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ENABLING A PARADIGM SHIFTING PRECOMPETITIVE
  OPEN ACCESS RESEARCH STRATEGY and ENGAGING
ADVOCATES IN RESEARCH/ BREAST CANCER CHALLENGE


     COLLABORATIVE SUMMIT ON BREAST CANCER RESEARCH
                      January 31, 2013
                       Washington DC

                  Stephen H Friend MD PhD
                Sage Bionetworks (non-profit)
Background: Information Commons for Biological Functions
.
Iterative Networked Approaches
To Generating Analyzing and Supporting New Models

                       Data




 Biological
  System                                Analysis




      Uncouple the automatic linkage between the
      data generators, analyzers, and validators
TENURE   FEUDAL STATES
Lessons Learned: Realities of Building Disease Models-
    Sharing , Rewards, Training, and Affordability




                  Stephen Friend MD PhD
We focus on a world where biomedical research is about
to fundamentally change. We think it will be often
conducted in an open, collaborative way where teams of
teams far beyond the current guilds of experts will
contribute to making better, faster, relevant discoveries
The Current R&D Ecosystem Is In Serious Need of a New
                Approach to Drug Development

•   $200B per year in biomedical and drug discovery R&D
•   Only a handful of new medicines are approved each year
•   Productivity in steady decline since 1950
•   >90% of novel drugs entering clinical trials fail, and negative POC
    information is not shared
•   >30,000 pharma employees laid off from downsizing in each of last four
    years
•   90% of 2013 prescriptions will be for generic drugs


                                                                          12
Issues With Drug Discovery

1. The greatest attrition is at clinical proof-of-concept – once a
   “target” is linked to a disease in the clinic, the risk of failure is
   far lower

2. Most novel targets are pursued by multiple companies in
   parallel (and most fail at clinical POC)

1. The complete data from failed trials are rarely, if ever,
   released to the public


                                                                     13
Open access research tools
          drive
  Precompetitive science




                             14
Structural Genomics Consortium:
                    Open Access Chemical Biology
                                 a great success
• PPP:
    - GSK, Pfizer, Novartis, Lilly, Abbott, Takeda
    - Genome Canada, Ontario, CIHR, Wellcome Trust

• Based in Universities of Toronto and Oxford

• 200 scientists

• Academic network of more than 250 labs

• Generate freely available reagents (proteins, assays, structures,
  inhibitors, antibodies) for novel, human, therapeutically relevant proteins

• Give these to academic collaborators to dissect pathways and disease
  networks, and thereby discover new targets for drug discovery        15
Schematic of project and current participants
                          GSK           Chemistry
SGC
                                Lilly
                                        Novartis
Biochemical assays
Chemical screening                        Pfizer
Protein structure
Computational chemistry
                                        Abbott

                                 U. North Carolina

                           Takeda
SGC EPIGENETICS PROBE PIPELINE (Mar.2012)
                                                                                                        G9a/GLP
                                                                                                                           Pan 2-OG
                                                                                                        BET
                        Probe/ Tool                                                                                        PHD2
                                                                                                        BET 2nd
                        Compound
                                                                                                                           L3MBTL3
                                                                                                        JMJD3

                                                                                 JMJD2                  FBXL11             WDR5
                          Potent &                                                                      CREBBP 1-3
                                                                                                                           G9a/GLP
                                                    BRD9                         FBXL11 2nd
                          Selective                                                                     SMARCA4
                                                                                 SETD7                                     DOT1L
                                                                                                        BAZ2B

                                        CECR2       SUV39H2                      GCN5L2
                                                                                                                  JMJD2 2nd
                                        PB1@5       EP300
                           Potent                              DNMT1
Screening / Chemistry




                                                                                 CREBBP 4th
                                        BAZ2A       53BP1                                                         JMJD3 2nd
                                        JMJD1       L3MBTL1                      PRMT3

                                        PCAF        ATAD2                        TIF1α
                                        PB1@2       PRMT5      UHRF1
                            Weak                                                 EZH2
                                        FALZ        SMYD2      HAT1
                                        BRPF3       SETDB1                       SETD8

                                        JMJD2A      MLL

                            None        JMJD2C      SMYD3      PHIP              JARID1A

                                        SPIN1       MYST3


                                              In vitro assay                   Cell assay                       Cell activity


                              2OG Oxygenase        BRD         HAT     (H)MT       KDM        Me Lys Binders         TUD           WD Domain
Some SGC Achievements
• Structural impact
   – SGC contributed ~25% of global output of human structures annually
   – SGC contributes >40% of global output of human parasite structures annually

