de novo design of bispecific ligands exscientia ukqsar16
TRANSCRIPT
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Scaling De novo Design of Bispecific Ligands
Jérémy Besnard
UK-QSAR & Chemoinformatics Group 19 October 2016
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exscientia
We are a young spin-out company from the University of Dundee (2012)
Employees working in multiple locations Platform built from revenues through partnerships
with pharmaceutical companies – organic yet exponential growth
Medicinal chemistry design as a routine process
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Scaling de novo design
1. Validate on single target Show that the method can generate patentable compounds
2. Expand to dual combinations Generate clinical candidate
3. Expand again to disease portfolio Design against a matrix of disease-related targets
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GA de novo design algorithm
Input Compounds
Enumerateusing 1k
TransformationsElite
PopulationFast Selection
by ModelVirtual Library
Iterate n times
Hundreds Millions 30 – 60k Tens
Analysis & Selection Designs
Besnard et al. Automated design of ligands to polypharmacological profiles, Nature 492, 215–220 http://doi.org/10.1038/nature11691
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1. Validating the method
Besnard et al. Automated design of ligands to polypharmacological profiles, Nature 492, 215–220 http://doi.org/10.1038/nature11691
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<30 compounds required to discover, synthesize and patent compound 27s, a selective D4 compound with early lead properties
> O(6) compounds evolved & scored for D4 and off-targets, but only most promising compounds were synthesized and screened
2 generations 2 generations 2 generations 2 generations
1 generation
Compound 27sD4 Ki = 90 nM, selective
Patent: PCT/GB2012/051194 / WO2012160392
Optimise D4 activity and selectivity
2 generations
+ ++
Donepezil
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Dual inhibitor for two unrelated targets
• Design against polypharmacology profiles• Confirmatory X-ray structures of both complexes
Metabolic Disorder Example
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Bispecific Compounds
Goal is to find first-in-class bispecific small molecule inhibiting two enzymes of unrelated families
Process: Gather public and patent data to build models De novo design with evolutionary algorithm Docking of top ranked compounds to assess if the compounds could fit
the two binding sites Synthesize top-ranking In-vitro assay followed by crystallography
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Protein-ligand X-ray structures of both complexes with the top prioritized compound
Structural validation
Enzyme BIC50= 10 nM
Enzyme AIC50= 350 nM
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Dual agonist for two distinct GPCRs
• Collaboration with Sumitomo Dainippon Pharma• Design against polypharmacology profile• in vitro assessment• Rapid delivery of candidate to in vivo safety study
2. Delivering a candidate
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DesignSimultaneous design
objectives deliver balanced compounds
Make10-30 compounds/cycle High information content
Test in disease relevant assays
LearnAssay data informs next design cycle
Technology in practiceAutomated lead generation with rapid design cycle and efficient evolution to drug candidate profile
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Lead Identification
Design of 5 chemotype
s
Compounds synthesized
25
5
30
45
5
Dual agonist activity
GPCR selectivity
*
Ease of synthesis
✓
✗
✓ ✓
✗
✗✓
✓ ✓ ✓
✗
✗
✗✗ ✗
Design, synthesis, assay: 5-15 compounds per 2-week cycle
to lead optimizatio
n
multiple compounds <150 nM at both targets. * <50% activity at 1µM over 20 GPCR receptors
80 nM70 nM
70 nM100 nM
Best Affinity
to lead optimizatio
n
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Lead Optimization80 further compounds for each prioritized scaffold
<20nM target 1
<20nM target 2
Dual agonist
✓✓ ✗
✓✓ ✓
scaffold designated as backupscaffold prioritizedadditional assays progressed further compounds made on this scaffold
Candidate Seeking40 compounds for prioritized scaffold
<20nM target 1
<20nM target 2
Dual agonist
✓✓ ✓ ✓✓ ✓ ✓solubility HERG >10µM GPCR
selectivityDMPK
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circle size number of assays performed for each compound
circle colour compound quality/proximity to objectives
compounds ranked by time
qual
ity o
f com
poun
ds
over
8 m
etric
sTowards candidate nominationSuccessful bispecific project for CNS disease 2 chemotypes progressing to candidate selection (ongoing) <400 compounds synthesized and assayed 12 month project
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From one pair to a matrix of pairs3. Scaling to disease level
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Big is relative, big compared to what? Philosophy is more important than size Collect and use all data rather than a small
sample Accept messiness of data - using more data
of variable quality outweighs using small, very exact data
All models are wrong but some are useful (link)
Big Data
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Design against a disease Find targets and select potential pairs
Generate designs for all pairs
Analyse the results
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Selecting target combinations
Drug like ligands affinity and
ADME
Commercial future
combination, generic
competition
Biological relevance
pathway, synergy
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Commonality between two targets Disease link
• Text mining for co-occurrence of target-disease and target-target
Chemical compatibility• Known compounds that bind both targets
• Take into account selectivity, “easy” to hit everything• Halfway compounds: known active against one,
predicted active against the other target• Similar chemical property space of known actives
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Druggable genome
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Clinically validated
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Emerging biology
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Target pool
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Chemical compatibility
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Matrix of targets linked to disease Automated design for all pairs ~1200 designs Scale: all results within ~ 2 days < 1% technical failures 100% result capture
Design against a disease
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Calculation details
Target matrix pairs 1235No model for target 60No inputs pass filters 1No evolution 26Technical failures 0
1150 successful runs 13 billion enumerated compounds 60 million compound designs captured A weekend on AWS
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What we are looking for per pair
Combinations with good model scores for both targets and drug-like molecules
Score Target 1
Score target 2
Ideal
Results from the evolutions
actives for target 1
activ
es fo
r tar
get 2
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“Distance to ideal” per target pair
Ideal pair: Score Target 1 = 1 Score Target 2 = 1
Want: small distance (yellow) Enough to chose from!
White = no result (not run or no desirable compound)
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Multiple series for a target pairData clustered and visualised in DataWarrior
Score target 2
Score target 1
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Selecting target combinations
Drug like ligands affinity and
ADME
Commercial future
combination, generic
competition
Biological relevance
pathway, synergy
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Scaling de novo design
1. Validate on single target Show that the method can generate patentable compounds
2. Expand to dual combinations Generate clinical candidate
3. Expand again to disease portfolio Design against a matrix of disease-related targets
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exscientia team
CTOJérémy BesnardCo-founder
CIORichard BickertonCo-founder.
Chief ChemistAndy Bell2 FDA drugs: sildenafil (Viagra) voriconazole (Vfend).
COOMark Swindells
Molecular Informatics Adrian Schreyer
Chemo-informatics Willem van Hoorn
CEOAndrew HopkinsFounder
ExecutiveOfficerSenga Oxenham
Systems ArchitectRichard Cox
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THANK YOU
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Back up slides