Advances in Predicting and Manipulating the Immunogenicity of Biotherapeutics and Vaccines


Therapeutic proteins are vital to the future of human health provision and the survival and profitability of the global pharmaceutical industry. Returns from protein therapeutics are experiencing unprecedented growth: both their number and their economic dividend have increased by an order of magnitude in the last 10 years. The potential immunogenicity of protein therapeutics raises many clinical and safety concerns. Many poorly understood factors relating to both product and host affect immune responses. Available laboratory measurement of immunogenicity is of little utility for predicting the clinical properties of biotherapeutics. Coupled with assay variability and standardization issues, this precludes adequate prediction of the biological or clinical responses of therapeutic proteins, arguing for the utilization of informatic strategies in the analysis and prediction of protein immunogenicity. Currently, many unresolved issues must be addressed and thus circumvented before effective prediction can become routine.

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  1. 1.

    Reichert JM. Trends in US approvals: new biopharmaceuticals and vaccines. Trends Biotechnol 2006; 24: 293–8

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    Reichert JM, Wenger JB. Development trends for new cancer therapeutics and vaccines. Drug Discov Today 2008; 13: 30–7

    PubMed  Article  CAS  Google Scholar 

  3. 3.

    Roger SD, Goldsmith D. Biosimilars: it's not as simple as cost alone. J Clin Pharm Ther 2008; 33: 459–64

    PubMed  Article  CAS  Google Scholar 

  4. 4.

    Swanson SJ. Immunogenicity issues in drug development. J Immunotoxicol 2006; 3: 165–72

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    Wadhwa M, Thorpe R. Strategies and assays for the assessment of unwanted immunogenicity. J Immunotoxicol 2006; 3: 115–21

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Flower DR. Bioinformatics for vaccinology. Chichester: Wiley, 2008

    Google Scholar 

  7. 7.

    Bayry J, Tchilian EZ, Davies MN, et al. In silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity in vaccination. Proc Natl Acad Sci U S A 2008; 105: 10221–6

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Bayry J, Flower DR, Tough DF, et al. From ‘perfect mix’ to ‘potion magique': regulatory T cells and anti-inflammatory cytokines as adjuvant targets. Nat Rev Microbiol 2008; 6: C1

    PubMed  Article  Google Scholar 

  9. 9.

    Doytchinova IA, Flower DR. Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine 2007; 25: 856–66

    PubMed  Article  CAS  Google Scholar 

  10. 10.

    Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007; 8: 4

    PubMed  Article  Google Scholar 

  11. 11.

    Flower DR. Towards in silico prediction of immunogenic epitopes. Trends Immunol 2003; 24: 667–74

    PubMed  Article  CAS  Google Scholar 

  12. 12.

    Vivona S, Gardy JL, Ramachandran S, et al. Computer-aided biotechnology: from immuno-informatics to reverse vaccinology. Trends Biotechnol 2008; 26: 190–200

    PubMed  Article  CAS  Google Scholar 

  13. 13.

    Evans MC. Recent advances in immunoinformatics: application of in silico tools to drug development. Curr Opin Drug Discov Devel 2008; 11: 233–41

    PubMed  CAS  Google Scholar 

  14. 14.

    Davies MN, Flower DR. Harnessing bioinformatics to discover new vaccines. Drug Disc Today 2007; 12: 389–95

    Article  CAS  Google Scholar 

  15. 15.

    Deluca DS, Blasczyk R. The immunoinformatics of cancer immunotherapy. Tissue Antigens 2007; 70: 265–71

    PubMed  Article  CAS  Google Scholar 

  16. 16.

    Guan P, Davies MN, Blythe MJ, et al. Using data mining and databases in vaccinology. Expert Opin Drug Discov 2007; 2: 19–35

    Article  Google Scholar 

  17. 17.

    Korber B, LaBute M, Yusim K. Immunoinformatics comes of age. PLoS Comput Biol 2006; 2: e71

    PubMed  Article  Google Scholar 

  18. 18.

    Peters B, Bui HH, Frankild S, et al. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol 2006; 2: e65

    PubMed  Article  Google Scholar 

  19. 19.

