Automated Design of ligands with targeted polypharmacology Jérémy Besnard PhD University of...
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Automated Design of ligands with targeted polypharmacology
Jérémy Besnard PhDUniversity of Dundee
ELRIG Drug Discovery '13 Manchester3rd September 2013
MedicinalInformatics
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Background• Increasing cost of R&D• High failure rate for compounds in
Phase II and III
Phase II failures: 2008–2010. The 108 failures are divided according to reason for failure when reported (87 drugs). The total success rate is 18 % between 2008 and 2009 (Arrowsmith, Nature Reviews Drug Discovery 2011)
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Possible solution• Improve efficacy and safety by better
understanding polypharmacological profile of a compound
Two proteins are deemed interacting in chemical space (joined by an edge) if both bind one or more compound. Paolini et al. Nature Biotechnology, 2006
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Designing Ligands• Challenging to test one compound
versus multiple targets: costs, which panel to use, more complicated SAR, increasing difficulty of multiobjective optimisation
• Computational methods can provide–Design ideas–Prediction of activities–When possible ADME predictions
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Design ideas – De Novo Drug Design
• Compound structures are generated by an algorithm
• Predefined rules to create/modify structures
• User defined filters–Molecular property space (MW,
LogP)–Primary activities to improve–Side activities to avoid
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Can we design against a polypharmacological profile?
• Drug Design is a multi-dimensional optimisation problem
• Polypharmacology profile design increases the number of dimensions but not the type of the problem– Multiple biological activities– ADMET properties– Drug-like properties
• Automating drug design is the strategy we have taken to deal with the design decision complexity of multi-target optimisation
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Drug Optimisation Road
• Decisions:–Exploration– Improvement
• Guides–Structure–Previous SAR–Med Chem
KnowledgeBiologically active chemical space
Synthesised CompoundsDecision to synthesize
Lead
ClinicalCandidate
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Algorithm
Compounds
Generate Virtual compounds
Backgroundknowledge
Med Chemdesign rules
MachineLearning
Predict propertiesPhys-Chem
Activities (primary and anti target)
Novelty
Define Objectives
Results expandknowledge-base
Patent WO2011061548A2
Multi-objective
prioritization
Final Population
Synthesisoptimal molecules
Test in bio-assays
Assess molecules
Top cpds + Random set
X run
Analyse
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Background knowledge• ChEMBL
– 30 years of publications
Total ~ 3M endpoints
Total 40,000 papers
Total ~ 660,000 cpds
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Algorithm
Compounds
Generate Virtual compounds
Backgroundknowledge
Med Chemdesign rules
MachineLearning
Predict propertiesPhys-Chem
Activities (primary and anti target)
Novelty
Define Objectives
Results expandknowledge-base
Patent WO2011061548A2
Multi-objective
prioritization
Final Population
Synthesisoptimal molecules
Test in bio-assays
Assess molecules
Top cpds + Random set
X run
Analyse
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Transformations
Try to find new transformations
• Set of ~700– Tactics to design analogs – Not synthetic reactions– Derived from literature
• Semi-automatic
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Algorithm
Compounds
Generate Virtual compounds
Backgroundknowledge
Med Chemdesign rules
MachineLearning
Predict propertiesPhys-Chem
Activities (primary and anti target)
Novelty
Define Objectives
Results expandknowledge-base
Patent WO2011061548A2
Multi-objective
prioritization
Final Population
Synthesisoptimal molecules
Test in bio-assays
Assess molecules
Top cpds + Random set
X run
Analyse
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Model• Categorical model
–Active if activity < 10μM• Use 2D structural information
O
O
O
O
1 0 0 1 1 0 0 0 1 0
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BayesianGood feature:23 times in training set,15 times in active molecule:Weight = 2.46
“A molecule”
Bad feature:360 times in training set,Never in active molecule:Weight = -1.91
Moderate good feature:389 times in training set,7 times in active molecule:Weight = 0.10
Moderate bad feature:4 times in training set,Never in active molecule:Weight = -0.06
Score= 2.46 + 0.10 -1.91 -0.06 = 0.59
High score means high confidence of activity.Low (negative) score means high confidence of inactivityScore ~ 0: either cancellation of good and bad, or unknown
W. Van Hoorn, Scitegic User Group Meeting, Feb 2006, La Jolla
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Algorithm
Compounds
Generate Virtual compounds
Backgroundknowledge
Med Chemdesign rules
MachineLearning
Predict propertiesPhys-Chem
Activities (primary and anti target)
Novelty
Define Objectives
Results expandknowledge-base
Patent WO2011061548A2
Multi-objective
prioritization
Final Population
Synthesisoptimal molecules
Test in bio-assays
Assess molecules
Top cpds + Random set
X run
Analyse
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Prioritization• Objectives
– Activity– CNS score or QED – Anti Target
• Example– Receptor 1 and 2
activity– Good CNS score– No α1 (a, b and d)
activity– -> n dimensions
Achievement Objective
Objective 1O
bjec
tive
2
QED: see Bickerton et al., Quantifying the chemical beauty of drugs. Nature Chemistry, 4(February 2012)
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Algorithm
Compounds
Generate Virtual compounds
Backgroundknowledge
Med Chemdesign rules
MachineLearning
Predict propertiesPhys-Chem
Activities (primary and anti target)
Novelty
Define Objectives
Results expandknowledge-base
Patent WO2011061548A2
Multi-objective
prioritization
Final Population
Synthesisoptimal molecules
Test in bio-assays
Assess molecules
Top cpds + Random set
X run
Analyse
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Experimental Validation • Does it actually work?• Evolution of a drug (SOSA)
– Look at possible side activity of drugs
• Donepezil: acetylcholinesterase inhibitor used for Alzheimer disease
• Potential activity for dopamine D4 receptor
• Confirmed experimentally at 600nM: design ligands with Donepezil as a hit to improve D4 activity
• Dopamine D2 receptor studied (lower prediction, not active)
Wermuth, C. G. Selective optimization of side activities: the SOSA approach. Drug discovery today, 11(3-4), 2006
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What are Dopamine D2 and D4 receptors?
