with elevated factor VIII. Thrombosis. 2014;2014:302861.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Kumar SR. Industrial production of clotting factors: challenges of expression, and choice of host cells. Biotechnol J. 2015;10(7):995–1004.PubMedCrossRefGoogle Scholar
  48. 48.
    Williams JA. Improving DNA vaccine performance through vector design. Curr Gene Ther. 2014;14(3):170–89.PubMedCrossRefGoogle Scholar
  49. 49.
    Gustafsson C, Minshull J, Govindarajan S, Ness J, Villalobos A, Welch M. Engineering genes for predictable protein expression. Protein Expr Purif. 2012;83(1):37–46.PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Van Der Kelen K, Beyaert R, Inze D, De Veylder L. Translational control of eukaryotic gene expression. Crit Rev Biochem Mol Biol. 2009;44(4):143–68.CrossRefGoogle Scholar
  51. 51.
    Ling C, Ermolenko DN. Structural insights into ribosome translocation. Wiley Interdiscip Rev RNA. 2016;7(5):620–36.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Welch M, Villalobos A, Gustafsson C, Minshull J. You’re one in a googol: optimizing genes for protein expression. J R Soc Interface. 2009;6(6 Suppl 4):S467–76.PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Itakura K, Hirose T, Crea R, Riggs AD, Heyneker HL, Bolivar F, et al. Expression in Escherichia coli of a chemically synthesized gene for the hormone somatostatin. Science. 1977;198(4321):1056–63.PubMedCrossRefGoogle Scholar
  54. 54.
    Athey J, Alexaki A, Osipova E, Rostovtsev A, Santana-Quintero LV, Katneni U, et al. A new and updated resource for codon usage tables. BMC Bioinform. 2017;18(1):391.CrossRefGoogle Scholar
  55. 55.
    Supek F. The code of silence: widespread associations between synonymous codon biases and gene function. J Mol Evol. 2016;82(1):65–73.PubMedCrossRefGoogle Scholar
  56. 56.
    Gardin J, Yeasmin R, Yurovsky A, Cai Y, Skiena S, Futcher B. Measurement of average decoding rates of the 61 sense codons in vivo. eLife. 2014;3.
  57. 57.
    Dana A, Tuller T. The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res. 2014;42(14):9171–81.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Dana A, Tuller T. Mean of the typical decoding rates: a new translation efficiency index based on the analysis of ribosome profiling data. G3. 2014;5(1):73–80.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, et al. Codon usage influences the local rate of translation elongation to regulate co-translational protein folding. Mol Cell. 2015;59(5):744–54.PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Paulet D, David A, Rivals E. Ribo-seq enlightens codon usage bias. DNA Res Int J Rapid Publ Rep Genes Genom. 2017;24(3):303–10.Google Scholar
  61. 61.
    Pouyet F, Mouchiroud D, Duret L, Semon M. Recombination, meiotic expression and human codon usage. eLife. 2017;6.
  62. 62.
    Dittmar KA, Goodenbour JM, Pan T. Tissue-specific differences in human transfer RNA expression. PLoS Genet. 2006;2(12):e221.PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Schmitt BM, Rudolph KL, Karagianni P, Fonseca NA, White RJ, Talianidis I, et al. High-resolution mapping of transcriptional dynamics across tissue development reveals a stable mRNA-tRNA interface. Genome Res. 2014;24(11):1797–807.PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Kirchner S, Cai Z, Rauscher R, Kastelic N, Anding M, Czech A, et al. Alteration of protein function by a silent polymorphism linked to tRNA abundance. PLoS Biol. 2017;15(5):e2000779.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Mauro VP, Chappell SA. A critical analysis of codon optimization in human therapeutics. Trends Mol Med. 2014;20(11):604–13.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Richardson SM, Wheelan SJ, Yarrington RM, Boeke JD. GeneDesign: rapid, automated design of multikilobase synthetic genes. Genome Res. 2006;16(4):550–6.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Villalobos A, Ness JE, Gustafsson C, Minshull J, Govindarajan S. Gene designer: a synthetic biology tool for constructing artificial DNA segments. BMC Bioinf. 2006;7:285.CrossRefGoogle Scholar
  68. 68.
    Angov E, Hillier CJ, Kincaid RL, Lyon JA. Heterologous protein expression is enhanced by harmonizing the codon usage frequencies of the target gene with those of the expression host. PLoS One. 2008;3(5):e2189.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Wang E, Wang J, Chen C, Xiao Y. Computational evidence that fast translation speed can increase the probability of cotranslational protein folding. Sci Rep. 2015;21(5):15316.CrossRefGoogle Scholar
  70. 70.
