Discovery of a first-in-class reversible DNMT1-selective inhibitor with improved tolerability and efficacy in acute myeloid leukemia (original) (raw)

Data availability

All data generated to support the findings of this study are available. The functional genomics data have been deposited in the NCBI Gene Expression Omnibus (GEO) and are accessible through the GEO SuperSeries accession number: GSE135207 (https://www.ncbi.nlm.nih.gov/geo). The atomic coordinates and structure factors of DNMT1–DNA (zebularine)–SAH (PDB 6X9I), DNMT1–DNA–GSK3830052 (PDB 6X9J) and DNMT1–DNA–GSK3685032 (PDB 6X9K) have been deposited in the Protein Data Bank (http://www.rcsb.org). Source data are provided with this paper. All other data are available from the corresponding authors upon reasonable request.

Code availability

The code generated to analyze Infinium Methylation EPIC array, RNA-seq gene expression and hERV expression data (Figs. 4–6 and Extended Data Fig. 5) can be found at https://github.com/ShawnWFoley-GSK/Pappalardi_et_al_2021. A detailed list of software and package versions can be found in the Methods section.

References

  1. Okano, M., Bell, D. W., Haber, D. A. & Li, E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99, 247–257 (1999).
    Article CAS PubMed Google Scholar
  2. Pradhan, S., Bacolla, A., Wells, R. D. & Roberts, R. J. Recombinant human DNA (cytosine-5) methyltransferase. I. Expression, purification, and comparison of de novo and maintenance methylation. J. Biol. Chem. 274, 33002–33010 (1999).
    Article CAS PubMed Google Scholar
  3. Bird, A. DNA methylation patterns and epigenetic memory. Genes Dev. 16, 6–21 (2002).
    Article CAS PubMed Google Scholar
  4. Jones, P. A. & Laird, P. W. Cancer epigenetics comes of age. Nat. Genet. 21, 163–167 (1999).
    Article CAS PubMed Google Scholar
  5. Baylin, S. B. & Herman, J. G. DNA hypermethylation in tumorigenesis: epigenetics joins genetics. Trends Genet. 16, 168–174 (2000).
    Article CAS PubMed Google Scholar
  6. Ting, A. H., McGarvey, K. M. & Baylin, S. B. The cancer epigenome—components and functional correlates. Genes Dev. 20, 3215–3231 (2006).
    Article CAS PubMed Google Scholar
  7. Sorm, F., Piskala, A., Cihak, A. & Vesely, J. 5-Azacytidine, a new, highly effective cancerostatic. Experientia 20, 202–203 (1964).
    Article CAS PubMed Google Scholar
  8. Jones, P. A. & Taylor, S. M. Cellular differentiation, cytidine analogs and DNA methylation. Cell 20, 85–93 (1980).
    Article CAS PubMed Google Scholar
  9. Silverman, L. R. et al. Randomized controlled trial of azacitidine in patients with the myelodysplastic syndrome: a study of the cancer and leukemia group B. J. Clin. Oncol. 20, 2429–2440 (2002).
    Article CAS PubMed Google Scholar
  10. Oki, Y., Jelinek, J., Shen, L., Kantarjian, H. M. & Issa, J. P. Induction of hypomethylation and molecular response after decitabine therapy in patients with chronic myelomonocytic leukemia. Blood 111, 2382–2384 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  11. Fenaux, P. et al. Efficacy of azacitidine compared with that of conventional care regimens in the treatment of higher-risk myelodysplastic syndromes: a randomised, open-label, phase III study. Lancet Oncol. 10, 223–232 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  12. Lubbert, M. et al. Low-dose decitabine versus best supportive care in elderly patients with intermediate- or high-risk myelodysplastic syndrome (MDS) ineligible for intensive chemotherapy: final results of the randomized phase III study of the European Organisation for Research and Treatment of Cancer Leukemia Group and the German MDS Study Group. J. Clin. Oncol. 29, 1987–1996 (2011).
    Article PubMed CAS Google Scholar
  13. Agrawal, K., Das, V., Vyas, P. & Hajduch, M. Nucleosidic DNA demethylating epigenetic drugs—a comprehensive review from discovery to clinic. Pharmacol. Ther. 188, 45–79 (2018).
    Article CAS PubMed Google Scholar
  14. Gnyszka, A., Jastrzebski, Z. & Flis, S. DNA methyltransferase inhibitors and their emerging role in epigenetic therapy of cancer. Anticancer Res. 33, 2989–2996 (2013).
    CAS PubMed Google Scholar
  15. Issa, J. P. & Kantarjian, H. M. Targeting DNA methylation. Clin. Cancer Res. 15, 3938–3946 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  16. Stresemann, C. & Lyko, F. Modes of action of the DNA methyltransferase inhibitors azacytidine and decitabine. Int. J. Cancer 123, 8–13 (2008).
    Article CAS PubMed Google Scholar
  17. Chabot, G. G., Bouchard, J. & Momparler, R. L. Kinetics of deamination of 5-aza-2ʹ-deoxycytidine and cytosine arabinoside by human liver cytidine deaminase and its inhibition by 3-deazauridine, thymidine or uracil arabinoside. Biochem. Pharmacol. 32, 1327–1328 (1983).
    Article CAS PubMed Google Scholar
  18. Brueckner, B. et al. Epigenetic reactivation of tumor suppressor genes by a novel small-molecule inhibitor of human DNA methyltransferases. Cancer Res. 65, 6305–6311 (2005).
    Article CAS PubMed Google Scholar
