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

Depression Cancer

Treating Symptoms v.s. Modifying Diseases

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?

Equipment capable of generating massive amounts of data

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

Standard Annotations

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 can 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 T

umor

s

Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization

Integration of Genotypic, Gene Expression & Trait Data

Causal Inference

Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009)

Chen et al. Nature 452:429 (2008) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005)

Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)

“Global Coherent Datasets” •  population based

•  100s-1000s individuals

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

  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

17

  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 Platform

Repository 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

  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

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

GLOBAL COHERENT DATASETS

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.

Curated GCD Data

Curated & QC d

GCD Data

Network Models

documented documented

Public Domain

How we share data- Build Models

Evolution of a Software Project

Evolution of a Biology Project


  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.


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

Public Domain

GCDs

Collaborators GCDs

Uncurated GCD

Database (Sage)

•  Public • Collaboration

•  Internal

Uncurated GCD

Sage

 Curated GCD

 Curated & QC’d GCD

 Network Models

Curated GCD •  Single common identifier to link datatypes

•  Gender mismatches removed

Curated & QC d GCD •  Gene expression data corrected for batch

effects, etc

Public Databases  dbGAP

Co-expression

Network Analysis

Bayesian Network Analysis

Integrated Network Analysis

Private Domain

GCDs

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/ 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


Hit/Probe/Lead ID



gy



Hit/Probe/Lead ID



gy



Hit/Probe/Lead ID



gy



Hit/Probe/Lead ID



gy



Hit/Probe/Lead ID



gy



Hit/Probe/Lead ID



gy



Hit/Probe/Lead ID



gy


“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

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

• Deliver more new drugs for patients by using compounds to understand disease biology

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.

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

• 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

Non-Responder Cancer Project Mission

Section 1 – Project Overview

Sage Bionetworks • Non-Responder Project

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

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



Ovarian Renal Breast AML Colon Lung

United States China

Seeking private sector funding

Likely to be funded by the

Federal Government

Pilot Funded by the Chinese private sector partners

GEOGRAPHY

TARGET CANCER

FUNDING SOURCE

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

Avoidance of Unnecessary Toxicity

Improved Clinical Outcomes

Reduced Medical Costs

Patient stratification results in:

e.g. Bisgrove Trials

•  Patients identified as non-responders can skip standard-of-care treatments and avoid experiencing side effects from a round of ineffective therapy

•  Selecting therapies based on molecular profiling improves treatment results

The Non-Responder Cancer Project Leadership Team



Stephen Friend, MD, PhD President and Co-Founder of Sage Bionetworks, Head of Merck Oncology 01-08, Founder of Rosetta Inpharmatics 97-01, co-Founder of the Seattle Project

Todd Golub, MD Founding Director Cancer Biology Program Broad Institute, Charles Dana Investigator Dana-Farber Cancer Institute, Professor of Pediatrics Harvard Medical School, Investigator, Howard Hughes Medical Institute

“This study aims to provide both a material near term improvement in cancer patient outcomes and a long term blueprint for the future of oncology trails, prognosis and care. I believe the team of scientific, clinical and patient advocate partners we have assembled is unique in its ability to execute this study. With public and private sector support, I know we will be able to change the future of cancer care and research around the world.”

“Having focused on molecular medicine in my decades of conducting clinical trials, I am excited by the opportunity for the Non-responder project to change the way we select treatments for patients. My passion for this project and for improving our ability to better target therapies is immeasurable and I look forward to being an active part of this research.”

The Non-Responder Cancer Project Leadership Team



Charles Sawyers, MD Chair, Human Oncology Memorial Sloan-Kettering Cancer Center, Investigator, Howard Hughes Medical Institute, Member, National Academy of Sciences, past President American Society of Clinical Investigation, 2009 Lasker-DeBakey Clinical Medical Research Award

Richard Schilsky, MD Chief, Hematology- Oncology, Deputy Director, Comprehensive Cancer Center, University of Chicago; Chair, National Cancer Institute Board of Scientific Advisors; past-President ASCO, past Chairman CALGB clinical trials group

“Stephen and I have worked together for many years on developing innovative network approaches to analyzing disease. Identifying signatures of non-response is the most exciting project I have been involved with in recent years and one which I believe can dramatically shift the way cancer patients receive treatment.”

