The Future of Open Innovation: Development and Use of Therapies

End of the Era of Medical Guilds and Alchemy

Moving beyond the Medical Industrial Complex

Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization)

Seattle/ Beijing/ Amsterdam

UC Berkeley Hass School of Business Topics in Innovation

March 5, 2012

•  New  ways  of  Building  Models  of  Disease  

•  What  prevents  us  from  building  them?  

•  What  is  Sage  Bionetworks?  

•  Review  of  Six  Pilots  

•  So  what  are  the  next  steps?  

What  is  the  problem?  

     Most  approved  therapies  were  assumed  to  be  monotherapies  for  diseases  represen4ng  homogenous  popula4ons  

 Our  exis4ng  disease  models  o9en  assume  pathway  knowledge  sufficient  to  infer  correct  therapies  

Familiar but Incomplete

Reality: Overlapping Pathways

The value of appropriate representations/ maps

Equipment capable of generating massive amounts of data

“Data Intensive” Science- Fourth Scientific Paradigm

Open Information System

IT Interoperability

Host evolving computational models in a “Compute Space”

WHY  NOT  USE    “DATA  INTENSIVE”  SCIENCE  

TO  BUILD  BETTER  DISEASE  MAPS?  

what will it take to understand disease?

                   DNA    RNA  PROTEIN  (dark  maOer)    

MOVING  BEYOND  ALTERED  COMPONENT  LISTS  

2002 Can one build a “causal” model?

Preliminary Probabalistic Models- Rosetta /Schadt

Gene symbol Gene name Variance of OFPM explained by gene expression*

Mouse model

Source

Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg

Mirochnitchenko (University of Medicine and Dentistry at New Jersey, NJ) [12]

Lactb Lactamase beta 52% tg Constructed using BAC transgenics Me1 Malic enzyme 1 52% ko Naturally occurring KO Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple

(UCLA) [13] Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg

(Columbia University, NY) [11] C3ar1 Complement component

3a receptor 1 46% ko Purchased from Deltagen, CA

Tgfbr2 Transforming growth factor beta receptor 2

39% ko Purchased from Deltagen, CA

Networks facilitate direct identification of genes that are

causal for disease Evolutionarily tolerated weak spots

Nat Genet (2005) 205:370

DIVERSE  POWERFUL  USE  OF  MODELS  AND  NETWORKS  

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

List of Influential Papers in Network Modeling

(Eric Schadt)

Equipment capable of generating massive amounts of data A-

“Data Intensive” Science- Fourth Scientific Paradigm Score Card for Medical Sciences

Open Information System D-

IT Interoperability D

Host evolving computational models in a “Compute Space F

.

We still consider much clinical research as if we were “hunter gathers”- not sharing

 TENURE      FEUDAL  STATES      

Clinical/genomic data are accessible but minimally usable

Little incentive to annotate and curate data for other scientists to use

Mathematical models of disease are not built to be

reproduced or versioned by others

Lack of standard forms for future rights and consents

Lack of data standards..

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, Johnson &Johnson

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  Foundations   Kauffman CHDI, Gates Foundation

  Government   NIH, LSDF, NCI

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

  Federation   Ideker, Califano, Nolan, Schadt

What  is  this?  

Bayesian  networks  enriched  in  inflammaVon  genes    correlated  with  disease  severity  in  pre-­‐frontal  cortex  of  250  Alzheimer’s  paVents.  

What  does  it  mean?  

InflammaVon    in  AD  is  an  interacVve  mulV-­‐pathway  system.    More  broadly,  network  structure  organizes  complex  disease  effects  into  coherent  sub-­‐systems  and  can  prioriVze  key  genes.  

Are  you  joking?  

