Stephen Friend Aug 1-6, 2011 IBC Next Generation Sequencing & Genomic Medicine San Francisco, CA

1. Arch2POCM
A Drug Development Approach
from Disease Targets to their Clinical Validation
Stephen Friend
Sage Bionetworks
(a non-profit foundation)
IBC
San Francisco
Aug 3, 2011

2. Alzheimers
Diabetes
Treating Symptoms v.s. Modifying Diseases
Depression
Cancer
Will it work for me?

3. r hh
mb Cp s
r Dmp

4. Personalized Medicine 101:
Capturing Single bases pair mutations = ID of responders

5. Reality: Overlapping Pathways

8. WHY
NOT
USE
“DATA
INTENSIVE”
SCIENCE
TO
BUILD
BETTER
DISEASE
MAPS?

9. “Data Intensive Science”- “Fourth Scientific Paradigm”
For building: “Better Maps of Human Disease”
Equipment capable of generating
massive amounts of data
IT Interoperability
Open Information System
Evolving Models hosted in a
Compute Space- Knowledge Expert

11. It is now possible to carry out comprehensive
monitoring of many traits at the population level
s h b h t
p
t s r
m Cm pbh pt h
b s
D
DCm
s t
o
Cp c
o Ctm l s
h t
t pbh p

12. How is genomic data used to understand biology?
RNA amplification
Tumors
Microarray hybirdization
Tumors
Gene Index
!Standard"GWAS Approaches Profiling Approaches
Identifies Causative DNA Variation but Genome scale profiling provide correlates of disease
provides NO mechanism  Many examples BUT what is cause and effect?
 Provide unbiased view of
molecular physiology as it
relates to disease phenotypes
trait
 Insights on mechanism
 Provide causal relationships
and allows predictions
Integrated"
! Genetics Approaches

13. Constructing Co-expression Networks
Start with expression measures for genes most variant genes across 100s ++ samples
1 2 3 4 Note: NOT a gene
expression heatmap
1
1 0.8 0.2 -0.8
Establish a 2D correlation matrix 2
for all gene pairs
expression
0.8 1 0.1 -0.6
3
0.2 0.1 1 -0.1
4
-0.8 -0.6 -0.1 1
Brain sample
Correlation Matrix
Define Threshold
eg >0.6 for edge
1 2 4 3 1 2 3 4
1 1
1 4 1 1 1 0 1 1 0 1
2 2
1 1 1 0 1 1 0 1
1 1 1 0 Hierarchically 3
Identify modules 4 0 0 1 0
2 3 cluster
4
3 0 0 0 1 1 1 0 1
Network Module Clustered Connection Matrix Connection Matrix
sets of genes for which many
pairs interact (relative to the
total number of pairs in that
set)

14. Integration of Genotypic, Gene Expression & Trait Data
Schadt et al. Nature Genetics 37: 710 (2005)
Millstein et al. BMC Genetics 10: 23 (2009)
Causal Inference
“Global Coherent Datasets”
• population based
• 100s-1000s individuals
Chen et al. Nature 452:429 (2008) Zhu et al. Cytogenet Genome Res. 105:363 (2004)
Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)

15. Preliminary Probabalistic Models- Rosetta /Schadt
Networks facilitate direct
identification of genes that are
causal for disease
Evolutionarily tolerated weak spots
Gene symbol Gene name Variance of OFPM Mouse Source
explained by gene model
expression*
Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics
Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics
Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg
Mirochnitchenko (University of
Medicine and Dentistry at New
Jersey, NJ) [12]
Lactb Lactamase beta 52% tg Constructed using BAC transgenics
Me1 Malic enzyme 1 52% ko Naturally occurring KO
Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple
(UCLA) [13]
Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg
(Columbia University, NY) [11]
C3ar1 Complement component 46% ko Purchased from Deltagen, CA
3a receptor 1
Tgfbr2 Transforming growth 39% ko Purchased from Deltagen, CA
Nat Genet (2005) 205:370 factor beta receptor 2