  19. Stresemann, C., Brueckner, B., Musch, T., Stopper, H. & Lyko, F. Functional diversity of DNA methyltransferase inhibitors in human cancer cell lines. Cancer Res. 66, 2794–2800 (2006).
    Article CAS PubMed Google Scholar
  20. Manara, M. C. et al. A quinoline-based DNA methyltransferase inhibitor as a possible adjuvant in osteosarcoma therapy. Mol. Cancer Ther. 17, 1881–1892 (2018).
    Article CAS PubMed Google Scholar
  21. Datta, J. et al. A new class of quinoline-based DNA hypomethylating agents reactivates tumor suppressor genes by blocking DNA methyltransferase 1 activity and inducing its degradation. Cancer Res. 69, 4277–4285 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  22. Valente, S. et al. Selective non-nucleoside inhibitors of human DNA methyltransferases active in cancer including in cancer stem cells. J. Med. Chem. 57, 701–713 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  23. Gros, C. et al. New insights on the mechanism of quinoline-based DNA methyltransferase inhibitors. J. Biol. Chem. 290, 6293–6302 (2015).
    Article CAS PubMed Google Scholar
  24. Shirahata, A. et al. Vimentin methylation as a marker for advanced colorectal carcinoma. Anticancer Res. 29, 279–281 (2009).
    CAS PubMed Google Scholar
  25. Cong, H. et al. DNA hypermethylation of the vimentin gene inversely correlates with vimentin expression in intestinal- and diffuse-type gastric cancer. Oncol. Lett. 11, 842–848 (2016).
    Article CAS PubMed Google Scholar
  26. Constantinides, P. G., Jones, P. A. & Gevers, W. Functional striated muscle cells from non-myoblast precursors following 5-azacytidine treatment. Nature 267, 364–366 (1977).
    Article CAS PubMed Google Scholar
  27. Song, J., Rechkoblit, O., Bestor, T. H. & Patel, D. J. Structure of DNMT1-DNA complex reveals a role for autoinhibition in maintenance DNA methylation. Science 331, 1036–1040 (2011).
    Article CAS PubMed Google Scholar
  28. Song, J., Teplova, M., Ishibe-Murakami, S. & Patel, D. J. Structure-based mechanistic insights into DNMT1-mediated maintenance DNA methylation. Science 335, 709–712 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  29. Joshi, M., Rajpathak, S. N., Narwade, S. C. & Deobagkar, D. Ensemble-based virtual screening and experimental validation of inhibitors targeting a novel site of human DNMT1. Chem. Biol. Drug Des. 88, 5–16 (2016).
    Article CAS PubMed Google Scholar
  30. Zhou, L. et al. Zebularine: a novel DNA methylation inhibitor that forms a covalent complex with DNA methyltransferases. J. Mol. Biol. 321, 591–599 (2002).
    Article CAS PubMed PubMed Central Google Scholar
  31. Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D 68, 352–367 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  32. Jones, P. A. & Baylin, S. B. The fundamental role of epigenetic events in cancer. Nat. Rev. Genet. 3, 415–428 (2002).
    Article CAS PubMed Google Scholar
  33. Siebenkas, C. et al. Inhibiting DNA methylation activates cancer testis antigens and expression of the antigen processing and presentation machinery in colon and ovarian cancer cells. PLoS ONE 12, e0179501 (2017).
    Article PubMed PubMed Central CAS Google Scholar
  34. Li, H. et al. Immune regulation by low doses of the DNA methyltransferase inhibitor 5-azacitidine in common human epithelial cancers. Oncotarget 5, 587–598 (2014).
    Article PubMed PubMed Central Google Scholar
  35. Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 162, 974–986 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  36. Roulois, D. et al. DNA-demethylating agents target colorectal cancer cells by inducing viral mimicry by endogenous transcripts. Cell 162, 961–973 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  37. Saba, H. I. Decitabine in the treatment of myelodysplastic syndromes. Ther. Clin. Risk Manag. 3, 807–817 (2007).
    CAS PubMed PubMed Central Google Scholar
  38. Hashimoto, H. et al. Recognition and potential mechanisms for replication and erasure of cytosine hydroxymethylation. Nucleic Acids Res. 40, 4841–4849 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  39. Gilmartin, A. G. et al. In vitro and in vivo induction of fetal hemoglobin with a reversible and selective DNMT1 inhibitor. Haematologica https://doi.org/10.3324/haematol.2020.248658 (2020).
  40. Ariazi, J. L. et al. Discovery and preclinical characterization of GSK1278863 (daprodustat), a small molecule hypoxia inducible factor-prolyl hydroxylase inhibitor for anemia. J. Pharmacol. Exp. Ther. 363, 336–347 (2017).
    Article CAS PubMed Google Scholar
  41. Dyachenko, V. D. K., Krivokolysko, S. G. & Litvinov, V. P. Synthesis and transformations of 6-amino-3,5-dicyano-4ethylpyridine-2(1H)-thione. Chem. Heterocycl. Compd. 32, 942–946 (1996).
    Article Google Scholar
  42. Stromgaard, K. et al. Ginkgolide derivatives for photolabeling studies: preparation and pharmacological evaluation. J. Med. Chem. 45, 4038–4046 (2002).
    Article CAS PubMed Google Scholar
  43. Sou, T. & Bergstrom, C. A. S. Automated assays for thermodynamic (equilibrium) solubility determination. Drug Discov. Today Technol. 27, 11–19 (2018).
    Article PubMed Google Scholar
