Arch2POCM        A Drug Development Approachfrom Disease Targets to their Clinical Validation               Stephen Friend...
Alzheimers	                             Diabetes	        Treating Symptoms v.s. Modifying Diseases Depression	            ...
r hh  mb   Cp s 	   r Dmp
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?	  
“Data Intensive Science”- “Fourth Scientific Paradigm”For building: “Better Maps of Human Disease”           Equipment cap...
It is now possible to carry out comprehensive        monitoring of many traits at the population level	   s h b h t       ...
How is genomic data used to understand biology?                                                              RNA amplifica...
Constructing Co-expression Networks             Start with expression measures for genes most variant genes across 100s ++...
Integration of Genotypic, Gene Expression & Trait Data                                               Schadt et al. Nature ...
Preliminary Probabalistic Models- Rosetta /Schadt                                                                         ...
List of Influential Papers in Network Modeling                                        50 network papers                  ...
(Eric Schadt)
Requires Data driven Science         Lots of data, tools, evolving models of diseaseRequires Scientists Clinicians & Citiz...
Sage Mission      Sage Bionetworks is a non-profit organization with a vision to   create a commons where integrative bion...
Sage Bionetworks Collaborators  Pharma Partners     Merck, Pfizer, Takeda, Astra Zeneca, Amgen  Foundations     CHDI, ...
Bin ZhangModel of Alzheimer’s Disease                                   Jun Zhu                                           ...
Bin ZhangModel of Breast Cancer: Integration                                                              Xudong Dai      ...
Clinical Trial Comparator Arm        Partnership (CTCAP)  Description: Collate, Annotate, Curate and Host Clinical Trial ...
Federation s Genome-wide Network and       Modeling Approach to Warburg EffectCalifano group at Columbia   Sage Bionetwork...
THE FEDERATIONButte   Califano Friend Ideker   Schadt                   vs
Software Tools Support Collaboration
Biology Tools Support Collaboration
Potential Supporting TechnologiesAddama                                   Taverna                pbs
Platform for Modeling      SYNAPSE	  
INTEROPERABILITY
Engaging Communities of Interest                                              NEW MAPS                                    ...
Arch2POCM	  A	  Fundamental	  Systems	  Change	           for	  Drug	  Discovery	     Stephen	  Friend	  Aled	  Edwards	  ...
“Absurdity”	  of	  Current	  R&D	  Ecosystem	  •    $200B	  per	  year	  in	  biomedical	  and	  drug	  discovery	  R&D	  ...
What	  is	  the	  problem?	  •     Regulatory	  hurdles	  too	  high?	  •     Low	  hanging	  fruit	  picked?	  •     Paye...
What	  is	  the	  problem?	  •  	  	  	  	  The	  current	  system	  is	  designed	  as	  if	  every	  new	     program	  ...
The current pharma model is redundant	   Target ID/         Hit/Probe/          Clinical   Toxicolog   Phase I            ...
What	  is	  the	  problem?	  The	  real	  problem	  is	  that	  the	  current	  system	  is	  unable	  to	  provide	  the	...
Let s imagine….	•  A pool of dedicated, stable funding	•  A process that attracts top scientists and clinicians	•  A proce...
Basic	  Concept	  of	  Arch2POCM	  •  Use	  NME’s	  to	  understand	  the	  biology	  of	  human	     diseases	  •  Focus	...
Entry Points For Arch2POCM Programs  Requires two compounds if first fails                                           - gen...
Arch2POCM and the Power of              Crowdsourcing                          	•  Crowdsourcing: the act of outsourcing t...
Why	  have	  a	  “Common	  Stream”	  where	       there	  is	  No-­‐IP	  on	  Compounds	  thru	  PhIIb?	  •    Broader	  d...
Why	  now?	  •  Economics	  •  Genomics	  is	  creaJng	  many	  high-­‐risk,	  high	     opportunity	  targets	  in	  all	...
General Benefits of Arch2POCM For The        Pharmaceutical Industry 1.  Arch2POCM s use of test compounds to de-risk prev...
