Advertisement

Investigational New Drugs

, Volume 26, Issue 2, pp 97–110 | Cite as

Atypical cytostatic mechanism of N-1-sulfonylcytosine derivatives determined by in vitro screening and computational analysis

  • Fran Supek
  • Marijeta Kralj
  • Marko Marjanović
  • Lidija Šuman
  • Tomislav Šmuc
  • Irena Krizmanić
  • Biserka Žinić
Preclinical Studies

Summary

We have previously shown that N-1-sulfonylpyrimidine derivatives have strong antiproliferative activity on human tumor cell lines, whereby 1-(p-toluenesulfonyl)cytosine showed good selectivity with regard to normal cells and was easily synthesized on a large scale. In the present work we have used an interdisciplinary approach to elucidate the compounds’ mechanistic class. An augmented number of cell lines (11) has allowed a computational search for compounds with similar activity profiles and/or mechanistic class by integrating our data with the comprehensive DTP–NCI database. We applied supervised machine learning methodology (Random Forest classifier), which offers information complementary to unsupervised algorithms commonly used for analysis of cytostatic activity profiles, such as self-organizing maps. The computational results taken together with cell cycle perturbation and apoptosis analysis of the cell lines point to an unusual mechanism of cytostatic action, possibly a combination of nucleic acid antimetabolite activity and a novel molecular mechanism.

Keywords

Nucleobases Antitumor agents In vitro screening Bioinformatics Random Forest 

Notes

Acknowledgement

This work was supported by the Rudjer Boskovic Institute’s spin-off company BioZyne d.o.o., and the Ministry of Science, Education and Sport of Croatia. We are very grateful to Dr. David Covell of the National Cancer Institute’s Developmental Therapeutics Program for supplying the data set with mechanistic classes of compounds.

