MaRS Future of Medicine™ Anticipating a world in which pharmaceutical companies outsource all R&D. Drug development: Skating to where the puck is. Speaker: Dr. Aled Edwards

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

2. Early-stage drug discovery research:
Skating to where the puck will be
NON CONFIDENTIAL
SGC Toronto SGC Oxford SGC Stockholm

3. Situation
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”

4. How drug discovery will evolve
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.

5. 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

6. 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

7. 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

8. 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
4. More community involvement
– Mechanisms to allow all scientists to participate in drug
discovery
– More patients involvement/understanding

9. Why public-private partnerships?
Better and broader understanding of biology will not
quickly come within existing academic structure

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

11. Is the H-K Effect a carry-over from pre-genome science?
Citations/kinase as a function of time
CITATIONS (normalized)
1950-2002
2003-2008
2009
*
* *
HUMAN PROTEIN KINASES
(ordered by most citations 1950-2002)

12. Academic research is highly redundant

13. CITATIONS
0
5000
10000
15000
20000
25000
30000
35000
ERa
AR
PR
PPARa
PPARg
GR
RARa
VDR
MR
TRb
PXR
ERb
LXRa
LXRb
PPARd
FXR
CAR
SF1
NGFIBa
RARb
RARg
NGFIBb
TRa
HNF4a
DAX
RORa
(1950-2010)
SHP
COUP2
ERRa
COUP1
RORg
LRH1
Rev-erba
ERRg
NUCLEAR HORMONE RECEPTOR
NGFIBg
GCNF
RXRg
Rev-erbb
TR2
RXRa
ERRb
PNR
Citation patterns for nuclear hormone receptors
TR4
RXRb
TLX
RORb
COUP3

14. CITATIONS
0
500
1000
1500
2000
2500
3000
ERa
AR
PPARa
PPARg
PR
GR
RARa
VDR
MR
PXR
LXRa
LXRb
PPARd
FXR
TRb
CAR
RORg
NGFIBa
ERb
NGFIBb
HNF4a
RORa
SHP
ERRa
SF1
Effect
DAX
Rev-erba
RARb
COUP2
RARg
TRa
ERRg
LRH1
NUCLEAR HORMONE RECEPTOR COUP1
NGFIBg
Rev-erbb
RXRa
RXRb
RXRg
PNR
ERRb
RORb
GCNF
TLX
NR citations in 2009 adhere to the H-K
TR2
TR4
COUP3
HNF4g

15. However, the relative order has changed
1990-1994
CITATIONS (normalized)
2009
* *
* * ** *
NUCLEAR HORMONE RECEPTOR

16. CITATIONS
0
500
1000
1500
2000
2500
3000
ERa
AR
PPARa
PPARg
PR
GR
RARa
VDR
MR
PXR
LXRa
LXRb
available
PPARd
FXR
Chemical probe
TRb
CAR
RORg
NGFIBa
ERb
NGFIBb
HNF4a
RORa
SHP
ERRa
SF1
Effect
DAX
Rev-erba
RARb
COUP2
RARg
TRa
available
ERRg
LRH1
chemical probes
COUP1
Few, if any quality
NUCLEAR HORMONE RECEPTOR
NGFIBg
Rev-erbb
RXRa
RXRb
RXRg
PNR
ERRb
RORb
GCNF
TLX
TR2
Tools are the key to overcome the Knapp
TR4
COUP3
HNF4g

17. 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)
4. More community involvement
– Mechanisms to allow all scientists to participate in drug
discovery (BioHub; Sage Bionetworks)
– More patients involvement/understanding (Open access
clinical PoC)

18. Reagent partnerships
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)
4. More community involvement
– Mechanisms to allow all scientists to participate in drug
discovery (BioHub; Sage Bionetworks)
– More patients involvement/understanding (Open access
clinical PoC)

19. SGC: A model for reagent generation
• 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

20. 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)

21. 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

22. 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

23. Applying the SGC model to uncover new
targets for drug discovery

24. Family member Epigenetics – going the way of protein kinases?
Number of Citations

25. Pre-Competitive Chemistry
Public/Private Public
Industry
Partnership Domain
Chemical Target Drug
Probes Validation Discovery
Screening No IP (re)Screening
Chemistry No restrictions Chemistry
Structure Publication Lead optimization
Bioavailability Pharmacology
DMPK
Toxicology
Chemical development
Clinical development
Creative commons Proprietary

26. Epigenetics Chemical Probes Consortium
Accessing expertise, assays and resource quickly
June 10
June 09
April 09
Jan 09
Pfizer OICR UNC Pharma
(8FTEs) (2FTEs) Well. Trust (£4.1M) (3FTEs) (8FTEs)
NCGC (20HTSs)
GSK (8FTEs)
Ontario ($5.0M)
15 acad. labs
Sweden ($3.0M)
….more than $50M of resource

27. Open access chemical probe shows Brd4 is
a potential oncology target
Selectivity Potency (ITC) Co-crystal Structure
(+)JQ1 but not stereoisomer (-)JQ1 binds
to BET BRD with Kd’s between 40 to 100
FRAP Assay nM.

28. A Network of Disease Institutes
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)
4. More community involvement
– Mechanisms to allow all scientists to participate in drug
discovery (BioHub; Sage Bionetworks)
– More patients involvement/understanding (Open access
clinical PoC)

29. 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

30. 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

31. Generating more targets with clinical PoC
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)
4. More community involvement
– Mechanisms to allow all scientists to participate in drug
discovery (BioHub; Sage Bionetworks)
– More patients involvement/understanding (Open access
clinical PoC)

32. 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

33. 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

34. Empowering scientists to get involved
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)
4. More community involvement
– Mechanisms to allow all scientists to participate in drug
discovery (BioHub; Sage Bionetworks)
– More patients involvement/understanding (Open access
clinical PoC)

35. 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

36. 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

37. Opportunities for Ontario in the new drug
discovery ecosystem
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

38. ACKNOWLEDGEMENTS
SGC SGC cont. Oxford Chemistry Harvard
Aled Edwards Frank von Delft Chris Schofield Jay Bradner
Chas Bountra Tom Heightman Nathan Rose Jun Qi
Cheryl Arrowsmith Martin Philpott Akane Kawamura
Johan Weigelt Oleg Fedorov Oliver King Cambridge
Udo Oppermann Frank Niesen Lars Hillringhaus Chris Abell
Stan Ng Tony Tumber Esther Woon Alessio Ciulli
Alice Grabbe Jing Yang
Michelle Daniel Oxford Biochemistry ICR
Atul Gadhave NCGC Rob Klose Julian Blagg
Stefan Knapp Anton Simeonov Shirley Li Rob van Montfort
Panagis Fillipakopoulos Dave Maloney Rosemary Burke
Sarah Picaud Ajit Jadhav
GSK UNC
Tracy Keates Amy Quinn
Tim Willson Stephen Frye
Ildiko Felletar
Ryan Trump Bill Janzen
Brian Marsden
Minghua Wang Tim Wigle
….and many
others
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