Quantitative Prioritization of Tool Compounds for Phenotypic Screening

  • Yuan Wang
  • Jeremy L. Jenkins
Part of the Methods in Molecular Biology book series (MIMB, volume 1787)


Phenotypic screens are increasingly utilized in drug discovery for multiple purposes such as lead and/or tool compound finding, and target discovery. Using potent and selective chemical tool compounds against well-defined targets in phenotypic screens can help elucidate biological processes modulating assay phenotypes. Unfortunately the identification of such tools from large heterogeneous bioactivity databases is nontrivial and there is repeated use of published unselective compounds as phenotypic tools. Here we describe a computational model, the compound-target tool score (TS), which is an evidence-based quantitative confidence metric that can be used to systematically rank tool compounds for targets. The identified selective and nonselective tool compounds have applications in phenotypic assays for target hypothesis validation as well as assay development.

Key words

Chemical probe Tool compound Phenotypic screen Selectivity Target hypothesis validation Bioactivity data integration 


  1. 1.
    Swinney DC (2013) Phenotypic vs. target-based drug discovery for first-in-class medicines. Clin Pharmacol Ther 93:299–301CrossRefPubMedGoogle Scholar
  2. 2.
    Moffat JG, Rudolph J, Bailey D (2014) Phenotypic screening in cancer drug discovery: past, present and future. Nat Rev Drug Discov 13:588–602CrossRefGoogle Scholar
  3. 3.
    Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690CrossRefGoogle Scholar
  4. 4.
    Lee JA, Uhlik MT, Moxham CM, Tomandl D, Sall DJ (2012) Modern phenotypic drug discovery is a viable, neoclassic pharma strategy. J Med Chem 55:4527–4538CrossRefGoogle Scholar
  5. 5.
    Jones JO, Diamond MI (2007) Design and implementation of cell-based assays to model human disease. ACS Chem Biol 2:718–724CrossRefGoogle Scholar
  6. 6.
    Hart CP (2005) Finding the target after screening the phenotype. Drug Discov Today 10:513–519CrossRefPubMedGoogle Scholar
  7. 7.
    Schirle M, Jenkins JL (2016) Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov Today 21:82–89CrossRefGoogle Scholar
  8. 8.
    Feng Y, Mitchison TJ, Bender A, Young DW, Tallarico JA (2009) Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat Rev Drug Discov 8:567–578CrossRefGoogle Scholar
  9. 9.
    King FJ, Selinger DW, Mapa FA, Janes J, Wu H, Smith TR et al (2009) Pathway reporter assays reveal small molecule mechanisms of action. J Assoc Lab Autom 14:374–382CrossRefGoogle Scholar
  10. 10.
    Frye SV (2010) The art of the chemical probe. Nat Chem Biol 6:159–161CrossRefGoogle Scholar
  11. 11.
    Arrowsmith CH, Audia JE, Austin C, Baell J, Bennett J, Blagg J et al (2015) The promise and peril of chemical probes. Nat Chem Biol 11:536–541CrossRefPubMedGoogle Scholar
  12. 12.
    Eggert US (2013) The why and how of phenotypic small-molecule screens. Nat Chem Biol 9:206–209CrossRefGoogle Scholar
  13. 13.
    Bunnage ME, Chekler EL, Jones LH (2013) Target validation using chemical probes. Nat Chem Biol 9:195–199CrossRefGoogle Scholar
  14. 14.
    Workman P, Collins I (2010) Probing the probes: fitness factors for small molecule tools. Chem Biol 17:561–577CrossRefPubMedGoogle Scholar
  15. 15.
    Uitdehaag JC, Verkaar F, Alwan H, De Man J, Buijsman RC, Zaman GJ (2012) A guide to picking the most selective kinase inhibitor tool compounds for pharmacological validation of drug targets. Br J Pharmacol 166:858–876CrossRefPubMedGoogle Scholar
  16. 16.
    Huang SM, Mishina YM, Liu S, Cheung A, Stegmeier F, Michaud GA et al (2009) Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature 461:614–620CrossRefGoogle Scholar
  17. 17.
    Gregori-Puigjane E, Setola V, Hert J, Crews BA, Irwin JJ, Lounkine E et al (2012) Identifying mechanism-of-action targets for drugs and probes. Proc Natl Acad Sci U S A 109:11178–11183CrossRefPubMedGoogle Scholar
  18. 18.
    Roth BL, Sheffler DJ, Kroeze WK (2004) Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat Rev Drug Discov 3:353–359CrossRefGoogle Scholar
  19. 19.
    Hopkins AL (2009) Drug discovery: predicting promiscuity. Nature 462:167–168CrossRefGoogle Scholar
  20. 20.