Gene  validaVon  shows  novel  key  drivers  increase  Abeta  uptake  and  decrease  neurite  length  through  an  ROS  burst.  (highly  relevant  to  AD  pathology)  

ALZHEIMER’S  

Liver   Adipose  

FaDy  acids  

Hypothalamus  

Macrophage/  inflamma4on  

Lep4n  signaling  

Phagocytosis-­‐  induced  lipolysis  

Phagocytosis-­‐  induced  lipolysis  

M1  macrophage  

A  mulV-­‐Vssue  immune-­‐driven  theory  of  weight  loss  

RULES GOVERN

PLAT

FORM

NEW

MAP

S

PLATFORM Sage Platform and Infrastructure Builders-

( Academic Biotech and Industry IT Partners...)

PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-

(Federation, CCSB, Inspire2Live....)

Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning

Leveraging Existing Technologies

Taverna

Addama

tranSMART

Watch What I Do, Not What I Say sage bionetworks synapse project

Most of the People You Need to Work with Don’t Work with You

sage bionetworks synapse project

My Other Computer is Cloudera Amazon Google

sage bionetworks synapse project

Sage Metagenomics Project

•  > 10k genomic and expression standardized datasets indexed in SCR •  Error detection, normalization in mG •  Access raw or processed data via download or API in downstream analysis •  Building towards open, continuous community curation

Processed Data (S3)

Sage Metagenomics using Amazon Simple Workflow

Full case study at http://aws.amazon.com/swf/testimonials/swfsagebio/

Synapse Roadmap

Q1-2012 Q2-2012 Q3-2012 Q4-2012 Q1-2013 Q2-2013

Synapse Platform Functionality

Data / Analysis Capabilities

Q3-2013 Q4-2013

Internal Alpha Public Beta Testing Synapse 1.0 Synapse 1.5 Future

•  Data Repository •  Projects and security •  R integration •  Analysis provenance

• Search • Controlled Vocabularies • Governance of restricted data

•  40+ manually curated clinical studies •  8000 + GEO / Array Express datasets •  Clinical, genomic, compound sensitivity •  Bioconductor and custom R analysis

• TCGA •  METABRIC breast cancer challenge

•  Workflow templates •  Publishing figures •  Wiki & collaboration tools •  Integrated management of cloud resources

•  Social networking •  User-customized dashboards •  R Studio integration •  Curation tool integration

•  Predictive modeling workflows •  Automated processing of common genomics platforms

•  TBD: Integrations with other visualization and analysis packages

CTCAP  Arch2POCM  The  FederaVon  Portable  Legal  Consent  Sage  Congress  Project  BRIDGE  

Six  Pilots  involving  Sage  Bionetworks  

RULES GOVERN

PLAT

FORM

NEW

MAP

S

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.

Started Sept 2010

Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery

•  Graphic  of  curated  to  qced  to  models  

Arch2POCM  

Restructuring  the  PrecompeVVve  Space  for  Drug  Discovery  

How  to  potenVally  De-­‐Risk      High-­‐Risk  TherapeuVc  Areas  

Arch2POCM: scale and scope

•  Proposed Goal: Initiate 2 programs. One for Oncology/Epigenetics/Immunology. One for Neuroscience/Schizophrenia/Autism. Both programs will have 8 drug discovery projects (targets) - ramped up over a period of 2 years

–  It is envisioned that Arch2POCM’s funding partners will select targets that are judged as slightly too risky to be pursued at the top of pharma’s portfolio, but that have significant scientific potential that could benefit from Arch2POCM’s crowdsourcing effort

•  These will be executed over a period of 5 years making a total of 16 drug discovery projects

–  Projected pipeline attrition by Year 5 (assuming 12 targets loaded in early discovery)

•  30% will enter Phase 1 •  20% will deliver Ph 2 POCM data 45

Arch2POCM: Highlights A PPP To De-Risk Novel Targets That The Pharmaceutical Industry Can

Then Use To Accelerate The Development of New and Effective Medicines •  The Arch2POCM will be a charitable Public Private Partnership (PPP) that will file no patents and

whose scientific plan (including target selection) will be endorsed by its pharmaceutical, private and public funders

