Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Integrating cell-level kinetic modeling into the design of engineered protein therapeutics

Nature Biotechnology volume 23, pages 191–194 (2005)Cite this article

Abstract

Functional genomics and proteomics are identifying many potential drug targets for novel therapeutic proteins, and both rational and combinatorial protein engineering methods are available for creating drug candidates. A central challenge is the definition of the most appropriate design criteria, which will benefit critically from computational kinetic models that incorporate integration from the molecular level to the whole systems level. Interpretation of these processes will require mathematical models that are refined in combination with relevant data derived from quantitative assays, to correctly set biophysical objectives for protein design.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Generalized schematic of binding and trafficking kinetic processes significant in the cellular pharmacodynamics and pharmacokinetics of protein drugs.

Bob Crimi

Similar content being viewed by others

References

  1. Horrobin, D.F. Realism in drug discovery-could Cassandra be right? Nat. Biotechnol. 19, 1099–1100 (2001).

    Article  CAS  Google Scholar 

  2. Rees, J. Complex disease and the new clinical sciences. Science 296, 698–700 (2002).

    Article  CAS  Google Scholar 

  3. Butte, A. The use and analysis of microarray data. Nat. Rev. Drug Discov. 1, 951–960 (2002).

    Article  CAS  Google Scholar 

  4. Wiley, H.S., Shvartsman, S.Y. & Lauffenburger, D.A. Computational modeling of the EGF-receptor system: a paradigm for systems biology. Trends Cell Biol. 13, 43–50 (2003).

    Article  CAS  Google Scholar 

  5. Mendelsohn, J. & Baselga, J. Status of epidermal growth factor receptor antagonists in the biology and treatment of cancer. J. Clin. Oncol. 21, 2787–2799 (2003).

    Article  CAS  Google Scholar 

  6. Lauffenburger, D.A. & Linderman, J.J. Receptors: Models for Binding, Trafficking, and Signaling. (Oxford University Press, Oxford, UK 1993).

    Google Scholar 

  7. Reddy, C.C., Niyogi, S.K., Wells, A., Wiley, H.S. & Lauffenburger, D.A. Engineering epidermal growth factor for enhanced mitogenic potency. Nat. Biotechnol. 14, 1696–1699 (1996).

    Article  CAS  Google Scholar 

  8. Sarkar, C.A. et al. Rational cytokine design for increased lifetime and enhanced potency using pH-activated histidine switching. Nat. Biotechnol. 20, 908–913 (2002).

    Article  CAS  Google Scholar 

  9. Stroud, R.M. & Wells, J.A. Mechanistic diversity of cytokine receptor signaling across cell membranes. Sci. STKE 231, RE7 (2004).

    Google Scholar 

  10. Brekke, O.H. & Sandlie, I. Therapeutic antibodies for human diseases at the dawn of the twenty-first century. Nat. Rev. Drug Discov. 2, 52–62 (2003).

    Article  CAS  Google Scholar 

  11. Goffe, B. & Cather, J.C. Etanercept: an overview. J. Am. Acad. Dermatol. 49, S105–S111 (2003).

    Article  Google Scholar 

  12. Holash, J. et al. VEGF-Trap: a VEGF blocker with potent antitumor effects. Proc. Natl. Acad. Sci. USA 99, 11393–11398 (2002).

    Article  CAS  Google Scholar 

  13. Pearce, K.H. Jr., Cunningham, B.C., Fuh, G., Teeri, T. & Wells, J.A. Growth hormone binding affinity for its receptor surpasses the requirements for cellular activity. Biochemistry 38, 81–89 (1999).

    Article  CAS  Google Scholar 

  14. Haugh, J.M. Mathematical model of human growth hormone (hGH)-stimulated cell proliferation explains the efficacy of hgh variants as receptor agonists or antagonists. Biotechnol. Prog. 20, 1337–1344 (2004).

    Article  CAS  Google Scholar 

  15. Foote, J. & Eisen, H.N. Kinetic and affinity limits on antibodies produced during immune responses. Proc. Natl. Acad. Sci. USA 92, 1254–1256 (1995).

    Article  CAS  Google Scholar 

  16. Sarkar, C.A. & Lauffenburger, D.A. Cell-level pharmacokinetic model of granulocyte colony-stimulating factor: implications for ligand lifetime and potency in vivo. Mol. Pharmacol. 63, 147–158 (2003).

    Article  CAS  Google Scholar 

  17. Lauffenburger, D.A., Fallon, E.M. & Haugh, J.M. Scratching the (cell) surface: cytokine engineering for improved ligand/receptor trafficking dynamics. Chem. Biol. 5, R257–R263 (1998).

    Article  CAS  Google Scholar 

  18. Fallon, E.M. & Lauffenburger, D.A. Computational model for effects of ligand/receptor binding properties on interleukin-2 trafficking dynamics and T cell proliferation response. Biotechnol. Prog. 16, 905–916 (2000).

