MaRS Future of Medicine™ Anticipating a world in which pharmaceutical companies outsource all R&D

Anticipating a world in which pharmaceutical companies outsource all R & D

SGC Oxford SGC Toronto SGC Stockholm

Early-stage drug discovery research:

Skating to where the puck will be

NON CONFIDENTIAL

1.  Global of funding for health and drug discovery research will not increase

2.  On average, over past 30 years, fewer drugs with novel mechanisms are being approved per dollar

3.  Pharma is retrenching from research; merging, “right-sizing”, seeking locations where costs are lower

4.  Pharma exiting completely from more challenging areas of drug discovery (i.e. neurosciences)

5.  Morgan Stanley report advises pharma to go the whole hawg: “get out of R+D altogether”

Situation

1.  Research and development outside pharma walls

2.  $20B R+D spent more globally – more competitive.

3.  Academia will feel pressure to become more “industry-like”

4.  More biotech’s will emerge

5.  Knowledge will become increasingly balkanized

6.  At the end of the day, this will only re-shuffle. If only 1-5 occur, cost of drug discovery and cost of healthcare will not decrease, and society’s unmet needs will remain unmet.

How drug discovery will evolve

What change is required to deliver new medicines more effectively/cheaper

1.  Better and broader understanding of biology -  Reagents and tools to facilitate research -  Deeper understanding of patient heterogeneity

2.  Assays that better reflect behaviour of drug candidates in humans -  Disease-relevant assays with clinical material

3.  Less duplication in drug development –  Partnerships to determine efficacy of “pioneer” targets

4.  More community involvement –  Mechanisms to allow all scientists to participate in drug

discovery –  More patients involvement/understanding

How to effect this change?

1.  Focus on science, not on IP generation

2.  Take advantage of the passion to make a difference

3.  Involve clinicians more closely in drug discovery

4.  Fund the effort through open access public-private partnerships - from discovery to clinical proof-of-concept

5.  Make data available from beginning to end to empower and involve scientists

Two possible ways forward for Ontario and Canada

1.  Try to compete within the existing drug discovery ecosystem -  Same strategy as always -  Not so sure that we have proven we can compete

2.  Be part of changing drug discovery ecosystem -  Get “first mover” opportunity -  Play to the strengths of Canadians and Canadian science

(effective, efficient, collaborative) -  Does not stop us from competing in existing system

What is required to deliver new medicines more effectively/cheaper

1.  Better and broader understanding of biology -  Reagents and tools to facilitate research -  Deeper understanding of patient heterogeneity

2.  Assays that better reflect behaviour of drug candidates in humans -  Disease-relevant assays with clinical material

3.  Less duplication in drug development –  Partnerships to determine efficacy of “pioneer” targets


discovery –  More patients involvement/understanding

Why public-private partnerships?

Better and broader understanding of biology will not quickly come within existing academic structure

Target discovery 10 years after the genome Protein kinase citation patterns (the “Harlow-Knapp Effect”)

Patents

Driver mutations

HUMAN PROTEIN KINASES (ordered by most citations 1950-2002)

CIT

ATIO

NS

(nor

mal

ized

)

Citations/kinase as a function of time

1950-2002 2003-2008

2009

* * *

Is the H-K Effect a carry-over from pre-genome science?

Academic research is highly redundant

NUCLEAR HORMONE RECEPTOR

CIT

ATIO

NS

Citation patterns for nuclear hormone receptors

(1950-2010)

0

5000

10000

15000

20000

25000

30000

35000 ER

a A

R

PR

PPA

Ra

PPA

Rg

GR

R

AR

a VD

R

MR

TR

b PX

R

ERb

LXR

a LX

Rb

PPA

Rd

FXR

C

AR

SF

1 N

GFI

Ba

RA

Rb

RA

Rg

NG

FIB

b TR

a H

NF4

a D

AX

RO

Ra

SHP

CO

UP2

ER

Ra

CO

UP1

R

OR

g LR

H1

Rev

-erb

a ER

Rg

NG

FIB

g G

CN

F R

XRg

Rev

-erb

b TR

2 R

XRa

ERR

b PN

R

TR4

RXR

b TL

X R

OR

b C

OU

P3

CIT

ATIO

NS

0

500

1000

1500

2000

2500

3000

ERa

AR

PP

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a PP

AR

g PR

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b PP

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d FX

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CA

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b N

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F4a

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NR citations in 2009 adhere to the H-K Effect