•  Arch2POCM will de-risk novel targets by developing and using pairs of test compounds (two different chemotypes) that interact with the selected targets: the compounds will be developed through Phase IIb clinical trials to determine if the selected target plays a role in the biology of human disease

•  Arch2POCM will work with and leverage patient groups and clinical CROs to enable patient recruitment, and with regulators to design novel studies and to validate novel biomarkers

•  Arch2POCM will make its GMP test compounds available to academic groups and foundations so they can use them to perform clinical studies and publish on a multitude of additional indications

•  Arch2POCM will release all reagents and data to the public at pre-defined stages in its drug development process. To ensure scientific quality, data and reagents will be released once they have been vetted by an independent scientific committee

•  Arch2POCM will publish all negative POCM data immediately in order to reduce the number of ongoing redundant proprietary studies (in pharma, biotech and academia) on an invalidated target and thereby –  minimize unnecessary patient exposure –  provide significant economic savings for the pharmaceutical industry

•  In the rare instance in which a molecule achieves positive POCM, Arch2POCM will ensure that the compound has the ability to reach the market by arranging for exclusive access to the proprietary IND database for the molecule 46

Arch2POCM: proposed funding strategy –  $160-200M over five years is projected as necessary to advance

up to 8 drug discovery projects within each of the two therapeutic programs

–  Arch2POCM funding will come from a combination of public funding from governments and private sector funding from pharmaceutical and biotechnology companies and from private philanthropists

–  By investing $1.6 M annually into one or both of Arch2POCM’s selected disease areas, partnered pharmaceutical companies:

1.  obtain a vote on Arch2POCM target selection 2.  have the opportunity to donate existing compounds from their

abandoned clinical programs for re-purposing on Arch2POCM’s  targets  

3.  gain real time data access to Arch2POCM’s 16 drug discovery projects

4.  have the strategic opportunity to expand their overall portfolio 47

Five Year Objective: Initiate ≈ 8 drug discovery projects with 6 entering in Early Discovery, one entering in pre-clinical and one entering in PH I

Months → 0-6 7-12 13-18 19-24 25-30 31-36 37-42 43-48 49-54 55-60

Pipeline flow for Arch2POCM

Early discovery (45% PTRS) Pre-clinical (70% PTRS) Ph I (65% PTRS)

Ph II (10% PTRS)

1.3

1

Ph 1 (1)

1

Year #2 Arch2POCM Target Load

Arch2POCM Snapshot at Year 5

Year #1 Arch2POCM Target Load

Early discovery (2)

1

Targets  Loaded   8  

Projected  INDs  filed   3-­‐4  

Ph  1  or  2  Trials  In  Progress   2  

Projected  Complete  Ph  2  (POCM)  Data  Sets  

1  

*PTRS = Probability of technical and regulatory success

Pre-clinical (1)

Early discovery (4)

Pre-clinical

Pre-clinical

Ph 1

Ph 1

Ph 1

Ph 2

Ph 2

Ph 2

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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 only recently 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

49

Arch2POCM epigenetics program: Assumptions for launch and completion of Year 1

•  Funding necessary to prosecute 8 epigenetic target-based projects o  ≈$85M for five years with $15M available for Year 1

•  $1.6M from each of 3 pharma partners ($4.8M) •  $5M from public funders and $5M from philanthropists

o  Year 1: load 3 targets with 2 in Early Discovery and 1 in pre-clinical stage of development o  Year 2: load 5 targets with at least one late stage clinical asset from a pharma partner

•  Partners –  In kind partners

o  GE Healthcare (imaging): open sharing of its experimental oncology biomarkers o  CRUK: through some of its drug discovery and development resources participating in Arch2POCM

–  Potential academic partner sites •  Institutions that have indicated willingness to let their scientists participate without patent filing: UCSF,

