Stephen Friend Institute of Development, Aging and Cancer 2011-11-29

Issues with Current Drug Discovery

A Proposal

Arch2POCM A Drug Development Approach

from Disease Targets to their Clinical Validation

Stephen Friend Sage Bionetworks

(a non-profit foundation) Sendai November 2011

Alzheimers Diabetes

Depression Cancer

Treating Symptoms v.s. Modifying Diseases

Will it work for me?

Familiar but Incomplete

Reality: Overlapping Pathways

"Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)

"Variations in DNA elucidate molecular networks that cause disease." Nature. (2008)

"Genetics of gene expression and its effect on disease." Nature. (2008)

"Validation of candidate causal genes for obesity that affect..." Nat Genet. (2009) ….. Plus 10 additional papers in Genome Research, PLoS Genetics, PLoS Comp.Biology, etc

"Identification of pathways for atherosclerosis." Circ Res. (2007)

"Mapping the genetic architecture of gene expression in human liver." PLoS Biol. (2008)

…… Plus 5 additional papers in Genome Res., Genomics, Mamm.Genome

"Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005)

“..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009)

"An integrative genomics approach to infer causal associations ...” Nat Genet. (2005)

"Increasing the power to detect causal associations… “PLoS Comput Biol. (2007)

"Integrating large-scale functional genomic data ..." Nat Genet. (2008)

…… Plus 3 additional papers in PLoS Genet., BMC Genet.

d

Metabolic Disease

CVD

Bone

Methods

Extensive Publications now Substantiating Scientific Approach Probabilistic Causal Bionetwork Models

• >80 Publications from Rosetta Genetics

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

List of Influential Papers in Network Modeling

(Eric Schadt)

Requires Data driven Science

Lots of data, tools, evolving models of disease

Requires Scientists Clinicians & Citizens to link in different ways

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

10

  Foundations   CHDI, Gates Foundation

  Government   NIH, LSDF

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

  Federation   Ideker, Califarno, Butte, Schadt

RULES GOVERN

Engaging Communities of Interest

PLAT

FORM

NEW

MAP

S NEW MAPS

Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...)

PLATFORM Sage Platform and Infrastructure Builders-

( Academic Biotech and Industry IT Partners...)

RULES AND GOVERNANCE Data Sharing Barrier Breakers-

(Patients Advocates, Governance and Policy Makers, Funders...)

NEW TOOLS Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape,

Clinical Trialists, Industrial Trialists, CROs…)

PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-

(Federation, CCSB, Inspire2Live....) Arch2POCM

Breast Cancer Bayesian Network Conserved Super-modules

mR

NA

proc

.

Chr

omat

in

Pathways & Regulators (Key drivers=yellow; key drivers validated in siRNA screen=green)

Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red)

Extract gene:gene relationships for selected super-modules from BN and define Key Drivers

Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)

Bin Zhang Xudong Dai Jun Zhu

Model of Breast Cancer: Integration

= predictive of survival

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 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

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%

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



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

Arch2POCM

A Fundamental Systems Change for Drug Discovery

Stephen Friend Aled Edwards Chas Bountra Lex vander Ploeg, Thea Norman, Keith Yamamoto

“Absurdity” of Current R&D Ecosystem

•  $200B per year in biomedical and drug discovery R&D •  Handful of new medicines approved each year •  Produc%vity in steady decline since 1950 •  90% of novel drugs entering clinical trials fail •  NIH and EU just started spending billions to duplicate process

•  98% of pharma revenues from compounds approved more than 5 years ago (average patent life 11 years)

•  >50,000 pharma employees fired in each of last three years •  Number of R&D sites in Europe down from 29 to 16 in 2009

What is the problem? •  Regulatory hurdles too high? •  Low hanging fruit picked? •  Payers unwilling to pay? •  Genome has not delivered? •  Valley of death? •  Companies not large enough to execute on strategy? •  Internal research costs too high? •  Clinical trials in developed countries too expensive?

•  In fact, all are true but none is the real problem

What is the problem? •  The current system is designed as if every new program is des%ned to deliver an approved drug

•  Each new therapy is pursued through use of proprietary compounds moving in parallel with no data being shared (ohen 5-‐10 companies at a %me)

•  Therefore it makes complete sense to maintain secrecy

•  But we have no clue what we’re doing •  Alzheimer’s, cancer, schizophrenia, au%sm.….