16. List of Influential Papers in Network Modeling
 50 network papers
 http://sagebase.org/research/resources.php

17. (Eric Schadt)

18. Requires Data driven Science
Lots of data, tools, evolving models of disease
Requires Scientists Clinicians & Citizens to link in different ways

19. Sage Mission
Sage Bionetworks is a non-profit organization with a vision to
create a commons where integrative bionetworks are evolved by
contributor scientists with a shared vision to accelerate the
elimination of human disease
Building Disease Maps Data Repository
Commons Pilots Discovery Platform
Sagebase.org

20. Sage Bionetworks Collaborators
 Pharma Partners
 Merck, Pfizer, Takeda, Astra Zeneca, Amgen
 Foundations
 CHDI, Gates Foundation
 Government
 NIH, LSDF
 Academic
 Levy (Framingham)
 Rosengren (Lund)
 Krauss (CHORI)
 Federation
 Ideker, Califarno, Butte, Schadt 20

21. Bin Zhang
Model of Alzheimer’s Disease Jun Zhu
AD
normal
AD
normal
AD
normal
Cell
cycle
http://sage.fhcrc.org/downloads/downloads.php

22. Bin Zhang
Model of Breast Cancer: Integration Xudong Dai
Jun Zhu
Conserved Super-modules
mRNA proc.
= predictive
Breast Cancer Bayesian Network
Chromatin
of survival
Extract gene:gene relationships for selected super-modules from BN and define Key Drivers
Pathways & Regulators
(Key drivers=yellow; key drivers validated in siRNA screen=green)
Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red)
Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)

23. Clinical Trial Comparator Arm
Partnership (CTCAP)
 Description: Collate, Annotate, Curate and Host Clinical Trial Data
with Genomic Information from the Comparator Arms of Industry and
Foundation Sponsored Clinical Trials: Building a Site for Sharing
Data and Models to evolve better Disease Maps.
 Public-Private Partnership of leading pharmaceutical companies,
clinical trial groups and researchers.
 Neutral Conveners: Sage Bionetworks and Genetic Alliance
[nonprofits].
 Initiative to share existing trial data (molecular and clinical) from
non-proprietary comparator and placebo arms to create powerful
new tool for drug development.

24. Federation s Genome-wide Network and
Modeling Approach to Warburg Effect
Califano group at Columbia Sage Bionetworks Butte group at Stanford

25. THE FEDERATION
Butte Califano Friend Ideker Schadt
vs

27. Software Tools Support Collaboration

28. Biology Tools Support Collaboration

29. Potential Supporting Technologies
Addama
Taverna
pbs

30. Platform for Modeling
SYNAPSE

33. INTEROPERABILITY

34. Engaging Communities of Interest
NEW MAPS
Disease Map and Tool Users-
( Scientists, Industry, Foundations, Regulators...)
PLATFORM
Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
RULES AND GOVERNANCE
Data Sharing Barrier Breakers-
(Patients Advocates, Governance
and Policy Makers, Funders...)
M
APS
FOR
NEW TOOLS
M
PLAT
Data Tool and Disease Map Generators-
NEW
(Global coherent data sets, Cytoscape,
Clinical Trialists, Industrial Trialists, CROs…)
RULES GOVERN
PILOTS= PROJECTS FOR COMMONS
Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)
Arch2POCM

35. Arch2POCM
A
Fundamental
Systems
Change
for
Drug
Discovery
Stephen
Friend
Aled
Edwards
Chas
Bountra
Lex
vander
Ploeg,
Thea
Norman,
Keith
Yamamoto

36. “Absurdity”
of
Current
R&D
Ecosystem
• $200B
per
year
in
biomedical
and
drug
discovery
R&D
• Handful
of
new
medicines
approved
each
year
• ProducJvity
in
steady
decline
since
1950
• 90%
of
novel
drugs
entering
clinical
trials
fail
• NIH
and
EU
just
started
spending
billions
to
duplicate
process
• 98%
of
pharma
revenues
from
compounds
approved
more
than
5
years
ago
(average
patent
life
11
years)
• >50,000
pharma
employees
fired
in
each
of
last
three
years
• Number
of
R&D
sites
in
Europe
down
from
29
to
16
in
2009