• High quality science (some publications from 2011)
      Vedadi et al, Nature Chem Biol, in press (2011); Evans et al, Nature Genetics in press
      (2011); Norman et al Science Transl Med. 3(88):88mr1 (2011); Kochan G et al PNAS
      108:7745 (2011); Clasquin MF et al Cell 145:969 (2011); Colwill et al, Nature Methods
      8:551 (2011); Ceccarelli et al, Cell 145:1075 (2011; Strushkevich et al, PNAS 108:10139
      (2011); Bian et al EMBO J in press (2011)
      Norman et al Science Trans. Med. 3:76cm10 (2011); Xu et al Nature Comm. 2: art. no.
      227 (2011); Edwards et al Nature 470:163 (2011); Fairman et al Nature Struct, and Mol.
      Biol. 18:316 (2011); Adams-Cioaba et al, Nature Comm. 2 (1) (2011); Carr et al EMBO J
      30:317 (2011); Deutsch et al Cell 144:566 (2011); Filippakopoulos et al Cell, in press;
      Nature Chem. Biol. in press, Nature in press
                                                                                      18
Moving the pre-competitive barrier:
    Open access to the clinic?



                                  19
Drug Discovery Is a Lottery Because:
Knowledge about clinical disease is limiting
  - patients are heterogeneous

  - do not know how some drugs work (i.e., paracetamol)

  - different doses effective in different patients

  - efficacy is short lived

  - poor biomarkers…..

Too many targets/preclinical assays do not prioritize

                                                          20
Other Problems With How We Do Drug Discovery


  • Most targets are worked on in parallel and in
    secret across pharma

  • No one organization has all capabilities

  • Early IP makes it even harder (slower, harder
     and more expensive)
                                                    21
Most Novel Targets Fail at Clinical POC

                    Hit/
 Target     HTS   Probe/   LO     Clinical
                                               Tox./    Phase          Phase
   ID/                           candidate
                   Lead                      Pharmacy     I             IIa/ b
Discovery                            ID
                     ID


            50%            10%                 30%       30%            90+%




                                                                this is killing
                                                                our industry

   …we can generate “safe” molecules, but they are not
   developable in the chosen patient group                                22
This Failure Is Repeated, Many Times
                    Hit/
 Target     HTS   Probe/   LO     Clinical
                                             Toxicology/   Phase   Phase
    ID/                          candidate
                   Lead                       Pharmacy       I      IIa/ b
Discovery           Hit/              ID
 Target               ID          Clinical
                  Probe/                     Toxicology/   Phase   Phase
   ID/                           candidate
                   Lead                       Pharmacy       I      IIa/ b
Discovery           Hit/             ID         30%         30%     90+%
 Target              ID           Clinical
                  Probe/                     Toxicology/   Phase   Phase
   ID/              Hit/         candidate
 Target            Lead           Clinical    Pharmacy       I      IIa/ b
Discovery         Probe/             ID      Toxicology/   Phase   Phase
   ID/               ID          candidate      30%         30%     90+%
                   Lead                       Pharmacy       I      IIa/ b
Discovery           Hit/             ID
 Target              ID           Clinical
                  Probe/                     Toxicology/
                                                30%        Phase
                                                            30%    Phase
                                                                     90+%
   ID/                           candidate
                   Lead                       Pharmacy       I      IIa/ b
Discovery           Hit/             ID
 Target              ID           Clinical      30%         30%     90+%
                  Probe/                     Toxicology/   Phase   Phase
   ID/                           candidate
                   Lead                       Pharmacy       I      IIa/ b
Discovery           Hit/             ID         30%         30%     90+%
 Target              ID           Clinical
                  Probe/                     Toxicology/   Phase   Phase
   ID/                           candidate
                   Lead                       Pharmacy       I      IIa/ b
Discovery                            ID
                     ID                         30%         30%     90+%

            50%            10%                  30%         30%     90+%

        …and neither data nor outcomes are shared                     23
A Possible Soution:Arch2POCM
              An Open Access Clinical Validation PPP
• A PPP to clinically validate (Ph IIa) pioneer targets

• Pharma, public, academia, regulators and patient groups are active participants

• Cultivate a common stream of knowledge
     – Avoid patents
     – Place all data into the public domain
     – Crowdsource the PPP’s drug-like compounds

