Stephen Friend Food & Drug Administration 2011-07-18

How Do We Study Network Perturbations in Clinical Specimens?
How do we select which targets are effective for what diseases and
which patients?- Stephen Friend July 18th FDA

1.  Clinical Trial Comparator Arm Project “CTCAP”
2.  “Arch2POCM”- Compounds to decode biology
3.  Oncology Non-Responders Project
4.  Freeing up Failed Compounds

What are the potential opportunities to participate in these projects?
What actions might the FDA take?
For actions needed beyond the FDA- executive or legislative?

Alzheimers Diabetes

Treating Symptoms v.s. Modifying Diseases
Depression Cancer
Will it work for me?

Personalized Medicine 101:
Capturing Single bases pair mutations = Rresponders
Illusion that Altered Component Lists = Correct decisions about who will benefit

Use of Sub-populations to ID Responders
Illusion that 1000 patients will provide Sub-populations

Reality: Overlapping Pathways generate Context Complexity

WHY NOT USE
“DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?

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

Equipment capable of generating
massive amounts of data

IT Interoperability

Standard Annotations
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

How can genomic data used to understand biology?

TumorsTumors
RNA amplification
Microarray hybirdization

Gene Index
!Standard"GWAS Approaches Profiling Approaches
Identifies Causative DNA Variation but Genome scale profiling provide correlates of disease
provides NO mechanism   Many examples BUT what is cause and effect?

  Provide unbiased view of
molecular physiology as it
relates to disease phenotypes
trait
  Insights on mechanism
  Provide causal relationships
and allows predictions

!Integrated"Genetics Approaches

Integration of Genotypic, Gene Expression & Trait Data
Schadt et al. Nature Genetics 37: 710 (2005)
Millstein et al. BMC Genetics 10: 23 (2009)

Causal Inference

“Global Coherent Datasets”
•  population based
•  100s-1000s individuals

Chen et al. Nature 452:429 (2008) Zhu et al. Cytogenet Genome Res. 105:363 (2004)
Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)

Preliminary Probabalistic Models- Rosetta /Schadt

Networks facilitate direct identification of
genes that are causal for disease
Evolutionarily tolerated weak spots

Gene symbol Gene name Variance of OFPM Mouse Source
explained by gene model
expression*
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 46% ko Purchased from Deltagen, CA
3a receptor 1
Tgfbr2 Transforming growth 39% ko Purchased from Deltagen, CA
Nat Genet (2005) 205:370 factor beta receptor 2

List of Influential Papers in Network Modeling

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

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

Building Disease Maps Data Repository

Commons Pilots Discovery Platform
Sagebase.org

Sage Bionetworks Collaborators

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

  Foundations
  CHDI, Gates Foundation

  Government
  NIH, LSDF

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

  Federation
  Ideker, Califarno, Butte, Schadt 17

Platform Commons Research
Cancer
Neurological Disease
Metabolic Disease
Curation/Annotation
Building
Data Disease
Repository Maps
CTCAP
Public Data Pfizer
Merck Data Outposts Merck
TCGA/ICGC Federation Takeda
CCSB Astra Zeneca
CHDI
Commons Gates
NIH
Pilots
LSDF-WPP
Inspire2Live
Hosting Data POC
Hosting Tools Bayesian Models
Co-expression Models
Hosting Models

Discovery Tools &
Platform Methods
KDA/GSVA
LSDF

Clinical Trial Comparator Arm
Partnership

  Sharon Terry
President & CEO, Genetic Alliance
  Stephen Friend
President, Sage Bionetworks

PROBLEM: Serious Need for Very Large Clinical and
Genomic Datasets to Build Better Disease Maps

Sage Bionetworks: Platform
GLOBAL COHERENT DATASETS
A data set containing genome-wide DNA variation and intermediate trait, as well as physiological phenotype
data across a population of individuals large enough to power association or linkage studies, typically 50 or
more individuals. To be coherent, the data needs to be matched with consistent identifiers. Intermediate traits
are typically gene expression, but may also include proteomic, metabolomic, and other molecular data.

See http://www.sagebase.org/commons/repository.php

MODELS

TOOLS
Key Driver Analysis (KDA) Tool (R package/Cystoscape plug in)
http://sagebase.org/research/tools.php

Sage Bionetworks Repository
Key Objective

Provide public access to curated, QC ed and documented global
coherent datasets (GCDs) and the network models derived from these
datasets.

documented documented

Curated Curated &
QC d Network
GCD Data
GCD Data Models

Public Domain

How we share data- Build
Models
Evolution of a Biology
Evolution of a Software Project
Project

Partnership
  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.

