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A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans


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.


  1. Burns, A.R. et al. High-throughput screening of small molecules for bioactivity and target identification in Caenorhabditis elegans. Nat. Protoc. 1, 1906–1914 (2006).

    CAS  PubMed  Google Scholar 

  2. Kwok, T.C.Y. et al. A small-molecule screen in C. elegans yields a new calcium channel antagonist. Nature 441, 91–95 (2006).

    CAS  PubMed  Google Scholar 

  3. Petrascheck, M., Ye, X. & Buck, L.B. An antidepressant that extends lifespan in adult Caenorhabditis elegans. Nature 450, 553–556 (2007).

    CAS  PubMed  Google Scholar 

  4. Kokel, D., Li, Y., Qin, J. & Xue, D. The nongenotoxic carcinogens naphthalene and para-dichlorobenzene suppress apoptosis in Caenorhabditis elegans. Nat. Chem. Biol. 2, 338–345 (2006).

    CAS  PubMed  Google Scholar 

  5. Kwok, T.C. et al. A genetic screen for dihydropyridine (DHP)-resistant worms reveals new residues required for DHP-blockage of mammalian calcium channels. PLoS Genet. 4, e1000067 (2008).

    PubMed  PubMed Central  Google Scholar 

  6. Jones, A.K., Buckingham, S.D. & Sattelle, D.B. Chemistry-to-gene screens in Caenorhabditis elegans. Nat. Rev. Drug Discov. 4, 321–330 (2005).

    CAS  PubMed  Google Scholar 

  7. Kaminsky, R. et al. A new class of anthelmintics effective against drug-resistant nematodes. Nature 452, 176–180 (2008).

    CAS  PubMed  Google Scholar 

  8. Kaletta, T. & Hengartner, M.O. Finding function in novel targets: C. elegans as a model organism. Nat. Rev. Drug Discov. 5, 387–398 (2006).

    CAS  PubMed  Google Scholar 

  9. Broeks, A., Janssen, H.W., Calafat, J. & Plasterk, R.H. A P-glycoprotein protects Caenorhabditis elegans against natural toxins. EMBO J. 14, 1858–1866 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Rand, J.B. & Johnson, C.D. Genetic pharmacology: interactions between drugs and gene products in Caenorhabditis elegans. in Methods in Cell Biology, 48 (eds. Epstein, H.F. & Shakes, D.C.) 187–204 (Academic, San Diego, 1995).

    CAS  PubMed  Google Scholar 

  11. Choy, R.K. & Thomas, J.H. Fluoxetine-resistant mutants in C. elegans define a novel family of transmembrane proteins. Mol. Cell 4, 143–152 (1999).

    CAS  PubMed  Google Scholar 

  12. Cox, G.N., Kusch, M. & Edgar, R.S. Cuticle of Caenorhabditis elegans: its isolation and partial characterization. J. Cell Biol. 90, 7–17 (1981).

    CAS  PubMed  Google Scholar 

  13. Avery, L. & Shtonda, B.B. Food transport in the C. elegans pharynx. J. Exp. Biol. 206, 2441–2457 (2003).

    PubMed  PubMed Central  Google Scholar 

  14. Lindblom, T.H. & Dodd, A.K. Xenobiotic detoxification in the nematode Caenorhabditis elegans. J. Exp. Zool. A. Comp. Exp. Biol. 305, 720–730 (2006).

    PubMed  PubMed Central  Google Scholar 

  15. Jospin, M., Jacquemond, V., Mariol, M.C., Segalat, L. & Allard, B. The L-type voltage-dependent Ca2+ channel EGL-19 controls body wall muscle function in Caenorhabditis elegans. J. Cell Biol. 159, 337–348 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Franks, C.J. et al. Ionic basis of the resting membrane potential and action potential in the pharyngeal muscle of Caenorhabditis elegans. J. Neurophysiol. 87, 954–961 (2002).

    CAS  PubMed  Google Scholar 

  17. Irwin, J.J. & Shoichet, B.K. ZINC—a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177–182 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Herre, S. & Pragst, F. Shift of the high-performance liquid chromatographic retention times of metabolites in relation to the original drug on an RP8 column with acidic mobile phase. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 692, 111–126 (1997).

    CAS  Google Scholar 

  19. Herzler, M., Herre, S. & Pragst, F. Selectivity of substance identification by HPLC–DAD in toxicological analysis using a UV spectra library of 2682 compounds. J. Anal. Toxicol. 27, 233–242 (2003).

