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Synergistic drug combinations tend to improve therapeutically relevant selectivity

An Erratum to this article was published on 01 September 2009

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Abstract

Drug combinations are a promising strategy to overcome the compensatory mechanisms and unwanted off-target effects that limit the utility of many potential drugs. However, enthusiasm for this approach is tempered by concerns that the therapeutic synergy of a combination will be accompanied by synergistic side effects. Using large scale simulations of bacterial metabolism and 94,110 multi-dose experiments relevant to diverse diseases, we provide evidence that synergistic drug combinations are generally more specific to particular cellular contexts than are single agent activities. We highlight six combinations whose selective synergy depends on multitarget drug activity. For one anti-inflammatory example, we show how such selectivity is achieved through differential expression of the drugs' targets in cell types associated with therapeutic, but not toxic, effects and validate its therapeutic relevance in a rat model of asthma. The context specificity of synergistic combinations creates many opportunities for therapeutically relevant selectivity and enables improved control of complex biological systems.

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Figure 1: Measuring selectivity bias.
Figure 2: Simulation results identify drug combinations that inhibit E. coli growth in fermentation (minimal glucose) rather than aerobic (minimal acetate) conditions.
Figure 3: Selectivity bias for 13 sets of combination data focused on six disease areas.
Figure 4: Examples of therapeutically and mechanistically selective synergistic combinations, showing the control (left) and test (center) matrices, and the test isobologram (right).
Figure 5: Selective synergy between glucocorticoids and tricyclic antidepressants (TCA).

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  • 08 July 2009

    In the version of this article initially published, in the legend of Figure 5b, line 2, “stress” is followed by a period. The period should be a comma, so that the sentence reads, “In response to stress, lymphoctyes…” The error has been corrected in the HTML and PDF versions of the article.

References

  1. Hopkins, A.L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).

    Article  CAS  Google Scholar 

  2. Hughes, B. 2007 FDA drug approvals: a year of flux. Nat. Rev. Drug Discov. 7, 107–109 (2008).

    Article  CAS  Google Scholar 

  3. Hartman, J.L.t., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001).

    Article  CAS  Google Scholar 

  4. Stelling, J., Sauer, U., Szallasi, Z., Doyle, F.J. III & Doyle, J. Robustness of cellular functions. Cell 118, 675–685 (2004).

    Article  CAS  Google Scholar 

  5. Kitano, H. A robustness-based approach to systems-oriented drug design. Nat. Rev. Drug Discov. 6, 202–210 (2007).

    Article  CAS  Google Scholar 

  6. Kassouf, W. et al. Uncoupling between epidermal growth factor receptor and downstream signals defines resistance to the antiproliferative effect of Gefitinib in bladder cancer cells. Cancer Res. 65, 10524–10535 (2005).

    Article  CAS  Google Scholar 

  7. Zarraga, I.G. & Schwarz, E.R. Coxibs and heart disease: what we have learned and what else we need to know. J. Am. Coll. Cardiol. 49, 1–14 (2007).

    Article  CAS  Google Scholar 

  8. Sharom, J.R., Bellows, D.S. & Tyers, M. From large networks to small molecules. Curr. Opin. Chem. Biol. 8, 81–90 (2004).

    Article  CAS  Google Scholar 

  9. Kaelin, W.G. Jr. The concept of synthetic lethality in the context of anticancer therapy. Nat. Rev. Cancer 5, 689–698 (2005).

    Article  CAS  Google Scholar 

  10. 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 

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

    Article  CAS  Google Scholar 

  12. 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 

  13. Boone, C., Bussey, H. & Andrews, B.J. Exploring genetic interactions and networks with yeast. Nat. Rev. Genet. 8, 437–449 (2007).

    Article  CAS  Google Scholar 

  14. St. Onge, R.P. et al. Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions. Nat. Genet. 39, 199–206 (2007).

    Article  CAS  Google Scholar 

  15. Hoon, S. et al. An integrated platform of genomic assays reveals small-molecule bioactivities. Nat. Chem. Biol. 4, 498–506 (2008).