  44. Valko, K., Bevan, C. & Reynolds, D. Chromatographic hydrophobicity index by fast-gradient RP-HPLC: a high-throughput alternative to log P/log D. Anal. Chem. 69, 2022–2029 (1997).
    Article CAS PubMed Google Scholar
  45. Bunally, S. Y. & Robert, J. Y. The role and impact of high throughput biomimetic measurements in drug discovery. ADMET DMPK 6, 74–84 (2018).
    Article Google Scholar
  46. Young, R. J., Green, D. V., Luscombe, C. N. & Hill, A. P. Getting physical in drug discovery II: the impact of chromatographic hydrophobicity measurements and aromaticity. Drug Discov. Today 16, 822–830 (2011).
    Article CAS PubMed Google Scholar
  47. Horiuchi, K. Y. et al. Assay development for histone methyltransferases. Assay Drug Dev. Technol. 11, 227–236 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  48. Anastassiadis, T., Deacon, S. W., Devarajan, K., Ma, H. & Peterson, J. R. Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1039–1045 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  49. McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  50. Moriarty, N. W., Grosse-Kunstleve, R. W. & Adams, P. D. electronic Ligand Builder and Optimization Workbench (eLBOW): a tool for ligand coordinate and restraint generation. Acta Crystallogr. D 65, 1074–1080 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  51. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
    Article CAS PubMed Google Scholar
  52. McCabe, M. T. et al. EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature 492, 108–112 (2012).
    Article CAS PubMed Google Scholar
  53. Fedoriw, A. et al. Anti-tumor activity of the type I PRMT inhibitor, GSK3368715, synergizes with PRMT5 inhibition through MTAP loss. Cancer Cell 36, 100–114 e125 (2019).
    Article CAS PubMed Google Scholar
  54. Savitski, M. M. et al. Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 173, 260–274 e225 (2018).
    Article CAS PubMed PubMed Central Google Scholar
  55. Franken, H. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat. Protoc. 10, 1567–1593 (2015).
    Article CAS PubMed Google Scholar
  56. Werner, T. et al. Ion coalescence of neutron encoded TMT 10-plex reporter ions. Anal. Chem. 86, 3594–3601 (2014).
    Article CAS PubMed Google Scholar
  57. Le, T., Kim, K. P., Fan, G. & Faull, K. F. A sensitive mass spectrometry method for simultaneous quantification of DNA methylation and hydroxymethylation levels in biological samples. Anal. Biochem. 412, 203–209 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  58. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
    Article CAS PubMed Google Scholar
  59. Jin, Y., Tam, O. H., Paniagua, E. & Hammell, M. TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets. Bioinformatics 31, 3593–3599 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  60. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).
    Article CAS PubMed Google Scholar
  61. Ewels, P., Magnusson, M., Lundin, S. & Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  62. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
    Article CAS PubMed PubMed Central Google Scholar
  63. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).
    Article CAS PubMed Google Scholar
  64. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
    Article PubMed PubMed Central CAS Google Scholar
  65. Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279–284 (2001).
    Article CAS PubMed Google Scholar
  66. Hulsen, T., de Vlieg, J. & Alkema, W. BioVenn—a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics 9, 488 (2008).
    Article PubMed PubMed Central CAS Google Scholar
  67. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
    Article CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We thank T. Tomaszek and P. Keller for their contributions toward the high-throughput screen, K. Morasco and D. Depagnier for execution of the CBC panel, D. Cooper and H. Tran for input regarding statistical analysis, and S. Rajapurkar for assistance with genomics data analysis and visualization. We also thank A.C. Wong for original development of the DNA-binding and fluorescence-coupled breaklight assays, B. Waszkowycz for early computational design input and H. Hashimoto for his effort preparing initial human DNMT1 constructs and protein for the crystallization studies. Lastly, we thank S. Pessagno, D. Wilson and M. Bottomley for alliance oversight. Work conducted at the Cancer Research UK Manchester Institute was wholly funded by Cancer Research UK (grant nos. C480/A11411 and C5759/A17098). Work at the MD Anderson Cancer Center was supported by the Cancer Prevention Research Institute of Texas (CPRIT) grant no. RR160029 and National Institutes of Health (NIH) grant no. R35GM134744 to X.C., who is a CPRIT Scholar in Cancer Research. Work at the Van Andel Research Institute (P.A.J.) was supported by National Cancer Institute grant no. R35CA209859 and by the Van Andel Research Institute–Stand Up to Cancer Epigenetics Dream Team. Stand Up to Cancer is a division of the Entertainment Industry Foundation, administered by AACR.