37. What
is
the
problem?
• Regulatory
hurdles
too
high?
• Low
hanging
fruit
picked?
• Payers
unwilling
to
pay?
• Genome
has
not
delivered?
• Valley
of
death?
• Companies
not
large
enough
to
execute
on
strategy?
• Internal
research
costs
too
high?
• Clinical
trials
in
developed
countries
too
expensive?
• In
fact,
all
are
true
but
none
is
the
real
problem

38. What
is
the
problem?
•
The
current
system
is
designed
as
if
every
new
program
is
desJned
to
deliver
an
approved
drug
• Each
new
therapy
is
pursued
through
use
of
proprietary
compounds
moving
in
parallel
with
no
data
being
shared
(oden
5-­‐10
companies
at
a
Jme)
• Therefore
it
makes
complete
sense
to
maintain
secrecy
• But
we
have
no
clue
what
we’re
doing
•
Alzheimer’s,
cancer,
schizophrenia,
auJsm.….

39. The current pharma model is redundant
Target ID/ Hit/Probe/ Clinical Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
Phase
Target ID/ Hit/Probe/ Clinical
ID Pharmaco
Toxicolog Phase I
Discovery Lead ID Candidate logy
y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical
logy
Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical Toxicolog
logy Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
logy
Target ID/ Hit/Probe/ Clinical Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical
logy
Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical Toxicolog
logy Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
logy
Attrition 50% 10% 30% 30% 90%
Negative POC information is not shared

40. What
is
the
problem?
The
real
problem
is
that
the
current
system
is
unable
to
provide
the
needed
insights
into
human
biology
and
disease
required
• It
has
been
assumed
that
academia’s
role
is
to
provide
fundamental
biological
insights
into
disease,
but
we
know
now
that
virtually
all
experiments
used
to
understand
disease
in
test
tubes
and
animals
are
not
predicJve
of
what
happens
in
people
We
need
to
develop
a
mechanism
to
be8er
understand
disease
biology
before
tes:ng
compe::ve
compounds
on
sick
people

41. Let s imagine….
• A pool of dedicated, stable funding
• A process that attracts top scientists and clinicians
• A process in which regulators can fully collaborate to solve key
scientific problems
• An engaged citizenry that promotes science and acknowledges
risk
• Mechanisms to decrease bureaucratic and administrative barriers
• Sharing of knowledge to more rapidly achieve understanding of
human biology
• A steady stream of targets whose links to disease have been
validated in humans

42. Basic
Concept
of
Arch2POCM
• Use
NME’s
to
understand
the
biology
of
human
diseases
• Focus
on
targets
that
are
deemed
too
risky
for
either
public
or
private
sectors
• Share
the
risk
by
pooling
public
and
private
resources
• Open
access
to
data
produces
stream
of
informaJon
about
human
biology
• NME,
not
burdened
by
IP,
expands
the
number
of
ideas
that
can
be
pursued,
without
addiJonal
funds

43. Entry Points For Arch2POCM Programs
Requires two compounds if first fails
- genomic/ genetic
Pioneer target sources - disease networks
- academic partners
- private partners
- Sage Bionetworks, SGC,
Lead Lead
Preclinical Phase I Phase II
identification optimisation
Assay
in vitro
probe
Lead Clinical Phase I Phase II
candidate asset asset
Early Discovery

44. Arch2POCM and the Power of
Crowdsourcing
• Crowdsourcing: the act of outsourcing tasks
traditionally performed by an employee to a large group
of people or community
• By making Arch2POCM s clinically characterized
probes available to all, Arch2POCM will seed
independently funded, crowdsourced experimental
medicine
• Crowdsourced studies on Arch2POCM probes will
provide clinical information about the pioneer targets in
MANY indications

