Using antitumor agents to probe the sensitivity contexts of cancer cells and assess the validity of target combinations

1
Global Medicinal Chemistry and GPCR Summit
The systematic use of highly profiled
antitumor agents in probing the
sensitivity contexts of cancer cells
and assessing the validity of target
combinations
Eduard R. Felder
Nerviano Medical Sciences

4
Outline
 Oncology complexity
 The Purinome Platform (targets, compounds)
 Chemical Collections, compound annotations
 Public
 commercial
 shared
 open innovation
 Proprietary
 Bioactivity profiling, Dissection of target involvements
 biochemical
 cell based
 Applications
 MELK relevance in carcinomas
 Chordoma targets
integration of ‘tools’, from crystal structures
to chemical probes to patient derived tissues
panels of annotated small molecules
chemical probes
HTS sets
‘chemical archives’
arrays
targeted libraries
diversity sets
chemogenomic sets

5
Oncology complexity
 Heterogeneity and variability embedded in cancer cells
 The complexity applies also to the tumor microenvironment, i.e. the supportive and
interactive stroma
 The redundancy in proliferative signaling pathways variably limits the efﬁcacy of
targeted therapies in different patient populations
 Emergence of secondary resistance to growth inhibitory drugs variably limits the
efﬁcacy as well (genetic drift → limited duration of clinical benefit)
Driving forces in
cancer cells
intra-tumoral metastatic variations Tumor-antagonizing vs. –promoting cell types
Complementary forces from
stromal cell constituents
M. De Palma, D. Hanahan, Mol. Oncol. 6, 111 (2012)
Signaling molecules play a critical role in these processes; several are now recognized as
therapeutic targets
Cells usually need to accrete several cancer-promoting, or oncogenic, mutations in separate
genes to acquire the hallmark properties of malignancy

6
Targeting the expanded set of cancer hallmarks
D. Hanahan, R.A. Weinberg, Cell 2011 144, 646-674

F. Collins, D. Barker, Sci. Am. (2007)
• A large degree of cross-talk and redundancy exists among
the different signaling pathways.
• This information is now being used to realize novel
therapeutic strategies, based on the combination of
different signalling inhibitors or the development of
multitargeted inhibitors.
• The aim is to block resistance due to the activation of
compensatory mitogenic pathways
Signaling networks regulate the cancer cell

renewed interest in phenotypic screening
Lack of validated targets in some disease areas
Emphasis on molecular targets in spite of new atypical therapeutic modalities
•Include polypharmacology as a
drug development option in the
early phases
•Challenge the reductionist ‘one-
target, one-disease’ approach

9R. Morphy, J. Med. Chem., 48, 6523 (2005)
readily approachable
challenging
Designed Multiple Ligand (DML) or …
Selected by evolutionary approaches?
Designed or evolutionary optimization of ligands
Multi-component reactions
and evolutionary chemistry
encoding molecules from a
combinatorial library and
applying the genetic
algorithmR. Morphy, J. Med. Chem., 48, 6523 (2005)

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Purinome Targets
• ATP- , GTP- , NAD-dependent enzymes
• Bind ligands possessing a purine substructure
• Modes of binding and the sites of interaction may vary considerably
• Interaction with phosphate groups of ATP/ADP is dominant in certain ATPases
• Need for new, purinome-targeted libraries (PTL), including diversified ATP-
mimicking designs
• Kinase Targeted Libraries (KTL) are viewed as a subset of PTL, without
implying a reduction of their important role in drug discovery projects
Purinome targets are widely diversified in terms of their function,
phylogenetic origins and structural architecture
Kinases
Non-Kinase targets
Is there a similarity of the purine binding site among the different
purinome members, sufficient to design a common chemistry?