“I have considered many opportunities to engage in personalized medicine, and believe the greatest value can be in developing assays to better target treatments for patients at the molecular level. I have worked with Stephen for 3 years and believe he is uniquely qualified to lead a project of this caliber to great success.”

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

Section 2 – Research Plan


Iden%fica%on and Enrollment

Data and Sample

Collec%on

Sample Processing

Clinical Data

Repor%ng

Disease Modeling

Feedback and Results

Payment and Reimbursement

Project Management

Non-Responder Project Workflow

The remaining parts of the study will be largely similar, and potentially shared, across all projects

Identification and enrollment, and data and sample collection may differ by tumor-type

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



Physicians & AMCs

Patient Advocacy Groups

Patient Consent

Pathology

Genome Sequencing

Core Bioinformatics

Analysis and Disease Modeling

Potential Non-Responder Project US partners include:

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



How many patients are required?

Who will be responsible for

enrolling patients?

What data will need to be collected at

enrollment?

What will be the cost of

identification and enrollment?

Ovarian Cancer

Since most ovarian cancer patients see a Gynecologic Oncologist who manages the entirety of their treatment, this tumor-type is well structured to use a select group of physicians/AMCs to target patients for enrollment 70% Physician-

driven

Ovarian Cancer Patients

Surgical removal and initial chemo

No initial response* 20%

+ Initial Response* 80%

No recurrence <24mo

Recurrence 6-24 months

Second series of Doublet Chemo

Responders 30-50%

Non-Responders 50-70%

In Ovarian Cancer, the target patient population will be those who experience 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

•  The number of patients differs according to the biology of each tumor-type being investigated

•  The sample will require enough patients to identify 100-150 patients for each arm (responders and non-responders) that have distinct biology

•  Enrollment sources will vary based on the makeup of the physician and patient communities

•  Each study will entail a mix of physician-driven and patient-initiated enrollment , with those with strong advocate communities trending towards patient-initiated, and those with leverageable physician relationships involving more physician targeting

•  Data will include a questionnaire to determine eligibility and to collect additional information that may inform analysis (e.g. age, race, etc.)

•  Additionally, patient consent will need to be obtained •  Genetic Alliance will own and standardize the consenting process

•  Costs to identify and enroll patients will vary by channel •  Patient-driven will be predominantly marketing and shipping costs

(e.g. marketing through the Love/Army of women costs $1500 until study is filled)

•  Physician-driven enrollment may require educating physicians and a grant of approximately $20,000 per patient plus some administrative expenses

Patient-initiated

30%

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



Patient presents with metastatic renal cancer

(25-30K annually)

Non-Responders Progress through treatment

(10-20%)

Treatment with VEGF receptor

inhibitors

Responders Stable Disease 30-40%

Partial Response 30-40%

Clinical Flow of Renal Cancer Patients The non-responder population will be those patients who experience disease progression throughout initial treatment with VEGFR TKIs(Tyrosine Kinase Inhibitors)

Definition of Non-

Response

Based on the number of renal cancer diagnoses and the proportion of non-response, the study will require enrollment of roughly 1,500 patients (500 patients/year) to identify a total of 100-150 patients for each arm (response, non-response)

Size of Sample

Population

Timeline for Enrollment

Enrollment Strategy

Nephrectomy Procedure (30-40%) It is estimated to take approximately 3 years to enroll and

collect viable samples from the required 1,500 patients

Patient-driven enrollment is expected to fulfill 25% of enrollees, as AMCs are seeing fewer first line patients

Enrollment and sample collection will require a network of approximately 25 targeted community hospitals and AMCs to ensure samples can be gathered and stored appropriately

With only 30-40% of patients having a nephrectomy procedure, the study will need to cover the cost of sample collection for at least 60% of patients