Massachusetts General Hospital, University of North Carolina, University of Toronto, Oxford University, Karolinska Institute

•  Costs to fund Arch2POCM academic partners will be de-frayed by crowd-sourcing: each funded investigator will use their own network to amplify what they can do and publish on Arch2POCM targets

–  Patient groups will enable patient recruitment and reduce costs for clinical studies –  FDA and EMEA team of regulators available

o  Oncology experts available o  Can provide in vitro screening assays for toxicities and biomarker development to improve patient

selection o  FDA to help build and host a compliant Arch2POCM data-sharing site

o  Infrastructure that needs to be in place to execute on time o  Align vendors and CROs prior to initiation of Arch2POCM projects o  IT and patient database management: harmonization of data-entry across participating clinical collaborators

in place well before start of first Arch2POCM trial 50

General benefits of Arch2POCM for drug development

1.  Arch2POCM’s use of test compounds to de-risk previously unexplored biology enables drug developers to initiate proprietary drug development starting from an array of unbiased, clinically validated targets

2.  Arch2POCM’s crowdsourced research and trials provides the pharmaceutical industry with “parallel shots on goal: by aligning test compounds to most promising unmet medical need”

3.  The positive and negative clinical trial data that Arch2POCM and the crowd produce and publish will increase clinical success rates (as one can pick targets and indications more smartly) and will save the pharmaceutical industry money by reducing redundant proprietary efforts on failed targets

51

Why is Arch2POCM a “smart bet” for Pharma investment?

Arch2POCM:  an  external  epigeneVc  think  tank  from  which  Pharma  can  load  the  most  likely  to  succeed  targets  as  proprietary  programs  or  leverage  Arch2POCM  results  for  its  other  internal  efforts  •  A  front  row  seat  on  the  progression  of  8  epigeneVc  targets  means  that:  

•  Pharma  can  select  the  epigeneVc  targets  that  best  compliment  their  internal  poriolio  and  for  which  there  is  the  greatest  interest  

•  Pharma  can  structure  Arch2POCM’s  projects  so  that  key  objecVves  line  up  with  internal  go/no-­‐go  decisions  

•  Pharma  can  use  Arch2POCM  data  to  trigger  its  internal  level  of  investment  on  a  parVcular  target  

•  Pharma  can  use  Arch2POCM  resources  to  enrich  their  internal  epigeneVcs  effort:  acVve  chemotypes,  assays,  pre-­‐clinical  models,  biomarkers,  geneVc  and  phenotypic  data  for  paVent  straVficaVon,  relaVonships  to  epigeneVc  experts  

•   Pharma  can  use  Arch2POCM’s  lead  compound  chemotypes  to:  •   inform  their  proprietary  medicinal  chemistry  efforts  on  the  target  

•   idenVfy  chemical  scaffolds  that  impact  epigeneVc  pathways:  a  proprietary  combinaVon  therapy  opportunity  

•   Toxicity  screening  of  Arch2POCM  compounds  with  FDA  tools  can  be  used  to  guide  internal  proprietary  chemistry  efforts  in  oncology,  inflammaVon  and  beyond    

•  Arch2POCM’s  crowd  of  scienVsts  and  clinicians  provides  its  Pharma  partners  with  parallel  shots  on  goal  at  the  best  context  for  Arch2POCM’s  compounds/targets   52

•  Arch2POCM’s Ph II validation of high risk high opportunity targets focuses Pharma’s NME efforts

•  Positive POCM data: De-risked validated targets for Pharma development •  Negative POCM data: public release of this data minimizes the amount of time

and money that Pharma and the industry place on failed targets

•  Arch2POCM’s clinical candidate compounds provide Pharma with multiple paths to new medicines

•  Arch2POCM compounds that achieve POCM can be advanced into Ph 3 by Arch2POCM Members

•  The purchaser of Arch2POCM’s IND database obtains a significant time advantage over competitors to generate Phase III data and proceed to market

•  NMEs that derive from Arch2POCM will launch with database exclusivity protections: 5-8 years to garner a return on investment

•  The crowd’s testing of Arch2POCM compounds may identify alternative/better contexts for agonizing/antagonizing the disease biology target

•  indications •  patient stratification •  combination therapy options

How will Arch2POCM provide “line of sight” to new medicines?

