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:

Systems biology and combination therapy in the quest for clinical efficacy

Abstract

Combinatorial control of biological processes, in which redundancy and multifunctionality are the norm, fundamentally limits the therapeutic index that can be achieved by even the most potent and highly selective drugs. Thus, it will almost certainly be necessary to use new 'targeted' pharmaceuticals in combinations. Multicomponent drugs are standard in cytotoxic chemotherapy, but their development has required arduous empirical testing. However, experimentally validated numerical models should greatly aid in the formulation of new combination therapies, particularly those tailored to the needs of specific patients. This perspective focuses on opportunities and challenges inherent in the application of mathematical modeling and systems approaches to pharmacology, specifically with respect to the idea of achieving combinatorial selectivity through use of multicomponent drugs.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: Application of traditional definitions of additivity to signaling networks.
Figure 2: Inhibitor combinations targeting two converging pathways.
Figure 3: Simulation of dual inhibition of a single target examined with mutually exclusive or nonexclusive inhibitors.
Figure 4: Targeting multiple levels of a simple signaling network leads to additivity or synergy depending on network topology.
Figure 5: Negative feedback alters inhibitor potency.
Figure 6: Comparing inhibitors under conditions in which parameter values vary between diseased and normal tissues.

Similar content being viewed by others

References

  1. Lipinski, C.A., Lombardo, F., Dominy, B.W. & Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26 (2001).

    Article  CAS  Google Scholar 

  2. Wong, S. & Witte, O.N. The BCR-ABL story: bench to bedside and back. Annu. Rev. Immunol. 22, 247–306 (2004).

    Article  CAS  Google Scholar 

  3. Saglio, G., Cilloni, D., Rancati, F. & Boano, L. Glivec and CML: a lucky date. J. Biol. Regul. Homeost. Agents 18, 246–251 (2004).

    CAS  PubMed  Google Scholar 

  4. Ikeda, A. et al. Molecular targets and the treatment of myeloid leukemia. Mol. Genet. Metab. 88, 216–224 (2006).

    Article  CAS  Google Scholar 

  5. Keith, C.T., Borisy, A.A. & Stockwell, B.R. Multicomponent therapeutics for networked systems. Nat. Rev. Drug Discov. 4, 71–78 (2005).

    Article  CAS  Google Scholar 

  6. The Nobel lectures in immunology. The Nobel prize for physiology or medicine, 1908, awarded to Elie Metchnikoff & Paul Ehrlich “in recognition of their work on immunity.” Scand. J. Immunol. 31, 1–13 (1990).

  7. Tortora, G., Bianco, R. & Daniele, G. Strategies for multiple signalling inhibition. J. Chemother. 16 Suppl 4, 41–43 (2004).

    Article  CAS  Google Scholar 

  8. Clarke, R. et al. Antiestrogen resistance in breast cancer and the role of estrogen receptor signaling. Oncogene 22, 7316–7339 (2003).

    Article  CAS  Google Scholar 

  9. Camirand, A., Zakikhani, M., Young, F. & Pollak, M. Inhibition of insulin-like growth factor-1 receptor signaling enhances growth-inhibitory and proapoptotic effects of gefitinib (Iressa) in human breast cancer cells. Breast Cancer Res. 7, R570–R579 (2005).

    Article  Google Scholar 

  10. Chakravarti, A., Loeffler, J.S. & Dyson, N.J. Insulin-like growth factor receptor I mediates resistance to anti-epidermal growth factor receptor therapy in primary human glioblastoma cells through continued activation of phosphoinositide 3-kinase signaling. Cancer Res. 62, 200–207 (2002).

    CAS  PubMed  Google Scholar 

  11. Scotlandi, K. et al. Prognostic and therapeutic relevance of HER2 expression in osteosarcoma and Ewing's sarcoma. Eur. J. Cancer 41, 1349–1361 (2005).

    Article  CAS  Google Scholar 

  12. du Manoir, J.M. et al. Strategies for delaying or treating in vivo acquired resistance to trastuzumab in human breast cancer xenografts. Clin. Cancer Res. 12, 904–916 (2006).

    Article  CAS  Google Scholar 

  13. Harrington, L.S. et al. The TSC1–2 tumor suppressor controls insulin-PI3K signaling via regulation of IRS proteins. J. Cell Biol. 166, 213–223 (2004).