The current pharma model is redundant

50% 10% 30% 30% 90%

Negative POC information is not shared Attrition

Target ID/ Discovery

Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology

Phase I Phase IIa/IIb


Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology



Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology



Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology



Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology



Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology



Hit/Probe/Lead ID

Clinical Candidate

ID

Toxicology/

Pharmacology


What is the problem?

The real problem is that the current system is unable to provide the needed insights into human biology and disease required

We need to develop a mechanism to be8er understand disease biology before tes:ng compe::ve compounds on sick people

A globally distributed public private partnership (PPP) committed to:

• Generate more clinically validated targets by sharing data

• Help deliver more new drugs for patients

The Solution: Arch2POCM

27 27

Arch2POCM: What Will It Do?

•  Arch2POCM will focus on targets that are deemed too risky for either public or private sectors in the disease areas of cancer/immunology and shizophrenia/autism

•  Arch2POCM will devleop and use test compounds to de-risk these targets and determine if the targets play a role in the biology of human disease

•  Arch2POCM will share the risk by pooling public and private resources

•  Arch2POCM will file no patents and place all data into the public domain: •  Enables the pharmaceutical sector to use this information to start, refine or shut down their

own proprietary efforts •  Crowdsourcing expands our understanding of human biology and the number of ideas that

can be pursued without additional funds

•  Arch2POCM will make the test compounds available to academic groups and foundations so they can use them to explore a multitude of additional indications

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Why Data Sharing Through To Phase IIb?

•  Most rapid approach to reveal limitations and opportunities associated with the target

•  Increases probability of success for internal proprietary programs

•  Scientific decisions are not influenced by market considerations or biased internal thinking

•  Target mechanism is only properly tested at Phase IIb

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Why No IP on “Common Stream” Compounds? •  Allows multiple groups to test compounds in diverse

indications without funds from Arch2POCM- crowdsourcing drug discovery

•  Broader and faster data dissemination

•  Far fewer legal agreements to negotiate

•  Generates “freedom to operate” on target because there are no patent thickets to wade through

•  Efficient way to access world’s top scientists and doctors without hassle

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The Benefits of the Arch2POCM Pre-competitive Model: Crowdsourced Studies

The Crowd

Clin

Arch2POCM Compounds And Data

Crowdsourced data on Arch2POCM test compound

• SAR med chem • Best indica%on • Clinical data

Crowdsourced studies on Arch2POCM test compounds will provide clinical informa:on about the pioneer targets in MANY indica:ons

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Arch2POCM: Scale and Scope

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

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

•  We project a five-year budget of $200-250M in order to advance up to 8 drug discovery projects within each of the two therapeutic programs.

•  Arch2POCM funding will come from a combination of public funding from governments (50%) and private sector funding from pharmaceutical and biotechnology companies (25%) and from private philanthropists (25%)

32

Epigenetics/ Chromatin Biology: Arch2POCM’s Selected Pioneer Area of Oncology/

Immunology Drug Discovery

33

Histone

DNA

Lysine

How We Define Epigenetics

Modification Write Read Erase

Acetyl HAT Bromo HDAC Methyl HMT MBT DeMethyl

34

The Case for Epigenetics/Chromatin Biology

1.  There are epigenetic oncology drugs on the market (HDACs)

2.  A growing number of links to oncology, notably many genetic links (i.e. fusion proteins, somatic mutations)

3.  A pioneer area: More than 400 targets amenable to small molecule intervention - most of which are only recently shown to be “druggable”, and only a few of which are under active investigation

4.  Open access, early-stage science is developing quickly – significant collaborative efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starting points

35

Examples of Epigenetic Links to Cancer •  Ezh2 methyltransferase (enhancer of zeste homolog 2)

–  Somatic mutations in B-cell lymphoma

•  JARID1B demethylase (jumonji, AT rich interactive domain 2 ) –  Linked to malignant transformation: expressed at high levels in breast and prostate

cancers; Knock-down inhibits proliferation of breast cancer lines and tumor growth

•  G9A methyltransferase (euchromatic histone-lysine N-methyltransferase 2)