CIT

ATIO

NS

(nor

mal

ized

) However, the relative order has changed


1990-1994 2009

* * * * *

* *

CIT

ATIO

NS

Chemical probe available

0

500

1000

1500

2000

2500

3000

ERa

AR

PP

AR

a PP

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Tools are the key to overcome the Knapp Effect

Few, if any quality chemical probes

available

Inter-linked partnerships to drive drug discovery 1.  Better and broader understanding of biology

-  Reagents and tools to facilitate research (SGC) -  Deeper understanding of patient heterogeneity (ICGP)

2.  Assays that better reflect behaviour in humans -  Disease-relevant assays with clinical material (Network of

Disease Institutes)

3.  Less duplication in drug development –  Partnerships to determine efficacy of untested targets

(Open access clinical PoC)


discovery (BioHub; Sage Bionetworks) –  More patients involvement/understanding (Open access

clinical PoC)

Reagent partnerships 1.  Better and broader understanding of biology



Disease Institutes)





clinical PoC)

•  Established 2003

•  Based in Univ. of Toronto, Karolinska Institutet and Univ. of Oxford

•  200 scientists

•  Funded by - Private: GSK, Merck, Novartis, (Pfizer) - Govt: Canada, Sweden - Charities: Wellcome Trust, Wallenberg Foundation

SGC: A model for reagent generation

SGC: the model works •  1000 human protein structures – all available without

restriction –  ~30% of novel human proteins in PDB per annum

•  2000 human proteins in purified form (milligram quantities)

•  >100 structures of proteins from parasitic protozoa –  Chemical validation for drug targets in toxoplasmosis (Nature, 2010)

and sleeping sickness (Nature, 2010)

•  500 cDNA clones distributed freely every year (academia, biotech, pharma)

•  ~2 publications per week (11 in past two years in Science, Cell, Nature journals)

Highlights of modus operandi •  SGC model allows opportunity to work with the very best

–  200+ collaborations

•  SGC model drives fast data dissemination –  On average, each SGC structure enters public domain

18-24 months in advance of academic norms

•  SGC model promotes collaboration –  Average of >3 non-SGC authors for each paper

•  SGC model focuses on milestones –  1000 structure target (2004-2011); 1,100 achieved to date

•  No IP is a fundamental tenet of the model

Impact of “no IP”

•  Collaborate quickly with any scientist, lab or institution

•  Work closely with multiple organisations, on same project

•  Generate data quickly

•  Place data in public domain quickly

Applying the SGC model to uncover new targets for drug discovery

Epigenetics – going the way of protein kinases?

Number of Citations

Fam

ily m

embe

r

Industry Public Domain

Public/Private Partnership

Chemical Probes

Screening Chemistry Structure Bioavailability

Target Validation

No IP No restrictions Publication

Drug Discovery

(re)Screening Chemistry Lead optimization Pharmacology DMPK Toxicology Chemical development Clinical development

Pre-Competitive Chemistry

Creative commons Proprietary

Jan 09

Well. Trust (£4.1M) NCGC (20HTSs)

GSK (8FTEs)

Ontario ($5.0M)

OICR (2FTEs)

UNC (3FTEs)

April 09 June 09 June 10

Pfizer (8FTEs)

Pharma (8FTEs)

Epigenetics Chemical Probes Consortium Accessing expertise, assays and resource quickly

Sweden ($3.0M)

15 acad. labs

….more than $50M of resource

Open access chemical probe shows Brd4 is a potential oncology target

Selectivity Potency (ITC)

(+)JQ1 but not stereoisomer (-)JQ1 binds to BET BRD with Kd’s between 40 to 100 nM.

