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A Drosophila Based Cancer Drug Discovery Framework

  • Erdem BangiEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1167)

Abstract

In recent years, there has been growing interest in using Drosophila for drug discovery as it provides a unique opportunity to screen small molecules against complex disease phenotypes in a whole animal setting. Furthermore, gene-compound interaction experiments that combine compound feeding with complex genetic manipulations enable exploration of compound mechanisms of response and resistance to an extent that is difficult to achieve in other experimental models. Here, I discuss how compound screening and testing approaches reported in Drosophila fit into the current cancer drug discovery pipeline. I then propose a framework for a Drosophila-based cancer drug discovery strategy which would allow the Drosophila research community to effectively leverage the power of Drosophila to identify candidate therapeutics and push our discoveries into the clinic.

Keywords

Drosophila Cancer drug discovery Compound screening 

Notes

Acknowledgements

I would like to thank Dr. Ross Cagan for feedback on this manuscript.

Conflicts of Interest

The author declares no potential conflicts of interest.

References

  1. 1.
    Pandey UB, Nichols CD (2011) Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery. Pharmacol Rev 63:411–436CrossRefGoogle Scholar
  2. 2.
    Sonoshita M, Cagan RL (2017) Modeling human cancers in Drosophila. Curr Top Dev Biol 121:287–309CrossRefGoogle Scholar
  3. 3.
    Graham P, Pick L (2017) Drosophila as a model for diabetes and diseases of insulin resistance. Curr Top Dev Biol 121:397–419CrossRefGoogle Scholar
  4. 4.
    McGurk L, Berson A, Bonini NM (2015) Drosophila as an in vivo model for human neurodegenerative disease. Genetics 201:377–402CrossRefGoogle Scholar
  5. 5.
    Bhandari P, Shashidhara LS (2001) Studies on human colon cancer gene APC by targeted expression in Drosophila. Oncogene 20:6871–6880CrossRefGoogle Scholar
  6. 6.
    Radimerski T, Montagne J, Hemmings-Mieszczak M, Thomas G (2002) Lethality of Drosophila lacking TSC tumor suppressor function rescued by reducing dS6K signaling. Genes Dev 16:2627–2632CrossRefGoogle Scholar
  7. 7.
    Micchelli CA et al (2003) γ-Secretase/presenilin inhibitors for Alzheimer’s disease phenocopy Notch mutations in Drosophila. FASEB J 17:79–81CrossRefGoogle Scholar
  8. 8.
    Vidal M, Wells S, Ryan A, Cagan R (2005) ZD6474 suppresses oncogenic RET isoforms in a Drosophila model for type 2 multiple endocrine neoplasia syndromes and papillary thyroid carcinoma. Cancer Res 65:3538–3541CrossRefGoogle Scholar
  9. 9.
    Desai UA et al (2006) Biologically active molecules that reduce polyglutamine aggregation and toxicity. Hum Mol Genet 15:2114–2124CrossRefGoogle Scholar
  10. 10.
    Chang S et al (2008) Identification of small molecules rescuing fragile X syndrome phenotypes in Drosophila. Nat Chem Biol 4:256–263CrossRefGoogle Scholar
  11. 11.
    Bangi E, Garza D, Hild M (2011) In vivo analysis of compound activity and mechanism of action using epistasis in Drosophila. J Chem Biol 4:55–68CrossRefGoogle Scholar
  12. 12.
    Jaklevic B et al (2006) Contribution of growth and cell cycle checkpoints to radiation survival in Drosophila. Genetics 174:1963–1972CrossRefGoogle Scholar
  13. 13.
    Yadav AK, Srikrishna S, Gupta SC (2016) Cancer drug development using Drosophila as an in vivo tool: from bedside to bench and Back. Trends Pharmacol Sci 37:789–806CrossRefGoogle Scholar
  14. 14.
    Strange K (2016) Drug discovery in fish, flies, and worms. ILAR J 57:133–143CrossRefGoogle Scholar
  15. 15.
    Gladstone M, Su TT (2011) Chemical genetics and drug screening in Drosophila cancer models. J Genet Genomics 38:497–504CrossRefGoogle Scholar
  16. 16.
    Markstein M (2013) Modeling colorectal cancer as a 3-dimensional disease in a dish: the case for drug screening using organoids, zebrafish, and fruit flies. Drug Discov Today Technol 10:e73–e81CrossRefGoogle Scholar
  17. 17.
    Das T, Cagan R (2010) Drosophila as a novel therapeutic discovery tool for thyroid cancer. Thyroid 20:689–695CrossRefGoogle Scholar
  18. 18.
    Das TK, Cagan RL (2013) A Drosophila approach to thyroid cancer therapeutics. Drug Discov Today Technol 10:e65–e71CrossRefGoogle Scholar
  19. 19.
    Swinney DC (2013) Phenotypic vs. target-based drug discovery for first-in-class medicines. Clin Pharmacol Ther 93:299–301CrossRefGoogle Scholar
