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Optimization of drug combinations using Feedback System Control

Nature Protocols volume 11, pages 302–315 (2016)Cite this article

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Abstract

We describe a protocol for the discovery of synergistic drug combinations for the treatment of disease. Synergistic drug combinations lead to the use of drugs at lower doses, which reduces side effects and can potentially lead to reduced drug resistance, while being clinically more effective than the individual drugs. To cope with the extremely large search space for these combinations, we developed an efficient combinatorial drug screening method called the Feedback System Control (FSC) technique. Starting with a broad selection of drugs, the method follows an iterative approach of experimental testing in a relevant bioassay and analysis of the results by FSC. First, the protocol uses a cell viability assay to generate broad dose-response curves to assess the efficacy of individual compounds. These curves are then used to guide the dosage input of each drug to be tested in combination. Data from applied drug combinations are input into the differential evolution (DE) algorithm, which predicts new combinations to be tested in vitro. This process identifies optimal drug-dose combinations, while saving orders of magnitude in experimental effort. The complete optimization process is estimated to take ∼4 weeks. FSC does not require insight into the disease mechanism, and it has therefore been applied to find combination therapies for many different pathologies, including cancer and infectious diseases, and it has also been used in organ transplantation.

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Figure 1: A schematic representation of the FSC technique, showing the five main components of the optimization process.
Figure 2: Illustration of the differential evolution algorithm.
Figure 3: Regression analysis of combinatorial data.
Figure 4: Schematic drawing of a smooth response surface.
Figure 5: Schematic drawing of reported FSC applications.

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References

  1. Sawyers, C.L. Cancer: mixing cocktails. Nature 449, 993–996 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. De Francesco, R. & Migliaccio, G. Challenges and successes in developing new therapies for hepatitis C. Nature 436, 953–960 (2005).

    Article  CAS  PubMed  Google Scholar 

  3. Lehar, J. et al. Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat. Biotechnol. 27, 659–666 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lindsay, M.A. Target discovery. Nat. Rev. Drug Discov. 2, 831–838 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Forrest, S. Genetic algorithms: principles of natural selection applied to computation. Science 261, 872–878 (1993).

    Article  CAS  PubMed  Google Scholar 

  6. Bonabeau, E., Dorigo, M. & Theraulaz, G. Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000).

    Article  CAS  PubMed  Google Scholar 

  7. Al-Shyoukh, I. et al. Systematic quantitative characterization of cellular responses induced by multiple signals. BMC Syst. Biol. 5, 88 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wang, H. et al. Mechanism-independent optimization of combinatorial nanodiamond and unmodified drug delivery using a phenotypically driven platform technology. ACS Nano 9, 3332–3344 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Ding, X., Xu, H., Hopper, C., Yang, J. & Ho, C.M. Use of fractional factorial designs in antiviral drug studies. Qual. Reliab. Eng. Int. 29, 229–304 (2013).

    Article  Google Scholar 

  10. Honda, Y. et al. Guiding the osteogenic fate of mouse and human mesenchymal stem cells through feedback system control. Sci. Rep. 3, 3420 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sun, C.P. et al. Integrative systems control approach for reactivating Kaposi's sarcoma-associated herpesvirus (KSHV) with combinatory drugs. Integr. Biol. (Camb) 1, 123–130 (2009).

    Article  CAS  Google Scholar 

  12. Tsutsui, H. et al. An optimized small molecule inhibitor cocktail supports long-term maintenance of human embryonic stem cells. Nat. Commun. 2, 167 (2011).

    Article  PubMed  CAS  Google Scholar 

  13. Weiss, A. et al. A streamlined search technology for identification of synergistic drug combinations. Sci. Rep. 5, 14508 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Weiss, A. et al. Rapid stochastic optimization of drug combination for inhibiting angiogenesis in cancer growth. Angiogenesis 9462, 233–244 (2015).

    Article  CAS  Google Scholar 

  15. Wong, P.K. et al. Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm. Proc. Natl. Acad. Sci. USA 105, 5105–5110 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yoon, B.J. Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics. BMC Bioinformatics 12 (suppl. 1): S18 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Yu, F. et al. Control of Kaposi's sarcoma-associated herpesvirus reactivation induced by multiple signals. PLoS ONE 6, e20998 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ding, X. et al. Cascade search for HSV-1 combinatorial drugs with high antiviral efficacy and low toxicity. Int. J. Nanomed. 7, 2281–2292 (2012).

    CAS  Google Scholar 

  19. Yu, H. et al. Optimizing combinations of flavonoids deriving from astragali radix in activating the regulatory element of erythropoietin by a feedback system control scheme. Evid. Based Complement. Altern. Med. 2013, 541436 (2013).

    Google Scholar 

  20. Storn, R.M. & Price, K.V. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997).

    Article  Google Scholar 

  21. Das, S. & Suganthan, P.N. Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011).

    Article  Google Scholar 

  22. Chakraborty, U.K. Advances in Differential Evolution (Springer Verlag, 2008).

  23. Yoon, H. et al. Modeling to predict growth/no growth boundaries and kinetic behavior of Salmonella on cutting board surfaces. J. Food Prot. 75, 2116–2121 (2012).

    Article  PubMed  Google Scholar 

  24. Van der Borght, K. et al. Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations. BMC Bioinformatics 12, 386 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wood, K., Nishida, S., Sontag, E.D. & Cluzel, P. Mechanism-independent method for predicting response to multidrug combinations in bacteria. Proc. Natl. Acad. Sci. USA 109, 12254–12259 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Seber, G.A.F. & Lee, A.J. Linear Regression Analysis 2nd edn (Wiley, 2003).

  27. Chou, T.C. Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer Res. 70, 440–446 (2010).

    Article  CAS  PubMed  Google Scholar 

  28. Feala, J.D. et al. Systems approaches and algorithms for discovery of combinatorial therapies. Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 181–193 (2010).

    Article  PubMed  Google Scholar 

  29. Dancey, J.E. & Chen, H.X. Strategies for optimizing combinations of molecularly targeted anticancer agents. Nat. Rev. Drug Discov. 5, 649–659 (2006).

    Article  CAS  PubMed  Google Scholar 

  30. Park, M., Nassar, M. & Vikalo, H. Bayesian active learning for drug combinations. IEEE Trans. Biomed. Eng. 60, 3248–3255 (2013).

    Article  PubMed  Google Scholar 

  31. Decker, S. & Sausville, E.A. Preclinical modeling of combination treatments: fantasy or requirement? Ann. NY Acad. Sci. 1059, 61–69 (2005).

    Article  CAS  PubMed  Google Scholar 

  32. Tol, J. et al. Chemotherapy, bevacizumab, and cetuximab in metastatic colorectal cancer. N. Engl. J. Med. 360, 563–572 (2009).

    Article  CAS  PubMed  Google Scholar 

  33. Jia, J. et al. Mechanisms of drug combinations: interaction and network perspectives. Nat. Rev. Drug Discov. 8, 111–128 (2009).

    Article  CAS  PubMed  Google Scholar 

  34. Calzolari, D. et al. Search algorithms as a framework for the optimization of drug combinations. PLoS Comput. Biol. 4, e1000249 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Zimmermann, G.R., Lehar, 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  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Potti, A. et al. Genomic signatures to guide the use of chemotherapeutics. Nat. Med. 12, 1294–1300 (2006).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Nelander, S. et al. Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4, 216 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zhao, B., Hemann, M.T. & Lauffenburger, D.A. Intratumor heterogeneity alters most effective drugs in designed combinations. Proc. Natl. Acad. Sci. USA 111, 10773–10778 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gamperle, R.M., Sibylle, D. & Koumoutsakos, P. A Parameter Study for Differential Evolution. in Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation (eds. Grmela, N. & Mastorakis, A.) 293–298 (WSEAS Press, 2002).

  43. Ding, X. et al. Discovery of a low-order drug-cell response surface for applications in personalized medicine. Phys. Biol. 11, 065003 (2014).

    Article  PubMed  Google Scholar 

  44. Tallarida, R.J. Drug synergism: its detection and applications. J. Pharmacol. Exp. Ther. 298, 865–872 (2001).

    CAS  PubMed  Google Scholar 

  45. Minto, C.F. et al. Response surface model for anesthetic drug interactions. Anesthesiology 92, 1603–1616 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Price, K.V., Storn, R.M. & Lampinen, J.A. Differential Evolution: A Practical Approach to Global Optimization (Springer, 2005).

  47. Storn, R.M. Differential Evolution (DE) http://www1.icsi.berkeley.edu/~storn/code.html.

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Acknowledgements

C.-M.H. acknowledges the support from the Ben Rich-Lockheed Martin Professor Endowment Fund and the Bill and Melinda Gates Foundation. P.N.-S. and A.W.G. acknowledge the support of KWF, the Dutch Cancer Society (Project VU 2014-7234). The authors acknowledge EPFL for financial support. We appreciate A.M. Silva for providing the sketch for Figure 2.

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Authors and Affiliations

  1. Department of Medical Oncology, Angiogenesis Laboratory, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands

    Patrycja Nowak-Sliwinska, Andrea Weiss & Arjan W Griffioen

  2. Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xianting Ding

  3. Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

    Paul J Dyson & Hubert van den Bergh

  4. Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, California, USA

    Chih-Ming Ho

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  1. Patrycja Nowak-Sliwinska
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Contributions

P.N.-S. conceived and designed the protocol, and wrote the manuscript; X.D. designed the protocol, modeled the data, delivered the code and wrote the manuscript; A.W. designed the protocol, delivered the code and wrote the manuscript; C.-M.H. conceived the FSC technology and participated in data modeling, data interpretation and writing of the manuscript; P.J.D. participated in data interpretation and writing of the manuscript; H.v.d.B. initiated the collaboration and participated in data interpretation, and in writing of the manuscript; A.W.G. designed the study, participated in data interpretation and contributed to writing of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Patrycja Nowak-Sliwinska or Chih-Ming Ho.

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Competing interests

All authors are the co-inventors on the FSC-related patents.

Supplementary information

Supplementary Text and Figures

Supplementary Note and Supplementary Tutorial (PDF 220 kb)

Supplementary Data

Example dataset for the refined search, providing an example of regression analysis performed in MATLAB. (XLS 111 kb)

Supplementary Software (ZIP 4 kb)

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Nowak-Sliwinska, P., Weiss, A., Ding, X. et al. Optimization of drug combinations using Feedback System Control. Nat Protoc 11, 302–315 (2016). https://doi.org/10.1038/nprot.2016.017

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