• Failed targets are identified before pharma makes a substantial proprietary investment
     – Reduces the number of redundant trials on bad targets
     – Reduces safety concerns

• Validated targets are de-risked for pharma investment
     – Pharma can initiate proprietary effort when risks are balanced with returns
     – PPP pharma members can acquire Arch2POCM IND for validated targets and benefit from
       shorter development timeline and data exclusivity for sales
                                                                                        24
Arch2POCM Pilots
• Initiate 1-2 projects, (1-2 novel target mechanisms), as pilots to
  assess Arch2POCM principles
• Epigenetic targets in Oncology and/or Neuroscience (new
  innovative target class applied to high risk disease areas in need of
  new approaches)
• Interested funders include disease foundations, pharma, public
  research foundations and venture philanthropists
• Objectives
    • Select two pre-clinical candidates: Leverage SGC PPP to identify two
      chemotypes for medicinal chemistry optimization
    • Develop a biomarker strategy for surrogate endpoints and/or patient
      stratification
    • Implement crowdsourced research on compounds
                                                                       25
The First Arch2POCM Oncology Pilot Project




                                       26
The Arch2POCM Project Team:
    Premier Oncology Institutions and Researchers
• Institute of Cancer Research (ICR)
     – Prof Paul Workman: Director of the Cancer Research UK Centre for Cancer
       Therapeutics at The Institute of Cancer Research and one of the world's
       leading experts in the discovery and development of new cancer drugs.
     – Prof Julian Blagg is Deputy Director of the Cancer Therapeutics Unit at the
       ICR and Head of Chemistry

•   Newcastle University
     – Prof Herbie Newell, world class expert in cancer pharmacology

• Structural Genomics Consortium (SGC)-Oxford University
     – Dr Chas Bountra: Chief Science Officer for SGC and involved in progressing
       over 30 candidates into clinical trials during his 20 years of pharmaceutical
       industry experience
     – Dr Paul Brennan: SGC Principal Investigator for Medicinal Chemistry and
       leads SGC’s effort to generate chemical probes for novel epigenetic targets.
                                                                                       27
Epigenetics: Exciting Science and Also A New Area
          For Innovative Drug Discovery


                                                 Lysine

                               DNA

                                     Histone




                      Modification   Write     Read   Erase

                      Acetyl         HAT       Bromo HDAC
                      Methyl         HMT       MBT   DeMethyl
                                                        28
The Case For Epigenetics/Chromatin Biology

1.   There are epigenetic oncology drugs on the market (HDACs)

2.   A growing number of links to oncology, notably many genetic links (i.e.
     fusion proteins, somatic mutations)

3.   A pioneer area: More than 400 targets amenable to small molecule
     intervention - most of which have only recently been shown to be
     “druggable”, and only a few of which are under active investigation

4.   Open access, early-stage science is developing quickly – significant
     collaborative efforts (e.g. SGC, NIH) to generate proteins, structures,
     assays and chemical starting points


                                                                           29
KDM4B:
an epigenetic target implicated in ER-positive breast cancer

• Member of KDM4 family of Histone Demethylase
  (HDM) “eraser” enzymes that site-specifically
  demethylate target histone lysines
• Silencing of KDM4B in ER-positive breast cancer cell
  lines attenuates ER gene expression and reduces
  proliferation (in vitro and in vivo)
• Elevated levels of KDM4B correlate with a worse
  patient outcome
• KDM4B depletion in the ER-negative cells fails to
  reduce proliferation: suggests an exclusive benefit for
  ER+-BC patients
Data/Findings That Are Already Being Shared on The
                  KDM4B Project
• Newcastle:
   – KDM4B is unique within the KDM family for an impact on breast cancer
        • siRNA screen shows that KDM4B but not other KDM4 family members, modulates ER activity
          and cell proliferation
   – Targeting KDM4B is likely to be tumor-specific
        • Data mining of publicly available data sets shows that post-natal expression of KDM4B in
          tissues is minimal
   – Opportunity for KDM4B biomarkers
        • Depletion of KDM4B in Bca effects global histone H3 methylation and acetylation
          levels
• ICR: KDM4B is a druggable target
   – Fragment screen against KDM4B resulted in 70 confirmed hits (peptides substrates and
     20G mimics)
• SGC
   – High throughput JDM4B biochemical screens available for the project
   – KDM4B crystal structure pending
The Ask:
    Why Breast Cancer Foundations Should Support this Arch2POCM
                          KDM4B Project
• Cancer Research UK (CRUK) has designated this KDM4B demethylase
  project for its support and is enthusiastic to apply an Arch2POCM strategy