45. Why
have
a
“Common
Stream”
where
there
is
No-­‐IP
on
Compounds
thru
PhIIb?
• Broader
data
disseminaJon
• Far
fewer
legal
agreements
to
negoJate
• Generates
FTO
• Only
way
to
access
KOL
without
hassle
• More
cost-­‐effecJve
• Not
constrained
by
market
consideraJons
or
biased
internal
thinking

46. Why
now?
• Economics
• Genomics
is
creaJng
many
high-­‐risk,
high
opportunity
targets
in
all
diseases
but
no
has
the
resources
to
take
the
risks
• Increased
level
of
interest
by
public
and
private
• Health
care
costs
bringing
drug
discovery
to
front
of
public
policy

48. General Benefits of Arch2POCM For The
Pharmaceutical Industry
1. Arch2POCM s use of test compounds to de-risk previously
unexplored biology enables pharma to initiate proprietary drug
development with an array of unbiased, clinically validated targets
 Arch2POCM operates without patents and advances pairs of test
compounds through Ph II
 Test compounds are used by Arch2POCM and the crowd to define
clinical mechanisms for epigenetic targets impacting oncology
2. Arch2POCM crowdsourced research and trials provides parallel
shots on goal: by aligning test compounds to most promising
unmet medical need
3. Negative clinical trial information generated by Arch2POCM and
the crowd will increase clinical success rates (as one can pick
targets and indications more smartly)
4. Build methods to track and visualize crowd sourced data, clinical
safety profiles and reporting of potential adverse event

49. Why Should Pharmaceutical Companies Invest in
Arch2POCM? Some Possible Answers
1. Directly shape the Arch2POCM scientific strategy around how to best
interrogate the high risk high opportunity epigenetics targets for Oncology
and Immunology
2. Chose the components and where they are to be executed: i.e., targets,
lead compounds, clinical indications and trial design
3. Potential to realize value for existing pharmaceutical assets that are not
currently getting developed
- Contribute these compounds at Gate 2 of Arch2POCM (i.e., pre-IND)
4. Partnering with Regulatory Sciences groups at the FDA and EMEA and
real time open dialogs on safety issues around the compounds being
used to understand biology as opposed to make products
5. Partnerships with academics and patient groups improve the
understanding around the importance of the Arch2POCM industry
partners in the full value chain and enable building of trust with key
leaders in both communities

50. Why Should Pharmaceutical Companies Invest in
Arch2POCM? Some Possible Answers (cont)
6. Real time access to the live stream of project information during the
Arch2POCM oncology projects: projected to provide lead time on key
information vs non-participating pharma
– Information on the leads
– Information from the preclinical studies
– Information from the Arch2POCM tirals in real time before aggregated
– Information from the crowd-sourced trials in real time before
aggregated
7. Direct/rapid access to epigenetic academic experts and their tools
8. Opportunity to take the Validated targets forward to approval or share in
auctioning of these assets

51. Arch2POCM Value Propositions For Academia
• Funding to pursue and publish disruptive discovery research
• Sharing of resources and data among private and academic
partners
• Development of basic discoveries toward therapies and cures
• Collaboration with private sector and regulatory scientists
• Education of students and public about nature and process of
discovery, and understanding disease
• Exit options: extend/branch studies beyond arch2POCM

52. Arch2POCM Value Propositions For Public
Funders
• The
benefits
of
public
funding
investment
in
Arch2POCM
are
compounded
by
the
efforts
of
the
crowd.
• Public
funding
will
support
experimental
medicine
studies.
• Public
funding
will
invest
in
a
drug
development
model
that
protects
paJent
safety
by
eliminaJng
redundant
negaJve
trials