Partnership
Aim 1: Identify, collect, QC, curate and host 4-6 CTCAP coherent
genomic datasets each year
Aim 2: Develop and host network models built from these datasets to
drive public target mining, biomarkers identification, and patient
stratification efforts
Aim 3: Establish a framework/process for ongoing release of clinical
genomics data
Challenges:
 Landscape of available datasets; Process & scale of project
 Behavioral challenges; Incentives for individuals to give data to Sage
 Move model from pull to push
 Compliance, Privacy, Data use, etc
 Independent Board to look at broader societal implications of providing
data

CTCAP Workstreams

Uncurated GCD

Curated GCD
•  Single common identifier to link datatypes
•  Gender mismatches removed

Public Sage
Domain
GCDs  Curated GCD
Curated & QC d GCD
•  Gene expression data corrected for batch
effects, etc
 Curated & QC’d
Uncurated GCD
GCD
Collaborators Database
GCDs (Sage)  Network Models
•  Public
• Collaboration
•  Internal

Private
Domain Public Databases
Co-
GCDs expression  dbGAP
Network
Analysis
Integrated
Network
Analysis

Bayesian
Network
Analysis

Benefits of working in CTCAP
shared generative environment
  Value: Represents a time and cost-efficient way to re-use and gain full
value from existing, expensive trial data. Reduced costs for patients,
payers and government when effective, tailored treatments become the
standard of care. Better outcomes for patients when appropriate
therapies are used first.

  Product Development: Reduction in cost, time and failure rate for drug
development; pharma, biotech companies and academic researchers will
have full access to the resultant platform without jeopardizing proprietary
molecules or therapies.

  A generative resource: No one company or research group has the
data or the tools to do this alone.

… 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

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

Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy

Attrition 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.

The current pharma model is redundant

Phase
Phase
Target ID/ Hit/Probe/ Clinical Toxicology/
gy Phase I
gy
Phase
gy
Phase
gy

Phase
gy
Phase
gy
Phase
gy

Attrition 50% 10% 30% 30% 90%

Negative POC information is not shared

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

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

•  We want to improve health

•  New medicines are part of this equation

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

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

Arch2POCM

A globally distributed public private partnership (PPP) committed to:
• Generate more clinically validated targets by sharing data
• Deliver more new drugs for patients by using compounds to understand disease biology

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.

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
- genomic/ genetic
Pioneer target sources - disease networks
- academic partners
- private partners
- Sage Bionetworks, SGC,

Lead Lead
Preclinical Phase I Phase II
identification optimisation

Assay
in vitro
probe
Lead Clinical Phase I Phase II
candidate asset asset
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

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

Section 1 – Project Overview

Non-Responder Cancer Project Mission

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

Sage Bionetworks • Non-Responder Project


The Non-Responder Project is an international initiative with funding for 6 initial
cancers anticipated from both the public and private sectors

GEOGRAPHY United States China

TARGET
CANCER
Ovarian Renal Breast AML Colon Lung

FUNDING Likely to be
SOURCE funded by the Pilot Funded by the Chinese private
Seeking private sector funding
Federal sector partners
Government



The study results will aid in the development of assays to identify non-
responders to current treatments, creating clinical and financial benefits

Assays developed based on the study’s results will allow for patient stratification by identifying those that will not respond to standard-of-care
therapies in advance of treatment, therefore accelerating access to second tier and experimental compounds

Patient stratification results in:

Avoidance of Unnecessary Improved Clinical
Reduced Medical Costs
Toxicity Outcomes
•  Patients identified as non- •  Selecting therapies based on
responders can skip molecular profiling improves
standard-of-care treatments treatment results
and avoid experiencing side
e.g. Bisgrove Trials
effects from a round of
ineffective therapy



The Non-Responder Cancer Project Leadership Team

Stephen Friend, MD, PhD Todd Golub, MD
President and Co-Founder of Sage Founding Director Cancer Biology
Bionetworks, Head of Merck Oncology Program Broad Institute, Charles
01-08, Founder of Rosetta Dana Investigator Dana-Farber
Inpharmatics 97-01, co-Founder of the Cancer Institute, Professor of
Seattle Project Pediatrics Harvard Medical School,
Investigator, Howard Hughes Medical
Institute

“This study aims to provide both a material near term “Having focused on molecular medicine in my
improvement in cancer patient outcomes and a long term decades of conducting clinical trials, I am excited by
blueprint for the future of oncology trails, prognosis and the opportunity for the Non-responder project to
care. I believe the team of scientific, clinical and patient change the way we select treatments for patients. My
advocate partners we have assembled is unique in its passion for this project and for improving our ability to
ability to execute this study. With public and private better target therapies is immeasurable and I look
sector support, I know we will be able to change the forward to being an active part of this research.”
future of cancer care and research around the world.”



