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

Autism Transverse Myelitis

Diabetes Cancer Treating Symptoms v.s. Modifying Diseases

Will it work for me?

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

Reality: Overlapping Pathways provide complexity

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 populations

Putative causal gene

Disease trait

trait trait trait trait trait trait trait trait trait trait trait 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

The

Evol

utio

n of

Sys

tem

s B

iolo

gy

Disease Models

Physiologic / Pathologic Phenotype Regulation

Literature

Structure Mol. Profiles

Model Evolution

Model Topology

Model Dynamics

Mol. Profiles

Genomic

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  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 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

15

  Foundations   CHDI, Gates Foundation

  Government   NIH, LSDF

  Academic   Levy (Framingham)

  Rosengren (Lund)

  Krauss (CHORI)

  Federation   Ideker, Califarno, Butte, Schadt

Research Platform Research Platform Commons

Data Repository

Discovery Platform

Building Disease

Maps

Tools & Methods

Repository

Discovery

Maps

Tools &

Repository

Discovery

Repository

Discovery

Repository

Discovery

Repository

Discovery

Repository

Discovery

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

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.

Objective Rewards vs Deeper Rewards

AUTONOMY MASTERY PURPOSE

Model of Alzheimer’s Disease Bin Zhang Jun Zhu

AD

normal

AD

normal

AD

normal

Cell cycle

http://sage.fhcrc.org/downloads/downloads.php

THE FEDERATION Butte Califano Friend Ideker Schadt

vs

Federated Aging Project : Combining analysis + narrative

=Sweave Vignette Sage Bionetworks Lab

Califano Lab Ideker Lab Califano Lab

Shared Data Repository

JIRA: Source code repository & wiki

R code + narrative

PDF(plots + text + code snippets) 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 IMPACT ON PATIENTS

ANY CITIZEN/ PATIENT

ANY FOUNDATION Or INDUSTRY PROJECT

ANY MODELER

ANY CITIZEN/ PATIENT

Assumption that genetic alterations in human conditions should be owned

. .

We still consider much clinical research as if we were hunter gathers!- not sharing soon enough

TENURE FEUDAL STATES

RULES GOVERN

Engaging Communities of Interest

PLAT

FORM

NEW

MAP

S 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...)

NEW TOOLS Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape,

Clinical Trialists, Industrial Trialists, CROs…)

PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-

(Federation, CCSB, Inspire2Live....)

http://sagecongress.org

!

Group A: ACTIVATING ACCESS

Group D LEGAL STACK-ENABLING PATIENTS: John Wilbanks

“… 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

Is the Industry managing itself into irrelevance?

$130 billion of patented drug sales will face generics in the 2011-2016 decade (55% of 2009 US sales)

Sales exposed to generics will double in 2012 (to $33 billion)

98% of big pharma sales come from products 5 years and older (avg patent life = 11 years)

6 big pharmas were lost in the last 10 years

R&D spending is flattening, threatening future innovation

How to help Science pay more attention to your disease- Aled Edwards

Are we starting with the right targets?

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/ Pharmacolo

gy

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/ Pharmacolo

gy

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate ID

Toxicology/ Pharmacolo

gy

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate ID

Toxicology/ Pharmacolo

gy

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate ID

Toxicology/ Pharmacolo

gy

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate ID

Toxicology/ Pharmacolo

gy

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate ID

Toxicology/ Pharmacolo

gy

Phase I Phase IIa/IIb

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate ID

Toxicology/ Pharmacolo

gy

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

• Is there sufficient incentive?

• Will universities forego IP ownership?

• Can we protect compounds that “make it”?

PPP

Pha

rma

Pub

lic fu

nder

s

Pat

ient

gro

ups

Aca

dem

ics

Reg

ulat

ors

CR

Os

Toronto Feb-2011 meeting: consensus among 5 pillars

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 (Oncology, CNS-Autism/Schizophrenia and Project 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

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

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

Arch2POCM Communities of Interest

• Arch2POCM Strategic Design Teams • Currently in place for oncology and CNS disease areas • Multiple pharmas represented in leadership • Charged to define detailed project workflow and timeline

• Private Foundations • Opportunity to seed an Arch2POCM Strategic Design Team • Opportunity to leverage the release of patient data for sponsored trials

Arch2POCM Strategic Design Teams: Target Selection Criteria

Pioneer

May be “high risk”

High patient value

POCM study must provide learnings

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

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

• Seed Arch2POCM strategic design team around a disease area of high interest to private foundation(s) to generate project workflow and timelines (Q4, 2011)

• 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 in order to launch operations in early 2012 in at least one disease area

Which disease areas?

Which pathways?

How will we select targets?

Costs/ Timelines/ Deliverables?

Strengths and Weaknesses?

Arch2POCM Strategic Design Teams: (One of the Breakout Groups for this Afternoon )

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