  20. 20.
    Hoelder S, Clarke PA, Workman P (2012) Discovery of small molecule cancer drugs: successes, challenges and opportunities. Mol Oncol 6:155–176CrossRefGoogle Scholar
  21. 21.
    Sams-Dodd F (2005) Target-based drug discovery: is something wrong? Drug Discov Today 10:139–147CrossRefGoogle Scholar
  22. 22.
    Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5:993–996CrossRefGoogle Scholar
  23. 23.
    Capdeville R, Buchdunger E, Zimmermann J, Matter A (2002) Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug. Nat Rev Drug Discov 1:493–502CrossRefGoogle Scholar
  24. 24.
    Barker AJ et al (2001) Studies leading to the identification of ZD1839 (IRESSA): an orally active, selective epidermal growth factor receptor tyrosine kinase inhibitor targeted to the treatment of cancer. Bioorg Med Chem Lett 11:1911–1914CrossRefGoogle Scholar
  25. 25.
    Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov 10:507–519CrossRefGoogle Scholar
  26. 26.
    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
  27. 27.
    Willoughby LF et al (2012) An in vivo large-scale chemical screening platform using Drosophila for anti-cancer drug discovery. Dis Model Mech 6:521–529CrossRefGoogle Scholar
  28. 28.
    Markstein M et al (2014) Systematic screen of chemotherapeutics in Drosophila stem cell tumors. Proc Natl Acad Sci U S A 111:4530–4535CrossRefGoogle Scholar
  29. 29.
    Levine BD, Cagan RL (2016) Drosophila lung cancer models identify trametinib plus statin as candidate therapeutic. Cell Rep 14:1477–1487CrossRefGoogle Scholar
  30. 30.
    Levinson S, Cagan RL (2016) Drosophila cancer models identify functional differences between ret fusions. Cell Rep 16:3052–3061CrossRefGoogle Scholar
  31. 31.
    Das TK, Esernio J, Cagan RL (2018) Restraining network response to targeted cancer therapies improves efficacy and reduces cellular resistance. Cancer Res 78:4344–4359CrossRefGoogle Scholar
  32. 32.
    Das TK, Cagan RL (2017) KIF5B-RET oncoprotein signals through a multi-kinase signaling hub. Cell Rep 20:2368–2383CrossRefGoogle Scholar
  33. 33.
    Bangi E, Murgia C, Teague AGS, Sansom OJ, Cagan RL (2016) Functional exploration of colorectal cancer genomes using Drosophila. Nat Commun 7:13615CrossRefGoogle Scholar
  34. 34.
    Enomoto M, Siow C, Igaki T (2018) Drosophila as a cancer model. Adv Exp Med Biol 1076:173–194CrossRefGoogle Scholar
  35. 35.
    Herranz H, Eichenlaub T, Cohen SM (2016) Cancer in Drosophila: imaginal discs as a model for epithelial tumor formation. Curr Top Dev Biol 116:181–199CrossRefGoogle Scholar
  36. 36.
    Hou SX, Singh SR (2017) Stem-cell-based tumorigenesis in adult Drosophila. Curr Top Dev Biol 121:311–337CrossRefGoogle Scholar
  37. 37.
    Garraway LA, Lander ES (2013) Lessons from the cancer genome. Cell 153:17–37CrossRefGoogle Scholar
  38. 38.
    Biankin AV, Piantadosi S, Hollingsworth SJ (2015) Patient-centric trials for therapeutic development in precision oncology. Nature 526:361–370CrossRefGoogle Scholar
  39. 39.
    Mendelsohn J (2013) Personalizing oncology: perspectives and prospects. J Clin Oncol 31:1904–1911CrossRefGoogle Scholar
  40. 40.
    Simon R, Roychowdhury S (2013) Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov 12:358–369CrossRefGoogle Scholar
  41. 41.
    Nass SJ et al (2018) Accelerating anticancer drug development — opportunities and trade-offs. Nat Rev Clin Oncol 15:777–786CrossRefGoogle Scholar
  42. 42.
    Wong CH (2017) Estimation of clinical trial success rates and related parametersGoogle Scholar
  43. 43.
    Rodon J, Dienstmann R, Serra V, Tabernero J (2013) Development of PI3K inhibitors: lessons learned from early clinical trials. Nat Rev Clin Oncol 10:143–153CrossRefGoogle Scholar
  44. 44.
    Casaluce F et al (2017) Selumetinib for the treatment of non-small cell lung cancer. Expert Opin Investig Drugs 26:973–984CrossRefGoogle Scholar
  45. 45.
    Infante JR et al (2012) Safety, pharmacokinetic, pharmacodynamic, and efficacy data for the oral MEK inhibitor trametinib: a phase 1 dose-escalation trial. Lancet Oncol 13:773–781CrossRefGoogle Scholar
  46. 46.
    Borthakur G et al (2016) Activity of the oral mitogen-activated protein kinase kinase inhibitor trametinib in RAS-mutant relapsed or refractory myeloid malignancies. Cancer 122:1871–1879CrossRefGoogle Scholar
  47. 47.
    Jänne PA et al (2013) Selumetinib plus docetaxel for KRAS-mutant advanced non-small-cell lung cancer: a randomised, multicentre, placebo-controlled, phase 2 study. Lancet Oncol 14:38–47CrossRefGoogle Scholar