• Continued CRUK funding of this project requires matching funds
    – This equates to funding 4-5 FTEs for the full POCM effort (i.e., 3-5 years).
    – This funding would currently support the following 4 FTEs: 3 chemistry, 1
      biology and 1 drug metabolism
    – As project evolves, FTE coverage will shift to cover new activities

• Because open data-sharing and crowdsourcing do not align with the early
  partnering business interests of a company that might otherwise bring
  matching funds to this project, we need to identify a different source of
  funds to cover the matching costs

• Therefore we are reaching out to all breast cancer disease foundations to
  seek this important seed funding for 3FTEs/3 years
THE POWER OF DISTRIBUTED ANALYSIS

THE SECOND BREAST CANCER CHALLENGE
The Sage Bionetworks/DREAM Breast Cancer
           Prognosis Challenge
        Building Better Models of Disease Together




                                                     34
The Sage Bionetworks/DREAM Breast Cancer
                Prognosis Challenge
Goal: use crowdsourcing to forge a computational model that accurately
predicts breast cancer survival

How it works:
• Training data set: genomic and clinical data from 2000
  women diagnosed with breast cancer (the Metabric data
  set)


• Data access and analysis tools: Synapse


• Compute resources: each participant provided with a
  standardized virtual machine donated by Google


• Model scoring: models submitted to Synapse for scoring on
  a real-time leaderboard                                                35
1ST Sage-DREAM Breast Cancer Prognosis Challenge
                            Three months of building better disease models together
                                                  Caldos/Aparicio




                                         breast cancer data
154 participants; 27 countries
                                                                                  354 participants; >35 countries
                                                              October 15 Status




Challenge Launch: July 17




                                                                          >500 models submitted to Leaderboard




                                                                                                           36
Unique Attributes
1. The First Challenge was designed to be open source and
   encouraged code-sharing to forge innovative computational
   models:
    – The standardized and shared computational infrastructure
      enables participants to use code submitted by others in their
      own model building
    – Winning code must be reproducible


2. The Challenge used a brand new dataset to select the
   winning model:
    – Derived from approx. 350 breast cancer samples
    – Data generation funded by Avon
    – Winning model: the one that, having been trained using Metabric data, is
      most accurate for survival prediction when applied to a brand new dataset


3. This Challenge’s overall winner is submitting a pre-approved
   article about his/her winning model to Science Translational                   37
   Medicine                                                                         37
Incentivizing Continuous
                Participation
• Monthly leaderboard winners
   – Winner is highlighted within the
     Challenge community
   – Winner posts a blog on winning
      model to Synapse


• Communities that link to the
  Leaderboard
   – Stackoverflow: Q&A site with
       1,000,000 users
   – Science Translational Medicine
     community

                                        38
“A MODEL CHALLENGE”




39
Next Generation Sage Bionetworks Challenges:
                what will they look like?




• Disease Communities/Groups that have contacted us to run a
  Challenge: GBM-NBTS, Colon, CHDI, NCI (pan-cancer), BROAD,
  NIEHS, Alzheimer’s- NIA




                                                           40
Next generation Sage Bionetworks Challenges:
    Opportunities for running an open Breast Cancer Challenge

Focus of Initial Challenge- Proving a challenge can be done
with Clinical data and in an open way

Focus of Second Challenge- Proving a challenge can answer an
important clinical question rapidly and affordably

Strategy- Let the question not the convenience of data drive
the Challenge

Approach- Form an Advisory Group of breast cancer thought
leaders


                                                               41
The Second Sage/DREAM Breast Cancer Challenge
Co Leaders: Stephen Friend and Dan Hayes
Scientific Advisory Board:
   Fabrice Andre- Inst. Gustave Roussy
   Jose Baselga- MSKCC
   John Bartlett- OICR
   Mitch Dowsett- Royal Marsden
   Daniel Hayes- University of Michigan
   Larry Norton- MSKCC
   Lisa McShane- NCI
   Martine Piccart- Universite Libre de Bruxelles
1) Determine the best clinical question regarding the
   treatment of breast cancer that can be developed using
   existing datasets
2) Determine the best clinical question regarding the
   treatment of breast cancer that can be developed not
   constrained by using existing datasets                   42
The Second Sage/DREAM Breast Cancer Challenge

One or more case control studies to determine patients with,
or without, residual risk to better guide enrollment into future
clinical trials. The Case Control studies could be broken into
categories based on ER, or HER2, or neither:

a.    ER pos:

   i. Those who got ET plus chemo: this is an important
group. If we can identify those who relapse anyway (vs. those
who don't) we could focus future trials on the former.

  ii. those who got ET only (like in TailorRx, plus B20, B14,
8814) - can we build a better oncotypeDx?

