Increasing compound collection value and diversity … diversity through collaborations,...
Transcript of Increasing compound collection value and diversity … diversity through collaborations,...
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Increasing compound collection value and diversity through collaborations, partnerships, and open-innovation
Thierry Kogej
AstraZeneca R&D - Discovery Sciences – Chemistry Innovation Centre
ChemAxon – User Group Meeting – 18-20th May 2015
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Current Challenges in early drug discovery
2 IMED Biotech Unit I Discovery Sciences
Declining success rates
Same old target space
Reduced investment in R&D
Increase collaboration/partnership
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Remit of AZ Early Discovery Partnering
3 IMED Biotech Unit I Discovery Sciences
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External Discovery Platform
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Many single disease
area collaborations
Support Neglected
Diseases
TBDA
IMI Lead Factory consortium
(European academia and EFPIA) Strategic Partnering
with Academic LG
Centres of Excellence
Multi-Target
opportunities
Increase
value of
screening
collections
AZ Collabn
National Cmpd
Collection
Innovative
chemistry:
Increase
diversity &
quality of
leads
AZ Compound
Collection
Multi-Target
First refusal
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Outlines of the talk
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Part 1. Cross HTS screening
AZ-Bayer screening collaboration
Part 2. Profiling novel compounds from Academia/CRO’s
Open Innovation portal
Part 3. Profiling external chemical libraries
Increase diversity & quality of lead molecules
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6 IMED Biotech Unit I Discovery Sciences
Part 1: Cross HTS screening
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Bayer Pharma HC - AstraZeneca ‘Boomerang’ project – a successful example of peer-peer collaboration
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• Pioneering Joint initiative established between AstraZeneca and Bayer in 2010 (extended to 2016)
• Enables both parties to seek chemistry starting points not available in their internal collections
Active / HitParties provides assay development to prosecute
HTS and lead cascade
Lead
Optimisation
Bayer Pharma HC AstraZeneca
Nominate HTS Target Nominate HTS Target
Screen
Target swap
Transfer chemical series
Progression through milestone and royalty payments
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Overlap of Bayer Pharma HC and AstraZeneca collection – Identical fingerprints
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Big pharma screening collections: more of the same or unique libraries? The AstraZeneca-Bayer Pharma AG case
Kogej T, Blomberg N, Greasley PJ, Mundt S, Vainio MJ, Schamberger J, Schmidt G, Huser J. Drug discovery today (2013).
*) As we are not sharing structures for analysis the overlap is based on exact
match of molecular fingerprints (ECFP4). This is an overestimate of identity
as a small fraction of non-identical compounds will have the same fingerprint
*
3.3% of the total collection (Bayer + AZ is overlapping)
95% of the overlap are public domain compounds
Screening of > 4.2 Millions unique cmpds
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9 IMED Biotech Unit I Discovery Sciences
Part 2: Profiling novel compounds from Academia/CRO’s
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AstraZeneca Open Innovation Website
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http://openinnovation.astrazeneca.com/
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New Molecule Profiling
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Description
Partner can submit novel compounds to be part of the AZ HTS collection
Quick assessment of the novelty and physchem profiles
Transfer of samples supported by AZ
AZ reports sample activities and discuss further options
Partner retain full intellectual property (IP) on their compounds
Requirements
No structure disclosure to AZ
no IP contamination
Check novelty and physchem profile need structure standardization
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New Molecule Profiling – Current process
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Academia/CRO’s
ChemAxon
ChemInformatic
assessment
AZ and Pubchem
(2D fingerprint format)
Update every quarter
AstraZeneca
Outsource the cheminformatic assessment to a third party
ChemAxon was selected as the preferred partner
Scope of work
Establish legal agreement with the submitters
Manage structures transfer to ChemAxon (formatting)
Structures standardization/molecular fingerprints generation
Novelty check against AZ and PubChem
Identify controlled substances
Physchem properties calculation
Generate report and submit to AZ for review
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Cheminformatic report
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MW cLogP
RotB Aliph
Ring
Arom
Ring
Nitrogen
Fsp3
Oxygen
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New Molecule Profiling – Future development
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Give access to
partners to a larger set
of predictions for their
cmpds
QSAR AZ models
Developed in
collaboration between
Genetta Soft AB,
Uppsala University, and
AstraZeneca R&D
www.bioclipse.net
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15 IMED Biotech Unit I Discovery Sciences
Part 3: Profiling external chemical libraries
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Cheminformatics assessment of external collections
16IMED Biotech Unit I Discovery Sciences
Making every SAR point count: the development of Chemistry Connect for the large-scale
integration of structure and bioactivity data.
Muresan S, Petrov P, Southan C, Kjellberg MJ, Kogej T, Tyrchan C, Várkonyi P, Xie PH. Drug
Discov Today. 2011, 16, 1019-30
Novelty to AstraZeneca
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Cheminformatics assessment of external collections
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Prop. Filters Chem. Filters
Core No violation No match
Back-Up 1 violation No match
Ugly no matter >= 1 match
Ugly >= 2 violations no matter
Chemical filters
~150 chemistry alerts (11 classes (e.g. reactives, unwanted structures,…)
Property filters
100 MW 550
-2 ClogP 6
1 PSA 160
AZFilters
Alerts:
Genotoxicity via Bursi alert
Reactive Metabolite
PAINS structures
Chemical predictive modelling to improve compound quality
Cumming JG, Davis AM, Muresan S, Haeberlein M, Chen H. Nature
Review Drug Discovery, 2013, 12, 948-62
Novelty to AstraZeneca
Compound
filters and alerts
AZ collection
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>20 % have 2 aromatic and 1 aliphatic rings
Cheminformatics assessment of external collections
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Novelty to AstraZeneca
Compound
filters and alerts
Physchem
Ex. Combined ring information
“many”
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Cheminformatics assessment of external collections
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0
20
40
60
80
100A’
B’
C’
% o
f cpds
AZ collection Alchemia ’sub-library’
Alchemia
compounds
populate more
3D space
O
NH
OOH
O
OO
NH2
Novelty to AstraZeneca
Compound
filters and alerts
Physchem
Molecular Complexity
Bioactive Molecules: Perfectly Shaped for Their Target?
