Transforming the Model for Target Identification and ...cancermeetings.org/PDW2014/Presentations/Breakout F... · Through a Collaboration between Biomedical Scientists from the Public

1 Partners for Innovation, Discovery, Health l www.fnih.org

Maria Vassileva, Ph.D. FNIH Senior Scientific Program Manager

Division of Research Partnerships

Transforming the Model for Target Identification and Validation Through a Collaboration between Biomedical Scientists from

the Public and Private Sector


FNIH Overview

Sole organization authorized by the U.S. Congress to support the mission of the NIH by creating and managing public-private partnerships

501(c)(3) non-profit organization Raised >$560 million to support >400 projects 100 currently active programs

Non-governmental Independent Board of Directors NIH Director/FDA Commissioner ex-officio FNIH Board Members

94 cents of every $ directly supports research programs

Consistently rated highly on Charity Navigator


The Role and Function of FNIH

Create innovative public-private biomedical partnerships that complement NIH priorities and advance the public health

Partner with corporations, foundations, academia,

federal agencies, and philanthropic individuals

Serve as “honest broker”, providing a neutral forum able to engage all partners

Enable efficient, effective collaboration

Structure flexible donor relationships

Manage grants, contracts, and projects efficiently


Major FNIH Research Partnerships Gates Foundation Projects $300M Partner: Bill & Melinda Gates Foundation (6 grants in global health, AIDS, tuberculosis and malnutrition)

Alzheimer’s Disease Neuroimaging Initiative (ADNI & ADNI 2) $50M Partners: NIA/NIBIB & 19 companies/2 non-profits Genetic Association Information Network (GAIN) $26M Partners: NHGRI, NLM & Pfizer, Affymetrix, Broad Institute, Perlegen Sciences

The Biomarkers Consortium $65M Partners: NIH, FDA, CMS, BIO, PhRMA, biopharmaceutical industry, non-profits Accelerating Medicines Partnership (AMP) $120M Partners: NIH OD, NIA, NIDDK, NIAMS, NIAID, NHGRI, AbbVie, Biogen Idec, BMS, Eli Lilly, GSK, JNJ, Merck, Pfizer, Sanofi, Takeda


A Significant Cause of Late Stage Drug Development Failures is the Insufficient Understanding of Targets

Preclinical Phase I Phase II Phase III Attrition rates

100%

50%

0% 1990-94 2004-08 1990-94 2004-08 1990-94 2004-08 1990-94 2004-08

∆15% ∆68% ∆69% ∆165%

Source: Pammoli et. al, "The productivity crisis in pharmaceutical R&D" Nature Reviews Drug Discovery June 2011

Is there anything systematic to be done to address this issue?


Accelerating Medicines Partnership (AMP) Problem Statement

Problem

We do not sufficiently understand the targets/pathways involved in diseases – leading to R&D investment against the wrong targets

No single group is positioned to change this efficiently and quickly – Scale is beyond that achievable even by large academic labs – Benefits are too diffuse for any one pharmaco to pursue – Necessary skills span these groups

Solution

Systematic characterization of complex, heterogeneous diseases, combining clinical & molecular information to:

– Facilitate rational selection of targets – Identify patients and specific subpopulations for trials and

customized prophylaxis, diagnosis and treatment decisions

Work collaboratively across government, academia and industry to harness collective capabilities, scale & resources


Initial Focus on Three Disease Areas

Alzheimer's Disease Program: aims to identify theragnostic biomarkers that predict clinical outcomes and, using an integrate network analysis in Alzheimer’s disease brain tissue, use human brain samples to identify biologic nodes and networks that are linked to the development or progression of Alzheimer’s Disease.

Rheumatoid Arthritis, Lupus & Related Autoimmune Diseases Program: aims to molecularly deconstruct and compare these two major autoimmune diseases by integrating individual proteomic and genomic data to provide a systems level understanding of disease mechanisms.

Type 2 Diabetes Program: aims to create a publicly available knowledge portal for the purpose of housing and analyzing integrated genomics data, and collecting new gene sequencing data in Type 2 Diabetes and its complications.


Alzheimer's disease Steering Committee

AMP Governance Structure

Executive and Steering Committee Support

• Steve Hoffmann, FNIH • David Wholley, FNIH • Maria Vassileva,FNIH • Sanya Whitaker, FNIH

Extended Executive Committee

Core Executive Committee • Bob Carter, NIAMS

• Francis Cuss, BMS • Mikael Dolsten,

PFIZER • Richard Hodes, NIA • Kathy Hudson, NIH

• Tom Insel, NIMH • Steve Katz, NIAMS • Rick Lifton, YALE • Jan Lundberg, LILLY • Griffin Rodgers,

