Automated Design of ligands with targeted polypharmacology

Jérémy Besnard PhDUniversity of Dundee

ELRIG Drug Discovery '13 Manchester3rd September 2013

MedicinalInformatics

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)

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

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

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

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

Drug Optimisation Road

• Decisions:–Exploration– Improvement

• Guides–Structure–Previous SAR–Med Chem

KnowledgeBiologically active chemical space

Synthesised CompoundsDecision to synthesize

Lead

ClinicalCandidate

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

Background knowledge• ChEMBL

– 30 years of publications

Total ~ 3M endpoints

Total 40,000 papers

Total ~ 660,000 cpds

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

Transformations

Try to find new transformations

• Set of ~700– Tactics to design analogs – Not synthetic reactions– Derived from literature

• Semi-automatic

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

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

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

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

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)

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

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

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

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

D2 as objective• 1st series of compounds with high D2

prediction

Results

CNS penetration for compound 3: brain/blood ratio = 0.5

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

Optimisation results for 5-HT1A/D2/D3/D4/CNS/α1 selectivity/CNS objectives

Highest ranked compound

Path

Results

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

D4 objective

• Improve D4 activity• Good ADME score Bayesian = 25

D4 Ki=614nM

Bayesian = 105D4 Ki=9nM

Screening data

Ki Binding Assays (nM)

Bayesian Model Predictions

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

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

Morpholino series• 24 analogues were synthesised

around 2 scaffolds

Matrix of results

Lead Series Criteria Met• Ki<100nM• Highly novel chemotype at level of

carbon framework• Chemotype is D4 selective• CNS penetrable • Patent filing (WO2012160392)

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

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

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)

• 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”)

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