    Article  CAS  Google Scholar 

  16. Apsel, B. et al. Targeted polypharmacology: discovery of dual inhibitors of tyrosine and phosphoinositide kinases. Nat. Chem. Biol. 4, 691–699 (2008).

    Article  CAS  Google Scholar 

  17. Araujo, R.P., Petricoin, E.F. & Liotta, L.A. A mathematical model of combination therapy using the EGFR signaling network. Biosystems 80, 57–69 (2005).

    Article  CAS  Google Scholar 

  18. Segrè, D., Deluna, A., Church, G.M. & Kishony, R. Modular epistasis in yeast metabolism. Nat. Genet. 37, 77–83 (2005).

    Article  Google Scholar 

  19. Yang, K., Bai, H., Ouyang, Q., Lai, L. & Tang, C. Finding multiple target optimal intervention in disease-related molecular network. Mol. Syst. Biol. 4, 228 (2008).

    Article  Google Scholar 

  20. Radhakrishnan, M.L. & Tidor, B. Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems. J. Chem. Inf. Model. 48, 1055–1073 (2008).

    Article  CAS  Google Scholar 

  21. Zimmermann, G.R., Lehár, J. & Keith, C.T. Multi-target therapeutics: when the whole is greater than the sum of the parts. Drug Discov. Today 12, 34–42 (2007).

    Article  CAS  Google Scholar 

  22. 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 

  23. Tong, A.H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004).

    Article  CAS  Google Scholar 

  24. Lehár, J. et al. Chemical combination effects predict connectivity in biological systems. Mol. Syst. Biol. 3, 80 (2007).

    Article  Google Scholar 

  25. Farr, M. & Bacon, P.A. How and when should combination therapy be used? The role of an anchor drug. Br. J. Rheumatol. 34, 100–103 (1995).

    Article  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  27. Loewe, S. Die quantitativen Probleme der Pharmakologie. Ergeb. Physiol. 27, 47–187 (1928).

    Article  Google Scholar 

  28. Bliss, C.I. The toxicity of poisons applied jointly. Ann. Appl. Biol. 26, 585–615 (1939).

    Article  CAS  Google Scholar 

  29. 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 

  30. Press, W.H., Teukolsky, S.A., Vetterling, W.T. & Flannery, B.P. Numerical Recipes in C: the Art of Scientific Computing, edn. 2, section 14.3.3 (Cambridge University Press, Cambridge, 1997).

    Google Scholar 

  31. Duarte, N.C., Herrgard, M.J. & Palsson, B.O. Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 14, 1298–1309 (2004).

    Article  CAS  Google Scholar 

  32. Edwards, J.S., Ibarra, R.U. & Palsson, B.O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 19, 125–130 (2001).

    Article  CAS  Google Scholar 

  33. Edwards, J.S. & Palsson, B.O. Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1, 1 (2000).

    Article  CAS  Google Scholar 

  34. Okamoto, M., Ono, M. & Baba, M. Potent inhibition of HIV type 1 replication by an antiinflammatory alkaloid, cepharanthine, in chronically infected monocytic cells. AIDS Res. Hum. Retroviruses 14, 1239–1245 (1998).

    Article  CAS  Google Scholar 

  35. Kleyman, T.R. & Cragoe, E.J. Jr. Amiloride and its analogs as tools in the study of ion transport. J. Membr. Biol. 105, 1–21 (1988).

    Article  CAS  Google Scholar 

  36. Virmani, R., Farb, A., Guagliumi, G. & Kolodgie, F.D. Drug-eluting stents: caution and concerns for long-term outcome. Coron. Artery Dis. 15, 313–318 (2004).

    Article  Google Scholar 

  37. Tischler, J., Lehner, B. & Fraser, A.G. Evolutionary plasticity of genetic interaction networks. Nat. Genet. 40, 390–391 (2008).

    Article  CAS  Google Scholar 

  38. Lehár, J., Krueger, A., Zimmermann, G. & Borisy, A. High-order combination effects and biological robustness. Mol. Syst. Biol. 4, 215 (2008).