    Article  CAS  Google Scholar 

  19. Smith, K.A. Cell growth signal transduction is quantal. Ann. NY Acad. Sci. 766, 263–271 (1995).

    Article  CAS  Google Scholar 

  20. Li, Q.J. et al. CD4 enhances T-cell sensitivity to antigen by coordinating Lck accumulation at the immunological synapse. Nat. Immunol. 5, 791–799 (2004).

    Article  CAS  Google Scholar 

  21. Goldbeter, A. & Koshland, D.E. Jr. An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl. Acad. Sci. USA 78, 6840–6844 (1981).

    Article  CAS  Google Scholar 

  22. Tyson, J.J., Chen, K.C. & Novak, B. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15, 221–231 (2003).

    Article  CAS  Google Scholar 

  23. Schoeberl, B., Eichler-Jonsson, C., Gilles, E.D. & Muller, G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat. Biotechnol. 20, 370–375 (2002).

    Article  Google Scholar 

  24. Gardner, T.S., Cantor, C.R. & Collins, J.J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    Article  CAS  Google Scholar 

  25. Angeli, D., Ferrell, J.E. Jr. & Sontag, E.D. Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc. Natl. Acad. Sci. USA 101, 1822–1827 (2004).

    Article  CAS  Google Scholar 

  26. Osbourn, J., Jermutus, L. & Duncan, A. Current methods for the generation of human antibodies for the treatment of autoimmune diseases. Drug Discov. Today 8, 845–851 (2003).

    Article  CAS  Google Scholar 

  27. Sanna, P.P., Ramiro-Ibanez, F. & De Logu, A. Synergistic interactions of antibodies in rate of virus neutralization. Virology 270, 386–396 (2000).

    Article  CAS  Google Scholar 

  28. Vasserot, A.P. et al. Optimization of protein therapeutics by directed evolution. Drug Discov. Today 8, 118–126 (2003).

    Article  CAS  Google Scholar 

  29. Forsten, K.E. & Lauffenburger, D.A. Interrupting autocrine ligand-receptor binding: comparison between receptor blockers and ligand decoys. Biophys. J. 63, 857–861 (1992).

    Article  CAS  Google Scholar 

  30. Jain, R.K. Transport of molecules, particles, and cells in solid tumors. Annu. Rev. Biomed. Eng. 1, 241–263 (1999).

    Article  CAS  Google Scholar 

  31. Flynn, A.A. et al. A model-based approach for the optimization of radioimmunotherapy through antibody design and radionuclide selection. Cancer 94, 1249–1257 (2002).

    Article  CAS  Google Scholar 

  32. Adams, G.P. et al. High affinity restricts the localization and tumor penetration of single-chain fv antibody molecules. Cancer Res. 61, 4750–4755 (2001).

    CAS  PubMed  Google Scholar 

  33. Graff, C.P. & Wittrup, K.D. Theoretical analysis of antibody targeting of tumor spheroids: importance of dosage for penetration, and affinity for retention. Cancer Res. 63, 1288–1296 (2003).

    CAS  PubMed  Google Scholar 

  34. Fujimori, K., Covell, D.G., Fletcher, J.E. & Weinstein, J.N. A modeling analysis of monoclonal antibody percolation through tumors: a binding-site barrier. J. Nucl. Med. 31, 1191–1198 (1990).

    CAS  Google Scholar 

  35. Egrie, J.C., Dwyer, E., Browne, J.K., Hitz, A. & Lykos, M.A. Darbepoetin alfa has a longer circulating half-life and greater in vivo potency than recombinant human erythropoietin. Exp. Hematol. 31, 290–299 (2003).

    Article  CAS  Google Scholar 

  36. Chang, T.W. The pharmacological basis of anti-IgE therapy. Nat. Biotechnol. 18, 157–162 (2000).

    Article  CAS  Google Scholar 

  37. Lu, Z.Y. et al. High amounts of circulating interleukin (IL)-6 in the form of monomeric immune complexes during anti-IL-6 therapy. Towards a new methodology for measuring overall cytokine production in human in vivo. Eur. J. Immunol. 22, 2819–2824 (1992).

    Article  CAS  Google Scholar 

  38. Montero-Julian, F.A., Klein, B., Gautherot, E. & Brailly, H. Pharmacokinetic study of anti-interleukin-6 (IL-6) therapy with monoclonal antibodies: enhancement of IL-6 clearance by cocktails of anti-IL-6 antibodies. Blood 85, 917–924 (1995).

    CAS  PubMed  Google Scholar 

  39. Harris, J.M. & Chess, R.B. Effect of pegylation on pharmaceuticals. Nat. Rev. Drug Discov. 2, 214–221 (2003).

    Article  CAS  Google Scholar 

  40. Ashkenazi, A. & Chamow, S.M. Immunoadhesins as research tools and therapeutic agents. Curr. Opin. Immunol. 9, 195–200 (1997).

    Article  CAS  Google Scholar 

  41. Dennis, M.S. et al. Albumin binding as a general strategy for improving the pharmacokinetics of proteins. J. Biol. Chem. 277, 35035–35043 (2002).