    Article  CAS  Google Scholar 

  14. Tremblay, F. & Marette, A. Amino acid and insulin signaling via the mTOR/p70 S6 kinase pathway. A negative feedback mechanism leading to insulin resistance in skeletal muscle cells. J. Biol. Chem. 276, 38052–38060 (2001).

    CAS  PubMed  Google Scholar 

  15. Manning, B.D. Balancing Akt with S6K: implications for both metabolic diseases and tumorigenesis. J. Cell Biol. 167, 399–403 (2004).

    Article  CAS  Google Scholar 

  16. Loewe, S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3, 285–290 (1953).

    CAS  PubMed  Google Scholar 

  17. Chou, T.C. & Talalay, P. A simple generalized equation for the analysis of multiple inhibitions of Michaelis-Menten kinetic systems. J. Biol. Chem. 252, 6438–6442 (1977).

    CAS  PubMed  Google Scholar 

  18. Chou, T.C. & Talalay, P. Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv. Enzyme Regul. 22, 27–55 (1984).

    Article  CAS  Google Scholar 

  19. Berenbaum, M.C. What is synergy? Pharmacol. Rev. 41, 93–141 (1989).

    CAS  PubMed  Google Scholar 

  20. Bliss, C.I. The calculation of microbial assays. Bacteriol. Rev. 20, 243–258 (1956).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Greco, W.R., Bravo, G. & Parsons, J.C. The search for synergy: a critical review from a response surface perspective. Pharmacol. Rev. 47, 331–385 (1995).

    CAS  PubMed  Google Scholar 

  22. Martinez-Irujo, J.J., Villahermosa, M.L., Mercapide, J., Cabodevilla, J.F. & Santiago, E. Analysis of the combined effect of two linear inhibitors on a single enzyme. Biochem. J. 329, 689–698 (1998).

    Article  CAS  Google Scholar 

  23. Prichard, M.N. & Shipman, C. Jr. A three-dimensional model to analyze drug-drug interactions. Antiviral Res. 14, 181–205 (1990).

    Article  CAS  Google Scholar 

  24. Martinez-Irujo, J.J., Villahermosa, M.L., Alberdi, E. & Santiago, E. A checkerboard method to evaluate interactions between drugs. Biochem. Pharmacol. 51, 635–644 (1996).

    Article  CAS  Google Scholar 

  25. Tan, M., Fang, H.B., Tian, G.L. & Houghton, P.J. Experimental design and sample size determination for testing synergism in drug combination studies based on uniform measures. Stat. Med. 22, 2091–2100 (2003).

    Article  Google Scholar 

  26. Tallarida, R.J., Stone, D.J. Jr. & Raffa, R.B. Efficient designs for studying synergistic drug combinations. Life Sci. 61, PL 417–425 (1997).

    Article  Google Scholar 

  27. Mead, R. & Pike, D.J. A review of response surface methodology from a biometric viewpoint. Biometrics 31, 803–851 (1975).

    Article  CAS  Google Scholar 

  28. Prichard, M.N., Prichard, L.E. & Shipman, C. Jr. Strategic design and three-dimensional analysis of antiviral drug combinations. Antimicrob. Agents Chemother. 37, 540–545 (1993).

    Article  CAS  Google Scholar 

  29. Natarajan, M., Lin, K.M., Hsueh, R.C., Sternweis, P.C. & Ranganathan, R. A global analysis of cross-talk in a mammalian cellular signalling network. Nat. Cell Biol. 8, 571–580 (2006).

    Article  CAS  Google Scholar 

  30. Yeh, P., Tschumi, A.I. & Kishony, R. Functional classification of drugs by properties of their pairwise interactions. Nat. Genet. 38, 489–494 (2006).

    Article  CAS  Google Scholar 

  31. Boulikas, T. & Vougiouka, M. Recent clinical trials using cisplatin, carboplatin and their combination chemotherapy drugs (review). Oncol. Rep. 11, 559–595 (2004).

    CAS  PubMed  Google Scholar 

  32. Peters, G.J. et al. Interaction between cisplatin and gemcitabine in vitro and in vivo. Semin. Oncol. 22, 72–79 (1995).

    CAS  PubMed  Google Scholar 

  33. Chou, T.C., Motzer, R.J., Tong, Y. & Bosl, G.J. Computerized quantitation of synergism and antagonism of taxol, topotecan, and cisplatin against human teratocarcinoma cell growth: a rational approach to clinical protocol design. J. Natl. Cancer Inst. 86, 1517–1524 (1994).

    Article  CAS  Google Scholar 

  34. Van Putte, B.P. et al. Combination chemotherapy with gemcitabine with isolated lung perfusion for the treatment of pulmonary metastases. J. Thorac. Cardiovasc. Surg. 130, 125–130 (2005).

    Article  CAS  Google Scholar 

  35. Sirotnak, F.M., Zakowski, M.F., Miller, V.A., Scher, H.I. & Kris, M.G. Efficacy of cytotoxic agents against human tumor xenografts is markedly enhanced by coadministration of ZD1839 (Iressa), an inhibitor of EGFR tyrosine kinase. Clin. Cancer Res. 6, 4885–4892 (2000).

    CAS  PubMed  Google Scholar 

  36. Thomas, H.D. et al. Randomized cross-over clinical trial to study potential pharmacokinetic interactions between cisplatin or carboplatin and etoposide. Br. J. Clin. Pharmacol. 53, 83–91 (2002).

    Article  CAS  Google Scholar 

  37. Borisy, A.A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl. Acad. Sci. USA 100, 7977–7982 (2003).

    Article  CAS  Google Scholar 

  38. Kholodenko, B.N., Demin, O.V., Moehren, G. & Hoek, J.B. Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274, 30169–30181 (1999).

    Article  CAS  Google Scholar 

  39. 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 

  40. Sasagawa, S., Ozaki, Y., Fujita, K. & Kuroda, S. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat. Cell Biol. 7, 365–373 (2005).

    Article  CAS  Google Scholar 

  41. Park, C.S., Schneider, I.C. & Haugh, J.M. Kinetic analysis of platelet-derived growth factor receptor/phosphoinositide 3-kinase/Akt signaling in fibroblasts. J. Biol. Chem. 278, 37064–37072 (2003).

    Article  CAS  Google Scholar 

  42. Christopher, R. et al. Data-driven computer simulation of human cancer cell. Ann. NY Acad. Sci. 1020, 132–153 (2004).

    Article  CAS  Google Scholar 

  43. Hornberg, J.J. et al. Control of MAPK signalling: from complexity to what really matters. Oncogene 24, 5533–5542 (2005).

    Article  CAS  Google Scholar 

  44. Nielsen, U.B. & Schoeberl, B. Using computational modeling to drive the development of targeted therapeutics. IDrugs 8, 822–826 (2005).

    CAS  PubMed  Google Scholar 

  45. 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 

  46. Jackson, R.C. Amphibolic drug combinations: the design of selective antimetabolite protocols based upon the kinetic properties of multienzyme systems. Cancer Res. 53, 3998–4003 (1993).

    CAS  PubMed  Google Scholar 

  47. Lichtner, R.B., Menrad, A., Sommer, A., Klar, U. & Schneider, M.R. Signaling-inactive epidermal growth factor receptor/ligand complexes in intact carcinoma cells by quinazoline tyrosine kinase inhibitors. Cancer Res. 61, 5790–5795 (2001).

    CAS  PubMed  Google Scholar 

  48. Anido, J. et al. ZD1839, a specific epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, induces the formation of inactive EGFR/HER2 and EGFR/HER3 heterodimers and prevents heregulin signaling in HER2-overexpressing breast cancer cells. Clin. Cancer Res. 9, 1274–1283 (2003).

    CAS  PubMed  Google Scholar 

  49. Goldstein, N.I., Prewett, M., Zuklys, K., Rockwell, P. & Mendelsohn, J. Biological efficacy of a chimeric antibody to the epidermal growth factor receptor in a human tumor xenograft model. Clin. Cancer Res. 1, 1311–1318 (1995).

    CAS  PubMed  Google Scholar 

  50. Matar, P. et al. Combined epidermal growth factor receptor targeting with the tyrosine kinase inhibitor gefitinib (ZD1839) and the monoclonal antibody cetuximab (IMC-C225): superiority over single-agent receptor targeting. Clin. Cancer Res. 10, 6487–6501 (2004).

    Article  CAS  Google Scholar 

  51. Ferrell, J.E. Jr. & Machleder, E.M. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 280, 895–898 (1998).

    Article  CAS  Google Scholar 

  52. Bhalla, U.S., Ram, P.T. & Lyengar, R. MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science 297, 1018–1023 (2002).

    Article  CAS  Google Scholar 

  53. Kholodenko, B.N. Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem. 267, 1583–1588 (2000).

    Article  CAS  Google Scholar 

  54. Asthagiri, A.R. & Lauffenburger, D.A. A computational study of feedback effects on signal dynamics in a mitogen-activated protein kinase (MAPK) pathway model. Biotechnol. Prog. 17, 227–239 (2001).

    Article  CAS  Google Scholar 

  55. Sauro, H.M. & Kholodenko, B.N. Quantitative analysis of signaling networks. Prog. Biophys. Mol. Biol. 86, 5–43 (2004).

    Article  CAS  Google Scholar 

  56. Manning, B.D. & Cantley, L.C. United at last: the tuberous sclerosis complex gene products connect the phosphoinositide 3-kinase/Akt pathway to mammalian target of rapamycin (mTOR) signalling. Biochem. Soc. Trans. 31, 573–578 (2003).

    Article  CAS  Google Scholar 

  57. Bjornsti, M.A. & Houghton, P.J. The TOR pathway: a target for cancer therapy. Nat. Rev. Cancer 4, 335–348 (2004).

    Article  CAS  Google Scholar 

  58. O'Reilly, K.E. et al. mTOR inhibition induces upstream receptor tyrosine kinase signaling and activates Akt. Cancer Res. 66, 1500–1508 (2006).

    Article  CAS  Google Scholar 

  59. Alves, R., Antunes, F. & Salvador, A. Tools for kinetic modeling of biochemical networks. Nat. Biotechnol. 24, 667–672 (2006).

    Article  CAS  Google Scholar 

  60. Kholodenko, B.N. Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165–176 (2006).

    Article  CAS  Google Scholar 

  61. Box, G.E.P. Robustness in the strategy of scientific model building. in Robustness in Statistics (Launer R.L & Wilkinson, G.N.) 202 (Academic Press, New York, 1979).

    Google Scholar 

  62. Loewe, S. Die quantitativen probleme der pharmakologie. Ergeb. Physiol. Biol. Chem. Exp. Pharmakol. 27, 47–187 (1928).

    Article  Google Scholar 

  63. Chou, T.C. & Talalay, P. Generalized equations for the analysis of inhibitions of Michaelis-Menten and higher-order kinetic systems with two or more mutually exclusive and nonexclusive inhibitors. Eur. J. Biochem. 115, 207–216 (1981).

    Article  CAS  Google Scholar 

  64. Webb, J.L. in Enzyme and Metabolic Inhibitors 55–79 (Academic, New York, 1963).

    Google Scholar 

  65. Berenbaum, M.C. Criteria for analyzing interactions between biologically active agents. Adv. Cancer Res. 35, 269–335 (1981).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank B. Harms for helpful suggestions and comments on the manuscript. This work was supported by National Institutes of Health grant GM68762 and by Merrimack Pharmaceuticals.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter K Sorger.

Ethics declarations

Competing interests

P.K.S. is a co-founder of Merrimack Pharmaceuticals and chair of the Scientific Advisory Board.

Supplementary information

Supplementary Table 1

Species, parameters and reactions that can be used to recreate the mechanistic models for Figure 2 (two ligands, two inhibitors). (PDF 235 kb)

Supplementary Table 2

Species, parameters and reactions that can be used to recreate the mechanistic models for Figure 3 (exclusive versus nonexclusive), Figure 4 (linear and ultrasensitive), Figure 5 (negative feedback) and Figure 6 (diseased linear and ultrasensitive). (PDF 239 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fitzgerald, J., Schoeberl, B., Nielsen, U. et al. Systems biology and combination therapy in the quest for clinical efficacy. Nat Chem Biol 2, 458–466 (2006). https://doi.org/10.1038/nchembio817

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio817

This article is cited by

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