The Non-Responder Cancer Project Leadership Team

Charles Sawyers, MD Richard Schilsky, MD
Chair, Human Oncology Memorial Chief, Hematology- Oncology, Deputy
Sloan-Kettering Cancer Center, Director, Comprehensive Cancer
Investigator, Howard Hughes Medical Center, University of Chicago; Chair,
Institute, Member, National Academy National Cancer Institute Board of
of Sciences, past President American Scientific Advisors; past-President
Society of Clinical Investigation, 2009 ASCO, past Chairman CALGB clinical
Lasker-DeBakey Clinical Medical trials group
Research Award

“I have considered many opportunities to engage in “Stephen and I have worked together for many years on
personalized medicine, and believe the greatest value can developing innovative network approaches to analyzing
be in developing assays to better target treatments for disease. Identifying signatures of non-response is the most
patients at the molecular level. I have worked with Stephen exciting project I have been involved with in recent years
for 3 years and believe he is uniquely qualified to lead a and one which I believe can dramatically shift the way
project of this caliber to great success.” cancer patients receive treatment.”


Section 2 – Research Plan

For each tumor-type, the non-responder project will follow a common workflow, with patient
identification and sample collection the most variable across studies

Non-Responder Project Workflow

Identification and enrollment, and data and sample The remaining parts of the study will be largely similar, and
collection may differ by tumor-type potentially shared, across all projects

Data
and
Clinical

Iden%ﬁca%on
and
Sample
Disease
Feedback

Sample
Data

Enrollment
Processing
Modeling
and
Results

Collec%on
Repor%ng

Payment and Reimbursement

Project Management



The non-responder project will require the coordination of a number of stakeholders to handle the
various components of the research process

Potential Non-Responder Project US partners include:

Physicians & AMCs Patient Consent

Pathology

Genome
Sequencing

Patient Advocacy
Groups
Core Bioinformatics

Analysis and
Disease Modeling



Identification and Enrollment
The number of patients and enrollment procedures will vary for each study based on the biology
and stage of the disease and the size of the advocate community

•  The number of patients differs according to the biology of each
tumor-type being investigated
Ovarian Cancer
How many patients •  The sample will require enough patients to identify 100-150 patients
are required? In Ovarian Cancer, the target patient population will be those who experience
for each arm (responders and non-responders) that have distinct
biology
recurrence within 6-24 months of stopping initial treatment. This population
will require enrollment of 150 patients to identify groups with distinct
response/non-response biology
•  Enrollment sources will vary based on the makeup of the physician
Ovarian Cancer Patients
Who will be and patient communities
responsible for •  Each study will entail a mix of physician-driven and patient-initiated
enrollment , with those with strong advocate communities trending
enrolling patients? + Initial Response* Surgical removal No initial response*
towards patient-initiated, and those with leverageable physician 80% and initial chemo 20%

relationships involving more physician targeting

No recurrence Recurrence Second series of
<24mo 6-24 months Doublet Chemo
•  Data will include a questionnaire to determine eligibility and to
What data will need collect additional information that may inform analysis (e.g. age,
to be collected at race, etc.) Responders Non-Responders
•  Additionally, patient consent will need to be obtained 30-50% 50-70%
enrollment?
•  Genetic Alliance will own and standardize the consenting process

Since most ovarian cancer patients see a Gynecologic 30% Patient-
Oncologist who manages the entirety of their treatment, initiated
•  Costs to identify and enroll patients will vary by channel
What will be the this tumor-type is well structured to use a select group of
•  Patient-driven will be predominantly marketing and shipping costs
cost of (e.g. marketing through the Love/Army of women costs $1500 until physicians/AMCs to target patients for enrollment 70% Physician-
identification and study is filled) driven
enrollment? •  Physician-driven enrollment may require educating physicians and a
grant of approximately $20,000 per patient plus some administrative
expenses



The renal cancer study will aim to identify the biomarkers related to patients whose disease
progresses during treatment with VEGF receptor inhibitors
Currently 10-20% of all patients diagnosed are considered to have no response to this therapy

