Collaborative medicinal chemistry research between AstraZeneca and external partners aims to build more open innovation organizations. AstraZeneca shares examples of compound collection collaborations and a case study of collaborating in real time on a design-make-test-analyze project. Challenges include defining roles and managing processes, but tools like ChemTraX help enable real-time collaboration. AstraZeneca's open innovation platform provides opportunities for target innovation and new molecule profiling to further external partnerships.

1. Collaborative MedicinalCollaborative Medicinal
Chemistry Research:
Building More Porous
OResearch Organisations
The Academic-Industrial Interface in 21st Century Drug Discovery
Tuesday 24 June 2014
David Andrews*, Andy Merritt, Martin Swarbrick

2. Outline of Talk
• Introduction to AstraZeneca’s Open Innovation Efforts
• Examples of compound collection collaborations
C t d ll b ti i l ti D i M k T t A l• Case study : collaborating in real time : Design–Make–Test–Analyze
- Opportunities
- IssuesIssues
- Solutions
• Open Innovation Platform
• Future Outlook
2 David M Andrews | 24 June 2014 R & D | Oncology

3. AstraZeneca is driving
science through
collaborations…
…more than 90
partnerships in the last
three years

4. Three examples of how we
are helping to drive Open Innovation across our industry:
Leveraging compound collections to share to
maximise value
TB D A l tTB Drug Accelerator
Delivering support for
neglected disease strategy
and sharing libraries to find
di i f lif
Delivering reciprocal access
to high quality chemical
start points with libraries
th $MM
Delivering early access to
new target ideas and
partnerships with
d i LG t new medicines for life-
threatening neglected
diseases
worth $MMs academic LG centres,
with first right of refusal on
targets at LO
P id ll i ( ) Hi F dProvide collection(s) → Hits → Freedom-to-use
‘Clean’; with a clear exit strategy

5. Medicinal chemistry within different collaborative
discovery model settingsy g
Model Description Advantages Challenges
Strategic long-term, shared risks motivation and engagement, ‘true- role definition, manage-
Model Description Advantages Challenges
Strategic long-term, shared risks motivation and engagement, ‘true- role definition, manage-S a eg c
Alliance*
o g te , s a ed s s
and incentives
o a o a d e gage e , ue
type’ collaboration,
learning/expertise, cost effective
o e de o , a age
ment, processes,
accountability, IP
Industry- risks & rights at clear roles IP and decisions utilizing full potential of
S a eg c
Alliance*
o g te , s a ed s s
and incentives
o a o a d e gage e , ue
type’ collaboration,
learning/expertise, cost effective
o e de o , a age
ment, processes,
accountability, IP
Industry- risks & rights at clear roles IP and decisions utilizing full potential ofIndustry-
sponsored
risks & rights at
industry sponsor
clear roles, IP and decisions,
speed, flexible
utilizing full potential of
the team, costs
Government/
Charity
research project grant
approval
neglected diseases, diverse groups
and skills longer term planning
bureaucracy, IP,
management
Industry-
sponsored
risks & rights at
industry sponsor
clear roles, IP and decisions,
speed, flexible
utilizing full potential of
the team, costs
Government/
Charity
research project grant
approval
neglected diseases, diverse groups
and skills longer term planning
bureaucracy, IP,
managementCharity-
funded**
approval and skills, longer term planning,
cost effective
management
Crowd- use of the entire Easy to accommodate, low cost, IP, management of
Charity-
funded**
approval and skills, longer term planning,
cost effective
management
Crowd- use of the entire Easy to accommodate, low cost, IP, management of
sourcing MedChem community powerful in idea generation design ideas
Innovation
incubator
on-campus model training, tool compounds, line of
sight
IP, limited to early
discovery phases
sourcing MedChem community powerful in idea generation design ideas
Innovation
incubator
on-campus model training, tool compounds, line of
sight
IP, limited to early
discovery phases
Precompeti-
tive consortia
common interest in
developing tools
cost effective, learning/expertise management, IP, limited
to early discov.
H Wild et al Angew Chem Int Ed 2013 52 2684
Precompeti-
tive consortia
common interest in
developing tools
cost effective, learning/expertise management, IP, limited
to early discov.
H. Wild et al., Angew. Chem. Int. Ed. 2013, 52, 2684.
* R. Wellenreuther et al., Drug Discov. Today 2012, 17, 1242.
* R. Williams et al., Drug Discov. Today 2012, 15, 1359.
* D. Andrews et al., Drug Discov. Today 2014, 19, 496. ** A.L. Hopkins et al. Nature 449, 166

