Searching for opportunities for WIN

it is more about how we do science than what

advantages of an open innovation compute space for building better models of disease

beyond siloed drug discovery- Arch2POCM

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

Reality: Overlapping Pathways: 90% Phase I Cpds do not make it

WHY  NOT  USE    “DATA  INTENSIVE”  SCIENCE  

TO  BUILD  BETTER  DISEASE  MAPS?  

Equipment capable of generating massive amounts of data

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

Open Information System

IT Interoperability

Evolving Models hosted in a Compute Space- Knowledge Expert

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

Monitor  disease  and  molecular  traits  in  populaFons  

PutaFve  causal  gene  

Disease  trait  

trait

How is genomic data used to understand biology?

“Standard” GWAS Approaches Profiling Approaches

“Integrated” Genetics Approaches

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

Identifies Causative DNA Variation but provides NO mechanism

  Provide unbiased view of molecular physiology as it

relates to disease phenotypes

  Insights on mechanism

  Provide causal relationships and allows predictions

RNA amplification Microarray hybirdization

Gene Index

Tum

ors

Tum

ors

12

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

List of Influential Papers in Network Modeling

(Eric Schadt)

Sage Mission

Sage Bionetworks is a non-profit organization founded in 2009 with a vision to create a “commons” where integrative bionetworks are evolved by contributor scientists with a shared vision to accelerate

the elimination of human disease

Sagebase.org

Data Repository

Discovery Platform

Building Disease Maps

Commons Pilots

Sage Bionetworks Collaborators

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

16

  Foundations   CHDI, Gates Foundation

  Government   NIH, LSDF

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

  Federation   Ideker, Califarno, Butte, Schadt

Research Platform Commons

Data Repository

Discovery Platform

Building Disease

Maps

Tools & Methods

Commons Pilots

Outposts Federation

CCSB

LSDF-WPP Inspire2Live

POC

Cancer Neurological Disease

Metabolic Disease

Pfizer Merck Takeda

Astra Zeneca CHDI Gates NIH

Curation/Annotation

CTCAP Public Data Merck Data TCGA/ICGC

Hosting Data Hosting Tools

Hosting Models

LSDF

Bayesian Models Co-expression Models

KDA/GSVA 17

4 Public Breast Cancer Datasets

NKI: van de Vijver et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002 Dec 19;347(25):1999-2009.

Wang Y et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005 Feb 19-25;365(9460):671-9.

Miller: Pawitan Y et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res. 2005;7(6):R953-64.

Christos: Sotiriou C et al.. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006 Feb 15;98(4):262-72.

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295 samples

286 samples

159 samples

189 samples

Example 1: Breast Cancer- Generation of Co-expression & Bayesian Networks from published Breast Cancer Studies

19 Zhang B et al., manuscript

Bayesian Network

Survival Analysis

Coexpression Networks Module combination

Partition BN

Comparison  of  Super-­‐modules  with  EGFR  and  Her2  signaling  and  resistance  pathways  

21

Key  Driver  Analysis  •  IdenFfy  key  regulators  for  a  list  of  genes  h    and  a  network  N  •  Check  the  enrichment  of  h in  the  downstream  of  each  node  in  N  •  The  nodes  significantly  enriched  for  h  are  the  candidate  drivers  

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A) Cell Cycle (blue)

C) Pre-mRNA Processing (brown)

B) Chromatin modification (black)

D) mRNA Processing (red)

Global driver

Global driver & RNAi validation

Signaling between Super Modules

Recovery  of  EGFR  and  Her2  oncoproteins  downstream  pathways  by  super  modules  

Example 2. The Sage Non-Responder Project in Cancer

Sage Bionetworks • Non-Responder Project

•  To identify Non-Responders to approved drug regimens so we can improve outcomes, spare patients unnecessary toxicities from treatments that have no benefit to them, and reduce healthcare costs

•  Co-Chairs Stephen Friend, Todd Golub, Charles Sawyers & Rich Schilsky

•  AML (at first relapse)-funded NIH •  Non-Small Cell Lung Cancer- Started Guangdong General

Hospital Prof Yi-long WU  •  Colon  Cancer  Sun  Yat  Sen  Univ-­‐Prof  WANG  •  Ovarian Cancer (at first relapse)

•  Breast Cancer •  Renal Cell

Purpose:

Leadership:

Initial Studies:

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.

Example 4: THE FEDERATION Butte Califano Friend Ideker Schadt

vs

Federated  Aging  Project  :    Combining  analysis  +  narraFve    

=Sweave Vignette Sage Lab

Califano Lab Ideker Lab

Shared  Data  Repository  

JIRA:  Source  code  repository  &  wiki  

R code + narrative

PDF(plots + text + code snippets)

Data objects

HTML

Submitted Paper

Synapse  as  a  Github  for  building  models  of  disease  

Platform for Modeling

SYNAPSE  

IMPACT  ON  PATIENTS  

 TENURE      FEUDAL  STATES      

“… the world is becoming too fast, too complex, and too

networked for any company to have all the

answers inside” Y. Benkler, The Wealth of Networks

Largest Attrition For Pioneer Targets is at Clinical POC (Ph II)

Target ID/ Discovery

50% 10% 30% 30% 90%

This is killing drug discovery

We can generate effective and “safe” molecules in animals, but they do not have sufficient efficacy and/or safety in the chosen patient group.