Wirth M, Sauer WHB. Molecular Informatics. 2011, 30, 677–88
A’B’
C’
Fsp3, Chirality,
PMI...
Principal Moment of Inertia
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Cheminformatics assessment of external collections
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DNP
148k
CCEPR
325k
Organic
HEV > 15
MW < 450
-2 < AlogP < 5
60k
311k
Random
selection
50k
50k
45+45k Bayesian classifier
FCFP_6
Number of rings
Number of aromatic rings
5k
5k
Test
model
[Have tried different variants]
AUC for ROC (TPR v. FPR): 0.999
Best split: -22.85
Score > -25 is associated with ”natural product like” cmpd
Novelty to AstraZeneca
Compound
filters and alerts
Physchem
Molecular Complexity
Natural Product-likeness Score and Its Application for Prioritization
of Compound Libraries
Peter Ertl,* Silvio Roggo, and Ansgar Schuffenhauer
J. Chem. Inf. Model. 2008, 48, 68-74
Natural Product
likeness zone
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Cheminformatics assessment of external collections
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Filter collection to include cmpds with 25 <
HAC < 40
Generate 3D struct. (max 100 conf/cmpd)
Run ROCS (different Tanimoto thresholds
depending the query)
3D Know mimetics:
a-helix i.e. nutlins
b-strand i.e. peptidomimetics
PPI co-crystallized X-ray structures
ACS Med. Chem. Lett. 2013, 4, 660
JACS 2011,133,14220
Novelty to AstraZeneca
Compound
filters and alerts
Physchem
Molecular Complexity
3D shape/2D search
2D structures:
Macrocycles, spiro scaffold, “ring of the
futures”, privileged structures
OpenEye tools
tools
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Cheminformatics assessment of external collections
22 IMED Biotech Unit I Discovery Sciences
Novelty to AstraZeneca
Compound
filters and alerts
Physchem
Molecular Complexity
3D shape/2D search
2D Fingerprint-based
Similarity/diversity
Clustering
Assessment based on 2D fingerprint (Tanimoto – ECFP4)
Similarity to active cmpds from ChEMBl, GVKBio, etc…to enrich
AZ biological active space
Diversity to AZ collection
2D fingerprint and “Murcko” scaffolds
Ensure good coverage of the external collection
Reduce singleton exposure
Visual inspection
… last but not least, review from
medicinal chemists
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Cheminformatics IT platform
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Linux cluster
2D Fingerprint-based Similarity/diversity
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Cheminformatics IT platform
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No IP issue
a priori no limitation on CPU time
Limited choice on the software platform (license issue)
Range of properties limited by the platform choice
Sharing protocols
(Pipeline Pilot)
Linux cluster
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Cheminformatics IT platform
25 IMED Biotech Unit I Discovery Sciences
No IP issue
a priori no limitation on CPU time
Limited choice on the software platform (license issue)
Range of properties limited by the platform choice
Use for projects with high IP restriction
(no network, disk format)
Current laptops are sufficiently powerful to
generate a rich collection analysis with limited time
Sharing protocols
(Pipeline Pilot)
Linux cluster
Isolated laptop
Novelty to AstraZeneca Compound filters and alerts
Molecular Complexity
3D shape/2D search
2D Fingerprint-based Similarity/diversity
Clustering
Physchem
~1 hour / 1 million cmpds
Murcko
Structure standardization
~12 hours, ECFP4, 2 million x 2 million cmpds
(ICore7, 8 threads, SSD)
Still challenging
but done on properly
limited sets
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Conclusion
26 IMED Biotech Unit I Discovery Sciences
Cross HTS partnership as a way to increase size of screening collections
AZ Open Innovation portal to gather and profile novel compounds from Academia/CRO’s
Profiling external chemical libraries
large set of relevant properties to access external collection value
Adapted platforms to fit different collaboration requirements
Increase diversity & quality of lead molecules
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Acknowledgement
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AZ/Bayer Alliance
Kirsty Rich, Peter Greasley, Niklas Blomberg, Peter Simpson, Isabella Feierberg
Bayer: Bernd Kalthof, Jens Schemberger, Georg Schmitt, Stefan Mundt, Jöerg Hϋser, Hanno Wild
New Molecule Profling
Phil Spencer, Dave Cosgrove, Ernst Ahlberg Helgee, Isabel Charles, Matthew McLoughlin, Clive Green
ChemAxon
Cheminformatics platform
Ola Engkvist, Michael Kossenjans, Ulf Börjesson, Wolfgang Klute, Loredana Spadola, Toan Mguyen, Jens Sadowski
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Thanks for your attention
Question?
IMED Biotech Unit I Discovery Sciences28
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Backup
29 IMED Biotech Unit I Discovery Sciences
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Current statistics of submission
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Low attribution between cmpds submission and incorporating them into HTS plates
Cheminformatics recommendation mainly driven by novelty to AZ collection
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