References

  1. 1.
    Erhardt PW (2002) Medicinal chemistry in the new millennium. A glance into the future. Pure Appl Chem 74:703–785CrossRefGoogle Scholar
  2. 2.
    Oprea TI, Gottfries J (2001) Chemography: the art of navigating in chemical space. J Com Chem 3:157–166CrossRefGoogle Scholar
  3. 3.
    MacCoss M, Robins MJ (1990) In: Wilman DEV (ed) Chemistry of antitumor agents. Blackie and Son, Glasgow, Scotland, p 261Google Scholar
  4. 4.
    Robins RK, Kirin GD (1990) In: Wilman DEV (ed) Chemistry of antitumor agents. Blackie and Son, Glasgow, Scotland, p 299Google Scholar
  5. 5.
    Robins RK, Revankar GR (1988) In: De Clercq E, Walker RT (eds) Antiviral drug development. Plenum, New York, p 11Google Scholar
  6. 6.
    Kašnar B, Krizmanić I, Žinić M (1997) Synthesis of the sulfonylpirimidine derivatives as a new type of sulfonylcycloureas. Nucleosides Nucleotides 16:1067–1071CrossRefGoogle Scholar
  7. 7.
    Žinić B, Krizmanić I, Vikić-Topić D, Žinić M (1999) 5-Bromo- and 5-iodo-N-1-sulfonylated cytosine derivatives. Exclusive formation of keto-imino tautomers. Croat Chem Acta 72:957–966Google Scholar
  8. 8.
    Žinić B, Žinić M, Krizmanić I (2003) Synthesis of the sulfonylpyrimidine derivatives with anticancer activity. EP 0 877 022 B1Google Scholar
  9. 9.
    Slišković DR, Krause BR, Bocan TMA (1999) In: Doherty AM, Greenlee W, Hagmann WK (eds) Annual Reports in Medicinal Chemistry 34:101–110Google Scholar
  10. 10.
    Melander A (1996) Oral antidiabetic drugs: an overview. Diabet Med 13:S143–S147PubMedGoogle Scholar
  11. 11.
    Furlong ET, Burkhardt MR, Gates PM, Werner SL, Battaglin WA (2000) Routine determination of sulfonylurea, imidazolinone, and sulfonamide herbicides at nanogram-per-liter concentrations by solid-phase extraction and liquid chromatography/mass spectrometry. Sci Total Environ 248:135–146PubMedCrossRefGoogle Scholar
  12. 12.
    Houghton PJ, Sosinski J, Thakar JH, Boder GB, Grindey GB (1995) Characterization of the intracellular distribution and binding in human adenocarcinoma cells of N-(4-azidophenylsulfonyl)-N′-(4-chlorophenyl)urea (LY219703), a photoaffinity analogue of the antitumor diarylsulfonylurea sulofenur. Biochem Pharmacol 49:661–668PubMedCrossRefGoogle Scholar
  13. 13.
    Schultz RM, Merriman RL, Toth JE, Zimmermann JE, Hertel LW, Andis SL, Dudley DE, Rutherford PG, Tanzer LR, Grindey GB (1993) Evaluation of new anticancer agents against the MIA PaCa-2 and PANC-1 human pancreatic carcinoma xenografts. Oncol Res 5:223–228PubMedGoogle Scholar
  14. 14.
    Mohamadi F, Spees MM, Grindey GB (1992) Sulfonylureas: a new class of cancer chemotherapeutic agents. J Med Chem 35:3012–3016PubMedCrossRefGoogle Scholar
  15. 15.
    Morre DJ, Wu LY, Morre DM (1998) Response of a cell-surface NADH oxidase to the antitumor sulfonylurea N-(4-methylphenylsulfonyl)-N′-(4-chlorophenylurea) (LY181984) modulated by redox. Biochim Biophys Acta 1369:185–192PubMedCrossRefGoogle Scholar
  16. 16.
    Toth JE, Grindey GB, Ehlhardt WJ, Ray JE, Boder GB, Bewley JR, Klingerman KK, Gates SB, Rinzel SM, Schultz RM, Weir LC, Worzalla JF (1997) Sulfonimidamide analogs of oncolytic sulfonylureas. J Med Chem 40:1018–1025PubMedCrossRefGoogle Scholar
  17. 17.
    Glavaš-Obrovac L, Karner I, Žinić B, Pavelić K (2001) Antineoplastic activity of novel N-1-sulfonypyrimidine derivatives. Anticancer Res 21:1979–1986PubMedGoogle Scholar
  18. 18.
    Glavaš-Obrovac L, Karner I, Štefanić M, Kašnar-Šamprec J, Žinić B (2005) Metabolic effects of novel N-1-sulfonylpyrimidine derivatives on human colon carcinoma cells. Farmaco 60:479–483PubMedCrossRefGoogle Scholar
  19. 19.
    Boyd MR, Paull KD (1995) Some practical considerations and applications of the national cancer institute in vitro anticancer drug discovery screen. Drug Dev Res 34:91–109CrossRefGoogle Scholar
  20. 20.
    Martirosyan A, Gunar VI, Zav’yalov SI (1970) Tosylation of nitrogenous components of nucleic acids. Akad Nauk SSSR, Ser Khim 8:1841–1844Google Scholar
  21. 21.
    Kaldrikyn MA, Geboyan VA, Ter-Yakharyn YZ, Paronikyan RV, Garibdzhanyan BT, Stepanyan GM, Paronikyan GM (1986) Synthesis and biological activity of N′-4-alkoxybenzenesulfonyl-5-halouracils. Khim Farm Zh 20:928–932Google Scholar
  22. 22.
    Tada M (1975) Antineoplastic agents. Synthesis of some 1-substituted 5-fluorouracil derivatives. Chem Lett 4:129–130CrossRefGoogle Scholar
  23. 23.