The Purinome addressed with a Chemical Biology approach
 Target (identify) pathway components that drive a deﬁned set of cancers and
contribute to cancer growth
 Target (identify) mechanisms that support the oncogenic process or represent a
vulnerability that can be exploited through synthetic lethality
 Discover bioactive New Chemical Entities with drug development potential
 One of the main mechanisms by which a normal cell appropriately transduces signals
is the reversible and dynamic process of protein phosphorylation
 Cross-proﬁling of inhibitors generated for one particular kinase, has traditionally been
a rich source for hits of other kinases. In case, one clinical candidate can be explored
as an inhibitor of more than one kinase
Objectives
Purinome Assets

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Functional Classification (M. Knapp et al.)
Protein Functional Class Total # of Proteins Substrate/Cofactor
Small G Proteins 750 GTP
Protein Kinases 518 ATP
Dehydrogenases * 456 NAD/NADP
ATPases 453 ATP
Motor proteins (Kinesins, Myosins, Dyneins) 22 ATP
Helicases 217 ATP
Non-conventional purine-utilizing proteins [such as HSP90] 357 ATP, ADP, AMP, GTP
Synthetases 213 ATP
Deaminases 85 ATP
Lipases 78 ATP
Sulfotransferases 40 ATP
CTK 34 ATP
Carboxylases 26 ATP
Puringenic receptors 17 Adenosine, ATP
P-Loop
Structural motif

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‘Chemical innovation’ , Chemical matter
3rd party cpds
Drugs
New Annotations New antitumor
treatments
Intellectual Property
Proprietary
existing
chemotypes
Common
chemotypes
New chemotypes
molecular targets
cell lines
and complex models

14
Open collections (annotated, curated, pre-competitive)
 In 2005, NIH launched the decade-long Molecular Libraries Program
 to innovate and broaden access to small-molecules
 enabling the exploration of biological pathways and therapeutic hypotheses
 In 2011, AstraZeneca and Bayer open mutual access to their libraries, but only on targets that
were not relevant to the other company
Years later AstraZeneca and Sanofi announced a swap of 210,000 compounds with no
restrictions on screening
 In 2014, AstraZeneca launched a partnership with the Academic Drug Discovery Consortium,
a network of more than 130 academic drug discovery centers formed in 2012.
Selected researchers get access to 250,000 AZ compounds for the assays they developed.
AstraZeneca, typically gets the first chance to license
 In 2015 in Europe the Joint European Compound Library (JECL) is formed:
 with 321,000 compounds that originated in seven pharmaceutical companies
 with additional 200,000 compounds (PCC) planned by 2019
 open to academics and biotech companies
 In 2016 comprehensive characterization of GlaxoSmithKline’s PKIS, a set of 367 kinase inhibitors
triaged and selected from 3000 kinase inhibitors previously published in 2014

Published Kinase Inhibitor Set (PKIS)
TK
TKL
STE
CK1
AGC
CAMK
CMGC
224 Caliper (Nanosyn) 68 DSF (SGC)
All structures and data in ChEMBL
232/518 Kinases
370Inhibitors
J. M. Elkins, Nature Biotechnology (2016) 34, 95-103.

Kinase Chemogenomic Set (KCGS) - expansion in progress
• A set of 1000 kinase inhibitors as a goal
• All kinases in the kinome will be inhibited by at least one
inhibitor in the set (ideally several)
• Each compound has a narrow kinase inhibition profile
• The set will be freely distributed for use in disease relevant
phenotypic screens, so that kinases involved in that disease
model can be identified
• Through broad screening of the set, the community will learn
which kinases to pursue (invest drug discovery effort) for which
disease
• Some approaches, such as whole-genome short interfering
RNA (siRNA) or CRISPR–Cas9 screening, may be carried out in
parallel to expedite target identification
D.H. Drewry, PLOS ONE | PLoS ONE 12 (8): e0181585 (2017)

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The Nerviano Compound Collection - Definitions
PTL
• Purinome Targeted Libraries – widely covering mimicks and surrogates of
purine analogs and derivatives, extending into space adjacent to the purine
binding site including:
• KTL Kinase Targeted Libraries
UniversalLibraryUL
• Primary libraries (File enrichment program → Expansion of screening sets)
• HIS
– Historical ‘non-kinase’ cpds (Legacy compounds Farmitalia, Pharmacia, Pfizer)
• GEN
– Generic cpds (non-kinase)
– Intermediates from ‘non-kinase’ NMS projects
• Commercial compounds
– Selection of 200,000 ChemDiv cpds complementary to KTL
– Other commercial sources
FBA
• Fragments
– Cpds with MW <300 Da, undecorated scaffolds, ‘rule-of-three’