53. Arch2POCM Value Propositions For
Regulators
• Opportunity
to
facilitate
a
greater
number
of
trials
on
pioneer
targets.
• Opportunity
to
leverage
exisJng
legacy
clinical
trial
data
to
support
improved
drug
development
strategies.
– By
focusing
on
pioneer
targets
instead
of
compounds,
regulators
can
parJcipate
pro-­‐acJvely
in
Arch2POCM
POCM
development.
• Opportunity
to
play
a
leadership
role
in
the
development
of
regulatory
and
legal
collaboraJons
that
would
incenJvize
the
pharmaceuJcal
industry
to
parJcpate
in
a
pre-­‐compeJJve
clinical
validaJon
model

54. Arch2POCM Value Propositions For Patients
• Arch2POCM
and
the
crowd
will
accelerate
the
development
of
NCEs
for
unmet
medical
needs
• Crowdsourced
research
and
trials
represent
an
opportunity
for
mulJple
shots
on
goal
that
will
serve
to
align
test
compounds
to
most
promising
unmet
medical
need
• PaJent
safety
is
protected
because
Arch2POCM
will
release
negaJve
clinical
trial
data
so
that
redundant
trials
can
stop

55. Epigene:cs/
Chroma:n
Biology
A
pioneer
area
of
drug
discovery

56. How We Define Epigenetics
Lysine
DNA
Histone
Modification Write Read Erase
Acetyl HAT Bromo HDAC
Methyl HMT MBT DeMethyl

57. The Case for Epigenetics/Chromatin
Biology
1. There
are
oncology
drugs
on
the
market
(HDACs)
2. A
growing
number
of
links
to
oncology,
notably
many
geneJc
links
(i.e.
fusion
proteins,
somaJc
mutaJons)
3. A
pioneer
area:
More
than
400
targets
amenable
to
small
molecule
intervenJon
-­‐
most
of
which
only
recently
shown
to
be
“druggable”,
and
only
a
few
of
which
are
under
acJve
invesJgaJon
4. Open
access,
early-­‐stage
science
is
developing
quickly
–
significant
collaboraJve
efforts
(e.g.
SGC,
NIH)
to
generate
proteins,
structures,
assays
and
chemical
starJng
points

58. Some Examples of Links to Cancer
• Ezh2
methyltransferase
(enhancer
of
zeste
homolog
2)
– SomaJc
mutaJons
in
B-­‐cell
lymphoma
• JARID1B
demethylase
(jumonji,
AT
rich
interac:ve
domain
2
)
– Linked
to
malignant
transformaJon:
expressed
at
high
levels
in
breast
and
prostate
cancers;
Knock-­‐down
inhibits
proliferaJon
of
breast
cancer
lines
and
tumor
growth
• G9A
methyltransferase
(euchroma:c
histone-­‐lysine
N-­‐methyltransferase
2)
– Expressed
in
aggressive
lung
cancer
cells:
high
expression
correlates
to
poor
prognosis;
G9a
knockdown
inhibits
metastasis
in
vivo.
• MLL:
myeloid/lymphoid
or
mixed-­‐lineage
leukemia
– MulJple
chromosomal
transloca,ons
involving
this
gene
are
the
cause
of
certain
acute
lymphoid
leukemias
and
acute
myeloid
leukemias
• Brd4:
(Bromodomain-­‐containing
protein
4)
– Implicated
in
t(15;
19)
aggressive
carcinoma:
Chromosome
19
transloca,on
breakpoint
interrupts
the
coding
sequence
of
a
bromodomain
gene,
BRD4
• CBP
bromodomain
– Oncogeneic
fusions
– Mutated
in
relapsing
AML

59. The Epigenetics Universe (2009)
Domain Family Typical substrate class* Total
Targets
Histone Lysine Histone/Protein K/R(me)n/ (meCpG) fq
demethylase
Bromodomain Histone/Protein K(ac) k/
R Tudor domain Histone Kme2/3 - Rme2s kC
O
Chromodomain Histone/Protein K(me)3 f:
Y
A MBT repeat Histone K(me)3 C
L
PHD finger Histone K(me)n C/
Acetyltransferase Histone/Protein K W/
Methyltransferase Histone/Protein K&R Eq
PARP/ADPRT Histone/Protein R&E W/
MACRO Histone/Protein (p)-ADPribose Wk
Histone deacetylases Histone/Protein KAc WW
f Ck
Druggable

