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

Alzheimers   Diabetes  

Depression   Cancer  

Treating Symptoms v.s. Modifying Diseases

Will it work for me?

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

Reality: Overlapping Pathways

WHY  NOT  USE    “DATA  INTENSIVE”  SCIENCE  

TO  BUILD  BETTER  DISEASE  MAPS?  

Equipment capable of generating massive amounts of data

“Data Intensive Science”- “Fourth Scientific Paradigm” For building: “Better Maps of Human Disease”

Open Information System

IT Interoperability

Evolving Models hosted in a Compute Space- Knowledge Expert

It is now possible to carry out comprehensive monitoring of many traits at the population level

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How is genomic data used to understand biology?

!Standard" GWAS Approaches Profiling Approaches

!Integrated" Genetics Approaches

Genome scale profiling provide correlates of disease   Many examples BUT what is cause and effect?

Identifies Causative DNA Variation but provides NO mechanism

  Provide unbiased view of molecular physiology as it

relates to disease phenotypes

  Insights on mechanism

  Provide causal relationships and allows predictions

RNA amplification Microarray hybirdization

Gene Index

Tum

ors

Tum

ors

Constructing Co-expression Networks

Start with expression measures for genes most variant genes across 100s ++ samples

Note: NOT a gene expression heatmap

1 -0.1 -0.6 -0.8

-0.1 1 0.1 0.2

-0.6 0.1 1 0.8

-0.8 0.2 0.8 1 1

2

3

4

1 2 3 4

Correlation Matrix Brain sample

expr

essi

on

1 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 1

2

3

4

1 2 3 4

Connection Matrix

1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1

2

4

3

1 2 4 3

4 1

3 2

4 1

2

Establish a 2D correlation matrix for all gene pairs

Define Threshold eg >0.6 for edge

Clustered Connection Matrix

Hierarchically cluster

sets of genes for which many pairs interact (relative to the total number of pairs in that

set)

Network Module

Identify modules

Integration of Genotypic, Gene Expression & Trait Data

Causal Inference

Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009)

Chen et al. Nature 452:429 (2008) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005)

Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)

“Global Coherent Datasets” •  population based

•  100s-1000s individuals

Preliminary Probabalistic Models- Rosetta /Schadt

Gene symbol Gene name Variance of OFPM explained by gene expression*

Mouse model

Source

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

3a receptor 1 46% ko Purchased from Deltagen, CA

Tgfbr2 Transforming growth factor beta receptor 2

39% ko Purchased from Deltagen, CA

Networks facilitate direct identification of genes that are

causal for disease Evolutionarily tolerated weak spots

Nat Genet (2005) 205:370

  50 network papers   http://sagebase.org/research/resources.php

List of Influential Papers in Network Modeling

(Eric Schadt)

Requires Data driven Science

Lots of data, tools, evolving models of disease

Requires Scientists Clinicians & Citizens to link in different ways

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

Sagebase.org

Data Repository

Discovery Platform

Building Disease Maps

Commons Pilots

Sage Bionetworks Collaborators

  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen

20

  Foundations   CHDI, Gates Foundation

  Government   NIH, LSDF

  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)

  Federation   Ideker, Califarno, Butte, Schadt

Model of Alzheimer’s Disease Bin Zhang Jun Zhu

AD

normal

AD

normal

AD

normal

Cell cycle

http://sage.fhcrc.org/downloads/downloads.php

Breast Cancer Bayesian Network Conserved Super-modules

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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)

Extract gene:gene relationships for selected super-modules from BN and define Key Drivers

Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)

Bin Zhang Xudong Dai Jun Zhu

Model of Breast Cancer: Integration

= predictive of survival

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.

Federation s Genome-wide Network and Modeling Approach to Warburg Effect

Califano group at Columbia Sage Bionetworks Butte group at Stanford

THE FEDERATION Butte Califano Friend Ideker Schadt

vs

Software Tools Support Collaboration

Biology Tools Support Collaboration

Potential Supporting Technologies

Taverna

Addama

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Platform for Modeling

SYNAPSE  

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INTEROPERABILITY

RULES GOVERN

Engaging Communities of Interest

PLAT

FORM

NEW

MAP

S 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...)

NEW TOOLS Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape,

Clinical Trialists, Industrial Trialists, CROs…)

PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-

(Federation, CCSB, Inspire2Live....) Arch2POCM

Arch2POCM  

A  Fundamental  Systems  Change  for  Drug  Discovery  

Stephen  Friend  Aled  Edwards  Chas  Bountra  Lex  vander  Ploeg,  Thea  Norman,  Keith  Yamamoto  

“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  

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  

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.….  