Author information

Author notes

  1. These authors contributed equally: Mark Cockerill, Wendy A. Kellner.

Authors and Affiliations

  1. Cancer Epigenetics Research Unit, Oncology, GlaxoSmithKline, Collegeville, PA, USA
    Melissa B. Pappalardi, Kathryn Keenan, Wendy A. Kellner, Christian Sherk, Kristen Wong, Charles F. McHugh, Lourdes Rueda, David T. Fosbenner, Cunyu Zhang, Jessica L. Handler, Susan Merrihew, Shawn W. Foley, Mei Li, Stuart P. Romeril, Elisabeth Minthorn, Helai P. Mohammad, Rab K. Prinjha, Christopher Carpenter, Dirk Heerding, Bryan W. King, Juan I. Luengo, Ryan G. Kruger & Michael T. McCabe
  2. Drug Discovery Unit, Cancer Research UK Manchester Institute, University of Manchester, Alderley Park, Macclesfield, UK
    Mark Cockerill, Alexandra Stowell, Philip Chapman, Emma Fairweather, Ali Raoof, Roger J. Butlin, Charlotte Burt, Kristin Goldberg, Christopher S. Kershaw, Donald Ogilvie, Allan M. Jordan & Ian Waddell
  3. Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, TX, USA
    Sarath Pathuri, John R. Horton, Xing Zhang & Xiaodong Cheng
  4. Medicinal Science & Technology, GlaxoSmithKline, Collegeville, PA, USA
    Jacques Briand, Nino Campobasso, Alan P. Graves, Makda Mebrahtu, Jon-Paul Jaworski, Dean E. McNulty, Amy N. Taylor, Thau Ho, Anthony J. Jurewicz & Mehul Patel
  5. Cellzome GmbH, Functional Genomics, GlaxoSmithKline, Heidelberg, Germany
    Michael Steidel, Thilo Werner, H. Christian Eberl, Anna Rutkowska, Marcus Bantscheff & Paola Grandi
  6. Future Pipeline Discovery, GlaxoSmithKline, Collegeville, PA, USA
    Arthur Groy, Andrew B. Benowitz & Aidan G. Gilmartin
  7. Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, USA
    Ashley K. Wiseman & Peter A. Jones
  8. Drug Metabolism and Pharmacokinetics Modelling, GlaxoSmithKline, Stevenage, UK
    Morris Muliaditan
  9. Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA
    Stephen B. Baylin