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225,000
Purinome Targeted Libraries (KTL): 70,000 compounds, 100 chemical classes
NMS chemical collection (~300,000 compounds)
N
N
N
N
R
O
R'
H
H
Pyrrolopyrazole library:
4300 compounds
Universal Collection (~225,000 compounds)
Explore diverse chemical space for non kinase targets
Universal Collection
KTL
70,000

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Chemical classes
• with wide scope
• 'expressing' selective individual compounds
Kinase Targeted Libraries
Scaffolds: Wide scope and high potential for selectivity
Pyrrolopyrazole Scaffold
e.g. 1,4,5,6-Tetrahydro-pyrrolo[3,4-c]pyrazoles for inhibition of kinases
PHA-E544 PHA-E363
PHA-E468 PHA-E779

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Purinophosphate mimicking libraries
3- and 8-substituted Pyrazolo[4,3-g]indolizines
3- and 9-substituted Pyrazolo[3,4-c]pyrrolo[1,2-a]azepines
Novel tricyclic
Adeninomimetics
N
N
H
N
N
O
NH
R2
R1
n
X
R3 6 n=1
7 n=2
X = -CO-,-CONH-,-SO2-
NN
H
N
O
N
H
CF3CO
OEt
N
N
N
O
N
H
CF3CO
OEt
N
N
N
OH
O
NH2
Pol, TEA, DCM NaOH, H2O/THF
500C, 72h
RT, 24h
NN
H
N
O
N
H
CF3CO
OEt
N
N
N
O
N
H
CF3CO
OEt
N
N
N
OH
O
NH2
Pol, TEA, DCM NaOH, H2O/THF
500C, 72h
RT, 24h
on solid support

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Scaffold → Extension → Phosphorylation
Extension 1
Scaffold 2

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Extensions / Scaffolds
O N
OH
NH2
O N
H
NH2
OH
O N
NH2
OH
O N
H
NH2
OH
O NH
NH2
OH
E1 E2 E3 E4 E5
N
N
NN
NH2
OH
O
N N
N
H
NH2
OH
O
NH
N
O
OH
O
O2
N
S
N
H
N
OH
O
NH2
N N
H
OH
O
O2
N
S2S1 S3 S4 S5

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Hit Rate = (#Cpds ≥70%I / #Cpds total)%
UL Hit Rate %
PTL Hit Rate %
Hit rate of entire PTL collection vs. “random” library (UL)

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PCTActivesperLibrary
Hit Rate = (#Cpds ≥70%I / #Cpds total)%
library ID
Hit Rates of PTL main chemical classes (>1000 cpds each)
E.R. Felder et al. Mol. Divers. 16, 27-51 (2012)

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1/IC50uM Kinase selectivity profile of active compounds from a
prototype ADP mimics library

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L025
L089
AGC CAMK CMGC Other STE TK
IC50 > 10 uM
No data
IC50 < 10 uM
Activities on selected kinases for two chemotypes

27
Specificity challenge: JAK2

2929
Antiproliferation vs. Kinase Panel Activity
At high antiproliferation activity for a
given cell line what targets do we hit?

Searching growth inhibitory targets of outstanding relevance in particular cell lines
compounds 1,2,3 etc. (selective or oligoactive)
IC50
Kinases hit by the most active cpd(s)
kinases a, b, c etc.
rank
Screening on tumor cell lines
Heatmaps (kinases hit by cell active cpds)