                                                                   43
The Second Sage/DREAM Breast Cancer Challenge
One or more case control studies to determine patients with, or
without, residual risk to better guide enrollment into future clinical
trials. The Case Control studies could be broken into categories
based on ER, or HER2, or neither:

b.     HER2 Pos (amplified or 3+).

     i. Those who got only chemo: Is there a group that does not
       NEED herceptin?
     ii. those who got Herceptin. This is the key group - who's cured,
       who's not? Focus future anti-HER2 trials on the latter.

c.       ER, PgR, HER2 neg.
     i. those who got "standard" chemo. There is a large group that
       are cured with standard chemo. Why enroll such patients in future
       trials? Focus future trials only on those who are likely to recur.   44
The Second Sage/DREAM Breast Cancer Challenge



                        The Ask?

Funds to coordinate and run the actual Challenge~ $250,000

Funds to coordinate the generation of new datasets including
trials/ sample collections= TBD




                                                               45
"Harnessing the power of teams to build
better models of disease in real time"




               If not

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Friend Collabsum 20130131

  • 1. ENABLING A PARADIGM SHIFTING PRECOMPETITIVE OPEN ACCESS RESEARCH STRATEGY and ENGAGING ADVOCATES IN RESEARCH/ BREAST CANCER CHALLENGE COLLABORATIVE SUMMIT ON BREAST CANCER RESEARCH January 31, 2013 Washington DC Stephen H Friend MD PhD Sage Bionetworks (non-profit)
  • 2.
  • 3.
  • 4.
  • 5. Background: Information Commons for Biological Functions
  • 6. .
  • 7. Iterative Networked Approaches To Generating Analyzing and Supporting New Models Data Biological System Analysis Uncouple the automatic linkage between the data generators, analyzers, and validators
  • 8. TENURE FEUDAL STATES
  • 9. Lessons Learned: Realities of Building Disease Models- Sharing , Rewards, Training, and Affordability Stephen Friend MD PhD
  • 10.
  • 11. We focus on a world where biomedical research is about to fundamentally change. We think it will be often conducted in an open, collaborative way where teams of teams far beyond the current guilds of experts will contribute to making better, faster, relevant discoveries
  • 12. The Current R&D Ecosystem Is In Serious Need of a New Approach to Drug Development • $200B per year in biomedical and drug discovery R&D • Only a handful of new medicines are approved each year • Productivity in steady decline since 1950 • >90% of novel drugs entering clinical trials fail, and negative POC information is not shared • >30,000 pharma employees laid off from downsizing in each of last four years • 90% of 2013 prescriptions will be for generic drugs 12
  • 13. Issues With Drug Discovery 1. The greatest attrition is at clinical proof-of-concept – once a “target” is linked to a disease in the clinic, the risk of failure is far lower 2. Most novel targets are pursued by multiple companies in parallel (and most fail at clinical POC) 1. The complete data from failed trials are rarely, if ever, released to the public 13
  • 14. Open access research tools drive Precompetitive science 14
  • 15. Structural Genomics Consortium: Open Access Chemical Biology a great success • PPP: - GSK, Pfizer, Novartis, Lilly, Abbott, Takeda - Genome Canada, Ontario, CIHR, Wellcome Trust • Based in Universities of Toronto and Oxford • 200 scientists • Academic network of more than 250 labs • Generate freely available reagents (proteins, assays, structures, inhibitors, antibodies) for novel, human, therapeutically relevant proteins • Give these to academic collaborators to dissect pathways and disease networks, and thereby discover new targets for drug discovery 15
  • 16. Schematic of project and current participants GSK Chemistry SGC Lilly Novartis Biochemical assays Chemical screening Pfizer Protein structure Computational chemistry Abbott U. North Carolina Takeda
  • 17. SGC EPIGENETICS PROBE PIPELINE (Mar.2012) G9a/GLP Pan 2-OG BET Probe/ Tool PHD2 BET 2nd Compound L3MBTL3 JMJD3 JMJD2 FBXL11 WDR5 Potent & CREBBP 1-3 G9a/GLP BRD9 FBXL11 2nd Selective SMARCA4 SETD7 DOT1L BAZ2B CECR2 SUV39H2 GCN5L2 JMJD2 2nd PB1@5 EP300 Potent DNMT1 Screening / Chemistry CREBBP 4th BAZ2A 53BP1 JMJD3 2nd JMJD1 L3MBTL1 PRMT3 PCAF ATAD2 TIF1α PB1@2 PRMT5 UHRF1 Weak EZH2 FALZ SMYD2 HAT1 BRPF3 SETDB1 SETD8 JMJD2A MLL None JMJD2C SMYD3 PHIP JARID1A SPIN1 MYST3 In vitro assay Cell assay Cell activity 2OG Oxygenase BRD HAT (H)MT KDM Me Lys Binders TUD WD Domain