60. The Epigenetics Universe (2011)
Domain Family Typical substrate class* Total
Targets
Histone Lysine Histone/Protein K/R(me)n/ (meCpG) fq
demethylase
Bromodomain Histone/Protein K(ac) k/
R Tudor domain Histone Kme2/3 - Rme2s kC
O
Chromodomain Histone/Protein K(me)3 f:
Y
A MBT repeat Histone K(me)3 C
L
PHD finger Histone K(me)n C/
Acetyltransferase Histone/Protein K W/
Methyltransferase Histone/Protein K&R Eq
PARP/ADPRT Histone/Protein R&E W/
MACRO Histone/Protein (p)-ADPribose Wk
Histone deacetylases Histone/Protein KAc WW
f Ck
Now known to be amenable to small molecule inhibition

61. Bromodomains Can Be Targeted by Small Molecules
(2010)
Co-crystal (JQ1 + BRD4)
Potency
Selectivity 40 nM (BRD4)
Anti-tumour activity (FDG-PET)
4 days
50mg/kg IP
JQ1distributed to more than 200 labs world wide

62. Methyltransferases Can be Selectively Inhibited
(Nature Chem Biol, in press 2011)
Structure based-design of a potent and selective G9a/GLP HMTase antagonist
• Global reduction in H3K9me2 levels
• Phenocopies shRNA knockdown
• Well tolerated by > 7 cell lines
• Cell line specific reduction in clonogenicity
• Reactivation of endogenous & exogenous genes in mouse iPS and ES cells
• Differential effects on DNA methylation between mouse ES cells and human adult
cancer cells
• modestly facilitates efficiency of iPS formation

63. Histone Demethylases Can be Targeted (JMJD3, unpublished)
• Alphascreen assay
• Validation by global H3K27me3 demethylation
assay in JMJD3 transfected cells
• 1.8 Å structure of JMJD3 catalytic domain with
8-hydroxyquinoline-5-carboxylic acid Overall fold of JMJD3
Binding mode of 8-
hydroxyquinoline-5-carboxylate
8-hydroxyquinoline-5-carboxylic acid tested in HEK293 cells
Alphascreen assays used for identifying small molecule binders

64. Methyl-lysine-binding Proteins Can be Targeted by
Small Molecules
Herold et al., J Med Chem (2011)
• First structure of small molecule inhibitor bound to methyl-
lysine binding domain.
• Potential relevance to reprogramming and regenerative
medicine

65. Bromodomain Field is Poised For Rapid Progress
• 31 crystal structures
• 45 purified Bromo domains
• Tm shift assay panel >30
bromodomains
• 5 Alpha binding assays
across 4 subfamilies

66. As are Human Methyltransferases
• Purifiable
HMTs:
– In
general,
all
the
targets
can
be
purified
to
>90%
purity
in
milligram
quanJJes
– AcJvity
of
most
of
the
proteins
have
been
tested

67. As are Human Demethylases
SGC structure
Non SGC structure
Catalytic domain purified
Alphascreen in place

68. Open Access Chemical Starting Points Will Be Available
Probe
Kd < 100 nM G9a/GLP
Selectivity > 30x
Criteria
Cell IC50 < 1 µM BET
SETD7 JMJD2
Kd < 500 nM PHF8 FBXL11
Selectivity > 30x
BET 2nd
Screening / Chemistry
SUV39H2
Active L3MBTL3 GCN5L2
Kd < 5 µM
EP300
L3MBTL1 JMJD3
WDR5
HAT1 PRMT3
BRD
Weak CREBBP BAZ2B PB1
KDM
FALZ SETD8
HAT
MYST3 EZH2
JARID1C Me Lys Binders
None DOT1L JMJD2A Tu
SMYD3 UHRF1 HMT
In vitro assay Cell assay
available available
Assay Development