    Article  CAS  Google Scholar 

  42. Rao, B.M., Driver, I., Lauffenburger, D.A. & Wittrup, K.D. IL-2 variants engineered for increased IL-2R affinity exhibit increased potency arising from a cell surface ligand reservoir effect. Mol. Pharmacol. 66, 864–869 (2004).

    CAS  PubMed  Google Scholar 

  43. Dubois, S., Mariner, J., Waldmann, T.A. & Tagaya, Y. IL-15R alpha recycles and presents IL-15 In trans to neighboring cells. Immunity 17, 537–547 (2002).

    Article  CAS  Google Scholar 

  44. Giovannoni, G. Strategies to treat and prevent the development of neutralizing anti-interferon-beta antibodies. Neurology 61, S13–S17 (2003).

    Article  CAS  Google Scholar 

  45. Rosenberg, A.S. Immunogenicity of biological therapeutics: a hierarchy of concerns. Dev. Biol. (Basel) 112, 15–21 (2003).

    CAS  Google Scholar 

  46. Schellekens, H. Bioequivalence and the immunogenicity of biopharmaceuticals. Nat. Rev. Drug Discov. 1, 457–462 (2002).

    Article  CAS  Google Scholar 

  47. Patten, P.A. & Schellekens, H. The immunogenicity of biopharmaceuticals. Lessons learned and consequences for protein drug development. Dev. Biol. (Basel) 112, 81–97 (2003).

    CAS  Google Scholar 

  48. Bander, N.H. et al. Targeted systemic therapy of prostate cancer with a monoclonal antibody to prostate-specific membrane antigen. Semin. Oncol. 30, 667–676 (2003).

    Article  CAS  Google Scholar 

  49. Wittrup, K.D. Protein engineering by cell-surface display. Curr. Opin. Biotechnol. 12, 395–399 (2001).

    Article  CAS  Google Scholar 

  50. Marshall, S.A., Lazar, G.A., Chirino, A.J. & Desjarlais, J.R. Rational design and engineering of therapeutic proteins. Drug Discov. Today 8, 212–221 (2003).

    Article  CAS  Google Scholar 

  51. Lazar, G.A., Marshall, S.A., Plecs, J.J., Mayo, S.L. & Desjarlais, J.R. Designing proteins for therapeutic applications. Curr. Opin. Struct. Biol. 13, 513–518 (2003).

    Article  CAS  Google Scholar 

  52. Chakraborty, A.K., Dustin, M.L. & Shaw, A.S. In silico models for cellular and molecular immunology: successes, promises and challenges. Nat. Immunol. 4, 933–936 (2003).

    Article  CAS  Google Scholar 

  53. Perelson, A.S. Modelling viral and immune system dynamics. Nat. Rev. Immunol. 2, 28–36 (2002).

    Article  CAS  Google Scholar 

  54. Bialek, W. & Botstein, D. Introductory science and mathematics education for 21st-Century biologists. Science 303, 788–790 (2004).

    Article  CAS  Google Scholar 

  55. Griffith, L.G. & Grodzinsky, A.J. Advances in biomedical engineering. J. Am. Med. Assoc. 285, 556–561 (2001).

    Article  CAS  Google Scholar 

  56. Grossmann, M., Leitolf, H., Weintraub, B.D. & Szkudlinski, M.W. A rational design strategy for protein hormone superagonists. Nat. Biotechnol. 16, 871–875 (1998).

    Article  CAS  Google Scholar 

  57. Kortemme, T. & Baker, D. A simple physical model for binding energy hot spots in protein-protein complexes. Proc. Natl. Acad. Sci. USA 99, 14116–14121 (2002).

    Article  CAS  Google Scholar 

  58. Steed, P.M. et al. Inactivation of TNF signaling by rationally designed dominant-negative TNF variants. Science 301, 1895–1898 (2003).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors are grateful for financial support from the National Science Foundation Biotechnology Process Engineering Center and National Institutes of Health grant CA96504. We are grateful for insightful comments on the manuscript by Jennifer Cochran, Jim Huston, Bob Kamen, Dasa Lipovsek, Dev Sidhu and Jeff Way, and the anonymous referees.

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, MIT 66-552, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Balaji M Rao, Douglas A Lauffenburger & K Dane Wittrup

  2. Biological Engineering Division, MIT 66-552, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Douglas A Lauffenburger & K Dane Wittrup

  3. Biotechnology Process Engineering Center, MIT 66-552, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Douglas A Lauffenburger & K Dane Wittrup

Authors
  1. Balaji M Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Douglas A Lauffenburger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K Dane Wittrup
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K Dane Wittrup.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rao, B., Lauffenburger, D. & Wittrup, K. Integrating cell-level kinetic modeling into the design of engineered protein therapeutics. Nat Biotechnol 23, 191–194 (2005). https://doi.org/10.1038/nbt1064

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt1064

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing