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Stephen Friend Food & Drug Administration 2011-07-18
 

Stephen Friend Food & Drug Administration 2011-07-18

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Stephen Friend, July 18, 2011. Food & Drug Administration, Washington, DC

Stephen Friend, July 18, 2011. Food & Drug Administration, Washington, DC

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

    • How Do We Study Network Perturbations in Clinical Specimens?How do we select which targets are effective for what diseases andwhich patients?- Stephen Friend July 18th FDA1.  Clinical Trial Comparator Arm Project “CTCAP”2.  “Arch2POCM”- Compounds to decode biology3.  Oncology Non-Responders Project4.  Freeing up Failed CompoundsWhat 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 = RrespondersIllusion that Altered Component Lists = Correct decisions about who will benefit
    • Use of Sub-populations to ID RespondersIllusion that 1000 patients will provide Sub-populations
    • Reality: Overlapping Pathways generate Context Complexity
    • WHY NOT USE “DATA INTENSIVE” SCIENCETO 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 levelMonitor 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, CANat Genet (2005) 205:370 factor beta receptor 2
    • List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
    • (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 diseaseBuilding Disease Maps Data RepositoryCommons 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 DiseaseCuration/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 BionetworksPROBLEM: 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 RepositoryKey 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 BiologyEvolution of a Software Project Project
    • Clinical Trial Comparator Arm 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.
    • Clinical Trial Comparator Arm PartnershipAim 1: Identify, collect, QC, curate and host 4-6 CTCAP coherentgenomic datasets each yearAim 2: Develop and host network models built from these datasets todrive public target mining, biomarkers identification, and patientstratification effortsAim 3: Establish a framework/process for ongoing release of clinicalgenomics dataChallenges:  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 toofast, 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 gyAttrition 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 Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I Phase Discovery Lead ID Candidate ID Pharmacolo IIa/IIb Phase Target ID/ Hit/Probe/ Clinical Toxicology/ gy Phase I Discovery Lead ID Candidate ID Pharmacolo IIa/IIb gy Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I Phase Discovery Lead ID Candidate ID Pharmacolo IIa/IIb gy Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I Phase Discovery Lead ID Candidate ID Pharmacolo IIa/IIb gy Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I Phase Discovery Lead ID Candidate ID Pharmacolo IIa/IIb gy Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I Phase Discovery Lead ID Candidate ID Pharmacolo IIa/IIb gy Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I Phase Discovery Lead ID Candidate ID Pharmacolo IIa/IIb gyAttrition 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
    • Arch2POCMA 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 FeasibilityPharma - 6 organisations supportiveAcademic Labs - access to discovery biology and test compoundsPatient 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 traditionallyperformed by an employee to a large group of people or community• By making Arch2POCM s clinically characterized probes availableto all, Arch2POCM will seed independently funded, crowdsourcedexperimental medicine• Crowdsourced studies on Arch2POCM probes will provide clinicalinformation about the pioneer targets in MANY indications
    • ArchPOCM Oncology Disease AreaFocus: Unprecedented targets and mechanisms Novelty  MOA and clinical findingsArc2POCM 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 projectworkflow plans and timelines (September)• Seed Arch2POCM strategic design team around a disease area of high interestto 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 leastone disease area
    • Section 1 – Project OverviewNon-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 costsSage Bionetworks • Non-Responder Project
    • Section 1 – Project OverviewThe Non-Responder Project is an international initiative with funding for 6 initialcancers 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 GovernmentSage Bionetworks • Non-Responder Project
    • Section 1 – Project OverviewThe 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 therapySage Bionetworks • Non-Responder Project
    • Section 1 – Project OverviewThe 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.”Sage Bionetworks • Non-Responder Project
    • Section 1 – Project OverviewThe 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.”Sage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanFor each tumor-type, the non-responder project will follow a common workflow, with patientidentification and sample collection the most variable across studiesNon-Responder Project WorkflowIdentification 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 ManagementSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanThe non-responder project will require the coordination of a number of stakeholders to handle thevarious components of the research processPotential Non-Responder Project US partners include: Physicians & AMCs Patient Consent Pathology Genome Sequencing Patient Advocacy Groups Core Bioinformatics Analysis and Disease ModelingSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanIdentification and EnrollmentThe number of patients and enrollment procedures will vary for each study based on the biologyand 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 expensesSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanThe renal cancer study will aim to identify the biomarkers related to patients whose diseaseprogresses during treatment with VEGF receptor inhibitorsCurrently 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 StrategyTarget 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 patientsSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanThe specific targets of the breast cancer study are still being defined, however there is acommitted clinical leader and support from a leading patient advocateClinical Lead: Craig Henderson PreliminaryPatient 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 350,000Target 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 treatmentSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanSample CollectionIn most cases, samples will be collected during required diagnostic procedures conducted by thepatient’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 prepSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanSample ProcessingSample processing will involve whole genome sequencing, conducted at leading TCGAparticipating 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  Sage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanClinical Data ReportingWhile the patient is undergoing treatment, the physician will be required to submit data regardingthe 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 cancerSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanData Collating and Disease ModelingThe genetic and clinical information will be combined and analyzed by Sage Bionetworks todesign 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 IPSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanFeedback and ResultsMaterial 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 scientificcommunity 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 occursSage Bionetworks • Non-Responder Project
    • Section 2 – Research PlanProject ManagementEach study will have an independent team to manage the tumor-specific study, which will roll up toa 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 managementSage Bionetworks • Non-Responder Project
    • 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 ratioWithout incentives or a mandated change to corporatebehavior 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 andwhich patients?- Stephen Friend July 18th FDA1.  Clinical Trial Comparator Arm Project “CTCAP”2.  “Arch2POCM”- Compounds to decode biology3.  Oncology Non-Responders Project4.  Freeing up Failed CompoundsWhat are the potential opportunities to participate in these projects?What actions might the FDA take?For actions needed beyond the FDA- executive or legislative?