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MELK , a controversial target
• Maternal embryonic leucine zipper kinase (MELK) is an AMPK-related
serine/threonine kinase
• MELK has active roles in a number of cancer cell lines, and in the physiological
cell cycle and embryogenesis
• Enhanced MELK activity was found in aggressive breast cancer cell lines, whose
proliferation can be modulated by siRNA knockdown
• Implications in glioblastoma, colon cancer, ovarian cancer have been reported
• OTSSP167, from OncoTherapy Science is in clinical studies
• Overexpression of MELK is observed in cancer stem cells
• Claims that MELK dependence is specific to basal-like breast cancer (BBC)
• BBC largely overlaps with triple-negative breast cancer (TNBC)
• Little is known about whether MELK plays a causal role in fueling these cancer
phenotypes
• J. Sheltzer et al. at Cold Spring Harbor found that knocking MELK out via
CRISPR treatment in a whole list of different cancer cell lines has no effect on
their growth

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MELK Crystal Structure
The N-terminal kinase domain is flanked by a smaller ubiquitin-associated (UBA) domain, a TP
dipeptide-rich domain and a C-terminal kinase associated domain (KA1)
G. Canevari et al. , Biochemistry 52, 6380–6387 (2013)
First reported structure
Enables Drug Design

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MELK Crystal Structure w. Inhibitors
(DFG)
Type I binding mode with key involvement of water molecules
G. Canevari et al. , Biochemistry 52, 6380–6387 (2013)
• Beware of force fitting ligands
(pharmacophores) without taking water
into account
• Displacing water from a binding site is a
key component of ligand binding
• But not all waters are equal …

34
N
N
N
O
O
N
N
N
Cl
Cl
N
N
N
O
O
N
N
N
O
Potent and selective MELK inhibitors

35
0.01 0.1 1 10 100
0
25
50
75
100 NMS-P664
NMS-P635
Dose uM
%ofSurvivalFraction
GBM-0627
IC50= 0.94 mM
IC50= 1.03 mM
0.01 0.1 1 10 100
0
25
50
75
100
NMS-P664
NMS-P635
Dose uM
%ofSurvivalFraction
GBM-080201
IC50= 1.9 mM
IC50= 1.56 mM
MELK inhibitors effective on Glioblastoma cancer stem cells
• Melk inhibitors are effective in inhibiting the growth of Glioblastoma cancer stem cells. GBM-0627 and GBM-080201
cancer stem cell lines were seeded and then treated with different concentrations of the Melk inhibitors.
After 7 days, the cell numbers were estimated using CellTiter-Glo assay.
P. Carpinelli et al. 26th EORTC-NCI-AACR (2014)

MELK as a marker for poor prognosis
Chemical proteomics reveals the target landscape of clinical kinase drugs
Bernhard Kuster & coworkers, Chair of Proteomics and Bioanalytics, Technical University of
Munich, Germany
Data revealing numerous novel targets for existing drugs
Offering a view on the druggable kinome, highlighting non-kinase off-targets and
suggesting potential applications in immune or cancer therapy
In this study, we elucidated the target space, selectivity and full dose response
characteristic of clinical kinase inhibitors in lysates of cancer cell lines using
Kinobeads and quantitative mass spectrometry
Other than protein kinases, Kinobeads also bind other nucleotide binding proteins
owing to the fact that the compounds immobilized on beads are ATP mimetics
Integration with phosphoproteomic data refined drug affected pathways, identified
response markers and provided rationale for combination treatment
The full proteomic data and multiple visualizations will be made publically available in
ProteomicsDB and proteomeXchange

Working towards better chordoma treatments
NMS
Chordoma
Foundation
INT
Milano
 Medical need
 Clinical knowledge
 Biological reagents
 Drug discovery
know-how
 Drug collection
 Biological reagents
 Support for efficacy experiments
New pharmacological approaches for the treatment of chordoma:
 New targets
 New drugs
Chordomas form when notochord cells left over in the skull or spine change over time and become cancerous

Anti-proliferative screening of NMS chemical collection
on U-CH1 and U-CH2 chordoma cell lines
1400 compounds including
200 reference drugs
Active compounds characterized on a broader panel
of chordoma cell lines
6 cell lines,
including new
Chor-IN-1
New targets
Ongoing, based on:
 Screening results
 Cell lines genomic
characterization
 Most drugs inactive on U-CH1 and U-CH2
 Focus on PDGFR, MET, EGFR inhibitors
 Afatinib EGFR inhibitor most active drug
Chordoma cell screening workflow
Active drugs
P. Magnaghi et al. 5th Int. Chordoma Research Workshop (2016)

