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BPS Pharmacology 2016 Meeting - Albert Antolin


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Identification of differential kinase off-targets among PARP inhibitors: new opportunities for precision oncology?

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BPS Pharmacology 2016 Meeting - Albert Antolin

  1. 1. in partnership with Identification of differential kinase off-targets among PARP inhibitors: new opportunities for precision oncology? Albert A. Antolin, Jordi Mestres, Paul Workman & Bissan Al-Lazikani Marie Curie Tecniospring PostDoctoral Fellow Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK & GRIB, IMIM Hospital del Mar Medical Research Institute and Pompeu Fabra University, Barcelona, Spain.
  2. 2. Polypharmacology and the limits of reductionism It is increasingly accepted that drugs tend to bind to more than one target, a behavior referred to as polypharmacology. Ehrlich, 1901 Vogt & Mestres, 2010 Tym JE, et al. 2016 Only 15% of drugs are currently known to interact with just one protein Jalencas & Mestres, 2013 2 How does polypharmacology influence clinical eficacy? Systems ApproachReductionism
  3. 3. Towards personalized and precision oncology 3 Antolin AA, et al. Curr Pharm Des, in press. Predictive Biomarker •  Biomarkers of response •  Polypharmacology can be exploited to extend the uses of cancer drugs without unacceptable toxicity.
  4. 4. •  Drug-target network of imatinib with polypharmacology biomarkers •  10-fold selectivity cutoff Current exploitation of drug polypharmacology 4 Antolin AA, et al. Curr Pharm Des, in press.
  5. 5. •  Identify new targets of known drugs •  Link them to predictive biomarkers using systems pharmacology data to identify new patient populations responding to these drugs through polypharmacology. Objective 5
  6. 6. 1.  Using chemical similarity we can predict new targets of compounds: 2.  PARP chemical probe PJ34 as an example: Predicting polypharmacology 6 Antolin AA, et al. ACS Chem Bio. 2012 PJ34 PARP1/2 (20nM) PIM1 kinase predicted CHEMBL572783 PIM1/2 (8 and 3 nM) superposition IC50 = 3.7 µM IC50 = 16 µM In vitro validation (isolated protein)
  7. 7. •  At the cellular level: 1.  Differential cancer cell line profile (Sanger) 2.  Differential siRNA sensitivity 3.  Differential anti-proliferative activities, cell cycle arrest and DNA damage 4.  Differential PARP trapping 5.  … Differential effects between clinical PARP inhibitors 7 Rucaparib Olaparib Veliparib Chuang HC, et al. Breast Cancer Res Treat. 2012
  8. 8. 1.  Does PJ34 polypharmacology translate into PARP clinical candidates? PARP inhibitors inhibit kinases off-target 8 Antolin AA & Mestres J. Oncotarget. 2014 PIM1 IC50 = 1.2 µM Rucaparib (PARP1 IC50 = 5 nM)
  9. 9. •  Pim kinases phosphorylate STAT3 Differential effects between PARP inhibitors 9 Rucaparib Olaparib Veliparib Chuang HC, et al. Breast Cancer Res Treat. 2012
  10. 10. 1.  Rucaparib Cmax: 2-9 µM > PIM1 IC50 = 1,2 µM 2.  Free drug concentration? Tumor retention? 3.  Different side-effect profile among PARP inhibitors PIM kinase inhibitor AZD1208 produces increased transaminases. Does PIM1 off-target inhibition have clinical implications? 10
  11. 11. Harnessing polypharmacology in precision oncology 11 •  Sir Henry Wellcome Postdoctoral Fellowship Drug (Olaparib) Primary Target (PARPs) Off-target (PIM1) Predictive Biomarker DNA
  12. 12. Summary 12 •  Many new targets of drugs remain to be identified and computational chemistry methods are becoming a cost-effective approach to off-target identification. •  Precision oncology offers a means to better exploit this polypharmacology through predictive biomarkers •  Different disciplines should work together to enable the clinical application of systems pharmacology and the maximum exploitation of currently available drugs to maximize patient benefit.
  13. 13. in partnership with Thank you! Bissan Al-Lazikani Paul Workman Jordi Mestres CBCG Team Elizabeth Coker Costas Mitsopoulos Joe Tym Carmen Rodriguez-Gonzalvez Veronica Garcia – Perez Sheng Yu Catherine Fletcher Sebastian Poetsrl James Campbell Patrizio di Micco STMP Team Paul Clarke Chi Zhang Alexia Hervieu Systems Pharmacology Group Xavier Jalencas Joaquim Olives Viktoria Szabo Nikita Remez (CT) David Vidal (CT) Ricard Garcia-Serna (CT) MariCarmen Carrascosa (CT) Johann de Bono DDU Team Udai Banerji Stan Kaye
  14. 14. Predicting new drug targets 15 drugs of interest 5658 targets Vidal et al. Methods Mol Biol 672 (2011) 489