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Efficient discovery of anti-inflammatory small-molecule combinations using evolutionary computing

Nature Chemical Biology volume 7, pages 902–908 (2011)Cite this article

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Abstract

The control of biochemical fluxes is distributed, and to perturb complex intracellular networks effectively it is often necessary to modulate several steps simultaneously. However, the number of possible permutations leads to a combinatorial explosion in the number of experiments that would have to be performed in a complete analysis. We used a multiobjective evolutionary algorithm to optimize reagent combinations from a dynamic chemical library of 33 compounds with established or predicted targets in the regulatory network controlling IL-1β expression. The evolutionary algorithm converged on excellent solutions within 11 generations, during which we studied just 550 combinations out of the potential search space of ∼9 billion. The top five reagents with the greatest contribution to combinatorial effects throughout the evolutionary algorithm were then optimized pairwise. A p38 MAPK inhibitor together with either an inhibitor of IκB kinase or a chelator of poorly liganded iron yielded synergistic inhibition of macrophage IL-1β expression. Evolutionary searches provide a powerful and general approach to the discovery of new combinations of pharmacological agents with therapeutic indices potentially greater than those of single drugs.

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Figure 1: Combinatorial evolutionary inhibition of IL-1β expression.
Figure 2: Combinatorial multiobjective optimization of reagents inhibiting IL-1β production.
Figure 3: Analysis of the evolutionary multiobjective optimization of the inhibition of IL-1β production.
Figure 4: Rank ordering of component reagents.
Figure 5: Concentration-dependent adaptive dose-matrix optimization of paired reagent combinations.

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Acknowledgements

We would like to thank the Biotechnology and Biological Sciences Research Council (BBSRC) for funding this project (BB/F018398/1) and the BBSRC, Engineering and Physical Sciences Research Council and Wellcome Trust for the funding of studentships and fellowships. We thank the Manchester Centre for Integrative Systems Biology for providing access to the Sciclone instrument. B.G.S. would like to thank S. Wasley and D. Stubbs of Caliper Life Sciences for their help and assistance in commissioning and their image of the Sciclone instrument (Fig. 1), respectively. The images of the computer and 96-well plate (Fig. 1) were used under the Creative Commons CC0 Public Domain Dedication and were obtained from Clker.com.

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Author notes

  1. Ben G Small and Barry W McColl: These authors contributed equally to this work.

Authors and Affiliations

  1. Doctoral Training Centre, Integrative Systems Biology Molecules to Life, Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK

    Ben G Small

  2. School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK

    Ben G Small

  3. The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush,, Midlothian, UK

    Barry W McColl

  4. School of Computer Science, The University of Manchester, Manchester, UK

    Richard Allmendinger, Jürgen Pahle, Joshua Knowles & Pedro Mendes

  5. NeuroSystems Section, Faculty of Life Sciences, The University of Manchester, Manchester, UK

    Gloria López-Castejón, Nancy J Rothwell & David Brough

  6. Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK

    Pedro Mendes & Douglas B Kell

  7. Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA

    Pedro Mendes

  8. School of Chemistry, The University of Manchester, Manchester, UK

    Douglas B Kell

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Contributions

B.G.S. designed, performed and analyzed the data from the single-reagent, combinatorial, evolution-directed and adaptive dose-matrix studies and wrote the first draft of the manuscript. B.W.M. designed, performed and analyzed the data from single-reagent and evolution-directed combinatorial studies. J.P. suggested and was involved in the design of the concentration-dependent adaptive dose-matrix search protocol of paired combinations and wrote the code in R to produce the combination response-shape plots. G.L.-C. conducted adaptive dose-matrix studies. D.B.K., D.B. and N.J.R. conceived the idea of applying an evolutionary algorithm to select combinations of reagents for inhibition of IL-1β expression; wrote the grant application; investigated and supervised, together with B.G.S.; and advised on experimental design. J.K. supervised R.A., generally advised on evolutionary algorithms and multiobjective optimization and, with R.A., evaluated and selected the evolutionary algorithm (IBEA) applied here. R.A. wrote the IBEA code in Java, helped implement its use and generated post hoc reagent-fitness measurements. P.M. supervised B.G.S. and advised on experimental design and data analysis. All authors contributed to the writing of the manuscript and approved its final form.

Corresponding author

Correspondence to Douglas B Kell.

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Competing interests

D.B.K. transferred his responsibility as principal investigator of Biotechnology and Biological Sciences Research Council (BBSRC) Grant BB/F018398/1 to N.J.R. and P.M. prior to taking up his appointment as chief executive of the BBSRC on 1 October 2008. N.J.R. is a nonexecutive director of AstraZeneca. B.G.S. is a former employee of AstraZeneca and Eli Lilly and Company and is a shareholder of stock from both companies.

Supplementary information

Supplementary Text and Figures

Supplementary Methods and Supplementary Results (PDF 1551 kb)

Supplementary Spreadsheet 1

NCHEMB-A110406380B-Kell_Supp_Spreadsheet.xls (XLS 145 kb)

Supplementary Source Code File 1

Cocktail Generator IBEA (ZIP 8 kb)

Supplementary Source Code File 2

Main (ZIP 3 kb)

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Small, B., McColl, B., Allmendinger, R. et al. Efficient discovery of anti-inflammatory small-molecule combinations using evolutionary computing. Nat Chem Biol 7, 902–908 (2011). https://doi.org/10.1038/nchembio.689

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