6. Setting Up the Collaborations
The Initial Model
Shared Series of
Initial HTS Hit
Shared Series of
Interest
The Problems
• What happens to the hits that don’t go anywhere?
• Led to a reluctance to unblind structuresLed to a reluctance to unblind structures
• ‘Two countries divided by a common language’ : we used slightly different terminology and
acronyms for the same things
The Solution
• Create an agreement that gives the chemists the maximum freedom to work innovatively and
i ti llsynergistically
• Control the risk of inadvertent reach-through into the broader proprietary information or the
parent organizations
• Agree common terminology, common ground rules
6 David M Andrews | 24 June 2014 R & D | Oncology

7. Opportunities in Compound Collaboration
• Ownership of compound series rests with the originator until initial liabilities are mitigated
• Prevents the non-originating parties collection becoming populated by compounds that can’t
progress
• Incentivizes teams to overcome initial liabilities
• ‘Productive SAR’ triggers shared ownership and a fully collaborative research optimization
programprogram
7 David M Andrews | 24 June 2014 R & D | Oncology

8. Additional Impacts of Clearer ground Rules
Allows testing of newer compounds
16
18
20
12
14
16
Years
6
8
10
Years
2
4
6
0 10 20 30 40 50 60 70 80 90 100
0
C l ti t
8
Cumulative percent
David M Andrews | 24 June 2014 R & D | Oncology

9. Additional Impacts of Clearer ground Rules
Allows testing of quality compounds
1
2
0
1
3
4 6
Calculated
logD
distribution-2
-1 4 Rotatable
bonds
2
12
8
10
-4 -3
2
5
6
7
12
3
4
4
5
7
8
Rings
≤2
5
6
…and expansion into full deck
screening
Number of
Acceptors
2
3
4
9
10
11 screening
9
2 11
David M Andrews | 24 June 2014 R & D | Oncology

10. Issues to Overcome….
….and solutions
Design Make Test Analyze
What should
Who?
External
database?
Preferred
we make?
Priority?
Who?
Route?
database?
Post data between
partners?
workflows /
analysis tools?
10 David M Andrews | 24 June 2014 R & D | Oncology

11. Collaboration Tools
Ch T X
• Capture of design ideas and outcomes (knowledge management)
ChemTraX
p g ( g g )
• Platform for real time collaboration between partners and service providers
• Easy visibility of on going chemistry within a project and planned next rounds
of chemistry, ensuring optimal deployment of resources
• Built to support today’s ways of working with partners and CROs where
information visibility and user functionality is easily controlled to fit all modes ofinformation visibility and user functionality is easily controlled to fit all modes of
operation
f f• Information access is set at the project level, enabling easy set up of multiple
projects to work with multiple organizations
11 David M Andrews | 24 June 2014 R & D | Oncology

12. Overview of Features
ChemTraX Tracking Board Process
• Steps a design set follows from conception
Overview of Features • Steps a design set follows from conception
through to completion
Color Design Set
• Multi parameter way of visualizing information
• Here we see color by organization that is
assigned the design set for synthesis
Design Set
• A collection of chemical structures
Swim Lanes
• Multi parameter way of separating the design sets
• This example shows split by priority of design set
12 David M Andrews | 24 June 2014 R & D | Oncology
designed to address a specific project issue
(potency, solubility etc)
• This example shows split by priority of design set

13. Design Sets
Sharing Ideas and Compounds to SynthesizeSharing Ideas and Compounds to Synthesize
Collaborative sharing of:
• Design hypotheses
C d t id f th i• Compounds to consider for synthesis
• Status of individual compounds (in
synthesis, complete etc)
R & D | Oncology
y , p )
• Design set outcomes

14. Data Sharing
Partner 2Partner 1
Visualisation Visualisation
Query &
retrieval
Query &
retrievalExport and
transfer
Corporate Database Corporate Database
transfer
p p
Input Input
14
Data generation Data generation