NIDDK

Co-chairs • Mike Hutton • Neil Buckholtz NIA

EC Liaison • Richard Hodes NIA

Members • Mike Decker, AbbVie • Xiaoming Guan, GSK • Tim Harris, Biogen Idec • Walter Koroshetz, NINDS • Pat Walicke, NINDS • Maria Carrillo, ALZ • Nicholas Kozauer, FDA

Type 2 Diabetes Steering Committee

Co-chairs

• Andy Plump, Sanofi • Phil Smith, NIDDK

EC Liaison • Griffin Rodgers, NIDDK

Members • Keith Demarest, J&J • Melissa Thomas, Lilly • Jeff Pfeffekorn, Pfizer • Liangsu Wang, Merck • Dan Rader, UPENN

RA, SLE & related diseases Steering Committee

Co-chairs

• Marty Hodge, Pfizer • Bob Carter, NIAMS

EC Liaison • Bob Carter NIAMS

Members • Christopher Arendt, Sanofi • Lisa Olson, AbbVie • Jonathan Zalevsky, Takeda • Dennis Zaller, Merck • Salwant Narula, BMS • Ellen Goldmuntz, NIAID • Susana Serrate-Sztein, NIAMS

Francis Collins, NIH • Bill Hait, J&J • Jim Sullivan, AbbVie • Patrick Vallance, GSK • Rupert Vessey, Merck

• Doug Williams, Biogen Idec • Tachi Yamada, Takeda • Elias Zerhouni, Sanofi

Representatives from academic, FDA and advocacy organizations will be added to Disease Area Steering Committees


Type 2 Diabetes AMP Program Overview

■ The five-year program with focus on: linking human genetic data on risk for type 2 diabetes &

phenotyping to identify novel drug targets Creation of tools to enable easy interrogation of all available data

in an integrated fashion

The $40.6M project has three specific aims: Create a knowledge portal containing comprehensive

genotype/phenotype T2D & diabetic micro- and macro-vascular complications data sets – apply informatics to identify predictors of risk and potential drug targets

Conduct targeted sequencing/genotyping of high priority targets of interest, as defined by T2D AMP Steering Committee

Conduct hypothesis-driven phenotyping on patients with high- priority Loss of Function/Gain of Function variants to validate potential T2DM/complications targets


Data Sets for the Knowledge Portal

■ T2D-GENES ■ SIGMA ■ GOT2D ■ NHLBI Exome Sequencing Project ■ DIAGRAM ■ MAGIC ■ CHARGE ■ SUMMIT ■ Asian cohorts ■ Family cohorts ■ Other


Intended Uses for the Knowledge Portal

The database created is going to have open access and will be used by academic researchers, pharmaceutical industry experts and clinicians to test hypothesis such as: Phenotype-based queries: what genetic variants contribute to the T2D

risk, or the risk of developing diabetes complications

Gene-based or Pathway-based queries: what genetic variation exists within a target or pathway of interest, and is it associated with a change in T2D risk

Variant-based queries: what clinical, biochemical, expression quantitative trait, or epigenetic phenotypes are associated with a given variant; are the results consistent across ancestry groups and across different studies


Deep Sequencing Project Components

■ Sequence target definition ■ Aggregation of samples ■ Assay validation ■ Genetic data generation ■ Data analysis ■ Deposition of analyses results into the knowledge portal ■ Interpretation to identify valuable targets

*Note: New data will be generated as a component of this project for targets of particular interest


Deep Sequencing Data Would Help Answer Whether

■ There is evidence that a perturbation of a target’s function leads to a change in T2D status consistent with expected outcome of therapeutic modulation

■ The desirable therapeutic modulation can be achieved through Loss of Function (LoF) or Gain of Function (GoF)

■ The perturbation of a target’s function leads to “on target” adverse risk effects that would compromise its value as a therapeutic target

■ Human genetics provides insight into the mechanism of action and whether it can help identify individuals carrying high value alleles such as rare variants of large effect

*


Statements about AMP

“Significant strides against diseases require bold initiatives that venture into uncharted territory and confront challenging biological questions,” said Maria Freire, Ph.D., President, Foundation for the NIH. “I am delighted that the Foundation for the NIH has the opportunity to be part of this trailblazing effort, managing and coordinating complex scientific alliances that can transform the speed and precision with which new medicines are developed.”

“Patients and their caregivers are relying on science to find better and faster ways to detect and treat disease and improve their quality of life,” said NIH Director Francis S. Collins, M.D., Ph.D. “Currently, we are investing a great deal of money and time in avenues with high failure rates, while patients and their families wait. All sectors of the biomedical enterprise agree that new approaches are sorely needed.”


AMP Participation by Disease Area 15

Alzheimer's disease Type 2 Diabetes RA, SLE & related

Industry members

Government members

Non-profit members

JDRF

Documents

Transforming the Model for Target Identification and ...cancermeetings.org/PDW2014/Presentations/Breakout F... · Through a Collaboration between Biomedical Scientists from the Public