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What to Make Next? Augmented Design

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What to Make Next? Augmented Design Building innovative drug discovery alliances What to make next? Augmented Design Cresset UGM, 29th June 2017 Building innovative drug discovery alliances What to make next? Augmented Design Or 2 Old blokes and a couple of buns Cresset UGM, 29th June 2017 What to make next? PAGE 2 What’s medicinal chemistry? PAGE 3 What do I make next? Lead Telemetry 1st Candidate • MPO scores comprised of: Potency MPO Solubility Score Protein binding Metabolic stability LogD Project progression PAGE 4 Good medicinal chemistry? Project Progress as a Function of Time & compound number pIC 50 Comp. 3 Comp. 4 (LLE 8.0) (LLE 8.0) First co-crystal structure delivered Structure based design: Comp. 2 - Targeting specific unconserved (LLE 7.9) Target Rapid progress increasing potency Core redesigned: cysteine residue Off Target 1 Docking based design: - Replacement of aromatic CH Off Target 2 - Introduction of axial methyl “lock” with heteroatom - Rigidification of molecule Comp. 1 - Intramolecular H-bonding (LLE 6.8) Rapidly regained potency Advanced Lead identified Ames liability removed AO liability identified No Ames liability … with drop in potency No AO activity Initial Hit (LLE 3.2) Ames liability Selectivity improved identified 6 months PAGE 5 Key tactics for early series evaluation Establishing the Pharmacophore Understanding Conformation • Determine which molecular features are driving or limiting • Assessing conformational potency landscape of hit series • Evolution of molecular • Exploration of molecular scaffolds using series features limiting conformational hybridisation and freedom and assessing their computational scaffold hopping impact on biological profile approaches Focus on Properties Ensuring Compound Efficiency • Focus on aligning molecular • Hypothesis driven, iterative properties of a series with approach to compound design desirable property space* • Preparing minimum number of • Establish independence compounds required to address between trends in molecular an issue or assess potential of properties and biological profile a series PAGE 6 * Desirable property space depends on route of admin, target organ etc What do chemists do well? An under-utilised resource lab notebooks (eLN) 200 chemists proprietary reaction ~10 reactions per week database Adding say ~100,000 reactions per year, strong medicinal chemistry bias PAGE 7 The ‘Bread & Butter’ A brief history of synthetic medicinal chemistry …. ‘Our study also shows a steady increase in the number of different reaction types used in pharmaceutical patents but a trend toward lower median yield for some of the reaction classes.’ Huge pool of known chemistry waiting to be tapped PAGE Landrum et al J. Med. Chem. 2016, 59, 4385−4402; Rougley & Jordan J Med Chem. 2011, 54,3451-79 Reaction Vectors O O + HO OH O R1 R2 P I 1 2 3 4 I 1 2 3 4 Bond C-C C=O C-OH C-OR Bond C-C C=O C-OH C-OR # 4 1 2 0 # 4 1 0 2 reactant vector, R = (R1 + R2) product vector, P I 1 2 3 4 Bond C-C C=O C-OH C-OR # 0 0 -2 2 reaction vector, D = P - R PAGE 9 Broughton, H. B., Hunt, P. & Mackey, M. (2003) Methods for Classifying and Searching Chemical Reactions. United States Patent Application 367550. Reaction Vectors in Structure Generation • The reaction vector, D, equals the difference between the product vector, P, and the reactant vector, R D = P – R Given a reaction vector, D, and a reactant vector, R, the product vector, P, can be obtained P = D + R Given a product vector, P, can we reconstruct the product molecule(s)? O O I 1 2 3 4 better descriptor Bond C-C C=O C-OH C-OR O # 4 1 0 2 O is required O O PAGE 10 J. Chem. Inf. Model., 2009, 49 (5), pp 1163–1184. Knowledge-Based Approach to de Novo Design Using Reaction Vectors So… Using known chemistry we can… PAGE 11 Augmented Design: scaffold hopping in CDK2 Feature Similarity + Docking • Reactions: 26K • Reagents: 18K extracted from Aldrich No rings < 4 Contain C, N, O, S, P, F, Cl, Br, I, B, Si, Se < 3 F Hann substructures removed Rb < 3 Max 15 heavy atoms • Scored feature similarity to 1H1S Hinge Polar Cyclohexyl Fragment 4400 products 16000 products 8 starting 10 starting materials materials PAGE 12 Augmented Design: Fast Follower Approach Find me a new IP free series? Type II Kinase Inhibitor PAGE 13 Spark meets Reaction Vectors Kinase inhibitor programme Evotec Program Target Starting Point End Point Timeline contribution Literature fast H2L Kinase inhibitor Advanced Lead 5-6 FTEs 9 Months follower Literature Type II Stage 1: Generation of new cores Stage 2: RV Backpocket expansion kinase inhibitor 1) Spark replacement of Cmpd A core 1) New structures generated with novel Cmpd A: 2) ROCs overlay, GOLD docking & building blocks shown pharmacophore match using Cmpd A 2) New structures filtered on phys. chem. crystal structure properties (cLogD 1.5-3.5, MW<500) 3) 4 new cores