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

.

Biological

System

Data

Analysis

Iterative Networked Approaches

To Generating Analyzing and Supporting New Models

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

• 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

Structural Genomics Consortium: Open Access Chemical Biology

a great success

Biochemical assays Chemical screening Protein structure Computational chemistry

GSK

Lilly

Novartis

Pfizer

U. North Carolina

Takeda

Abbott

SGC Chemistry

Schematic of project and current participants

Scre

enin

g /

Ch

emis

try

In vitro assay Cell assay

SGC EPIGENETICS PROBE PIPELINE (Mar.2012)

MYST3

UHRF1

SMYD3

JMJD2A MLL

HAT1

PRMT3

SETDB1

FALZ

GCN5L2

JARID1A

Potent & Selective

Potent

Weak

None

Probe/ Tool Compound

BRD9

PCAF

JMJD2 2nd

JMJD3 2nd CREBBP 4th

PB1@2

TIF1α

PHIP JMJD2C

SPIN1

SUV39H2

EP300

L3MBTL1

53BP1

DNMT1

PRMT5

SETD8

EZH2 SMYD2

JMJD3

BET 2nd

G9a/GLP

BET PHD2

L3MBTL3

BRPF3

ATAD2

PB1@5

JMJD1

CECR2

BAZ2A

WDR5

DOT1L BAZ2B SETD7

JMJD2 FBXL11

FBXL11 2nd CREBBP 1-3

G9a/GLP SMARCA4

Cell activity

Pan 2-OG

Me Lys Binders BRD (H)MT KDM HAT TUD 2OG Oxygenase 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

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Tox./

Pharmacy

Phase

I

Phase

IIa/ b

HTS LO

10% 30% 30% 90+% 50%

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

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Toxicology/

Pharmacy

Phase

I

Phase

IIa/ b

HTS

30% 30% 90+%

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Toxicology/

Pharmacy

Phase

I

Phase

IIa/ b

30% 30% 90+%

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Toxicology/

Pharmacy

Phase

I

Phase

IIa/ b

30% 30% 90+%

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Toxicology/

Pharmacy

Phase

I

Phase

IIa/ b

30% 30% 90+%

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Toxicology/

Pharmacy

Phase

I

Phase

IIa/ b

30% 30% 90+%

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Toxicology/

Pharmacy

Phase

I

Phase

IIa/ b

30% 30% 90+%

Target

ID/

Discovery

Hit/

Probe/

Lead

ID

Clinical

candidate

ID

Toxicology/

Pharmacy

Phase

I

Phase

IIa/ b

10% 30% 30% 90+% 50%

LO

…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

Histone

DNA

Lysine

Epigenetics: Exciting Science and Also A New Area

For Innovative Drug Discovery

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

• 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 Ask: Why Breast Cancer Foundations Should Support this Arch2POCM

KDM4B Project

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

154 participants; 27 countries

354 participants; >35 countries

>500 models submitted to Leaderboard

breast cancer data

Challenge Launch: July 17

October 15 Status

Caldos/Aparicio

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

Medicine 37

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

MO

DE

L C

HA

LL

EN

GE

”

39

Next Generation Sage Bionetworks Challenges:

what will they look like?

40

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

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

41

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

The Second Sage/DREAM Breast Cancer Challenge

42

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

The Second Sage/DREAM Breast Cancer Challenge

43

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?

The Second Sage/DREAM Breast Cancer Challenge

44

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.

The Second Sage/DREAM Breast Cancer Challenge

45

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

If not

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