  48. 48.
    Blumenschein GR Jr et al (2015) A randomized phase II study of the MEK1/MEK2 inhibitor trametinib (GSK1120212) compared with docetaxel in KRAS-mutant advanced non-small-cell lung cancer (NSCLC)†. Ann Oncol 26:894–901CrossRefGoogle Scholar
  49. 49.
    Sonoshita M et al (2018) A whole-animal platform to advance a clinical kinase inhibitor into new disease space. Nat Chem Biol 14:291–298CrossRefGoogle Scholar
  50. 50.
    Gleeson MP, Hersey A, Montanari D, Overington J (2011) Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat Rev Drug Discov 10:197–208CrossRefGoogle Scholar
  51. 51.
    Huggins DJ, Sherman W, Tidor B (2012) Rational approaches to improving selectivity in drug design. J Med Chem 55:1424–1444CrossRefGoogle Scholar
  52. 52.
    Davis MI et al (2011) Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29:1046–1051CrossRefGoogle Scholar
  53. 53.
    Ciardiello F et al (2004) Antitumor activity of ZD6474, a vascular endothelial growth factor receptor tyrosine kinase inhibitor, in human cancer cells with acquired resistance to antiepidermal growth factor receptor therapy. Clin Cancer Res 10:784–793CrossRefGoogle Scholar
  54. 54.
    Wedge SR et al (2002) ZD6474 inhibits vascular endothelial growth factor signaling, angiogenesis, and tumor growth following oral administration. Cancer Res 62:4645–4655PubMedGoogle Scholar
  55. 55.
    McCarty MF et al (2004) ZD6474, a vascular endothelial growth factor receptor tyrosine kinase inhibitor with additional activity against epidermal growth factor receptor tyrosine kinase, inhibits orthotopic growth and angiogenesis of gastric cancer. Mol Cancer Ther 3:1041–1048PubMedGoogle Scholar
  56. 56.
    Wells SA et al (2012) Vandetanib in patients with locally advanced or metastatic medullary thyroid cancer: a randomized, double-blind phase III trial. J Clin Oncol 30:134–141CrossRefGoogle Scholar
  57. 57.
    Wong CH, Siah KW, Lo AW (2018) Estimation of clinical trial success rates and related parameters. Biostatistics 20(2):273–286.  https://doi.org/10.1093/biostatistics/kxx069CrossRefPubMedCentralGoogle Scholar
  58. 58.
    Massacesi C et al (2016) PI3K inhibitors as new cancer therapeutics: implications for clinical trial design. Onco Targets Ther 9:203–210CrossRefGoogle Scholar
  59. 59.
    Guha R (2013) On exploring structure–activity relationships. Methods Mol Biol 993:81–94CrossRefGoogle Scholar
  60. 60.
    Dar AC, Das TK, Shokat KM, Cagan RL (2012) Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 486:80–84CrossRefGoogle Scholar
  61. 61.
    Cagan R (2016) Drug screening using model systems: some basics. Dis Model Mech 9:1241–1244CrossRefGoogle Scholar
  62. 62.
    Lonial S, Anderson KC (2014) Association of response endpoints with survival outcomes in multiple myeloma. Leukemia 28:258–268CrossRefGoogle Scholar
  63. 63.
    Harvey AL, Edrada-Ebel R, Quinn RJ (2015) The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov 14:111–129CrossRefGoogle Scholar
  64. 64.
    Li JW-H, Vederas JC (2009) Drug discovery and natural products: end of an era or an endless frontier? Science 325:161–165CrossRefGoogle Scholar
  65. 65.
    Cha Y et al (2018) Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol 175:168–180CrossRefGoogle Scholar
  66. 66.
    Pushpakom S et al (2018) Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov.  https://doi.org/10.1038/nrd.2018.168CrossRefGoogle Scholar
  67. 67.
    Breckenridge A, Jacob R (2019) Overcoming the legal and regulatory barriers to drug repurposing. Nat Rev Drug Discov 18:1–2CrossRefGoogle Scholar
  68. 68.
    Garralda E, Dienstmann R, Tabernero J (2017) Pharmacokinetic/Pharmacodynamic modeling for drug development in oncology. Am Soc Clin Oncol Educ Book 37:210–215CrossRefGoogle Scholar
  69. 69.
    Lavé T, Caruso A, Parrott N, Walz A (2016) Translational PK/PD modeling to increase probability of success in drug discovery and early development. Drug Discov Today Technol 21–22:27–34CrossRefGoogle Scholar
  70. 70.
    Stricker-Krongrad A, Shoemake CR, Bouchard GF (2016) The miniature swine as a model in experimental and translational medicine. Toxicol Pathol 44:612–623CrossRefGoogle Scholar
  71. 71.
    Lipton SA, Nordstedt C (2016) Partnering with big pharma—what academics need to know. Cell 165:512–515CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biological ScienceFlorida State UniversityTallahasseeUSA

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