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Attrition

The current pharma model is redundant

50% 10% 30% 30% 90%

Negative POC information is not shared Attrition

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb

“Remember the two

benefits of failure. First if

you do fail, you learn

what doesn’t work and

second the failure gives

you the opportunity to try

a new approach.”

Roger van Oech

Cost of Negative Ph II POC Estimated at $12.5 Billion Annually

•  We want to improve health

•  New medicines are part of this equation

•  In this, we are failing, and we want to find a solution

”

Innovation is the ability to see change as an opportunity – not a threat

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 avoid 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

A globally distributed public private partnership (PPP) committed to:

• Generate more clinically validated targets by sharing data

• Help deliver more new drugs for patients

Arch2POCM

Arch2POCM: what’s in a name?

Arch: as in archipelago and referring to the distributed network of academic labs, pharma partners and clinical sites that will contribute to Arch2POCM programs

POCM: Proof Of Clinical Mechanism: demonstration in a Ph II setting that the mechanism of the selected disease target can be safely and usefully modulated.

Arch2POCM: a new drug development model?

•  Pool public and private sector funding into an independent organization •  Public sector provides stability and new ideas •  Private sector brings focus and experience •  Funding can focus explicitly on high-risk targets

•  Pre-competitive model to test hypotheses from financial gain •  Will attract top scientists and clinicians •  Will allow regulators to participate as scientists •  Will reduce perceived conflicts of interests – engages citizens/

patients •  Will reduce bureaucratic and administrative overhead •  Will allow rapid dissemination of information without restriction

- informs public and private sectors and reduces duplication

Toronto Feb-2011 meeting: ���output on Arch2POCM Feasibility

Pharma - 6 organisations supportive

Academic Labs - access to discovery biology and test compounds

Patient groups - access to patients more quickly and cheaply

- access to “personal data”

Regulators

- access to historical data

- want to help with new clinical endpoints and study designs

Arch2POCM: April San Francisco Meeting

•  Selected Disease Areas of Focus: Oncology,, Neuroscience and Opportunistic (O, CNS and X, respectively)

•  Defined primary entry points of Arch2POCM test compounds into overall development pipeline

•  Committed academic centers identified: UCSF, Toronto, Oxford

•  CROs engaged

•  Evaluated Arch2POCM business model

•  Two Science Translational Medicine manuscripts published

Entry Points For Arch2POCM Programs

Lead identification Phase I Phase II Preclinical

Lead optimisation

Assay in vitro probe

Lead Clinical candidate

Phase I asset

Phase II asset

- genomic/ genetic Pioneer target sources - disease networks

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

Early Discovery

Arch2POCM and the Power of Crowdsourcing

• “Crowdsourcing:” the act of outsourcing tasks traditionally performed by an employee to a large group of people or community- such as WIN

• By making Arch2POCM’s clinically characterized probes available to all, Arch2POCM will seed independently funded, crowdsourced experimental medicine- advantage WIN

• Crowdsourced studies on Arch2POCM probes will provide clinical information about the pioneer targets in MANY indications- opportunity for WIN

ROI for Pharma Partner

•  Option to in-licence asset after positive POCM

•  Early data for new clinically validated (and invalidated) targets

•  Easier access to the crowd of “proven” experts/ centers: leverage the crowd’s learnings to ID the most promising unmet medical need

•  Collaborate in more open way with regulatory agencies and patient groups

•  Jointly invalidate a larger number of pioneer targets

ArchPOCM Oncology Disease Area

Focus: Unprecedented targets and mechanisms

Novelty MOA and clinical findings

Arc2POCM Capacity: 5 targets/year for ~ 4 years

Gate 1: ~75% effort •  New target with lead and Sage bionetworks insights on MOA

(increase likelihood of success), or •  New target (enabled by Sage) with assay

Gate 2: ~25% effort •  Pharma failed or deprioritized/parked compounds •  Compound ID is followed by a Sage systems biology effort to define

MOA and clinical entry point

ArchPOCM Oncology: Epigenetics selected as the target area of choice

Top Targets:

• Discovery • Jard1 • Ezh1 • G9A

• Lead • Dyrk1

• Pre-Clin • ̀Brd4

ArchPOCM Oncology: Epigenetics selected as the target area of choice

ArchPOCM Oncology: Epigenetics selected as the target area of choice

Arch2POCM: Next Steps • Oncology and CNS Arch2POCM strategic design teams to generate project workflow plans and timelines (September)

• Define critical details of Arch2POCM leadership, organizational and decision-making structures • (Q3-Q4, 2011)

• Develop business case to support Arch2POCM programs (Q3-Q4, 2011)

• Obtain financial backing and launch operations in early 2012

Arch2POCM: ���an idea whose time has come

Ideas are only as good as your ability to make them happen.

"In a world of abundant knowledge, hoarding technology is a self-limiting strategy. Nor can any organization, even the largest, afford any longer to ignore the tremendous external pools of knowledge that exist.“ Henry Chesbrough

it is more about how we do science than what

advantages of an open innovation compute space for building better models of disease

beyond siloed drug discovery- Arch2POCM

Each of these are opportunities For the WIN Consortium