Met and PDGFR inhibitors do not impair proliferation of chordoma
cell lines
PDGFR
inhibitors
MET
inhibitors
Cell line
MET
amplif.
Compound
IC50 (uM)
U-CH1 UM-Chor1
MUG-
Chor1
U-CH2
U-CH2
(ATCC)
Chor-IN-1 JHC7
MKN-45
Ctrl +
A2780
Ctrl -
Crizotinib 3.972 >10 2.611 3.318 6.421 4.856 8.061 0.098 1.086
Cabozantinib 7.047 4.755 8.651 2.919 2.398 7.508 8.444 0.129 1.269
PHA-665752 4.926 4.681 2.332 3.439 4.073 5.480 3.560 0.119 3.289
Doxorubicin 0.166 0.067 0.455 0.152 0.348 0.340 0.881 0.659 0.010
Cell line
PDGFR
amplif.
Compound
IC50 (uM)
U-CH1 UM-Chor1
MUG-
Chor1
U-CH2 U-CH2
(ATCC)
Chor-IN-1 JHC7
NCI-H1703
Ctrl +
A2780
Ctrl -
Imatinib >10 >10 >10 >10 >10 >10 >10 1.936 10.000
Sunitinib 5.739 >10 3.897 3.636 4.441 3.764 9.349 0.088 1.576
Crenolanib 8.635 >10 >10 5.786 >10 8.117 >10 0.505 1.702
Doxorubicin 0.166 0.067 0.455 0.152 0.348 0.340 0.881 0.171 0.010
IC50= drug concentration that inhibits 50% cell proliferation
(Red: active drug= IC50< 1 uM)

Activity of EGFR approved drugs on chordoma cell lines
Chordoma cell lines
EGFR
amplif.
Compound
IC50 (uM)
(StdDev)
U-CH1 UM-Chor1
MUG-
Chor1
U-CH2
U-CH2
(ATCC)
Chor-IN-1 JHC7
A-431
Ctrl+
A2780
Ctrl-
Afatinib 0.014 0.023 0.258 0.494 0.531 0.668 1.346 0.026 1.915
(0.005) (0.007) (0.072) (0.409) (0.203) (0.351) (0.394) (0.009) (0.594)
Erlotinib 0.144 0.617 3.006 8.042 7.776 2.329 2.281
0.346
3.919
(0.049) (0.069) (0.977) (1.714) (1.953) (0.774) (0.848) (0.033) (0.898)
Lapatinib 0.656 0.516 >10 >10 >10 >10 >10 0.562 3.578
(0.257) (0.080) (-) (-) (-) (-) (-) (0.061) (0.771)
Gefitinib 0.791 0.751 6.241 6.259 5.936 9.040 7.010 0.333 4.762
(0.446) (0.055) (1.390) (2.502) (2.115) (1.389) (0.856) (0.069) (0.911)
Dacomitinib < 0.019 <0.019 2.075 2.400 2.145 0.418 1.230 0.043 2.715
(-) (-) (0.191) (0.042) (0.276) (0.054) (0.156) (0.014) (0.106)
 U-CH1 and UM-Chor-1 are sensitive to all EGFR inhibitors, with different sensitivity
 Afatinib is the only EGFR inhibitor active across the chordoma cell line panel
 Sensitivity is comparable to EGFR-dependent cell lines
 The JHC7 cell line is resistant to Afatinib (note: JHC7 line is not driven by EGFR signaling)

0
1000
2000
0 14 28 42 56
Day
CubicMillimeters
Control
Afatinib
Tumor Volume (Mean ± SEM)
53-15101-UCH-1 10-6-15
0
500
1000
0 14 28 42 56
Day
CubicMillimeters
Control
Afatinib
53-15105-SF8894, 10-19-2015
0
500
1000
0 14 28 42 56
Day
CubicMillimeters
Control
Afatinib
53-15105-SF8894, 10-19-2015
In vivo efficacy of Afatinib in U-CH1 Xenograft and SF8894 PDX
models
20 mg/kg; po; qdx2820 mg/kg; po; qdx42
 Daily oral treatment with afatinib induces tumor regression
in both chordoma models
U-CH1 SF8894 PDX