Authors

  1. Melissa B. Pappalardi
    You can also search for this author inPubMed Google Scholar
  2. Kathryn Keenan
    You can also search for this author inPubMed Google Scholar
  3. Mark Cockerill
    You can also search for this author inPubMed Google Scholar
  4. Wendy A. Kellner
    You can also search for this author inPubMed Google Scholar
  5. Alexandra Stowell
    You can also search for this author inPubMed Google Scholar
  6. Christian Sherk
    You can also search for this author inPubMed Google Scholar
  7. Kristen Wong
    You can also search for this author inPubMed Google Scholar
  8. Sarath Pathuri
    You can also search for this author inPubMed Google Scholar
  9. Jacques Briand
    You can also search for this author inPubMed Google Scholar
  10. Michael Steidel
    You can also search for this author inPubMed Google Scholar
  11. Philip Chapman
    You can also search for this author inPubMed Google Scholar
  12. Arthur Groy
    You can also search for this author inPubMed Google Scholar
  13. Ashley K. Wiseman
    You can also search for this author inPubMed Google Scholar
  14. Charles F. McHugh
    You can also search for this author inPubMed Google Scholar
  15. Nino Campobasso
    You can also search for this author inPubMed Google Scholar
  16. Alan P. Graves
    You can also search for this author inPubMed Google Scholar
  17. Emma Fairweather
    You can also search for this author inPubMed Google Scholar
  18. Thilo Werner
    You can also search for this author inPubMed Google Scholar
  19. Ali Raoof
    You can also search for this author inPubMed Google Scholar
  20. Roger J. Butlin
    You can also search for this author inPubMed Google Scholar
  21. Lourdes Rueda
    You can also search for this author inPubMed Google Scholar
  22. John R. Horton
    You can also search for this author inPubMed Google Scholar
  23. David T. Fosbenner
    You can also search for this author inPubMed Google Scholar
  24. Cunyu Zhang
    You can also search for this author inPubMed Google Scholar
  25. Jessica L. Handler
    You can also search for this author inPubMed Google Scholar
  26. Morris Muliaditan
    You can also search for this author inPubMed Google Scholar
  27. Makda Mebrahtu
    You can also search for this author inPubMed Google Scholar
  28. Jon-Paul Jaworski
    You can also search for this author inPubMed Google Scholar
  29. Dean E. McNulty
    You can also search for this author inPubMed Google Scholar
  30. Charlotte Burt
    You can also search for this author inPubMed Google Scholar
  31. H. Christian Eberl
    You can also search for this author inPubMed Google Scholar
  32. Amy N. Taylor
    You can also search for this author inPubMed Google Scholar
  33. Thau Ho
    You can also search for this author inPubMed Google Scholar
  34. Susan Merrihew
    You can also search for this author inPubMed Google Scholar
  35. Shawn W. Foley
    You can also search for this author inPubMed Google Scholar
  36. Anna Rutkowska
    You can also search for this author inPubMed Google Scholar
  37. Mei Li
    You can also search for this author inPubMed Google Scholar
  38. Stuart P. Romeril
    You can also search for this author inPubMed Google Scholar
  39. Kristin Goldberg
    You can also search for this author inPubMed Google Scholar
  40. Xing Zhang
    You can also search for this author inPubMed Google Scholar
  41. Christopher S. Kershaw
    You can also search for this author inPubMed Google Scholar
  42. Marcus Bantscheff
    You can also search for this author inPubMed Google Scholar
  43. Anthony J. Jurewicz
    You can also search for this author inPubMed Google Scholar
  44. Elisabeth Minthorn
    You can also search for this author inPubMed Google Scholar
  45. Paola Grandi
    You can also search for this author inPubMed Google Scholar
  46. Mehul Patel
    You can also search for this author inPubMed Google Scholar
  47. Andrew B. Benowitz
    You can also search for this author inPubMed Google Scholar
  48. Helai P. Mohammad
    You can also search for this author inPubMed Google Scholar
  49. Aidan G. Gilmartin
    You can also search for this author inPubMed Google Scholar
  50. Rab K. Prinjha
    You can also search for this author inPubMed Google Scholar
  51. Donald Ogilvie
    You can also search for this author inPubMed Google Scholar
  52. Christopher Carpenter
    You can also search for this author inPubMed Google Scholar
  53. Dirk Heerding
    You can also search for this author inPubMed Google Scholar
  54. Stephen B. Baylin
    You can also search for this author inPubMed Google Scholar
  55. Peter A. Jones
    You can also search for this author inPubMed Google Scholar
  56. Xiaodong Cheng
    You can also search for this author inPubMed Google Scholar
  57. Bryan W. King
    You can also search for this author inPubMed Google Scholar
  58. Juan I. Luengo
    You can also search for this author inPubMed Google Scholar
  59. Allan M. Jordan
    You can also search for this author inPubMed Google Scholar
  60. Ian Waddell
    You can also search for this author inPubMed Google Scholar
  61. Ryan G. Kruger
    You can also search for this author inPubMed Google Scholar
  62. Michael T. McCabe
    You can also search for this author inPubMed Google Scholar