69. Aided By an Archipelago of Scientists
Chemistry
Biology
• GlaxoSmithKline
• Assam El-Osta, Baker Inst, Australia
• University
of
North
Carolina
– SETD7, Diabetes
(CICBDD)
• Or Gozani, Stanford
• NovarJs
– Biochemistry
• Pfizer
Nov,
2010
• James Ellis, Hospital for Sick Children,
Toronto
• Eli
Lilly
Dec
2010
– induced pluripotent stem cells
• Broad
InsJtute
Feb
2011
• Thea Tlsty, UCSF
• James
Bradner,
Harvard
– breast epithelial models
• NCGC
• Broad Institute, Harvard
• Julian
Blagg,
ICR
– cancer cell line panel
• Chris
Abell,
Cambridge
• Rossi, U British Columbia
– mouse leukemia model/G9a inhibitor
• Ulrich
Guenther,
Birmingham
• James Bradner Harvard
• Stefan
Lauffer,
Tuebingen
– DOT1L in leukemia
– BET family
Biological Reagents • David Wilson, Kevin Lee, GSK
– Assays in disease tissue from RA and OA patients
• Anthony Kossiakov, Chicago – Cell based assays
– renewable binders
• Roland Schuele, Freiburg
• Alan Sawyer, EMBL – JMJD2C in prostate cancer
– monoclonal Antibodies
• Juerg Schwaller, Basel
• Karen Kennedy, Bill Skarnes – Bromo domains in MLL
Sanger
– knockout mice, knockout Embryonic
Stem cells

70. Epigenetics Archipelago Publications (2011)
• Identification of Macro Domain Proteins as Novel O-Acetyl-ADP-Ribose Deacetylases. J Biol Chem (2011)
• UNC0638: Potent, Selective, and Cell-penetrant Chemical Probe of Protein Lysine Methyltransferases G9a and GLP.
Nature Chem Biol. in press
• Recognition and specificity determinants of the human Cbx chromodomains. J Biol Chem. (2011)
• Animal Models of Epigenetic Regulation in Neuropsychiatric Disorders. Curr Top Behav Neurosci. (2011)
• A Selective Inhibitor and Probe of the Cellular Functions of Jumonji C Domain-Containing Histone Demethylases. J Am
Chem Soc. Epub ahead of print (2011)
• Inhibition of the histone demethylase JMJD2E by 3-substituted pyridine 2,4-dicarboxylates. Org Biomol Chem. (2011)
• Inhibition of histone demethylases by 4-carboxy-2,2'-bipyridyl compounds. ChemMedChem. 6(5):759-64. (2011)
• Recognition of multivalent histone states associated with heterochromatin by UHRF1. J Biol Chem. (2011)
• Small-molecule ligands of methyl-lysine binding proteins. J Med Chem. 54(7):2504-11 (2011)
• The Replication Focus Targeting Sequence (RFTS) Domain Is a DNA-competitive Inhibitor of Dnmt1. J Biol Chem. 286
(17):15344-51 (2011)
• Structural Chemistry of the Histone Methyltransferases Cofactor Binding Site. J Chem Inf Model. 51(3):612-623. (2011)
• The structural basis for selective binding of non-methylated CpG islands by the CFP1 CXXC domain. Nature Commun.
Epub ahead of print (2011)
• Somatic mutations at EZH2 Y641 act dominantly through a mechanism of selectively altered PRC2 catalytic activity, to
increase H3K27 trimethylation. Blood. 117(8):2451-9. (2011)
• Structural basis for the recognition and cleavage of histone H3 by cathepsin L. Nature Commun. (2011)

71. The Benefits of the Arch2POCM Pre-
competitive Model: Crowdsourced Studies
The
Crowd
Arch2POCM
Compounds
And Data
Crowdsourced data on
Arch2POCM test compound
• SAR
med
chem
• Best
indicaJon
Clin
• Clinical
data