15. PIP5K and PI4K – Complex Biology
A ideal target area for risk-sharing collaboration
N
O
H
O
O
PI4K
O
O
LY294002
O
O
O
O
OO
Wortmannin
Pharmacological
manipulation of
cellular PI4P
LY294002 Wortmannin
pIC50
PI4Kα <4.3
pIC50
PI4Kα 5.9
levels
A challenge due to
non-specificity
and lack of
PIP5K
PI4Kβ 4.4
PI3Kα 6.2
PI4Kβ 5.8
PI3Kα 8.1
and lack of
potency of PI 4-
kinase inhibitors
• AZ/CRT team identified potent and
selective small molecule inhibitors:
• Of both type III PI 4-kinase isoforms
• Cross-subtype selective inhibitors of PIP5K
Phosphoinositides in cell regulation and membrane dynamicsPhosphoinositides in cell regulation and membrane dynamics
Nature 443, 651-657 (12 October 2006) | doi:10.1038/nature05185
Author | 00 Month Year15 R & D | Oncology

16. PI4Kβ Lead Generation
Starting with a potent non-selective hit
1 2
HTS Hit – 1 2
PI4Kα pIC50 8.0 5.3
PI4Kβ pIC50 8.3 7.2
PI3Kα pIC50 8.5 6.1
PIP5Kγ pIC50 6.2 6.2
Potent, selective small molecule inhibitors of type III phosphatidylinositol-4-kinase-α…
Ch C 2014 50 5388 5390 htt //d d i /10 1039/C3CC48391F
16 David M Andrews | 24 June 2014 R & D | Oncology
Chem. Commun., 2014, 50, 5388-5390 http://dx.doi.org/10.1039/C3CC48391F

17. PI4Kβ Series – Amide SAR
pIC50
PI4Kα 5.3
pIC50
PI4Kα 5.0
pIC50
PI4Kα 5.5
pIC50
PI4Kα 6.0
pIC50
PI4Kα 5.0
PI4Kβ 7.2
PI3Kα 6.2
PIP5K 4 9
PI4Kβ 7.7
PI3Kα 4.8
PIP5K 4 0
PI4Kβ 8.0
PI3Kα 5.4
PIP5K 4 4
PI4Kβ 8.2
PI3Kα 5.9
PIP5K 4 7
PI4Kβ 8.0
PI3Kα 4.5
PIP5K 4 9PIP5Kγ 4.9
LogD 2.5
PIP5Kγ <4.0
LogD -
PIP5Kγ <4.4
LogD 3.6
PIP5Kγ 4.7
LogD 1.6
PIP5Kγ 4.9
LogD 3.0
PI4KβPI3Kα
17 David M Andrews | 24 June 2014 R & D | Oncology

18. Kinase selectivity of inhibitors
N
O
N H 2
S
N
@10μM
60
70
80
90
100
60
70
80
90
100
@10μM
FGR 98%
ZIPK 72%
STK17A 68%
inhibition@
20
30
40
50
20
30
40
50
inhibition@
%
Millipore 125 kinase panel
0
10
0 50 100 150 200 250 300
0
10
0 20 40 60 80 100 120 140
%
Millipore 259 kinase panel
18 David M Andrews | 24 June 2014 R & D | Oncology

19. Live cell imaging
• The PH domain of PLCδ1 binds specifically to PI(4,5)P2
• U2OS cells overexpressing PH-PLCδ1 pre-incubated with inhibitors for 60 min atp g p
37ºC before reading fluorescence
• In this system, Wortmannin and the PI4Kα inhibitor modulate PI(4,5)P2 levels,
the PI4Kβ inhibitor is inactivethe PI4Kβ inhibitor is inactive
19 David M Andrews | 24 June 2014 R & D | Oncology

20. Open Innovation – Industry Perspective
• Stefan Lindegaard survey – 2010
• http://www.15inno.com/2010/03/29/oibigpharma/
• Quick and dirty survey 10 largest pharma + ‘Open Innovation’• Quick and dirty survey – 10 largest pharma + Open Innovation
• GSK – ‘Innovation at GSK’ – the only well-developed web site
• Four years on….
20 David M Andrews | 24 June 2014