Contributions

M.P. and A.J.J. contributed to the high-throughput screen. A.N.T. and T.H. generated DNMT proteins. M.B.P., A.S., K.W. and C.B. designed, executed and/or analyzed enzymatic studies. M.C. conducted the DNMT3B −/− HCT-116 vimentin assays. M. Mebrahtu and J.-P.J. executed the wild-type HCT-116 vimentin assay. A.S. and E.F. executed the DNA binding assay. M.S., T.W., H.C.E., A.Rutkowska, M.B. and P.G. contributed to thermal shift assays. J.B. and N.C. conducted the covalent modification and photoaffinity labeling studies. A.K.W. executed the mouse embryonic fibroblast studies under guidance from P.A.J. A.P.G. performed sequence alignment. A.G. executed the mutagenesis studies under guidance from A.G.G. and A.B.B. S.P. performed protein purification, complex formation and crystallization under guidance from X.Z. J.R.H. collected X-ray diffraction data and determined structures. K.K. and S.M. executed the AML proliferation studies. M.T.M., K.K., J.L.H., C.F.M. and M.B.P. designed, executed and/or analyzed the AML in vitro studies. K.K. executed the western blot studies with advice from S.B.B.; D.E.M. developed the LC–MS/MS methodology for the 5-methylcytosine assay. W.A.K. and S.W.F. analyzed the genomics data. C.S., C.F.M., E.M. and M.B.P. designed, executed and/or analyzed the in vivo studies. M. Muliaditan modeled the pharmacokinetic data. A.Raoof, R.J.B., L.R., D.T.F., C.Z., M.L., S.P.R., K.G., C.S.K., A.B.B., D.H., B.W.K., J.I.L. and A.M.J. designed and/or synthesized compounds. X.C. organized and designed the scope of the crystallography study. M.C., P.C., H.P.M., R.K.P., D.O., C.C., R.G.K., I.W., M.T.M. and M.B.P. provided conceptual advice. M.B.P. and M.T.M. wrote the manuscript.

Corresponding authors

Correspondence toMelissa B. Pappalardi or Michael T. McCabe.

Ethics declarations

Competing interests

M.B.P., K.K., W.A.K., C.S., K.W., J.B., M.S., A.G., C.F.M., N.C., A.P.G., T.W., L.R., D.T.F., C.Z., J.L.H., M.Muliaditan, M.Mebrahtu, J.P.J., D.E.M., H.C.E., A.N.T., T.H., S.M., S.W.F., A. Rutkowska, M.L., S.P.R., M.B., A.J.J., E.M., P.G., M.P., A.B.B., H.P.M., A.G.G., R.K.P., C.C., D.H., B.W.K., J.I.L., R.G.K. and M.T.M. are/were employees and/or shareholders of GlaxoSmithKline (GSK). The remaining authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Characterization of GSK3484862 and GSK3685032.