    Article  Google Scholar 

  39. Qiu, Y.H., Cheng, C., Dai, L. & Peng, Y.P. Effect of endogenous catecholamines in lymphocytes on lymphocyte function. J. Neuroimmunol. 167, 45–52 (2005).

    Article  CAS  Google Scholar 

  40. Salicrú, A.N., Sams, C.F. & Marshall, G.D. Cooperative effects of corticosteroids and catecholamines upon immune deviation of the type-1/type-2 cytokine balance in favor of type-2 expression in human peripheral blood mononuclear cells. Brain Behav. Immun. 21, 913–920 (2007).

    Article  Google Scholar 

  41. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–6067 (2004).

    Article  CAS  Google Scholar 

  42. Schäcke, H., Docke, W.D. & Asadullah, K. Mechanisms involved in the side effects of glucocorticoids. Pharmacol. Ther. 96, 23–43 (2002).

    Article  Google Scholar 

  43. Kvien, T.K. et al. Efficacy and safety of a novel synergistic drug candidate, CRx-102, in hand osteoarthritis. Ann. Rheum. Dis. 67, 942–948 (2007).

    Article  Google Scholar 

  44. Zimmermann, G.R. et al. Selective amplification of glucocorticoid anti-inflammatory activity through synergistic multi-target action of a combination drug. Arthritis Res. Ther. 11, R12 (2009).

    Article  Google Scholar 

  45. Schäcke, H., Rehwinkel, H. & Asadullah, K. Dissociated glucocorticoid receptor ligands: compounds with an improved therapeutic index. Curr. Opin. Investig. Drugs 6, 503–507 (2005).

    PubMed  Google Scholar 

  46. Keasling, J.D. Synthetic biology for synthetic chemistry. ACS Chem. Biol. 3, 64–76 (2008).

    Article  CAS  Google Scholar 

  47. Holm, S. A simple sequential rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

    Google Scholar 

  48. Filliben, J.J. in Engineering Statistics Handbook, vol. 2007 (National Institute of Standards and Technology, Gaithersburg, MD, 2005) http://www.itl.nist.gov/div898/handbook/eda/section3/eda356.htm.

    Google Scholar 

  49. Berenbaum, M.C. The expected effect of a combination of agents: the general solution. J. Theor. Biol. 114, 413–431 (1985).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors are grateful to many people at CombinatoRx who provided technical materials for this paper. We also thank T. Golub and F. Roth for comments on the manuscript. The simulations were performed using Boston University's computing facilities. Antiviral research was conducted in collaboration with CombinatoRx Singapore, funded by the Singapore Economic Development Board. Anthrax studies were funded through the National Institutes of Health/National Institute of Allergy and Infectious Diseases under grant U01 AI61345. Cardiovascular screens were done in collaboration with Angiotech. Huntington's disease experiments were done in collaboration with and funded by the CHDI Foundation. MM.1R and MM.1S were kindly provided by S. Rosen, Northwestern University.

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Contributions

J.L. drafted and edited most of this paper, in addition to developing and performing the selectivity analyses. A.A.B. conceived the underlying premise and made major contributions to the abstract, introduction and conclusion. G.R.Z. wrote the in vivo validation sections and oversaw many of the screening projects. M.S.L. and J.E.S. oversaw the remaining screening projects reported. A.S.K. performed the theoretical simulations. W.A. performed and analyzed the preclinical experiments; A.M.H. designed and conducted the cancer 180×180 2005 screen; L.M.J. designed and directed the viral infection, bacterial and anthrax experiments; E.R.P. planned and directed most of the in vitro inflammatory cytokine experiments; R.J.R. planned and conducted two of the cancer screens; G.F.S. designed and oversaw the cardiovascular experiments; and X.J. designed and directed the Huntington's disease screen.

Corresponding authors

Correspondence to Joseph Lehár or Alexis A Borisy.

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All of the authors are currently, or were previously, employed by CombinatoRx, Inc.

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Lehár, J., Krueger, A., Avery, W. et al. Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat Biotechnol 27, 659–666 (2009). https://doi.org/10.1038/nbt.1549

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