A rationale for clinical trials with Afatinib in chordoma patients
 A subset of chordoma cell lines are highly sensitive to EGFR inhibitors
 Afatinib is the only EGFR inhibitor with activity across the chordoma panel
 This activity appears to be strongly contributed by its unique ability to down-
modulate the total level of EGFR and the transcription factor Brachyury *, a
feature not shared by the other inhibitors
 The most sensitive cell lines display stronger EGFR activation and lower
expression of Axl, a putative resistance pathway
 Afatinib has high efficacy against chordoma tumors in vivo
 These data support the use of afatinib in clinical trials and provide the rationale for
the upcoming European phase II study on afatinib in advanced chordoma.
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*Brachyury is highly expressed in all cells in nearly every chordoma tumor

NMS-Probe12 Kinome Profile
NMS internal Kinase Selectivity Screen + external KSS services
>400 Kinases tested
Narrowing the circle of relevant targets
%I >60 @ 100nM
or
KSS IC50 < 100nM
73 Kinases
inhibited
INVALIDATED
34 Kinases
(by at least 2 inhibitors)
QUESTIONED
30 Kinases
(by only 1 compound)
TIER 1
9 Kinases
(no disproof)
Kd/IC50<50nM
AND
IC50 > 2uM on Chordoma cell lines (UCH1/2)
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200 inhibitors
Kd/IC50 Kinases
IC50 on Chordoma cell lines
(probes)

Compound groups of closer interest
Four groups of compounds are under investigation based on potency, cellular selectivity and
perspectives for therapeutic applications in Chordomas
 EGFR inhibitors: Afatinib has been identified as the most advanced high profile compound
suitable for clinical studies
 HSP90 inhibitors: very potent cellular activity, correlated to Brachyury and EGFR
degradation in multiple cell lines. Good in vivo efficacy in UCH-1 SCID mice
 CDKs
 NMS-Probe12 and analogs: multi-kinase inhibitors with cellular selectivity.
Tool for the identification of new potential kinase targets involved in chordoma cell line
proliferation

Chordoma cell line inactive kinase inhibitors (probe cpds)
45
Compounds with IC50 > 2uM
on UCH-1/2
UCH-1
(IC50 uM)
UCH-2
(IC50 uM)
Excluded Target
Number of
Excluded
Targets with
compound
AC-220/Quizartinib >10 >10
CSF1R/FMS; FLT1/VEGFR1; FLT3; FLT4/VEGFR3; KIT; PDGFRA;
PDGFRB; RET;
8
Barasertib; AZD1152-HQPA >10 >10 AURKC; FLT3; KIT; MEK5/MAP2K5; PDGFRA; PDGFRB; 6
Cabozantinib 6.561 5.806 RET; VEGFR2/KDR; 2
Crizotinib 2.943 3.830 LCK; LOK/STK10; SLK/STK2; TRKB; 4
Dabrafenib
37% @
0.3uM
46% @ 0.3uM BRAF; RAF1/CRAF; 2
Dasatinib 2.093 3.911
ABL; ABL2/ARG; BLK; CSF1R/FMS; DDR1; DDR2; EPHA2; EPHA8;
FGR; FYN; KIT; LCK; LYN; MEK5/MAP2K5; p38-alpha; PDGFRA;
PDGFRB; ZAK/MLTK;
18
Doramapimod, BIRB-796 (p38-
alpha)
>10 >10 DDR1; DDR2; JNK2; LOK/STK10; p38-alpha; p38-beta; 6
Imatinib >10 >10
ABL; ABL2/ARG; CSF1R/FMS; DDR1; DDR2; KIT; LCK; PDGFRA;
PDGFRB;
9
MLN-518/Tandutinib >10 >10 CSF1R/FMS; FLT3; KIT; PDGFRA; PDGFRB; 5
Nilotinib 2.407 2.975
ABL; ABL2/ARG; CSF1R/FMS; DDR1; DDR2; EPHA8; KIT; LCK;
p38-beta; ZAK/MLTK;
10
NMS-probe1
21.1% @
0.3uM
6.1% @ 0.3uM AKT3; 1
NMS-probe2
-16% @
0.3uM
-14% @ 0.3uM AKT3; 1
NMS-probe69A
14% @
0.3uM
0% @ 0.3uM SULU1; 1
NMS-probe3
11.4% @
0.3uM
11.8% @
0.3uM
AKT3; 1
NMS-probe9 >10 >10 BRAF; RAF1/CRAF; 2
NMS-probe76 >10 >10 BRAF; RAF1/CRAF; 2
NMS-probe83 6.790 6.430 BRAF; 1
NMS-probe16 (KIT) 8.519 9.465 CSF1R/FMS; FLT1/VEGFR1; FLT3; KIT; (LOK/STK10; ZAK/MLTK;) 6
NMS-probes etc.
etc. ↓ ↓
etc. ↓ ↓
↓ ↓
VX-745 (Vertex p38) >10 >10 p38-alpha; 1