building blocks selected and 3) ROCs overlay / GOLD docking & Hinge Core Back conformational analysis completed: pharmacophore match completed binder pocket 4) Compounds docked in key kinase crystal structures to test for selectivity IC50 = 10 nM 5) Predictive DMPK workflow run Crystal structure 6) Scifinder IP searches conducted published 7) 14 New compounds selected for synthesis Novel lead compound generated with desirable potency suitable for further SAR expansion IC50 = 80 nM PAGE 14 So… PAGE 15 What do I make next? Options RVs & Bayes FMO Pan-Omics eAPPS PAGE 16 What to make next? GPR Bayesian optimisation meets reaction sequence vectors Balance between Exploitation Exploration Acquisition Functions • For finding the maximum: Probability of improvement (PI) Expected improvement (EI) Upper confidence bound (UCB) • For finding the minimum: Lower confidence bound (LCB) PAGE 17 Nafisa Sharif & Mike Osborne 2016 The Dataset The Chemist’s QSAR Apply acquisition The Experiment function Train Train +1+2+3…n Test Train Build model (50) pIC50 Train +1+2+3…n Calculate acquisition function. Add top scoring compound to model. Rebuild model. Test Predict on final compounds (42). Repeat 100x… (50+1+2…151) Compounds (ordered chronologically) PAGE 18 Results: Observing Signal Formation Application of the acquisition function to build the training set Test set r set Test No Bayesian Optimisation r set Test PI UCB 2 2 value value Test set r set Test Number of compounds added to training set Number of compounds added to training set 2 value Test set r set Test Test set r set Test 2 LCB Number of compounds added to training set EI 2 value value Chronologically Ordered Number of compounds added to training set Number of compounds added to training set PAGE 19 Balancing Exploration with Exploitation Mixing the acquisition functions No Bayesian Optimisation LCB followed by UCB LCB followed by UCB Test Train +1+2+3…n Test Test set r set Test 2 value kappa = 2 Number of compounds added to training set Exploration followed 2 r2 = 0.627 r = -0.345 by Exploitation No Correlation Good Correlation PAGE 20 Modelling “By definition all models are wrong … it’s just that some are more useful than others” George EP Box (1919-2013) “Modelling isn’t about getting it right … It’s about understanding why you get the answer you get” If you understand the why then you can build a better model PAGE 21 Summary • Demonstrated that given a molecule we can automatically suggest what chemistry we can apply and hence what molecules we can make • Successful impact of RVs in combination with Cresset tools • Described research into the application of Acquisition Functions to drive choice of compound(s) for synthesis from within the RV networks PAGE 22 Acknowledgments Dimitar Hristozov Val Gillet Mike Osborne Atanas Patronov Beining Chen Nafisa Sharif Craig Johnstone Hina Patel Michelle Southey Ben Allen Chris Stimson James Wallace 23 PAGE Building innovative drug discovery alliances Your contact: Mike Bodkin VP Research Informatics 114 Innovation Drive, Milton Park, Abingdon Oxfordshire OX14 4RZ, UK T: +44 (0)1235 44 1207 [email protected] What do I make next? Sequence vector network PAGE 25 AI & Augmenting Design Local QSAR Global QSAR 7 y = 0.7044x + 0.9289 6 R2 = 0.6091 5 4 3 2 1 0 0 1 2 3 4 5 6 7 8 Given a single change, Take all the data, What is the effect? What is the effect? Bench Chemist Computational Chemist PAGE 26 Augmented Design Load Molecule Generate new molecules Any scoring tool Multi-objective Best new molecules Input Mutate Score Rank Output Q2 = 0.76 Activity XQSAR Pool of possible products Predicted Local Models Multi-objective Pareto optimisation Actual algorithm In-silico reaction Reaction Vectors Score Select Starting material Docking Score Q2 = 0.68 ADMETQSAR Global Models Predicted Cream-off top scoring molecules for evolution Actual PAGE 27 1. Multi-dimensional de novo design of drug-like compounds. 2013, De Novo Molecular Design ISBN 978-3-527-67700-9. 2. Validation of Reaction Vectors for de Novo Design. 2011, Library Design, Search Methods, and Applications of Fragment-Based Drug Design. ISBN 9780841224926. 3. Knowledge-Based Approach to de-Novo Design using Reaction Vectors. 2009, 49 (5), pp 1163–1184. J. Chem. Inf. Model. Ion channel dual Inhibitor design QSARs drive the objective function Coloured by Pareto Rank: Red (High)-> Blue (Low) 2nd/3rd Iterations V1.5 pIC50 Prediction (log scale) (log Prediction pIC50 V1.5 K NaV1.5 pIC50 Prediction (log scale) Results (3 Iterations) PAGE 28 Antipsychotic polypharmacology 4 objectives: QSAR + Pharmacophore Similarity The chart shows the known affinity (Ki) values of antipsychotic drugs for a panel of receptors. How can we go about designing a novel antipsychotic? 26K Reactions 93K Reagents PAGE 29 Naunyn Schmiedebergs Arch Pharmacol. 2015 Mar 14 Fragment Growth in-situ PHIP2 Bromo-Domain Reactions based around Objective function PAGE 30 Cox, O.
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