• Belong to the GPCR family• Mainly present in the CNS• Involved in cognition, memory,
learning…• Targets for several neuropsychiatric
disorders like Parkinson’s disease, Schizophrenia, Attention-deficit hyperactivity disorder, Bipolar disorder…
• Data (4,400 activities for D2 and 1,500 for D4) and screening facilities available
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Two studies• Two receptors as objectives
–D2: will lead to work on selectivity toward multiple receptors
–D4: will lead to work on selectivity and novelty
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D2 as objective• 1st series of compounds with high D2
prediction
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Results
CNS penetration for compound 3: brain/blood ratio = 0.5
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Next objectives: reduce anti-target activity
• Polypharmacology primary activity– Combination profile of multiple GPCRs: 5HT1a, D2, D3, D4
• Selectivity over alpha 1 anti-targets– Alpha 1a, 1b and 1d– Inhibitors induce vasodilatation
• Novelty: remove known scaffolds• Good phys-chem properties: need to cross blood-
brain-barrier
• Multiple calculations and look at the results for synthetically attractive compounds
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Optimisation results for 5-HT1A/D2/D3/D4/CNS/α1 selectivity/CNS objectives
Highest ranked compound
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Path
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Results
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N
O
N
N
X
1 - 2
R
NN N
O
Y
X1 - 3
Selectivity• Need to include selectivity in the
algorithm:–Alpha adrenoreceptor 1 inhibitors
versus other targetsavg_selectivity
0.001
0.01
0.1
1
10
100
GFR-VII-266
GFR-VII-269
GFR-VII-273
GFR-VII-274
GFR-VII-280-HCl
GFR-VII-280
GFR-VII-281
GFR-VII-285
GFR-VII-287
GFR-VII-290
GFR-VII-327
GFR-VII-328
GFR-VII-329
GFR-VII-330
GFR-VII-331
GFR-VII-332
Ratio Ki D2 receptor / Ki α Receptor
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D4 objective
• Improve D4 activity• Good ADME score Bayesian = 25
D4 Ki=614nM
Bayesian = 105D4 Ki=9nM
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Screening data
Ki Binding Assays (nM)
Bayesian Model Predictions
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Experimental Data• Compound 13 is selective for D4
receptor with pKi = 8• It crosses the BBB (Ratio of 7.5)• In vivo experiments on with
comparison to D4-KO mouse showed effects that the compound acts on target
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Morpholino series• However Cpd 13 is commercial and
thus not novel• New objective: starting from 13, keep
activity, filter non-novel chemotype, D4 selectivity over other targets, CNS penetrant
• Example of top ranked compound
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Morpholino series• 24 analogues were synthesised
around 2 scaffolds
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Matrix of results
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Lead Series Criteria Met• Ki<100nM• Highly novel chemotype at level of
carbon framework• Chemotype is D4 selective• CNS penetrable • Patent filing (WO2012160392)
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Further characterization• Functional data
– Compounds are antagonist or inverse agonist• hERG (K ion channel): inhibition can cause sudden
death– 27s: EC50 = 3μM
• Blood-Brain-Barrier– 27s: in vivo brain/blood ratio of 2.0
• Stability– Compound itself: oxidation possible indoline > indole– Metabolic stability: high clearance > need improvement(Cli, = 25 mL/min/g)
Compound 27s can be classified as a lead for D4 selective inverse agonist. From the series, there is also a potential of dual 5HT1A/D4 ligands
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How to improve the algorithm
• Better model: better prediction can help reducing false positives and detect potential other activities
• Different methods– Predictions: other machine learning, 2D/3D
similarity (USR-USRCAT), docking– Idea generator: real synthetic reactions, group
replacements (MMPs)• More knowledge on the method itself
– Where it works– When to stop
Hussain. Computationally Efficient Algorithm to Identify Matched Molecular Pairs ( MMPs ) in Large Data Sets, J.Chem.Inf.Model., 4, 2010Ballester. Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of computational chemistry, 28(10), 2007Schreyer. USRCAT: real-time ultrafast shape recognition with pharmacophoric constraints. Journal of Cheminformatics, 4(27), 2012
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Conclusion• We have designed an algorithm to generate
and predict compounds against polypharmacological profile
• The algorithm can adapt to the situation: improve activity, selectivity, novelty
• We have shown proof of concept that we can automatically invent patentable compounds
• Results were experimentally validated and it generated a lead compound – this study has been published (Besnard al,. Nature, 492(7428), 2012)
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• The technology has been licensed to Ex Scientia Ltd (spin off - http://www.exscientia.co.uk/ )
• Ex Scientia in its first year has had further successes applying the algorithm to the design of various other gene families including ion channels, GPCRs and enzymes (“stay tuned”)
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Acknowledgments• Pr. Andrew Hopkins• Richard Bickerton• ALH group
• Pr. Ian Gilbert• Gian Filippo Ruda• Karen Abecassis
• Kevin Read and DMPK group
• Barton group• Brenk group
• Pr. Bryan Roth (UNC-CH - NIH)• Vincent Setola• Roth lab• Pr. William Wetsel (Duke
University Medical School)• Wetsel group
• CLS IT support (Jon)• Accelrys support