  • 18. Some SGC Achievements • Structural impact – SGC contributed ~25% of global output of human structures annually – SGC contributes >40% of global output of human parasite structures annually • High quality science (some publications from 2011) Vedadi et al, Nature Chem Biol, in press (2011); Evans et al, Nature Genetics in press (2011); Norman et al Science Transl Med. 3(88):88mr1 (2011); Kochan G et al PNAS 108:7745 (2011); Clasquin MF et al Cell 145:969 (2011); Colwill et al, Nature Methods 8:551 (2011); Ceccarelli et al, Cell 145:1075 (2011; Strushkevich et al, PNAS 108:10139 (2011); Bian et al EMBO J in press (2011) Norman et al Science Trans. Med. 3:76cm10 (2011); Xu et al Nature Comm. 2: art. no. 227 (2011); Edwards et al Nature 470:163 (2011); Fairman et al Nature Struct, and Mol. Biol. 18:316 (2011); Adams-Cioaba et al, Nature Comm. 2 (1) (2011); Carr et al EMBO J 30:317 (2011); Deutsch et al Cell 144:566 (2011); Filippakopoulos et al Cell, in press; Nature Chem. Biol. in press, Nature in press 18
  • 19. Moving the pre-competitive barrier: Open access to the clinic? 19
  • 20. Drug Discovery Is a Lottery Because: Knowledge about clinical disease is limiting - patients are heterogeneous - do not know how some drugs work (i.e., paracetamol) - different doses effective in different patients - efficacy is short lived - poor biomarkers….. Too many targets/preclinical assays do not prioritize 20
  • 21. Other Problems With How We Do Drug Discovery • Most targets are worked on in parallel and in secret across pharma • No one organization has all capabilities • Early IP makes it even harder (slower, harder and more expensive) 21
  • 22. Most Novel Targets Fail at Clinical POC Hit/ Target HTS Probe/ LO Clinical Tox./ Phase Phase ID/ candidate Lead Pharmacy I IIa/ b Discovery ID ID 50% 10% 30% 30% 90+% this is killing our industry …we can generate “safe” molecules, but they are not developable in the chosen patient group 22
  • 23. This Failure Is Repeated, Many Times Hit/ Target HTS Probe/ LO Clinical Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ b Discovery Hit/ ID Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ b Discovery Hit/ ID 30% 30% 90+% Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ Hit/ candidate Target Lead Clinical Pharmacy I IIa/ b Discovery Probe/ ID Toxicology/ Phase Phase ID/ ID candidate 30% 30% 90+% Lead Pharmacy I IIa/ b Discovery Hit/ ID Target ID Clinical Probe/ Toxicology/ 30% Phase 30% Phase 90+% ID/ candidate Lead Pharmacy I IIa/ b Discovery Hit/ ID Target ID Clinical 30% 30% 90+% Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ b Discovery Hit/ ID 30% 30% 90+% Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ b Discovery ID ID 30% 30% 90+% 50% 10% 30% 30% 90+% …and neither data nor outcomes are shared 23
  • 24. A Possible Soution:Arch2POCM An Open Access Clinical Validation PPP • A PPP to clinically validate (Ph IIa) pioneer targets • Pharma, public, academia, regulators and patient groups are active participants • Cultivate a common stream of knowledge – Avoid patents – Place all data into the public domain – Crowdsource the PPP’s drug-like compounds • Failed targets are identified before pharma makes a substantial proprietary investment – Reduces the number of redundant trials on bad targets – Reduces safety concerns • Validated targets are de-risked for pharma investment – Pharma can initiate proprietary effort when risks are balanced with returns – PPP pharma members can acquire Arch2POCM IND for validated targets and benefit from shorter development timeline and data exclusivity for sales 24
  • 25. Arch2POCM Pilots • Initiate 1-2 projects, (1-2 novel target mechanisms), as pilots to assess Arch2POCM principles • Epigenetic targets in Oncology and/or Neuroscience (new innovative target class applied to high risk disease areas in need of new approaches) • Interested funders include disease foundations, pharma, public research foundations and venture philanthropists • Objectives • Select two pre-clinical candidates: Leverage SGC PPP to identify two chemotypes for medicinal chemistry optimization • Develop a biomarker strategy for surrogate endpoints and/or patient stratification • Implement crowdsourced research on compounds 25
  • 26. The First Arch2POCM Oncology Pilot Project 26
