This document discusses using computational methods to predict new targets of existing drugs, with a focus on polypharmacology of PARP inhibitors. Specifically: 1. Computational chemistry methods can predict new targets of drugs, identifying PIM1 kinase as a potential off-target of the PARP inhibitor PJ34. 2. Differential effects were observed between clinical PARP inhibitors rucaparib, olaparib, and veliparib in cancer cell lines and siRNA sensitivity, suggesting different off-target profiles. 3. Rucaparib inhibition of PIM1 kinase may have clinical implications, as PIM1 inhibition has been linked to increased liver toxicity, and ruc

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. 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. 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. • 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. • 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. 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. • 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. 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. • Pim kinases phosphorylate STAT3
Differential effects between PARP inhibitors
9
Rucaparib Olaparib Veliparib
Chuang HC, et al. Breast Cancer Res Treat. 2012

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. Harnessing polypharmacology in precision
oncology
11
• Sir Henry Wellcome Postdoctoral Fellowship
Drug
(Olaparib)
Primary
Target
(PARPs)
Off-target
(PIM1)
Predictive
Biomarker
DNA

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. 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

15. Predicting new drug targets
15
drugs of interest
5658 targets
Vidal et al. Methods Mol Biol 672 (2011) 489