Invalidated Chordoma driver targets : 34 kinases
46
Target Compounds with Kd or IC50 <50nM on target and inactive on UCH1/2KSS 1
(% I)
KSS2
(% I)
KSS
(IC50
uM)
ABL1 98 94 0.056 Dasatinib; Imatinib; NMS-probe8; Nilotinib;
ABL2/ARG 98 84 Dasatinib; Imatinib; Nilotinib
AKT3 100 -2 NMS-probe1; NMS-probe2; NMS-probe3
AURKC 81 3 R406 (Fostamatinib active metab); AZD-1152HQPA
BLK 99 74 R406; Dasatinib; NMS-probe8
BRAF 77 37 2.939 Dabrafenib, SB-590885, Plexxikon (PLX-4720), and other NMS-probes......
CSF1R/FMS 95 61
MLN-518/Tandutinib; Pazopanib; PTK-787; R406; Sorafenib; Sunitinib;
AC220; Dasatinib; Imatinib; NMS-probe16 (Kd exper 0.88nM); Nilotinib
DDR1 100 93 R406; Sorafenib; BIRB-796; Dasatinib; Imatinib; Nilotinib
DDR2 88 100 R406; Sorafenib; BIRB-796; Dasatinib; Imatinib; Nilotinib
EPHA2 87 95 0.050 Dasatinib; NMS-probe8 (KSS 58nM); more NMS cpds available
EPHA8 99 92 Dasatinib; Nilotinib
FGR 60 64 R406; Dasatinib;
FLT1/VEGF
R1
95 68 Pazopanib; PTK-787; R406; Sorafenib; Sunitinib; AC220; NMS-probe16;
FLT3 100 89 0.071
MLN-518/Tandutinib; R406; Sorafenib; Sunitinib; AC220; AZD-1152HQPA;
NMS-probe16; NMS-probe15; NMS-probe97
FLT4/VEGF
R3
94 86 Pazopanib; R406; Sunitinib; AC220;
etc. Cpds X, Y , Z etc.
etc. etc.
NMS-Probe12
A Chordoma cell line
active
multikinase inhibitor
Chordoma cell line inactive kinase inhibitors
34 targets overall

47
Conclusions
 The increasing relevance of context dependent poly-pharmacology in treating
complex diseases, such as cancer, has a significant impact on the strategic
configuration of compound collections, screening and optimization methods
 Multiparametric optimizations from the onset of hit-to-lead phases and leverage on
the combination of effects are bound to have a consolidated role in drug discovery
 Chemogenomic screening helps to convert phenotypic screening endeavors into
target-based drug discovery, at times involving multiple targets simultaneously
 Multiple, structurally distinct chemotypes with affinity against a particular target
provide confidence in a target.
 Developing and applying selective chemical probes against novel (unannotated)
targets is an area for collaborative partnerships between academic institutions and
pharmaceutical companies
 Opportunities to repurpose existing drugs into new applications are created

48
Presented at the Global Medicinal
Chemistry and GPCR Summit
To find out more, visit:
www.global-engage.com

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