    Jester BW, Gaj A, Shomin CD, Cox KJ, Ghosh I (2012) Testing the promiscuity of commercial kinase inhibitors against the AGC kinase group using a split-luciferase screen. J Med Chem 55:1526–1537CrossRefPubMedGoogle Scholar
  21. 21.
    Graczyk PP (2007) Gini coefficient: a new way to express selectivity of kinase inhibitors against a family of kinases. J Med Chem 50:5773–5779CrossRefGoogle Scholar
  22. 22.
    Uitdehaag JC, Zaman GJ (2011) A theoretical entropy score as a single value to express inhibitor selectivity. BMC Bioinformatics 12:94CrossRefPubMedGoogle Scholar
  23. 23.
    Kalliokoski T, Kramer C, Vulpetti A, Gedeck P (2013) Comparability of mixed IC(5)(0) data: a statistical analysis. PLoS One 8:e61007CrossRefPubMedGoogle Scholar
  24. 24.
    Kramer C, Kalliokoski T, Gedeck P, Vulpetti A (2012) The experimental uncertainty of heterogeneous public K(i) data. J Med Chem 55:5165–5173CrossRefGoogle Scholar
  25. 25.
    Tang J, Szwajda A, Shakyawar S, Xu T, Hintsanen P, Wennerberg K et al (2014) Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model 54:735–743CrossRefGoogle Scholar
  26. 26.
    Tiikkainen P, Bellis L, Light Y, Franke L (2013) Estimating error rates in bioactivity databases. J Chem Inf Model 53:2499–2505CrossRefGoogle Scholar
  27. 27.
    Maglott D, Ostell J, Pruitt KD, Tatusova T (2011) Entrez gene: gene-centered information at NCBI. Nucleic Acids Res 39:D52–D57CrossRefGoogle Scholar
  28. 28.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29CrossRefPubMedGoogle Scholar
  29. 29.
    Gene Ontology C (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res 43:D1049–D1056CrossRefGoogle Scholar
  30. 30.
    Uniprot C (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212CrossRefGoogle Scholar
  31. 31.
    Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y et al (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–D1097CrossRefGoogle Scholar
  32. 32.
    Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M et al (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083–D1090CrossRefGoogle Scholar
  33. 33.
    Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35:D198–D201CrossRefGoogle Scholar
  34. 34.
    Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2015) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045–D1053CrossRefPubMedGoogle Scholar
  35. 35.
    Heller S, Mcnaught A, Stein S, Tchekhovskoi D, Pletnev I (2013) InChI: the worldwide chemical structure identifier standard. J Chem 5:7CrossRefGoogle Scholar
  36. 36.
    Heller SR, Mcnaught A, Pletnev I, Stein S, Tchekhovskoi D (2015) InChI, the IUPAC international chemical identifier. J Chem 7:23CrossRefGoogle Scholar
  37. 37.
    Hoare CAR (1969) An axiomatic basis for computer programming. Commun Acm 12:576–583CrossRefGoogle Scholar
  38. 38.
    Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D230CrossRefGoogle Scholar
  39. 39.
    Emig D, Kacprowski T, Albrecht M (2011) Measuring and analyzing tissue specificity of human genes and protein complexes. EURASIP J Bioinform Syst Biol 2011:5CrossRefPubMedGoogle Scholar
  40. 40.
    Gujral TS, Peshkin L, Kirschner MW (2014) Exploiting polypharmacology for drug target deconvolution. Proc Natl Acad Sci U S A 111:5048–5053CrossRefPubMedGoogle Scholar
  41. 41.
    Wang Y, Cornett A, King FJ, Mao Y, Nigsch F, Paris CG et al (2016) Evidence-based and quantitative prioritization of tool compounds in phenotypic drug discovery. Cell Chem Biol 23:862–874CrossRefGoogle Scholar
  42. 42.
    Shin YJ, Kim JJ, Kim YJ, Kim WH, Park EY, Kim IY et al (2015) Protective effects of quercetin against HgCl(2)-induced nephrotoxicity in Sprague-Dawley rats. J Med Food 18:524–534CrossRefGoogle Scholar
  43. 43.
    Gryglewski RJ, Korbut R, Robak J, Swies J (1987) On the mechanism of antithrombotic action of flavonoids. Biochem Pharmacol 36:317–322CrossRefGoogle Scholar
  44. 44.
    Vita JA (2005) Polyphenols and cardiovascular disease: effects on endothelial and platelet function. Am J Clin Nutr 81:292S–297SCrossRefGoogle Scholar
  45. 45.
    Sankari SL, Babu NA, Rani V, Priyadharsini C, Masthan KM (2014) Flavonoids—clinical effects and applications in dentistry: a review. J Pharm Bioallied Sci 6:S26–S29CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Novartis Institutes for BioMedical Research Inc.CambridgeUSA

Personalised recommendations