Friend Collabsum 20130131


Published on

Stephen Friend, Jan 31, 2013. Collaborative Summit on Breast Cancer Research, Washington DC

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Friend Collabsum 20130131

  2. 2. Background: Information Commons for Biological Functions
  3. 3. .
  4. 4. Iterative Networked ApproachesTo Generating Analyzing and Supporting New Models Data Biological System Analysis Uncouple the automatic linkage between the data generators, analyzers, and validators
  6. 6. Lessons Learned: Realities of Building Disease Models- Sharing , Rewards, Training, and Affordability Stephen Friend MD PhD
  7. 7. We focus on a world where biomedical research is aboutto fundamentally change. We think it will be oftenconducted in an open, collaborative way where teams ofteams far beyond the current guilds of experts willcontribute to making better, faster, relevant discoveries
  8. 8. 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
  9. 9. Issues With Drug Discovery1. 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 lower2. 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
  10. 10. Open access research tools drive Precompetitive science 14
  11. 11. 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
  12. 12. Schematic of project and current participants GSK ChemistrySGC Lilly NovartisBiochemical assaysChemical screening PfizerProtein structureComputational chemistry Abbott U. North Carolina Takeda
  13. 13. 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 DNMT1Screening / 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
  14. 14. 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
  15. 15. Moving the pre-competitive barrier: Open access to the clinic? 19
  16. 16. 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
  17. 17. 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
  18. 18. Most Novel Targets Fail at Clinical POC Hit/ Target HTS Probe/ LO Clinical Tox./ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery 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
  19. 19. This Failure Is Repeated, Many Times Hit/ Target HTS Probe/ LO Clinical Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID 30% 30% 90+% Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ Hit/ candidate Target Lead Clinical Pharmacy I IIa/ bDiscovery Probe/ ID Toxicology/ Phase Phase ID/ ID candidate 30% 30% 90+% Lead Pharmacy I IIa/ bDiscovery Hit/ ID Target ID Clinical Probe/ Toxicology/ 30% Phase 30% Phase 90+% ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID Target ID Clinical 30% 30% 90+% Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID 30% 30% 90+% Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery ID ID 30% 30% 90+% 50% 10% 30% 30% 90+% …and neither data nor outcomes are shared 23
  20. 20. 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
  21. 21. 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
  22. 22. The First Arch2POCM Oncology Pilot Project 26
  23. 23. 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 worlds 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
  24. 24. 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
  25. 25. The Case For Epigenetics/Chromatin Biology1. 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 investigation4. 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
  26. 26. 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
  27. 27. 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
  28. 28. 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
  30. 30. The Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge Building Better Models of Disease Together 34
  31. 31. The Sage Bionetworks/DREAM Breast Cancer Prognosis ChallengeGoal: use crowdsourcing to forge a computational model that accuratelypredicts breast cancer survivalHow 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
  32. 32. 1ST Sage-DREAM Breast Cancer Prognosis Challenge Three months of building better disease models together Caldos/Aparicio breast cancer data154 participants; 27 countries 354 participants; >35 countries October 15 StatusChallenge Launch: July 17 >500 models submitted to Leaderboard 36
  33. 33. Unique Attributes1. 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 reproducible2. 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 dataset3. This Challenge’s overall winner is submitting a pre-approved article about his/her winning model to Science Translational 37 Medicine 37
  34. 34. 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
  35. 35. “A MODEL CHALLENGE”39
  36. 36. 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
  37. 37. Next generation Sage Bionetworks Challenges: Opportunities for running an open Breast Cancer ChallengeFocus of Initial Challenge- Proving a challenge can be donewith Clinical data and in an open wayFocus of Second Challenge- Proving a challenge can answer animportant clinical question rapidly and affordablyStrategy- Let the question not the convenience of data drivethe ChallengeApproach- Form an Advisory Group of breast cancer thoughtleaders 41
  38. 38. The Second Sage/DREAM Breast Cancer ChallengeCo Leaders: Stephen Friend and Dan HayesScientific 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 Bruxelles1) Determine the best clinical question regarding the treatment of breast cancer that can be developed using existing datasets2) Determine the best clinical question regarding the treatment of breast cancer that can be developed not constrained by using existing datasets 42
  39. 39. The Second Sage/DREAM Breast Cancer ChallengeOne or more case control studies to determine patients with,or without, residual risk to better guide enrollment into futureclinical trials. The Case Control studies could be broken intocategories based on ER, or HER2, or neither:a. ER pos: i. Those who got ET plus chemo: this is an importantgroup. If we can identify those who relapse anyway (vs. thosewho dont) 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
  40. 40. The Second Sage/DREAM Breast Cancer ChallengeOne or more case control studies to determine patients with, orwithout, residual risk to better guide enrollment into future clinicaltrials. The Case Control studies could be broken into categoriesbased 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 - whos cured, whos 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
  41. 41. The Second Sage/DREAM Breast Cancer Challenge The Ask?Funds to coordinate and run the actual Challenge~ $250,000Funds to coordinate the generation of new datasets includingtrials/ sample collections= TBD 45
  42. 42. "Harnessing the power of teams to buildbetter models of disease in real time" If not