Clinical Leads: Bob Motzer, James Hseih
Definition of The non-responder population will be those patients who
Clinical Flow of Renal Cancer Patients Non- experience disease progression throughout initial treatment
Response with VEGFR TKIs(Tyrosine Kinase Inhibitors)
Patient presents with metastatic
renal cancer
(25-30K annually) Size of Based on the number of renal cancer diagnoses and the
Sample proportion of non-response, the study will require enrollment
Population of roughly 1,500 patients (500 patients/year) to identify a
Nephrectomy Nephrectomy is not a required total of 100-150 patients for each arm (response, non-
Procedure treatment for the study but will aid response)
in sample collection
(30-40%) Timeline for It is estimated to take approximately 3 years to enroll and
Enrollment collect viable samples from the required 1,500 patients

Treatment with
VEGF receptor Patient-driven enrollment is expected to fulfill 25% of
inhibitors Enrollment enrollees, as AMCs are seeing fewer first line patients
Strategy
Target Population
Enrollment and sample collection will require a network of
approximately 25 targeted community hospitals and
AMCs to ensure samples can be gathered and stored
Responders appropriately
Non-Responders
Stable Disease 30-40%
Progress through treatment With only 30-40% of patients having a nephrectomy
Partial Response 30-40% Sample
(10-20%) procedure, the study will need to cover the cost of sample
Collection
collection for at least 60% of patients



The specific targets of the breast cancer study are still being defined, however there is a
committed clinical leader and support from a leading patient advocate

Clinical Lead: Craig Henderson Preliminary
Patient Advocate Group: AVON/Love Army of Women
Clinical Flow of Breast Cancer Patients Definition of The clinical team is still selecting the ideal patient population
Non- to study
Response
Patient diagnosed with The leading population being considered is patients with
metastatic breast cancer metastatic breast cancer who are being treated with Avastin
or a similar therapy

Treatment with With a highly active patient advocate community, the breast
Avastin Enrollment cancer study is likely to be filled largely by patient-initiated
Strategy enrollment

Leveraging the relationship with the AVON/Love Army of
Women will provide access to a network of over 350,000
Target Population women interested in participating in studies related to breast
cancer

This network can help to virally spread the word about the
study and generate national interest in participation
Non-Responders
Responders
Progress through treatment



Sample Collection
In most cases, samples will be collected during required diagnostic procedures conducted by the
patient’s treating surgeon and shipped to a central location

•  Both tumor and normal tissue samples will be Ovarian Cancer
What type of sample required in all cases, where possible an adjacent
or recurrence sample should be obtained Since the treatment plan for Ovarian Cancer lends
is required? itself to a physician-driven enrollment plan, ten to
twelve AMC partners will be selected to be primary
enrollment and treatment sites for the study
•  Sample collection will be conducted during a
Where will the These sites will be expected to enroll roughly one
patient’s required biopsy procedure
samples be patient per month to reach the 150 patient target
•  The location of collection will vary based on the specific projects; projects being completed
collected and by
through physician enrollment at targeted AMCs will require collection at these sites, while
whom? An estimated two-thirds of ovarian cancer patients
patient-driven studies will allow for collection at any community location
will not have a medically necessary surgical
procedure after their first recurrence, requiring the
study to fund biopsy procedures to collect samples
•  Procedures for collection will require standard medical materials available to participating
What materials will from these patients
physicians
be required for •  Physicians will be provided a copy of instructions for storing shipping and handling of the Of the 150 patients enrolled, approximately 99
collection? samples
patients will require biopsies to collect samples
•  All samples will need to be shipped FedEx overnight to the sequencing location
specifically for the study

•  Sample collection will leverage procedures that are already being conducted and (in many
What is the cost of cases) reimbursed by insurers or paid out of pocket by patients
sample collection? •  Cost for additional samples in cases where biopsies were not medically required will cost
approximately $5,400 per patient, including sample prep



Sample Processing
Sample processing will involve whole genome sequencing, conducted at leading TCGA
participating sequencing centers, as well as bioinformatics and pathological review

Labs
&
Pathology
Gene%c
Analysis
Core
Bioinforma%cs

•  Each
cancer
type
will
•  Analysis
will
include:
•  Bioinforma%cs
will
be

have
designated
sites
Whole
Genome
conducted
by
the
most

for
conduc%ng
rou%ne
Sequencing,
cost-‐eﬀec%ve,
trusted

labs
and
pathological
transcriptome
gene
provider
to
ensure
the

review
to

ensure
expression
and
copy
quality
and
consistency

consistency
of
analysis
number
varia%on
of
data
for
analysis

•  Each
study
will
have
a
•  The
core

primary
processing
bioinforma%cs

site,
which
will
be
processing
will
turn
the

selected
from
among
raw
data
into
usable

leaders
in
gene%c
altera%on
component

sequencing
that
have
lists
of
muta%ons
and

par%cipated
in
similar
dele%ons

projects,
such
as
The

Cancer
Genome
Atlas



Clinical Data Reporting
While the patient is undergoing treatment, the physician will be required to submit data regarding
the patient’s therapies and outcomes
Clinical Reporting