21. Open Innovation offerings across all stages of
hresearch
R & D | Oncology
http://openinnovation.astrazeneca.com

22. Target Innovation
How Does it Work process flow diagram
Proposals can seek
How Does it Work – process flow diagram
Continuous call for proposalsContinuous call for proposals
①Seed funding (up to $100K) to strengthen
hypothesis
②Request an AZ compound library for them to
screen
Continuous call for proposalsContinuous call for proposals
High
th h t
High
th h t
AZAZSeed fundingSeed funding screen
③Request to run a full HTS at AZ facility
Tools provided to help investigators:
throughput
screen in
AZ facility
throughput
screen in
AZ facility
AZ
compound
library
AZ
compound
library
Seed funding
for Target
validation
Seed funding
for Target
validation
• “Instructions to Authors”
• AZ interests and proposal scoring criteria
• Review feedback
AZ scientific ReviewAZ scientific Review
Typical arrangement is risk/reward sharing:
• AZ provides compound supply or seed funding or
screening capability
‘Full Project Proposal’ under CDA‘Full Project Proposal’ under CDA
screening capability
• PI has obtained funding via grant (unless grant
awarded by AZ)
• Rewards include: publication(s) background info
AZ scientific ReviewAZ scientific Review
Project ExecutedProject Executed Rewards include: publication(s), background info.
for follow-on studies, royalties (if successful)
Project ExecutedProject Executed
David M Andrews | 24 June 2014 R & D | Oncology

23. New Molecule Profiling
How Does it Work process flow diagram
Two step process:
How Does it Work – process flow diagram
New molecules submittedNew molecules submitted
• Cheminformatics evaluation
• Screening evaluation
More details:
to external cheminformatics service providerto external cheminformatics service provider
Cheminformatics evaluationCheminformatics evaluation
More details:
• Molecules are submitted securely to an external
cheminformatics service provider so that AZ does not
see the structures
Report of property calculations and novelty
checks sent to AZ/submitter
Report of property calculations and novelty
checks sent to AZ/submitter
• Physicochemical and biological properties are
calculated and molecules are checked for novelty
against the AZ and public collections
AZ d b itt i t f h i f ti
AZ scientific reviewAZ scientific review
• AZ and submitter receive a report of cheminformatics
evaluation results
• AZ reviews report and accepts/rejects compounds
into the HTS screening collection
MTAMTA
g
• MTA between AZ and submitter and samples added
to the HTS screening collection
• HTS screening report generated yearly and sent to
b itt
Samples added to Screening collectionSamples added to Screening collection
submitter
• If ‘screening hit’ then AZ/submitter
negotiate/collaborate.
Negotiate/collaborate if ‘screening hit’Negotiate/collaborate if ‘screening hit’

24. The Future?
M t li d / il• More streamlined / agile
start-up?
• Further vendor tools to
facilitate
• Remote working?g
• E.g. virtual whiteboards :
• http://www.chemaxon.com/wp-content/uploads/2012/10/Patcore.pdf
• ‘Skype for chemists’
• Refinement of interaction models
Addi i l ll b i d l
24 David M Andrews | 24 June 2014 R & D | Oncology
• Additional collaboration models

25. What you may hear about collaborative MedChem…
We can’t
l h IP
Remember
the Boeing
We are
control the IP
risks!
Dreamliner
project!
giving away
our crown
jewels!
This is too
complicated
and can neverand can never
work!
cf. H. Wild et al., Angew. Chem. Int. Ed. 2013, 52, 2684.

26. Summary
• Collaborative MedChem in our hands has been
versatile and successful, projects have advanced fast
d t hi h d i litand at high design quality
• An incentive to invest in novel therapeutic approaches
over the longer term – e.g. AZ/CRT Cancer metabolism
Alliance
• Many ways of ‘constructing’ the collaborative DMTA
teams (project dependent)(p j p )
• Pursuit of more than one project in partnership brings
many synergies
• We have found ways to incentivize teams to overcome• We have found ways to incentivize teams to overcome
initial liabilities with novel chemical series / hits
• Future areas for focus
Technology sharing / access• Technology sharing / access
• Derisking area of biology
• Exploring new target classes
• Should be tried more often• Should be tried more often

27. Acknowledgements
• A large number of bench scientists at AstraZeneca, Cancer Research UK and
MRCT
• ….in particular – Mike Waring
• Jörg Holenz AZNeuro
• Phil Spencer AZ Discovery Sciencep y
• Peter Simpson AZ Discovery Science
• David Hollinshead
• Martin Harrison
• Paul Faulder
• Andrew Griffin
The ChemTraX team at Elixir
• Andrew Griffin
http://www elixirsoftware co uk/chemTrax htmlhttp://www.elixirsoftware.co.uk/chemTrax.html
27 David M Andrews | 24 June 2014 R & D | Oncology

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