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The  FederaVon  

2008   2009   2010   2011  

How can we accelerate the pace of scientific discovery?

Ways to move beyond “traditional” collaborations?

Intra-lab vs Inter-lab Communication

Colrain/ Industrial PPPs Academic Unions

(Nolan  and  Haussler)  

sage federation: model of biological age

Faster Aging

Slower Aging

Clinical Association -  Gender -  BMI -  Disease Genotype Association Gene Pathway Expression Pr

edicted  Age  (liver  expression)  

Chronological  Age  (years)  

Age Differential

Reproducible  science==shareable  science  

Sweave: combines programmatic analysis with narrative

Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reports using literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 –

Proceedings in Computational Statistics,pages 575-580. Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9

Dynamic generation of statistical reports using literate data analysis

Federated  Aging  Project  :    Combining  analysis  +  narraVve    

=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

1)  Data  management  APIs  to  load  standaridzed  objects,  e.g.  R  ExpressionSets  (MaD  Furia):  

         ccleFeatureData  <-­‐  getEnVty(ccleFeatureDataId)            ccleResponseData  <-­‐  getEnVty(ccleResponseDataId)  

         tcgaFeatureData  <-­‐  getEnVty(tcgaFeatureDataId)            tcgaResponseData  <-­‐  getEnVty(tcgaResponseDataId)  

=!

Observed Data!=! +!

+!

Random Variation!Systematic Variation!

+!

Normalization: Remove the influence of adjustment variables on data...!

=! +!

2)    Automated,  standardized  workflows  for  cura4on  and  QC  of  large-­‐scale  datasets  (Brig  Mecham).  

A.  TCGA:  Automated  cloud-­‐based  processing.  B. GEO  /  Array  Expression:  NormalizaVon  workflows,  curaVon  of  phenotype  using  standard  ontologies.  C. AddiVonal  studies  with  geneVc  and  phenotypic  data  in  Sage  repository  (e.g.  CCLE  and  Sanger  cell  line  datasets)  

custom  model  1   custom  model  2   custom  model  N  

4)  Sta4s4cal  performance  assessment  across  models.  

custom  model  1   custom  model  2   custom  model  N  

5)  Output  of  candidate  biomarkers  and  feature  evalua4on  (e.g.  GSEA,  pathway  analysis)  

6)  Experimental  follow-­‐up  on  top  predic4ons  (TBD)        E.g.  for  cell  lines:  medium  throughput  suppressor  /  enhancer  screens  of  drug  sensiVvity  for  knockdown  /  overexpression  of  predicted  biomarkers.  

3)  Pluggable  API  to  implement  predic4ve  modeling  algorithms.  

A)  Support  for  all  commonly  used  machine  learning  methods  (for  automated  benchmarking  against  new  methods)  

B)  Pluggable  custom  methods  as  R  classes  implemenVng  customTrain()  and  customPredict()  methods.  

A)  Can  be  arbitrarily  complex  (e.g.  pathway  and  other  priors)  

B)  Support  for  parallelizaVon  in  for  each  loops.  

Portable  Legal  Consent  

(AcVvaVng  PaVents)  

John  Wilbanks  

weconsent.us  

Sage  Congress  Project  April  20  2012  

RealNames  Parkinson’s  Project  RevisiVng  Breast  Cancer  Prognosis  

Fanconi’s  Anemia  

(Responders  CompeVVons-­‐  IBM-­‐DREAM)  

Networking  Disease  Model  Building