    Deavin AJ, Auton TR, Greaney PJ. Statistical comparison of established T-cell epitope predictors against a large database of human and murine antigens. Mol Immunol 1996; 33: 145–55

    PubMed  Article  CAS  Google Scholar 

  20. 20.

    El-Manzalawy Y, Dobbs D, Honavar V. On evaluating MHC-II binding peptide prediction methods. PLoS ONE 2008; 3: e3268

    PubMed  Article  Google Scholar 

  21. 21.

    Lin HH, Zhang GL, Tongchusak S, et al. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 2008; 9 Suppl. 12: S22

    Article  Google Scholar 

  22. 22.

    Gowthaman U, Agrewala JN. In silico tools for predicting peptides binding to HLA-class II molecules: more confusion than conclusion. J Proteome Res 2008; 7: 154–63

    PubMed  Article  CAS  Google Scholar 

  23. 23.

    Knapp B, Omasits U, Frantal S, et al. A critical cross-validation of high throughput structural binding prediction methods for pMHC. J Comput Aided Mol Des 2009 May; 23 5: 301–7

    Article  Google Scholar 

  24. 24.

    Ponomarenko JV, Bourne PE. Antibody-protein interactions: benchmark datasets and prediction tools evaluation. BMC Struct Biol 2007; 7: 64

    PubMed  Article  Google Scholar 

  25. 25.

    Blythe MJ, Flower DR. Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Sci 2005; 14: 246–8

    PubMed  Article  CAS  Google Scholar 

  26. 26.

    Zhang H, Lundegaard C, Nielsen M. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics 2009; 25: 83–9

    PubMed  Article  Google Scholar 

  27. 27.

    Nielsen M, Lundegaard C, Blicher T, et al. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput Biol 2008; 4: e1000107

    PubMed  Article  Google Scholar 

  28. 28.

    Doytchinova IA, Flower DR. In silico identification of supertypes for class II MHCs. J Immunol 2005; 174: 7085–95

    PubMed  CAS  Google Scholar 

  29. 29.

    Doytchinova IA, Guan P, Flower DR. Identifiying human MHC supertypes using bioinformatic methods. J Immunol 2004; 172: 4314–23

    PubMed  CAS  Google Scholar 

  30. 30.

    Boulianne GL, Hozumi N, Shulman MJ. Production of functional chimaeric mouse/human antibody. Nature 1984; 312: 643–6

    PubMed  Article  CAS  Google Scholar 

  31. 31.

    Morrison SL, Johnson MJ, Herzenberg LA, et al. Chimeric human antibody molecules: mouse antigen-binding domains with human constant region domains. Proc Natl Acad Sci USA 1984; 81: 6851–5

    PubMed  Article  CAS  Google Scholar 

  32. 32.

    Jones PT, Dear PH, Foote J, et al. Replacing the complementaritydetermining regions in a human antibody with those from a mouse. Nature 1986; 321: 522–5

    PubMed  Article  CAS  Google Scholar 

  33. 33.

    Anderson PJ. Tumor necrosis factor inhibitors: clinical implications of their different immunogenicity profiles. Semin Arthritis Rheum 2005; 34: 19–22

    PubMed  Article  CAS  Google Scholar 

  34. 34.

    De Groot AS, Knopp PM, Martin W. De-immunization of therapeutic proteins by T-cell epitope modification. Dev Biol (Basel) 2005; 122: 171–94

    Google Scholar 

  35. 35.

    De Groot AS, Moise L. Prediction of immunogenicity for therapeutic proteins: state of the art. Curr Opin Drug Discov Devel 2007; 10: 332–40

    Google Scholar 

  36. 36.

    De Groot AS, Scott DW. Immunogenicity of protein therapeutics. Trends Immunol 2007; 28: 482–90

    Article  Google Scholar 

  37. 37.

    De Groot AS, McMurry J, Moise L. Prediction of immunogenicity: in silico paradigms, ex vivo and in vivo correlates. Curr Opin Pharmacol 2008; 8: 620–6

    Article  Google Scholar 

  38. 38.

