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A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans

Nature Chemical Biology volume 6, pages 549–557 (2010)Cite this article

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Abstract

The resistance of Caenorhabditis elegans to pharmacological perturbation limits its use as a screening tool for novel small bioactive molecules. One strategy to improve the hit rate of small-molecule screens is to preselect molecules that have an increased likelihood of reaching their target in the worm. To learn which structures evade the worm's defenses, we performed the first survey of the accumulation and metabolism of over 1,000 commercially available drug-like small molecules in the worm. We discovered that fewer than 10% of these molecules accumulate to concentrations greater than 50% of that present in the worm's environment. Using our dataset, we developed a structure-based accumulation model that identifies compounds with an increased likelihood of bioavailability and bioactivity, and we describe structural features that facilitate small-molecule accumulation in the worm. Preselecting molecules that are more likely to reach a target by first applying our model to the tens of millions of commercially available compounds will undoubtedly increase the success of future small-molecule screens with C. elegans.

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Figure 1: A survey of exogenous drug-like molecule accumulation in C. elegans.
Figure 2: A machine-learned structure-based model predicts the accumulation of drug-like molecules in C. elegans.
Figure 3: Prominent substructures that influence small-molecule accumulation in C. elegans.
Figure 4: The C. elegans structure-based accumulation model (SAM) enriches for structurally diverse compounds with distinct bioactivities in the worm.

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Acknowledgements

We thank C. Cummins for critical comments on the manuscript; V. Wong and G. Selman for technical assistance early on in the project; S. Pan at the University of California, Riverside, for MS and MS-MS analyses; and A. Young of the Advanced Instrumentation for Molecular Structure Mass Spectrometry Laboratory at the University of Toronto for accurate mass MS analyses. This work was supported by Canadian Institutes of Health Research (CIHR) operating grants to P.J.R. (grant number 68813) and G.G. and C.N. (MOP-81340), Natural Sciences and Engineering Research Council of Canada support to G.D.B., a Natural Sciences and Engineering Research Council of Canada Graduate Scholarship doctoral award to A.R.B. and a Marie Curie Fellowship to I.M.W. G.G. and P.J.R. are Canadian Research Chairs in Chemical Biology and Molecular Neurobiology, respectively.

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Authors and Affiliations

  1. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Andrew R Burns, Mike Tyers, Guri Giaever, Gary D Bader, Corey Nislow & Peter J Roy

  2. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada

    Andrew R Burns, Iain M Wallace, Guri Giaever, Gary D Bader, Corey Nislow & Peter J Roy

  3. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada

    Jan Wildenhain & Mike Tyers

  4. Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, United Kingdom

    Jan Wildenhain & Mike Tyers

  5. Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada

    Guri Giaever

  6. Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada

    Gary D Bader & Corey Nislow

  7. Department of Botany and Plant Sciences, Center for Plant Cell Biology, University of California, Riverside, California, USA

    Sean R Cutler

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Contributions

A.R.B. did the wet-lab work and analyzed the MS data with guidance from S.R.C. and P.J.R. I.M.W. did the computational analysis with guidance from A.R.B., J.W., M.T., G.D.B., G.G., C.N. and P.J.R. The project was conceived by P.J.R., S.R.C. and A.R.B., and the paper was written by A.R.B. and P.J.R.

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Correspondence to Peter J Roy.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–10, Supplementary Table 1 and Supplementary Methods (PDF 15463 kb)

Supplementary Data 1

An Excel file of the top-scoring 5% of the ZINC collection of commercially available small molecules as ranked by our structure-accumulation model. In the version of this supplementary file originally posted online, the file contained only 65,536 lines instead of 683,493 lines. The error has been corrected in this file as of 18 June 2010. Please unzip and open with the 2007 or a newer version of Excel. (ZIP 34824 kb)

Supplementary Data 2

The xml file of our structure-accumulation model script for use with Pipeline Pilot. (XML 77 kb)

Supplementary Data 3

An sdf file of the small-molecule training set used to build our structure-accumulation model. In the version of this supplementary file originally posted online, the file was displayed as a pdf rather than in the correct sdf file format. The error has been corrected in this file as of 18 June 2010. (SDF 749 kb)

Supplementary Data 4

A list of available open-source software that could be used to generate a similar structure-accumulation model if Pipeline Pilot is not available. (PDF 40 kb)

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Burns, A., Wallace, I., Wildenhain, J. et al. A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans. Nat Chem Biol 6, 549–557 (2010). https://doi.org/10.1038/nchembio.380

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