    Kašnar-Šamprec J, Glavaš-Obrovac L, Pavlak M, Mihaljević I, Mrljak V, Štambuk N, Konjevoda P, Žinić B (2005) Synthesis, spectroscopic characterization and biological activity of N-1-sulfonylcytosine derivatives. Croat Chem Acta 78:261–267Google Scholar
  24. 24.
    Breiman L (2001) Random Forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  25. 25.
    Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464CrossRefGoogle Scholar
  26. 26.
    Supek F: i2SOM (computer program). http://www.lis.irb.hr/∼fran/i2SOM/
  27. 27.
    Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98:5116–5121PubMedCrossRefGoogle Scholar
  28. 28.
    Paull KD, Shoemaker RH, Hodes L, Monks A, Scudiero DA, Rubinstein L, Plowman J, Boyd MR (1989) Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and Compare algorithm. J Natl Cancer Inst 81:1088–1092PubMedCrossRefGoogle Scholar
  29. 29.
    Rabow AA, Shoemaker RH, Sausville EA, Covell DG (2002) Mining the National Cancer Institute’s tumor-screening database: identification of compounds with similar cellular activities. J Med Chem 45:818–840PubMedCrossRefGoogle Scholar
  30. 30.
    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Statist 6:65–70Google Scholar
  31. 31.
    Chua MS, Kashiyama E, Bradshaw TD, Stinson SF, Brantley E, Sausville EA, Stevens MF (2000) Role of Cyp1A1 in modulation of antitumor properties of the novel agent 2-(4-amino-3-methylphenyl)benzothiazole (DF 203, NSC 674495) in human breast cancer cells. Cancer Res 60:5196–5203PubMedGoogle Scholar
  32. 32.
    Monks A, Harris E, Hose C, Connelly J, Sausville EA (2003) Genotoxic profiling of MCF-7 breast cancer cell line elucidates gene expression modifications underlying toxicity of the anticancer drug 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole. Mol Pharmacol 63:766–772PubMedCrossRefGoogle Scholar
  33. 33.
    Qi Y, Bar-Joseph Z, Klein-Seetharaman J (2006) Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins 63:490–500PubMedCrossRefGoogle Scholar
  34. 34.
    Topić G, Šmuc T (2007) PARF—Parallel Random Forest algorithm (computer program). http://www.parf.irb.hr
  35. 35.
    Capranico G, Binaschi M (1998) DNA sequence selectivity of topoisomerases and topoisomerase poisons. Biochim Biophys Acta 1400:185–194PubMedGoogle Scholar
  36. 36.
    Grem JL (2000) 5-Fluorouracil: forty-plus and still ticking. A review of its preclinical and clinical development. Invest New Drugs 18:299–313PubMedCrossRefGoogle Scholar
  37. 37.
    Koch-Paiz CA, Amundson SA, Bittner ML, Meltzer PS, Fornace AJ, Jr (2004) Functional genomics of UV radiation responses in human cells. Mutat Res 549:65–78PubMedGoogle Scholar
  38. 38.
    Marchal JA, Boulaiz H, Suarez I, Saniger E, Campos J, Carrillo E, Prados J, Gallo MA, Espinosa A, Aranega A (2004) Growth inhibition, G(1)-arrest, and apoptosis in MCF-7 human breast cancer cells by novel highly lipophilic 5-fluorouracil derivatives. Invest New Drugs 22:379–389PubMedCrossRefGoogle Scholar
  39. 39.
    Marjanović M, Kralj M, Supek F, Frkanec L, Piantanida I, Šmuc T, Tušek-Božić L (2007) Antitumor potential of crown ethers: structure–activity relationships, cell cycle disturbances, and cell death studies of a series of ionophores. J Med Chem 50:1007–1018PubMedCrossRefGoogle Scholar
  40. 40.
    Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San FranciscoGoogle Scholar
  41. 41.
    Covell DG, Wallqvist A, Huang R, Thanki N, Rabow AA, Lu XJ (2005) Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases. Proteins 59:403–433PubMedCrossRefGoogle Scholar
  42. 42.
    Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman & Hall, New YorkGoogle Scholar
  43. 43.
    Quinlan JR (2006) C4.5: programs for machine learning. Morgan Kaufmann, San FranciscoGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Fran Supek
    • 2
  • Marijeta Kralj
    • 3
  • Marko Marjanović
    • 3
  • Lidija Šuman
    • 3
  • Tomislav Šmuc
    • 2
  • Irena Krizmanić
    • 4
    • 5
  • Biserka Žinić
    • 1
  1. 1.Laboratory of Supramolecular and Nucleoside Chemistry, Division of Organic Chemistry and BiochemistryRuđer Bošković InstituteZagrebCroatia
  2. 2.Division of Electronics, Laboratory for Information SystemsRuđer Bošković InstituteZagrebCroatia
  3. 3.Division of Molecular Medicine, Laboratory of Functional GenomicsRuđer Bošković InstituteZagrebCroatia
  4. 4.HERBOS Chemical IndustrySisakCroatia
  5. 5.PLIVA—Research and Development Ltd.ZagrebCroatia

Personalised recommendations