    CAS  PubMed  Google Scholar 

  20. 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).

    CAS  PubMed  Google Scholar 

  21. Kocisko, D.A. et al. New inhibitors of scrapie-associated prion protein formation in a library of 2000 drugs and natural products. J. Virol. 77, 10288–10294 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Eddershaw, P. & Dickins, M. Phase I metabolism. in A Handbook of Bioanalysis and Drug Metabolism (ed. Evans, G.) 208–221 (CRC Press, Boca Raton, Florida, USA, 2004).

  23. Manchee, G., Dickins, M. & Pickup, E. Phase II enzymes. in A Handbook of Bioanalysis and Drug Metabolism (ed. Evans, G.) 222–243 (CRC Press, Boca Raton, Florida, USA, 2004).

  24. Xia, X., Maliski, E.G., Gallant, P. & Rogers, D. Classification of kinase inhibitors using a Bayesian model. J. Med. Chem. 47, 4463–4470 (2004).

    CAS  PubMed  Google Scholar 

  25. Rogers, D., Brown, R.D. & Hahn, M. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen. 10, 682–686 (2005).

    CAS  PubMed  Google Scholar 

  26. Durant, J.L., Leland, B.A., Henry, D.R. & Nourse, J.G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273–1280 (2002).

    CAS  PubMed  Google Scholar 

  27. Kerwar, S.S. Pharmacologic properties of fenbufen. Am. J. Med. 75, 62–69 (1983).

    CAS  PubMed  Google Scholar 

  28. Flower, D.R. On the properties of bit string-based measures of chemical similarity. J. Chem. Inf. Comput. Sci. 38, 379–386 (1998).

    CAS  Google Scholar 

  29. Hert, J., Irwin, J.J., Laggner, C., Keiser, M.J. & Shoichet, B.K. Quantifying biogenic bias in screening libraries. Nat. Chem. Biol. 5, 479–483 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Bemis, G.W. & Murcko, M.A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).

    CAS  PubMed  Google Scholar 

  31. Shelat, A.A. & Guy, R.K. Scaffold composition and biological relevance of screening libraries. Nat. Chem. Biol. 3, 442–446 (2007).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  33. Young, D.W. et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol. 4, 59–68 (2008).

    CAS  PubMed  Google Scholar 

  34. Horton, D.A., Bourne, G.T. & Smythe, M.L. The combinatorial synthesis of bicyclic privileged structures or privileged substructures. Chem. Rev. 103, 893–930 (2003).

    CAS  PubMed  Google Scholar 

  35. Klekota, J. & Roth, F.P. Chemical substructures that enrich for biological activity. Bioinformatics 24, 2518–2525 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Evans, B.E. et al. Methods for drug discovery: development of potent, selective, orally effective cholecystokinin antagonists. J. Med. Chem. 31, 2235–2246 (1988).

    CAS  PubMed  Google Scholar 

  37. Mason, J.S. et al. New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J. Med. Chem. 42, 3251–3264 (1999).

    CAS  PubMed  Google Scholar 

  38. Hajduk, P.J., Bures, M., Praestgaard, J. & Fesik, S.W. Privileged molecules for protein binding identified from NMR-based screening. J. Med. Chem. 43, 3443–3447 (2000).

    CAS  PubMed  Google Scholar 

  39. Chen, Y. & Shoichet, B.K. Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat. Chem. Biol. 5, 358–364 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Garzon-Aburbeh, A., Poupaert, J.H., Claesen, M. & Dumont, P. A lymphotropic prodrug of L-dopa: synthesis, pharmacological properties, and pharmacokinetic behavior of 1,3-dihexadecanoyl-2-[(S)-2-amino-3-(3,4-dihydroxyphenyl)prop anoyl]propane-1,2,3-triol. J. Med. Chem. 29, 687–691 (1986).

    CAS  PubMed  Google Scholar 

  41. Inturrisi, C.E. et al. Evidence from opiate binding studies that heroin acts through its metabolites. Life Sci. 33 Suppl 1: 773–776 (1983).

    CAS  PubMed  Google Scholar 

  42. Hou, B., Lim, E.K., Higgins, G.S. & Bowles, D.J. N-glucosylation of cytokinins by glycosyltransferases of Arabidopsis thaliana. J. Biol. Chem. 279, 47822–47832 (2004).

    CAS  PubMed  Google Scholar 

  43. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

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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



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.

Corresponding author

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).

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