    Bali V, Bebok Z. Decoding mechanisms by which silent codon changes influence protein biogenesis and function. Int J Biochem Cell Biol. 2015;64:58–74.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Diederichs S, Bartsch L, Berkmann JC, Frose K, Heitmann J, Hoppe C, et al. The dark matter of the cancer genome: aberrations in regulatory elements, untranslated regions, splice sites, non-coding RNA and synonymous mutations. EMBO Mol Med. 2016;8(5):442–57.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Hanson G, Coller J. Codon optimality, bias and usage in translation and mRNA decay. Nat Rev Mol Cell Biol. 2018;19(1):20–30.PubMedCrossRefGoogle Scholar
  73. 73.
    Rudolph KL, Schmitt BM, Villar D, White RJ, Marioni JC, Kutter C, et al. Codon-driven translational efficiency is stable across diverse mammalian cell states. PLoS Genet. 2016;12(5):e1006024.PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Gingold H, Tehler D, Christoffersen NR, Nielsen MM, Asmar F, Kooistra SM, et al. A dual program for translation regulation in cellular proliferation and differentiation. Cell. 2014;158(6):1281–92.PubMedCrossRefGoogle Scholar
  75. 75.
    Ingolia NT, Lareau LF, Weissman JS. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell. 2011;147(4):789–802.PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Park JH, Kwon M, Yamaguchi Y, Firestein BL, Park JY, Yun J, et al. Preferential use of minor codons in the translation initiation region of human genes. Hum Genet. 2017;136(1):67–74.PubMedCrossRefGoogle Scholar
  77. 77.
    Stadler M, Fire A. Wobble base-pairing slows in vivo translation elongation in metazoans. RNA. 2011;17(12):2063–73.PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Wang H, McManus J, Kingsford C. Accurate recovery of ribosome positions reveals slow translation of wobble-pairing codons in yeast. J Comput Biol. 2017;24(6):486–500.PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Gamble CE, Brule CE, Dean KM, Fields S, Grayhack EJ. Adjacent codons act in concert to modulate translation efficiency in yeast. Cell. 2016;166(3):679–90.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Harigaya Y, Parker R. The link between adjacent codon pairs and mRNA stability. BMC Genom. 2017;18(1):364.CrossRefGoogle Scholar
  81. 81.
    McCarthy C, Carrea A, Diambra L. Bicodon bias can determine the role of synonymous SNPs in human diseases. BMC Genom. 2017;18(1):227.CrossRefGoogle Scholar
  82. 82.
    Lorenz FK, Wilde S, Voigt K, Kieback E, Mosetter B, Schendel DJ, et al. Codon optimization of the human papillomavirus E7 oncogene induces a CD8 + T cell response to a cryptic epitope not harbored by wild-type E7. PLoS One. 2015;10(3):e0121633.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Saikia M, Wang X, Mao Y, Wan J, Pan T, Qian SB. Codon optimality controls differential mRNA translation during amino acid starvation. RNA. 2016;22(11):1719–27.PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Gotea V, Gartner JJ, Qutob N, Elnitski L, Samuels Y. The functional relevance of somatic synonymous mutations in melanoma and other cancers. Pigm Cell Melanoma Res. 2015;28(6):673–84.CrossRefGoogle Scholar
  85. 85.
    Hunt RC, Simhadri VL, Iandoli M, Sauna ZE, Kimchi-Sarfaty C. Exposing synonymous mutations. Trends Genet. 2014;30(7):308–21.PubMedCrossRefGoogle Scholar
  86. 86.
    Firth AE. Mapping overlapping functional elements embedded within the protein-coding regions of RNA viruses. Nucleic Acids Res. 2014;42(20):12425–39.PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Fahraeus R, Marin M, Olivares-Illana V. Whisper mutations: cryptic messages within the genetic code. Oncogene. 2016;35(29):3753–9.PubMedCrossRefGoogle Scholar
  88. 88.
    Cheong DE, Ko KC, Han Y, Jeon HG, Sung BH, Kim GJ, et al. Enhancing functional expression of heterologous proteins through random substitution of genetic codes in the 5′ coding region. Biotechnol Bioeng. 2015;112(4):822–6.PubMedCrossRefGoogle Scholar
  89. 89.
    Martinez MA, Jordan-Paiz A, Franco S, Nevot M. Synonymous virus genome recoding as a tool to impact viral fitness. Trends Microbiol. 2016;24(2):134–47.PubMedCrossRefGoogle Scholar
  90. 90.
    de Fabritus L, Nougairede A, Aubry F, Gould EA, de Lamballerie X. Attenuation of tick-borne encephalitis virus using large-scale random codon re-encoding. PLoS Pathog. 2015;11(3):e1004738.PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Wang B, Yang C, Tekes G, Mueller S, Paul A, Whelan SP, et al. Recoding of the vesicular stomatitis virus L gene by computer-aided design provides a live, attenuated vaccine candidate. MBio. 2015;6(2):1–10.CrossRefGoogle Scholar
  92. 92.