Sample Collection

Target Population

Nephrectomy is not a required treatment for the study but will aid in sample collection

Clinical Leads: Bob Motzer, James Hseih

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



Patient diagnosed with metastatic breast cancer

Non-Responders Progress through treatment

Treatment with Avastin

Responders

Clinical Flow of Breast Cancer Patients The clinical team is still selecting the ideal patient population to study

The leading population being considered is patients with metastatic breast cancer who are being treated with Avastin or a similar therapy

Definition of Non-

Response

Enrollment Strategy

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

Leveraging the relationship with the AVON/Love Army of Women will provide access to a network of over 350,000 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

Target Population

Clinical Lead: Craig Henderson Patient Advocate Group: AVON/Love Army of Women

Preliminary

Ovarian Cancer

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



What type of sample is required?

Where will the samples be

collected and by whom?

What materials will be required for

collection?

What is the cost of sample collection?

•  Both tumor and normal tissue samples will be required in all cases, where possible an adjacent or recurrence sample should be obtained

•  Sample collection will be conducted during a patient’s required biopsy procedure

•  The location of collection will vary based on the specific projects; projects being completed through physician enrollment at targeted AMCs will require collection at these sites, while patient-driven studies will allow for collection at any community location

•  Procedures for collection will require standard medical materials available to participating physicians

•  Physicians will be provided a copy of instructions for storing shipping and handling of the samples

•  All samples will need to be shipped FedEx overnight to the sequencing location

•  Sample collection will leverage procedures that are already being conducted and (in many cases) reimbursed by insurers or paid out of pocket by patients

•  Cost for additional samples in cases where biopsies were not medically required will cost approximately $5,400 per patient, including sample prep

Since the treatment plan for Ovarian Cancer lends 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

These sites will be expected to enroll roughly one patient per month to reach the 150 patient target

An estimated two-thirds of ovarian cancer patients will not have a medically necessary surgical procedure after their first recurrence, requiring the study to fund biopsy procedures to collect samples from these patients

Of the 150 patients enrolled, approximately 99 patients will require biopsies to collect samples specifically for the study

Labs & Pathology

• Each cancer type will have designated sites for conduc%ng rou%ne labs and pathological review to ensure consistency of analysis

Gene%c Analysis

• Analysis will include: Whole Genome Sequencing, transcriptome gene expression and copy number varia%on

• Each study will have a primary processing site, which will be selected from among leaders in gene%c sequencing that have par%cipated in similar projects, such as The Cancer Genome Atlas

Core Bioinforma%cs

• Bioinforma%cs will be conducted by the most cost-‐effec%ve, trusted provider to ensure the quality and consistency of data for analysis

• The core bioinforma%cs processing will turn the raw data into usable altera%on component lists of muta%ons and dele%ons

Sample Processing

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



Clinical Data Reporting

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



•  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

Clinical Reporting

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



Combines genomic and clinical data

Applies sophisticated mathematical modeling

Generates a map of drivers of non-response

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

resulting IP

1 2 3

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

Long-term

Within one year after project

Longer than one year after project completion

Occasionally, specific insights will be shared with the patient through their physician – mainly related to the potential benefits of specific treatments

Over time, as the study results facilitate guidance on therapy selection, patients may be notified of a specific signature of response/non-response that can be used to make treatment decisions if relapse occurs

The first versions of disease maps will be available publicly identifying hypotheses of non-response signatures for use by physicians and scientists to validate

Once the initial maps are published they data and maps will be dynamically updated as new patients and tests are added to the results, with scientists globally able to refine the disease maps

Project Management

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



Project Coordinator Clinical Coordinator

Non-Responder Project Coordinator Overall PMO

Study-specific PMO

(Ovarian example)

Entity/Person --- Role and responsibilities

Administrative support, coordination and marketing

Oversees overall operations

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

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?

Stephen Friend Food & Drug Administration 2011-07-18

Health & Medicine

Transcript of Stephen Friend Food & Drug Administration 2011-07-18