  • 27. The Arch2POCM Project Team: Premier Oncology Institutions and Researchers • Institute of Cancer Research (ICR) – Prof Paul Workman: Director of the Cancer Research UK Centre for Cancer Therapeutics at The Institute of Cancer Research and one of the world's leading experts in the discovery and development of new cancer drugs. – Prof Julian Blagg is Deputy Director of the Cancer Therapeutics Unit at the ICR and Head of Chemistry • Newcastle University – Prof Herbie Newell, world class expert in cancer pharmacology • Structural Genomics Consortium (SGC)-Oxford University – Dr Chas Bountra: Chief Science Officer for SGC and involved in progressing over 30 candidates into clinical trials during his 20 years of pharmaceutical industry experience – Dr Paul Brennan: SGC Principal Investigator for Medicinal Chemistry and leads SGC’s effort to generate chemical probes for novel epigenetic targets. 27
  • 28. Epigenetics: Exciting Science and Also A New Area For Innovative Drug Discovery Lysine DNA Histone Modification Write Read Erase Acetyl HAT Bromo HDAC Methyl HMT MBT DeMethyl 28
  • 29. The Case For Epigenetics/Chromatin Biology 1. There are epigenetic oncology drugs on the market (HDACs) 2. A growing number of links to oncology, notably many genetic links (i.e. fusion proteins, somatic mutations) 3. A pioneer area: More than 400 targets amenable to small molecule intervention - most of which have only recently been shown to be “druggable”, and only a few of which are under active investigation 4. Open access, early-stage science is developing quickly – significant collaborative efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starting points 29
  • 30. KDM4B: an epigenetic target implicated in ER-positive breast cancer • Member of KDM4 family of Histone Demethylase (HDM) “eraser” enzymes that site-specifically demethylate target histone lysines • Silencing of KDM4B in ER-positive breast cancer cell lines attenuates ER gene expression and reduces proliferation (in vitro and in vivo) • Elevated levels of KDM4B correlate with a worse patient outcome • KDM4B depletion in the ER-negative cells fails to reduce proliferation: suggests an exclusive benefit for ER+-BC patients
  • 31. Data/Findings That Are Already Being Shared on The KDM4B Project • Newcastle: – KDM4B is unique within the KDM family for an impact on breast cancer • siRNA screen shows that KDM4B but not other KDM4 family members, modulates ER activity and cell proliferation – Targeting KDM4B is likely to be tumor-specific • Data mining of publicly available data sets shows that post-natal expression of KDM4B in tissues is minimal – Opportunity for KDM4B biomarkers • Depletion of KDM4B in Bca effects global histone H3 methylation and acetylation levels • ICR: KDM4B is a druggable target – Fragment screen against KDM4B resulted in 70 confirmed hits (peptides substrates and 20G mimics) • SGC – High throughput JDM4B biochemical screens available for the project – KDM4B crystal structure pending
  • 32. The Ask: Why Breast Cancer Foundations Should Support this Arch2POCM KDM4B Project • Cancer Research UK (CRUK) has designated this KDM4B demethylase project for its support and is enthusiastic to apply an Arch2POCM strategy • Continued CRUK funding of this project requires matching funds – This equates to funding 4-5 FTEs for the full POCM effort (i.e., 3-5 years). – This funding would currently support the following 4 FTEs: 3 chemistry, 1 biology and 1 drug metabolism – As project evolves, FTE coverage will shift to cover new activities • Because open data-sharing and crowdsourcing do not align with the early partnering business interests of a company that might otherwise bring matching funds to this project, we need to identify a different source of funds to cover the matching costs • Therefore we are reaching out to all breast cancer disease foundations to seek this important seed funding for 3FTEs/3 years
  • 33. THE POWER OF DISTRIBUTED ANALYSIS THE SECOND BREAST CANCER CHALLENGE
  • 34. The Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge Building Better Models of Disease Together 34
  • 35. The Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge Goal: use crowdsourcing to forge a computational model that accurately predicts breast cancer survival How it works: • Training data set: genomic and clinical data from 2000 women diagnosed with breast cancer (the Metabric data set) • Data access and analysis tools: Synapse • Compute resources: each participant provided with a standardized virtual machine donated by Google • Model scoring: models submitted to Synapse for scoring on a real-time leaderboard 35