a, Stability of GSK3484862 (1,000 nM) as determined by LC-MS/MS in media without or with cells (MV4-11) at 37 °C versus decitabine (1,000 nM). b, Thermal profile (Tm, 77.5 °C, n = 2 biologically independent experiments with two technical replicates each) of the hemi-methylated hairpin oligonucleotide with DMSO or GSK3484862 (100 µM). c, DNMT1 activity (average of technical replicates, data is representative of two biologically independent experiments) for uninhibited reaction (DMSO, n = 2) and recovery of inhibited enzyme activity following rapid dilution (100-fold) of pre-complexed DNMT1:GSK3484862 (n = 3) or DNMT1:SAH (n = 3). d, Dose-dependent increase in vimentin expression (n = 2 biologically independent experiments with two technical replicates each) following treatment of DNMT3B -/- HCT-116 cells with GSK3484862 or decitabine. e, Table summarizing up-regulation of vimentin expression in wild-type or DNMT3B -/- HCT-116 cells after treatment with GSK3484862 or decitabine (average ± s.d.; n = independently fitted EC50 values). f, IC50 values (bar represents average, n = biologically independent determinations) following a 0-minute (n = 15), 60-minute DNMT1:Inhibitor (EI, n = 2), or 60-minute DNMT1:Inhibitor:hemi-methylated DNA (ESI, n = 2) preincubation. g, Intact protein mass spectrometry for mDNMT1 (731-1602) following incubation with hemi-methylated DNA in the absence or presence of GSK3685032 showed no covalent adduct. h, Inhibition of a kinase panel (n = 369) by 10 µM GSK3685032. i, Inhibition of a methyltransferase panel by 10 µM GSK3685032. j, Isothermal dose-response curves for DNMT1 following treatment with GSK3685032 in a recombinant system (DNMT1 601-1600 in the presence of 40-mer hemi-methylated DNA) or in a cellular system (HepG2). k, Dose response curves (average ± s.d., n = biologically independent samples) for full-length DNMT1 using a 40-mer hemi-methylated or poly(dIdC) DNA substrate in a radioactive SPA assay with GSK3685032 (n = 4 or 5, respectively) or SAH (n = 4 or 6), respectively).

Source data

Extended Data Fig. 2 DNMT1 residues important for compound binding and inhibition.

a, Analogues containing a photoreactive benzophenone or diazerine moiety. b, c, Murine DNMT1 (731-1602) spectra in the absence or presence of a 45-minute photolysis step with 14-mer hemi-methylated DNA and GSK3844831 (b) or GSK3901839 (c). d, Dose response curves for HEK293 cells expressing either wild-type or site-directed alanine mutant DNMT1 (n = 2; technical replicates) treated for 6 days with decitabine or GSK3685032. Dashed line represents starting cell number (T0). e, Dose response curves (n = 4 biologically independent samples; average ± s.d.) for full-length wild-type or H1507Y DNMT1 activity in a radioactive SPA assay.

Source data

Extended Data Fig. 3 Sequence alignment of the methyltransferase domains of human DNMT1, DNMT3A, and DNMT3B.

Identical residues are shaded blue while similar residues are shaded yellow. The boxes indicate the target recognition domain of DNMT1 (dashed, black) and the active-site loop (solid, red). Residues that were photoaffinity labeled*, residues that conferred resistance to GSK3685032 upon mutation to alanine (gIC50 > 10 µM)† and are reported to be involved in recognition of the methylated cytosine‡, or the catalytic cysteine (C1226)♯ are marked within the DNMT1 sequence.

Extended Data Fig. 4 Crystal structure of DNMT1-DNA in complex with DNMT1 inhibitor.

a, View of the active-site loop bound in the space left by the flipped-out zebularine in the DNMT1-DNA complex. b, The omit electron density map in mesh for GSK3685032 contoured at 4σ above the mean. c, d, Orthogonal views of DNMT1-DNA in the presence of GSK3685032. The active-site loop is colored brown and adopts an open conformation. e, The omit electron density map in mesh for GSK3830052 contoured at 4σ above the mean. f, Superimposition of inhibitor (pink) and the active-site loop in the native complex (cyan). g, The inhibitor intercalates into DNA between two G:C base pairs. h, Two hydrogen bonds formed between G1 and zebularine. i, Inhibitor interacts with 5-methylcytosine (5mC) of the parent DNA strand and Trp1510 of DNMT1. j, The end of the inhibitor N-methyl-N-phenylmethanesulfonamide moiety is close to the DNMT1 active-site Cys1226.