Co-crystal Structure

FRAP Assay

A Network of Disease Institutes 1.  Better and broader understanding of biology



Disease Institutes)





clinical PoC)

Better prediction of drug efficacy Problem

Animal and cell-based models of disease poorly predict the efficacy of drug candidates (small molecule or biologic); most diseases are poorly understood

Solution Create a network of science-based Disease-Focused Institutes to tackle the diseases of greatest societal importance

Outcome 1.  New targets for therapeutic intervention 2.  Disease relevant assays 3.  Biomarkers for disease progression and treatment

A Model for Disease Focused Institutes

1.  Each Institute focused on specific disease [e.g. pancreatic cancer(s)]

2.  Science funded stably by public and private sectors 3.  Human is the disease model; must link to clinicians 4.  All data generated, assays, ideas within the four walls are

open access 5.  Industry scientists welcome to work at the Institute and have

complete access to science 6.  Set up quantitative objectives; impact measured against

milestones as well as customary academic assessment criteria

7.  The science must be genome or system-based, not biased by current thinking

Generating more targets with clinical PoC 1.  Better and broader understanding of biology



Disease Institutes)





clinical PoC)

Reducing waste in clinical development Problem

Most novel targets are pursued by multiple companies without disclosure of the data. With a 90% failure rate in clinical trials, this wastes resource, hampers learning and subjects patients to compounds destined to fail (and likely to cause harm).

Solution Pursue large numbers of “never before been drugged” targets to clinical PoC within an open access consortium

Outcome 1.  Clinically-validated targets at reduced cost 2.  Reduced patient harm 3.  More knowledge about role of target in human biology

A model for open access clinical PoC Consortia

1.  Funded by public and private sectors 2.  Science, not market, driven 3.  Governed by funders; all stakeholders part of

equation (funders, patients, regulators) 4.  Set up quantitative objectives; impact measured

against milestones as well as customary academic assessment criteria

5.  All data open access

Empowering scientists to get involved 1.  Better and broader understanding of biology



Disease Institutes)





clinical PoC)

Social networks to reduce waste in research Problem

$20B of funding spent on laboratory equipment/reagents. Much funding wasted on poor products. >$1B alone wasted on poor quality antibodies

Solution Create mechanism for scientists to contribute to the global knowledge

Outcome 1.  Better experiments, faster 2.  Reduced cost of research

BioHub 1.  Toronto-based company 2.  Initial focus on providing mechanism to provide

feedback on efficacy of commercial antibodies 3.  No charge for academics to comment or view

comments (like Trip Advisor) 4.  The idea is only as good as the willingness of

scientists to participate

Market size: ~$20B up for grabs

Potential opportunities for research and business

1.  Academic partnerships that deliver new targets 2.  High value clinical trials 3.  Contract research organizations with leading edge science 4.  Biotech companies with compounds and technologies

Potential impact

1.  More industry funding for University and Hospital-based research 2.  A business community built on high value service 3.  A clinical trial network that works on innovative targets 4.  Better business climate for biotech due to enhanced links with

industry

Opportunities for Ontario in the new drug discovery ecosystem

ACKNOWLEDGEMENTS

FUNDING PARTNERS Canadian Institutes for Health Research, Canadian Foundation for Innovation, Genome Canada through the Ontario Genomics Institute, GlaxoSmithKline, Knut and Alice Wallenberg Foundation, Merck & Co., Inc., Novartis Research Foundation, Ontario Innovation Trust, Ontario Ministry for Research and Innovation, Swedish Agency for Innovation Systems, Swedish Foundation for Strategic Research, and Wellcome Trust. www.thesgc.org

SGC Aled Edwards Chas Bountra Cheryl Arrowsmith Johan Weigelt Udo Oppermann Stan Ng Alice Grabbe Michelle Daniel Atul Gadhave Stefan Knapp Panagis Fillipakopoulos Sarah Picaud Tracy Keates Ildiko Felletar Brian Marsden Minghua Wang

SGC cont. Frank von Delft Tom Heightman Martin Philpott Oleg Fedorov Frank Niesen Tony Tumber Jing Yang

GSK Tim Willson Ryan Trump

Oxford Chemistry Chris Schofield Nathan Rose Akane Kawamura Oliver King Lars Hillringhaus Esther Woon

Oxford Biochemistry Rob Klose Shirley Li

NCGC Anton Simeonov Dave Maloney Ajit Jadhav Amy Quinn

….and many others

UNC Stephen Frye Bill Janzen Tim Wigle

Cambridge Chris Abell Alessio Ciulli

ICR Julian Blagg Rob van Montfort Rosemary Burke

Harvard Jay Bradner Jun Qi

Health & Medicine

MaRS Future of Medicine™ Anticipating a world in which pharmaceutical companies outsource all R&D