  • 36. 1ST Sage-DREAM Breast Cancer Prognosis Challenge Three months of building better disease models together Caldos/Aparicio breast cancer data 154 participants; 27 countries 354 participants; >35 countries October 15 Status Challenge Launch: July 17 >500 models submitted to Leaderboard 36
  • 37. Unique Attributes 1. The First Challenge was designed to be open source and encouraged code-sharing to forge innovative computational models: – The standardized and shared computational infrastructure enables participants to use code submitted by others in their own model building – Winning code must be reproducible 2. The Challenge used a brand new dataset to select the winning model: – Derived from approx. 350 breast cancer samples – Data generation funded by Avon – Winning model: the one that, having been trained using Metabric data, is most accurate for survival prediction when applied to a brand new dataset 3. This Challenge’s overall winner is submitting a pre-approved article about his/her winning model to Science Translational 37 Medicine 37
  • 38. Incentivizing Continuous Participation • Monthly leaderboard winners – Winner is highlighted within the Challenge community – Winner posts a blog on winning model to Synapse • Communities that link to the Leaderboard – Stackoverflow: Q&A site with 1,000,000 users – Science Translational Medicine community 38
  • 40. Next Generation Sage Bionetworks Challenges: what will they look like? • Disease Communities/Groups that have contacted us to run a Challenge: GBM-NBTS, Colon, CHDI, NCI (pan-cancer), BROAD, NIEHS, Alzheimer’s- NIA 40
  • 41. Next generation Sage Bionetworks Challenges: Opportunities for running an open Breast Cancer Challenge Focus of Initial Challenge- Proving a challenge can be done with Clinical data and in an open way Focus of Second Challenge- Proving a challenge can answer an important clinical question rapidly and affordably Strategy- Let the question not the convenience of data drive the Challenge Approach- Form an Advisory Group of breast cancer thought leaders 41
  • 42. The Second Sage/DREAM Breast Cancer Challenge Co Leaders: Stephen Friend and Dan Hayes Scientific Advisory Board: Fabrice Andre- Inst. Gustave Roussy Jose Baselga- MSKCC John Bartlett- OICR Mitch Dowsett- Royal Marsden Daniel Hayes- University of Michigan Larry Norton- MSKCC Lisa McShane- NCI Martine Piccart- Universite Libre de Bruxelles 1) Determine the best clinical question regarding the treatment of breast cancer that can be developed using existing datasets 2) Determine the best clinical question regarding the treatment of breast cancer that can be developed not constrained by using existing datasets 42
  • 43. The Second Sage/DREAM Breast Cancer Challenge One or more case control studies to determine patients with, or without, residual risk to better guide enrollment into future clinical trials. The Case Control studies could be broken into categories based on ER, or HER2, or neither: a. ER pos: i. Those who got ET plus chemo: this is an important group. If we can identify those who relapse anyway (vs. those who don't) we could focus future trials on the former. ii. those who got ET only (like in TailorRx, plus B20, B14, 8814) - can we build a better oncotypeDx? 43
  • 44. The Second Sage/DREAM Breast Cancer Challenge One or more case control studies to determine patients with, or without, residual risk to better guide enrollment into future clinical trials. The Case Control studies could be broken into categories based on ER, or HER2, or neither: b. HER2 Pos (amplified or 3+). i. Those who got only chemo: Is there a group that does not NEED herceptin? ii. those who got Herceptin. This is the key group - who's cured, who's not? Focus future anti-HER2 trials on the latter. c. ER, PgR, HER2 neg. i. those who got "standard" chemo. There is a large group that are cured with standard chemo. Why enroll such patients in future trials? Focus future trials only on those who are likely to recur. 44
  • 45. The Second Sage/DREAM Breast Cancer Challenge The Ask? Funds to coordinate and run the actual Challenge~ $250,000 Funds to coordinate the generation of new datasets including trials/ sample collections= TBD 45
  • 46. "Harnessing the power of teams to build better models of disease in real time" If not