Extended Data Fig. 5 Biochemical, phenotypic, and mechanistic activity of DNMT inhibitors.

a, Table reporting GSK3685032 activity in a panel of AML cell lines (day 6, average ± s.d., n = biologically independent experiments). b, Heatmap showing induction of caspase-glo 3/7 activity (Promega, average log2 fold change, n = 2 biologically independent experiments) in MV4-11 cells following treatment with GSK3510477, GSK3484862 (with 2 technical replicates), or GSK3685032 at days 1, 2, 4 & 6 (0.06-7,340 nM). c, Compound structures for reported DNMT inhibitors. d, Table containing output parameters (average ± SEM, n = biologically independent experiments) following biochemical, phenotypic (MV4-11, day 6) or mechanistic (MV4-11, day 4) assessment using a panel of DNMT inhibitors. NA, not applicable to assay format. 5mC, 5-methylcytosine. e, Top, Venn diagram for significantly increased genes in MV4-11 (RNA-seq, FDR < 0.05, |log2 fold-change| > 1, day 4, 400 nM) following treatment with GSK3685032 or decitabine. Bottom, Heatmap of log2 fold change differential expression (RNA-seq, day 4) following treatment with GSK3685032 or decitabine (3.2-10,000 nM) for overlapping genes (n = 1,542) from the Venn diagram.

Source data

Extended Data Fig. 6 Pharmacokinetic evaluation of GSK3685032.

a, Summary of mouse pharmacokinetic parameters for GSK3685032. NA, not applicable to dosing route. ND, value not determined. IV, intravenous. SC, subcutaneous. b, Blood concentration of GSK3685032 at multiple timepoints following a single dose of 2 mg/kg IV (male CD-1 mice), 2 mg/kg SC (male C57/BL6 mice), or 30 mg/kg SC (female Nu/Nu mice). Individual data shown (n = 3 animals/group). c, Dose proportional blood concentration of GSK3685032 following twice daily subcutaneous dosing for 8.5 days in a SKM-1 subcutaneous xenograft model (NOD-scid) collected 6 hours post last dose. Individual concentrations (n = 3 animals/group) with linear regression (R square = 0.9780) fit to the mean concentration for each group. d, Simulated profile of GSK3685032 over a 24 hour time frame adjusted for unbound fraction (2.5%) in the blood following twice daily subcutaneous dosing. 5-mC, 5-methylcytosine.

Source data

Extended Data Fig. 7 Compound effect in subcutaneous MV4-11 and SKM-1 xenograft models.

a, b, Animal body weight measurements for MV4-11 (a) or SKM-1 (b) xenograft models spanning the dosing duration of the study (average ± s.d.; n = 10 animals/group, # represents day first animal came off study due to tumor volume). c-f, Individual tumor volume measurements for MV4-11 (c, day 35) or SKM-1 (d, day 20). Solid line represents the median for each group (n = 10 animals unless noted). Dotted line represents the median tumor volume for vehicle. Statistical significance* of treatment versus vehicle was calculated using one-way ANOVA, Dunnett’s multiple comparisons test. Table summarizes adjusted P values to account for multiple comparisons and corresponding tumor growth inhibition (TGI) values for each group within the MV4-11 (e) or SKM-1 (f) xenograft models. g, h, Individual tumor volume measurements for the 45 mg/kg GSK3685032 group in MV4-11 (g) or SKM-1 (h) xenograft models during the dosing segment (orange bar) and continuing for ≥ 27 days off drug (blue bar) to monitor durability (n = 10 animals unless noted). The minimum measurable tumor volume was set to 10 mm3.

Source data

Extended Data Fig. 8 Effects of GSK3685032 and decitabine on complete blood cell counts.

a, b, Complete blood cell count (a) at day 28 across all dose groups (n = 5 animals/group; mean ± s.d.). Statistical significance* of treatment versus vehicle was calculated using one-way ANOVA, Dunnett’s multiple comparisons test. Each P value was adjusted to account for multiple comparisons. Table (b) showing output parameters following statistical analysis. ^Used log10 transformed values due to unequal variance between groups. Ratio represents treatment group normalized to vehicle. c, Complete blood cell count (mean ± s.d.) at day 28 on treatment (n = 5 animals/group) followed by 27 days off treatment (n = 5 animals/group for 15 and 45 mg/kg or n = 8 animals for 30 mg/kg group) with GSK3685032.

Source data

Supplementary information

Source data

Rights and permissions

About this article

Cite this article

Pappalardi, M.B., Keenan, K., Cockerill, M. et al. Discovery of a first-in-class reversible DNMT1-selective inhibitor with improved tolerability and efficacy in acute myeloid leukemia.Nat Cancer 2, 1002–1017 (2021). https://doi.org/10.1038/s43018-021-00249-x

Download citation