    Magistrelli G, Poitevin Y, Schlosser F, Pontini G, Malinge P, Josserand S, et al. Optimizing assembly and production of native bispecific antibodies by codon de-optimization. mAbs. 2017;9(2):231–9.PubMedCrossRefGoogle Scholar
  93. 93.
    Perez-De-Lis M, Retamozo S, Flores-Chavez A, Kostov B, Perez-Alvarez R, Brito-Zeron P, et al. Autoimmune diseases induced by biological agents. A review of 12,731 cases (BIOGEAS Registry). Expert Opin Drug Saf. 2017;16(11):1255–71.PubMedCrossRefGoogle Scholar
  94. 94.
    Strand V, Balsa A, Al-Saleh J, Barile-Fabris L, Horiuchi T, Takeuchi T, et al. Immunogenicity of biologics in chronic inflammatory diseases: a systematic review. BioDrugs. 2017;31(4):299–316.PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Piga M, Chessa E, Ibba V, Mura V, Floris A, Cauli A, et al. Biologics-induced autoimmune renal disorders in chronic inflammatory rheumatic diseases: systematic literature review and analysis of a monocentric cohort. Autoimmun Rev. 2014;13(8):873–9.PubMedCrossRefGoogle Scholar
  96. 96.
    Zucchelli E, Pema M, Stornaiuolo A, Piovan C, Scavullo C, Giuliani E, et al. Codon optimization leads to functional impairment of RD114-TR envelope glycoprotein. Mol Ther Methods Clin Dev. 2017;17(4):102–14.CrossRefGoogle Scholar
  97. 97.
    Casadevall N, Nataf J, Viron B, Kolta A, Kiladjian JJ, Martin-Dupont P, et al. Pure red-cell aplasia and antierythropoietin antibodies in patients treated with recombinant erythropoietin. N Engl J Med. 2002;346(7):469–75.PubMedCrossRefGoogle Scholar
  98. 98.
    Cournoyer D, Toffelmire EB, Wells GA, Barber DL, Barrett BJ, Delage R, et al. Anti-erythropoietin antibody-mediated pure red cell aplasia after treatment with recombinant erythropoietin products: recommendations for minimization of risk. J Am Soc Nephrol. 2004;15(10):2728–34.PubMedCrossRefGoogle Scholar
  99. 99.
    Katsnelson A. Breaking the silence. Nat Med. 2011;17(12):1536–8.PubMedCrossRefGoogle Scholar
  100. 100.
    Derdeyn CA, Moore PL, Morris L. Development of broadly neutralizing antibodies from autologous neutralizing antibody responses in HIV infection. Curr Opin HIV AIDS. 2014;9(3):210–6.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    McCoy LE, Burton DR. Identification and specificity of broadly neutralizing antibodies against HIV. Immunol Rev. 2017;275(1):11–20.PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Kimchi-Sarfaty C, Schiller T, Hamasaki-Katagiri N, Khan MA, Yanover C, Sauna ZE. Building better drugs: developing and regulating engineered therapeutic proteins. Trends Pharmacol Sci. 2013;34(10):534–48.PubMedCrossRefGoogle Scholar
  103. 103.
    Chen S, Li K, Cao W, Wang J, Zhao T, Huan Q, et al. Codon-resolution analysis reveals a direct and context-dependent impact of individual synonymous mutations on mRNA level. Mol Biol Evol. 2017;34(11):2944–58.PubMedCrossRefGoogle Scholar
  104. 104.
    Zhou Z, Dang Y, Zhou M, Li L, Yu CH, Fu J, et al. Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc Natl Acad Sci USA. 2016;113(41):E6117–25.PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Newman ZR, Young JM, Ingolia NT, Barton GM. Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9. Proc Natl Acad Sci USA. 2016;113(10):E1362–71.PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Gustafsson C, Vallverdu J. The best model of a cat is several cats. Trends Biotechnol. 2016;34(3):207–13.PubMedCrossRefGoogle Scholar
  107. 107.
    Kaur P, Kiselar J, Yang S, Chance MR. Quantitative protein topography analysis and high-resolution structure prediction using hydroxyl radical labeling and tandem-ion mass spectrometry (MS). Mol Cell Proteomics. 2015;14(4):1159–68.PubMedPubMedCentralCrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Scripps Research InstituteLa JollaUSA

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