    Liang M, Klakamp SL, Funelas C, et al. Detection of high-and low-affinity antibodies against a human monoclonal antibody using various technology platforms. Assay Drug Dev Technol 2007; 5: 655–62

    PubMed  Article  CAS  Google Scholar 

  39. 39.

    Shankar G, Devanarayan V, Amaravadi L, et al. Recommendations for the validation of immunoassays used for detection of host antibodies against biotechnology products. J Pharm Biomed Anal 2008; 48: 1267–81

    PubMed  Article  CAS  Google Scholar 

  40. 40.

    Bugelski PJ, Treacy G. Predictive power of preclinical studies of recombinant therapeutic proteins in animals for the immunogenicity humans. Curr Opin Mol Ther 2004; 6: 10–6

    PubMed  CAS  Google Scholar 

  41. 41.

    Atassi MZ, Dolimbek BZ. Mapping of the antibody-binding regions on the H Ndomain (residues 449-859) of botulinum neurotoxin A with antitoxin antibodies from four host species: full profile of the continuous antigenic regions of the H-chain of botulinum neurotoxin A. Protein J 2004; 23: 39–52

    PubMed  Article  CAS  Google Scholar 

  42. 42.

    Dolimbek BZ, Aoki KR, Steward LE, et al. Mapping of the regions on the heavy chain of botulinum neurotoxin A (BoNT/A) recognized by antibodies of cervical dystonia patients with immunoresistance to BoNT/A. Mol Immunol 2007; 44: 1029–41

    PubMed  Article  CAS  Google Scholar 

  43. 43.

    Mueller CM, Minnerath JM, Jemmerson R. B lymphocyte recognition of the self antigen mouse cytochrome C in different mouse strains: targeting of the same dominant epitope by naturally-occurring cells expressing distinct VH genes. Mol Immunol 1997; 34: 843–53

    PubMed  Article  CAS  Google Scholar 

  44. 44.

    Jin L, Fendly BM, Wells JA. High resolution functional analysis of antibodyantigen interactions. J Mol Biol 1992; 226: 851–65

    PubMed  Article  CAS  Google Scholar 

  45. 45.

    Mitchell AJ, Edwards MR, Collins AM. Valency or wahlency: is the epitope diversity of the B-cell response regulated or chemically determined? Immunol Cell Biol 2001; 79: 507–11

    PubMed  Article  CAS  Google Scholar 

  46. 46.

    Onda M, Nagata S, FitzGerald DJ, et al. Characterization of the B cell epitopes associated with a truncated form of Pseudomonas exotoxin (PE38) used to make immunotoxins for the treatment of cancer patients. J Immunol 2006; 177: 8822–34

    PubMed  CAS  Google Scholar 

  47. 47.

    Onda M, Beers R, Xiang L, et al. An immunotoxin with greatly reduced immunogenicity by identification and removal of B cell epitopes. Proc Natl Acad Sci USA 2008; 105: 11311–6

    PubMed  Article  CAS  Google Scholar 

  48. 48.

    Laroche Y, Heymans S, Capaert S, et al. Recombinant staphylokinase variants with reduced antigenicity due to elimination of B-lymphocyte epitopes. Blood 2000; 96: 1425–32

    PubMed  CAS  Google Scholar 

  49. 49.

    Mayer A, Sharma SK, Tolner B, et al. Modifying an immunogenic epitope on a therapeutic protein: a step towards an improved system for antibodydirected enzyme prodrug therapy (ADEPT). Br J Cancer 2004; 90: 2402–10

    PubMed  CAS  Google Scholar 

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DRF received salary support from a Senior Jenner Fellowship and the Wellcome Trust Grant WT079287MA; he is a Jenner Institute Investigator. No sources of funding were used to assist in the preparation of this review. The author has no conflicts of interest that are directly relevant to the content of this review.

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Correspondence to Dr Darren R. Flowerc.

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Flowerc, D.R. Advances in Predicting and Manipulating the Immunogenicity of Biotherapeutics and Vaccines. BioDrugs 23, 231–240 (2009).

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