•  The treating physician will submit data on the patient’s treatment and outcomes to the CRO on a
regular basis

•  This information will include:
–  Type and dosage of treatment the patient is receiving (i.e. a specific platinum-doublet
chemotherapy)
–  Details on the progress of the patient’s cancer



Data Collating and Disease Modeling
The genetic and clinical information will be combined and analyzed by Sage Bionetworks to
design a disease model identifying the causes of non-response
1 2 3
Combines genomic and Applies sophisticated Generates a map of drivers
clinical data mathematical modeling of non-response

All scientific output will be publicly available and
no members of the research group will own any
resulting IP



Feedback and Results
Material findings related to a patient’s potential treatment will be communicated when discovered;
The resulting disease maps will be publicly available to be revised and validated by the scientific
community

Patients/Physicians Scientific Community

Near-term Occasionally, specific insights will The first versions of disease maps
Within one year be shared with the patient through will be available publicly identifying
after project their physician – mainly related to hypotheses of non-response
the potential benefits of specific signatures for use by physicians
treatments and scientists to validate

Long-term Over time, as the study results Once the initial maps are published
Longer than one year facilitate guidance on therapy they data and maps will be
after project selection, patients may be notified dynamically updated as new
completion
of a specific signature of response/ patients and tests are added to the
non-response that can be used to results, with scientists globally able
make treatment decisions if relapse to refine the disease maps
occurs



Project Management
Each study will have an independent team to manage the tumor-specific study, which will roll up to
a central project coordinator

Overall
Non-Responder Project Coordinator
PMO
Entity/Person --- Role and responsibilities

Study-specific Project Coordinator Clinical Coordinator
PMO
Oversees overall operations
(Ovarian example)
Administrative support, coordination
and marketing

Genetic Alliance

Manage and standardize the enrollment and consenting process

CRO
Data management, clinical operations and monitoring activities , safety management


These Data May Teach Valuable Lessons About:

•  mechanism of action/target heterogeneity
•  off target effects
•  experimental design flaws
•  duration of effect/compensatory pathways
•  genotype/phenotype relationships
•  predictive power of disease models

With These Insights Researchers Could:

•  build better maps/more predictive models of disease
•  identify patient subsets that benefit
•  identify repurposing opportunities
•  reduce off target toxicity/side effects
•  form new hypothesis about pathophysiology
•  avoid replicating others failures
•  design more informed future trials

Sponsors Perceive A Negative Risk to Reward

•  reputational/legal concerns
•  competitor intelligence
•  potential damage to existing product franchises
•  potential damage to IP portfolios
•  collaborator restrictions must be negotiated
•  nobody wants to be first

Today, disclosing negative data is all risk and no reward
for a sponsor company

We need creative solutions to balance the risk/reward
ratio

Without incentives or a mandated change to corporate
behavior assets will be wasted, mistakes repeated and
opportunities for innovation missed

The Carrot Approach
•  Priority Review Vouchers
- in exchange for committing to disclose failed studies over a 5 year period
a sponsor will receive a priority review voucher that can be used for any
submission or transferred for economic value

- similar to FDAAA Section 524 establishing a priority review voucher for
sponsors that pursue therapeutics for tropical disease

- legislative framework is already established and can be appropriated

- NPV of voucher calculated at US$300 million

The Stick Approach
•  Shareholder Activism
- a new take on demanding corporate social responsibility
- educate shareholders on benefits of disclosing data sets
- dialogue with management and seek compliance
- file shareholder resolution for vote at annual meeting
- coordinate media campaign to raise public awareness

The Stick Approach
•  Enlist Payors To Call Out Pricing Dichotomy
- the cost of new drug development is estimated to range from $800 million -
$1.3 billion
- new drug launches must price at levels to recoup those costs in order to
drive further innovation
- DiMasi et al shows that 40% of drug development costs are due to clinical
failures
- payors and patients are subsidizing failed clinical trials but never benefit
from the data
- insurers and Medicare should press for fairness
either hold drug prices constant and disclose the failed data or
cut prices by 40% so that payments accurately reflect the goods delivered

Stephen Friend Food & Drug Administration 2011-07-18

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Stephen Friend Food & Drug Administration 2011-07-18