Why Should Pharmaceutical Companies Invest in      Arch2POCM? Some Possible Answers1.  Directly shape the Arch2POCM scient...
Why Should Pharmaceutical Companies Invest in Arch2POCM? Some Possible Answers (cont)6. Real time access to the live strea...
Arch2POCM Value Propositions For Academia  •  Funding to pursue and publish disruptive discovery research  •  Sharing of r...
Arch2POCM Value Propositions For Public                Funders•  The	  benefits	  of	  public	  funding	  investment	  in	 ...
Arch2POCM Value Propositions For                    Regulators•  Opportunity	  to	  facilitate	  a	  greater	  number	  of...
Arch2POCM Value Propositions For Patients•  Arch2POCM	  and	  the	  crowd	  will	  accelerate	  the	  development	  of    ...
Epigene:cs/	  Chroma:n	  Biology	   A	  pioneer	  area	  of	  drug	  discovery	  
How We Define Epigenetics                                       Lysine                    DNA                            H...
The Case for Epigenetics/Chromatin                     Biology1.  There	  are	  oncology	  drugs	  on	  the	  market	  (HD...
Some Examples of Links to Cancer•  Ezh2	  methyltransferase	  (enhancer	  of	  zeste	  homolog	  2)	        –  SomaJc	  mu...
The Epigenetics Universe (2009)        Domain Family          Typical substrate class*             Total                  ...
The Epigenetics Universe (2011)        Domain Family          Typical substrate class*             Total                  ...
Bromodomains Can Be Targeted by Small Molecules                   (2010)                                                  ...
Methyltransferases Can be Selectively Inhibited              (Nature Chem Biol, in press 2011)Structure based-design of a ...
Histone Demethylases Can be Targeted (JMJD3, unpublished)•  Alphascreen assay•  Validation by global H3K27me3 demethylatio...
Methyl-lysine-binding Proteins Can be Targeted by                 Small Molecules                                         ...
Bromodomain Field is Poised For Rapid Progress•  31 crystal structures•  45 purified Bromo domains•  Tm shift assay panel ...
As are Human Methyltransferases•  Purifiable	  HMTs:	      –  In	  general,	  all	  the	  targets	  can	  be	         purifi...
As are Human Demethylases                      SGC structure                      Non SGC structure                      C...
Open Access Chemical Starting Points Will Be Available Probe                          Kd < 100 nM                         ...
Aided By an Archipelago of ScientistsChemistry	                                     Biology•  GlaxoSmithKline	            ...
Epigenetics Archipelago Publications (2011)•    Identification of Macro Domain Proteins as Novel O-Acetyl-ADP-Ribose Deace...
The Benefits of the Arch2POCM Pre-competitive Model: Crowdsourced Studies   The	  Crowd	                             Arch2...
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03
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Stephen Friend Aug 1-6, 2011 IBC Next Generation Sequencing & Genomic Medicine San Francisco, CA

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Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03

  1. 1. Arch2POCM A Drug Development Approachfrom Disease Targets to their Clinical Validation Stephen Friend Sage Bionetworks (a non-profit foundation) IBC San Francisco Aug 3, 2011
  2. 2. Alzheimers   Diabetes   Treating Symptoms v.s. Modifying Diseases Depression   Cancer   Will it work for me?
  3. 3. r hh mb Cp s   r Dmp
  4. 4. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  5. 5. Reality: Overlapping Pathways
  6. 6. WHY  NOT  USE     “DATA  INTENSIVE”  SCIENCE  TO  BUILD  BETTER  DISEASE  MAPS?  