–  Expressed in aggressive lung cancer cells: high expression correlates to poor prognosis; G9a knockdown inhibits metastasis in vivo

•  MLL: myeloid/lymphoid or mixed-lineage leukemia –  Multiple chromosomal translocations involving this gene are the cause of certain acute

lymphoid leukemias and acute myeloid leukemias

•  Brd4: (Bromodomain-containing protein 4) –  Implicated in t(15; 19) aggressive carcinoma: Chromosome 19 translocation

breakpoint interrupts the coding sequence of a bromodomain gene, BRD4

•  CBP bromodomain –  Oncogeneic fusions –  Mutated in relapsing AML

36

Domain Family Typical substrate class* Total Targets

Histone Lysine demethylase

Histone/Protein K/R(me)n/ (meCpG) 30

Bromodomain Histone/Protein K(ac) 57

R O Y A L

Tudor domain Histone Kme2/3 - Rme2s 59

Chromodomain Histone/Protein K(me)3 34

MBT repeat Histone K(me)3 9

PHD finger Histone K(me)n 97

Acetyltransferase Histone/Protein K 17

Methyltransferase Histone/Protein K&R 60

PARP/ADPRT Histone/Protein R&E 17

MACRO Histone/Protein (p)-ADPribose 15

Histone deacetylases Histone/Protein KAc 11

395

The Current Epigenetics Universe (2011)

Now known to be amenable to small molecule inhibition 37

Some Potential Arch2POCM Epigenetic Oncology/Immunology Targets

38

Assay Development

Open Access Test Compounds and Tools Are Available For Arch2POCM Teams

Scre

enin

g /

Chem

istr

y

Kd < 500 nM Selectivity > 30x

Active Kd < 5 µM

Weak

None

In vitro assay available

Cell assay available

Me Lys Binders

BRD

HMT

KDM HAT

SUV39H2 EP300

L3MBTL1 L3MBTL3

WDR5

MYST3 DOT1L

UHRF1 SMYD3 JMJD2A Tu EZH2

HAT1 PRMT3

SETD8 CREBBP FALZ

BAZ2B PB1

GCN5L2 JMJD3

SETD7

BET 2nd PHF8

JMJD2 FBXL11

G9a/GLP BET

JARID1C

Probe Criteria

Kd < 100 nM Selectivity > 30x Cell IC50 < 1 µM

39

Proposed IT Infrastructure For Arch2POCM Data Sharing

CRO or funded Basic

IT structure and

compliance with

reduced control

Good QC control and IT data base

structure

Independent Basic Res

Independent Clin Res

CRO preclinical

Academic Basic Clinical Research

Research

ac%vi%es

Data managem

ent

CRO Clinical

1.  Arch2POCM QC data and IT structure 2.  Database from crowdsourcing with volunteer contr. 3.  Published data

Harmonize data management

Data base design for crow

dsou

rced

and internal use re

quire

s significant

investmen

t in data base structure

Crowdsourced Research Arch2POCM funded Research

40

General Benefits of Arch2POCM For Drug Development

1.  Arch2POCM’s use of test compounds to de-risk previously unexplored biology enables drug developers to initiate proprietary drug development with an array of unbiased, clinically validated targets   Arch2POCM operates without patents and advances pairs of test

compounds through Ph II   Test compounds are used by Arch2POCM and the crowd to define clinical

mechanisms for epigenetic targets impacting oncology

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

3.  Negative clinical trial information generated by Arch2POCM and the crowd will increase clinical success rates (as one can pick targets and indications more smartly)

4.  Build methods to track and visualize crowd sourced data, clinical safety profiles and reporting of potential adverse event

41

•  Funding to pursue and publish disruptive discovery research

•  Sharing of resources and data among private and academic partners

•  Development of basic discoveries toward therapies and cures

•  Collaboration with private sector and regulatory scientists

•  Education of students and public about nature and process of discovery, and understanding disease

•  Exit options: extend/branch studies beyond arch2POCM

Arch2POCM Value Propositions For Academia

42

Example 6: Sage Congress 2012 Pa%ent Project

Health & Medicine

Stephen Friend Institute of Development, Aging and Cancer 2011-11-29