Automated Design of ligands with targeted polypharmacology Jérémy Besnard PhD University of Dundee ELRIG Drug Discovery

1. Medicinal Informatics • Automated Design of ligands with targeted polypharmacology Jérémy Besnard PhD University of Dundee • ELRIG Drug Discovery '13 Manchester 3rdSeptember 2013

2. 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)

3. 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

4. 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

5. 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

6. 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

7. Biologically active chemical space Synthesised Compounds Decision to synthesize Drug Optimisation Road Lead • Decisions: • Exploration • Improvement • Guides • Structure • Previous SAR • Med Chem Knowledge Clinical Candidate

8. Algorithm Define Objectives Analyse Test in bio-assays X run Compounds Synthesis optimal molecules Med Chem design rules GenerateVirtualcompounds Top cpds + Random set Background knowledge Assess molecules Predict properties Phys-Chem Activities (primary and anti target) Novelty Machine Learning Multi-objective prioritization Final Population Results expand knowledge-base Patent WO2011061548A2

9. Background knowledge • ChEMBL • 30 years of publications Total 40,000 papers Total ~ 3M endpoints Total ~ 660,000 cpds

10. Algorithm Define Objectives Analyse Test in bio-assays X run Compounds Synthesis optimal molecules Med Chem design rules GenerateVirtualcompounds Top cpds + Random set Background knowledge Assess molecules Predict properties Phys-Chem Activities (primary and anti target) Novelty Machine Learning Multi-objective prioritization Final Population Results expand knowledge-base Patent WO2011061548A2

11. Set of ~700 Tactics to design analogs Not synthetic reactions Derived from literature Semi-automatic Transformations Try to find new transformations

12. Algorithm Define Objectives Analyse Test in bio-assays X run Compounds Synthesis optimal molecules Med Chem design rules GenerateVirtualcompounds Top cpds + Random set Background knowledge Assess molecules Predict properties Phys-Chem Activities (primary and anti target) Novelty Machine Learning Multi-objective prioritization Final Population Results expand knowledge-base Patent WO2011061548A2

13. Model • Categorical model • Active if activity < 10μM • Use 2D structural information

14. Bayesian Bad feature: 360 times in training set, Never in active molecule: Weight = -1.91 Good feature: 23 times in training set, 15 times in active molecule: Weight = 2.46 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 “A molecule” 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 inactivity Score ~ 0: either cancellation of good and bad, or unknown W. Van Hoorn, Scitegic User Group Meeting, Feb 2006, La Jolla

15. Algorithm Define Objectives Analyse Test in bio-assays X run Compounds Synthesis optimal molecules Med Chem design rules GenerateVirtualcompounds Top cpds + Random set Background knowledge Assess molecules Predict properties Phys-Chem Activities (primary and anti target) Novelty Machine Learning Multi-objective prioritization Final Population Results expand knowledge-base Patent WO2011061548A2

16. 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 2 Objective 1 QED: see Bickerton et al., Quantifying the chemical beauty of drugs. Nature Chemistry, 4(February 2012)

17. Algorithm Define Objectives Analyse Test in bio-assays X run Compounds Synthesis optimal molecules Med Chem design rules GenerateVirtualcompounds Top cpds + Random set Background knowledge Assess molecules Predict properties Phys-Chem Activities (primary and anti target) Novelty Machine Learning Multi-objective prioritization Final Population Results expand knowledge-base Patent WO2011061548A2

18. 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

19. 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

20. 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

21. D2 as objective • 1st series of compounds with high D2 prediction

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

23. 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

24. Optimisation results for 5-HT1A/D2/D3/D4/CNS/α1 selectivity/CNS objectives Highest ranked compound

25. Path

26. Results

27. Selectivity • Need to include selectivity in the algorithm: • Alpha adrenoreceptor 1 inhibitors versus other targets Ratio Ki D2 receptor / Ki α Receptor

28. D4 objective • Improve D4 activity • Good ADME score Bayesian = 25 D4 Ki=614nM Bayesian = 105 D4 Ki=9nM

29. Screening data Bayesian Model Predictions Ki Binding Assays (nM)

30. 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

31. 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

32. Morpholino series • 24 analogues were synthesisedaround 2 scaffolds

33. Matrix of results

34. Lead Series Criteria Met • Ki<100nM • Highly novel chemotype at level of carbon framework • Chemotype is D4 selective • CNS penetrable • Patent filing (WO2012160392)

35. 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

36. 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, 2010 Ballester. Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of computational chemistry, 28(10), 2007 Schreyer. USRCAT: real-time ultrafast shape recognition with pharmacophoric constraints. Journal of Cheminformatics, 4(27), 2012

37. 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)

38. The technology has been licensed to Ex ScientiaLtd (spin off - http://www.exscientia.co.uk/ ) • Ex Scientiain 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”)

39. Acknowledgments • Pr. Andrew Hopkins • Richard Bickerton • ALH group • Pr. Ian Gilbert • GianFilippoRuda • 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