  7. 7. “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
  8. 8. 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
  9. 9. 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
  10. 10. 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 pairsexpression 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)
  11. 11. 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)
  12. 12. 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, CANat Genet (2005) 205:370 factor beta receptor 2
  13. 13. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  14. 14. (Eric Schadt)
  15. 15. Requires Data driven Science Lots of data, tools, evolving models of diseaseRequires Scientists Clinicians & Citizens to link in different ways
  16. 16. 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 diseaseBuilding Disease Maps Data RepositoryCommons Pilots Discovery Platform Sagebase.org
  17. 17. 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
  18. 18. Bin ZhangModel of Alzheimer’s Disease Jun Zhu AD normal AD normal AD normal Cell cycle http://sage.fhcrc.org/downloads/downloads.php
  19. 19. Bin ZhangModel 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)
  20. 20. 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.
  21. 21. Federation s Genome-wide Network and Modeling Approach to Warburg EffectCalifano group at Columbia Sage Bionetworks Butte group at Stanford
  22. 22. THE FEDERATIONButte Califano Friend Ideker Schadt vs
  23. 23. Software Tools Support Collaboration
  24. 24. Biology Tools Support Collaboration
  25. 25. Potential Supporting TechnologiesAddama Taverna pbs
  26. 26. Platform for Modeling SYNAPSE  
  27. 27. INTEROPERABILITY
  28. 28. 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 TOOLSM 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
  29. 29. Arch2POCM  A  Fundamental  Systems  Change   for  Drug  Discovery   Stephen  Friend  Aled  Edwards  Chas  Bountra  Lex  vander  Ploeg,  Thea  Norman,  Keith  Yamamoto  
  30. 30. “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  
  31. 31. 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  
  32. 32. 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.….  
  33. 33. 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 logyAttrition 50% 10% 30% 30% 90% Negative POC information is not shared
  34. 34. 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  
  35. 35. 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
  36. 36. 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  
  37. 37. 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
  38. 38. Arch2POCM and the Power of Crowdsourcing •  Crowdsourcing: the act of outsourcing taskstraditionally performed by an employee to a large groupof people or community• By making Arch2POCM s clinically characterizedprobes available to all, Arch2POCM will seedindependently funded, crowdsourced experimentalmedicine• Crowdsourced studies on Arch2POCM probes willprovide clinical information about the pioneer targets inMANY indications
  39. 39. 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  
  40. 40. 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  
  41. 41. 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
  42. 42. Why Should Pharmaceutical Companies Invest in Arch2POCM? Some Possible Answers1.  Directly shape the Arch2POCM scientific strategy around how to best interrogate the high risk high opportunity epigenetics targets for Oncology and Immunology2.  Chose the components and where they are to be executed: i.e., targets, lead compounds, clinical indications and trial design3.  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 products5.  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
  43. 43. Why Should Pharmaceutical Companies Invest in Arch2POCM? Some Possible Answers (cont)6. Real time access to the live stream of project information during theArch2POCM oncology projects: projected to provide lead time on keyinformation 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 aggregated7. Direct/rapid access to epigenetic academic experts and their tools8. Opportunity to take the Validated targets forward to approval or share inauctioning of these assets
  44. 44. 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
  45. 45. 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  
  46. 46. 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  
  47. 47. 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  
  48. 48. Epigene:cs/  Chroma:n  Biology   A  pioneer  area  of  drug  discovery  
  49. 49. How We Define Epigenetics Lysine DNA Histone Modification Write Read Erase Acetyl HAT Bromo HDAC Methyl HMT MBT DeMethyl
  50. 50. The Case for Epigenetics/Chromatin Biology1.  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  
  51. 51. 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  
  52. 52. 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 CkDruggable
  53. 53. 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 CkNow known to be amenable to small molecule inhibition
  54. 54. 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
  55. 55. 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
  56. 56. 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 cellsAlphascreen assays used for identifying small molecule binders
  57. 57. 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
  58. 58. 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
  59. 59. 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  
  60. 60. As are Human Demethylases SGC structure Non SGC structure Catalytic domain purified Alphascreen in place
  61. 61. Open Access Chemical Starting Points Will Be Available Probe Kd < 100 nM G9a/GLP Selectivity > 30xCriteria 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
  62. 62. Aided By an Archipelago of ScientistsChemistry   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 familyBiological 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
  63. 63. 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)
  64. 64. 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  

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