The current pharma model is redundant

50% 10% 30% 30% 90%

Negative POC information is not shared Attrition

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

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  

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

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  

Entry Points For Arch2POCM Programs Requires two compounds if first fails

Lead identification Phase I Phase II Preclinical

Lead optimisation

Assay in vitro probe

Lead Clinical candidate

Phase I asset

Phase II asset

- genomic/ genetic Pioneer target sources - disease networks

- academic partners - private partners - Sage Bionetworks, SGC,

Early Discovery

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

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  

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  

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

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

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

•  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

Arch2POCM Value Propositions For Academia

•  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  

Arch2POCM Value Propositions For Public Funders

•  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  

Arch2POCM Value Propositions For Regulators

•  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  

Arch2POCM Value Propositions For Patients

Epigene:cs/  Chroma:n  Biology  

A  pioneer  area  of  drug  discovery  

Histone

DNA

Lysine Lysine

How We Define Epigenetics

Modification Write Read Erase

Acetyl HAT Bromo HDAC Methyl HMT MBT DeMethyl

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  

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  

Domain Family Typical substrate class* Total Targets

Histone Lysine demethylase

Histone/Protein K/R(me)n/ (meCpG) f q�

Bromodomain Histone/Protein K(ac) k/ �

R O Y A L

Tudor domain Histone Kme2/3 - Rme2s kC�

Chromodomain Histone/Protein K(me)3 f :�

MBT repeat Histone K(me)3 C�

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�

The Epigenetics Universe (2009)

Druggable

Domain Family Typical substrate class* Total Targets

Histone Lysine demethylase

Histone/Protein K/R(me)n/ (meCpG) f q�

Bromodomain Histone/Protein K(ac) k/ �

R O Y A L

Tudor domain Histone Kme2/3 - Rme2s kC�

Chromodomain Histone/Protein K(me)3 f :�

MBT repeat Histone K(me)3 C�

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�

The Epigenetics Universe (2011)

Now known to be amenable to small molecule inhibition

JQ1distributed to more than 200 labs world wide

Bromodomains Can Be Targeted by Small Molecules (2010)

Anti-tumour activity (FDG-PET)

4 days 50mg/kg IP

Selectivity Potency

40 nM (BRD4)

Co-crystal (JQ1 + BRD4)

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

•  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

Histone Demethylases Can be Targeted (JMJD3, unpublished)

•  First structure of small molecule inhibitor bound to methyl-lysine binding domain.

•  Potential relevance to reprogramming and regenerative medicine

Methyl-lysine-binding Proteins Can be Targeted by Small Molecules

Herold et al., J Med Chem (2011)

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

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  

As are Human Demethylases

SGC structure

Non SGC structure

Alphascreen in place

Catalytic domain purified

Assay Development

In vitro assayavailable

Cell assayavailable

Open Access Chemical Starting Points Will Be Available Sc

reen

ing

/ Ch

emis

try

Me Lys Binders

BRD

HMT

KDM HAT

Kd < 500 nM Selectivity > 30x

Active Kd < 5 µM

Weak

None

SUV39H2 EP300

L3MBTL1 L3MBTL3

WDR5

MYST3 DOT1L

UHRF1 SMYD3 JMJD2A Tu EZH2

HAT1 PRMT3

SETD8 CREBBP FALZ

BAZ2B PB1

GCN5L2 JMJD3

SETD7

BET 2nd PHF8

JMJD2 FBXL11

G9a/GLP BET

JARID1C

Probe Criteria

Kd < 100 nM Selectivity > 30x Cell IC50 < 1 µM

Aided By an Archipelago of Scientists Chemistry  •  GlaxoSmithKline  •  University  of  North  Carolina  

(CICBDD)  •  NovarJs  •  Pfizer    Nov,  2010  •  Eli  Lilly    Dec  2010  •  Broad  InsJtute    Feb  2011  •  James  Bradner,  Harvard  •  NCGC  •  Julian  Blagg,  ICR  •  Chris  Abell,  Cambridge  •  Ulrich  Guenther,  Birmingham  •  Stefan  Lauffer,  Tuebingen  

Biology •  Assam El-Osta, Baker Inst, Australia

–  SETD7, Diabetes •  Or Gozani, Stanford

–  Biochemistry •  James Ellis, Hospital for Sick Children,

Toronto –  induced pluripotent stem cells

•  Thea Tlsty, UCSF –  breast epithelial models

•  Broad Institute, Harvard –  cancer cell line panel

•  Rossi, U British Columbia –  mouse leukemia model/G9a inhibitor

•  James Bradner Harvard –  DOT1L in leukemia –  BET family

•  David Wilson, Kevin Lee, GSK –  Assays in disease tissue from RA and OA patients –  Cell based assays

•  Roland Schuele, Freiburg –  JMJD2C in prostate cancer

•  Juerg Schwaller, Basel –  Bromo domains in MLL

Biological Reagents •  Anthony Kossiakov, Chicago

–  renewable binders •  Alan Sawyer, EMBL

–  monoclonal Antibodies •  Karen Kennedy, Bill Skarnes

Sanger –  knockout mice, knockout Embryonic

Stem cells

•  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)

Epigenetics Archipelago Publications (2011)

The Benefits of the Arch2POCM Pre-competitive Model: Crowdsourced Studies

The  Crowd  

Clin  

Arch2POCM Compounds And Data

Crowdsourced data on Arch2POCM test compound

• SAR  med  chem  • Best  indicaJon  • Clinical  data