Challenges and Perspectives on Computational Chemistry & GlaxoSmithKline

Transcription

1 Challenges and Perspectives on Computational Chemistry & Informatics in Drug Discovery Darren Green GlaxoSmithKline

2 Drug discovery and computational chemistry Target ID Lead Discovery Lead ptimisation Preclinical Development PC Full Development Registration Target Selection Candidate Selection Product Differentiation Market Entry Predictive ADMET Molecular Diversity HTS analysis Array Design Molecular Similarity (Q)SAR analysis Structure Based Design Ligand based Design Attrition analysis/risk assessment Structural Genomics Sequence homology

3 Lead ID options Screening collection Knowledge Based Virtual Screening Hit ID Commit H2L Commit L2C Diversity Screening Applies equally to Drug like, lead like or Fragmentcollections

4 Chemical Descriptors Molecular fields Shape fingerprint Reduced Graphs A P D/A Ar D Reference Shapes 3D Pharmacophores H Shape 3 point Pharmacophores H H 2 H 2 Fragments D fingerprints Atom pairs and paths D fingerprints

5 Chemical Descriptors Which h ones to use? Which are best? GSK 2D fingerprints for diversity Reduced dgraphs, 3D pharmacophores. h 3D fields and shape for knowledge based work The most important ingredient for success is the quality of the computational chemist, not the software (or descriptors) they have to hand (assuming a base level of scientific validation!)

6 The World we live in is not 1 dimensional Most decisions we make involve more than one parameter Time and place Size, pattern, colour, style Relative importance varies with season, currenttrends, trends, likely use, personal preference (bias) The drug discovery process is no different!

7 Strategically managing the growth of a Screening Collection How large does the collection need to be? How do we balance across different design strategies? Success in Screening GSK Collection Model Developability Compound properties Chemical Tractability Property and substructure filters Chemist eyeballing Amenable to array synthesis Compromise between these three goals

8 Physical Properties the recent literature at. Rev Drug Disc. 2011, 10, 197 at. Rev Drug Disc. 2007, 6, 681 Pfizer 3/75 and toxicology Chem. Res. Toxicol., 2011, 24,1420

9 Essential Reading Poor permeability (PSA < 132) Poor permeability (PSA < 132) permeability < 100 nm/s (MWt <400) Poor solubility for Mpt 150 C (Yalkowsky) Kinetic solubility >250 mg/ml Renal clearance Low solubility/ionisation dependent Human mice int >8.6 Human earnance >7.8 ml/min/kg Bioprint receptor promiscuity (AZ) Adapted d from: (AZ) Lipophilicity in Drug Discovery (Roche) M. J. Waring Expertpin pin. Drug Disc., 5, (2010) Bioprint receptor promiscuity in vitro rat toxicology herg 10μM μ (neutral molecules) herg 10μM (bases) Phospholipidosis (bases of pka 9) Phospholipidosis (all bases) logd/p

10 Virtual Screening The in silico equivalent of wet assays/hts Requires a method of predicting active compounds protein structure (+docking) (see Chris) 3D pharmacophore QSAR equation Capable of screening many more molecules than can be made or tested in reality This high throughput use of comp. chem. requires us to make the most approximations

11 Pharmacophores A reductionist approach to bioactivity A pharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response IUPAC Hdon Hacc C 2 H 9.3 angs 7.6 Hdon 2.9 eglect of entropy, solvation and anything subtle!

12 Example: 3D pharmacophore lead hopping Endothelin antagonists H H 2 C H H H H + H 2 C H H H 5 BQ123 (IC50 22nM) 6 Shionogi (IC50 78nM) C 2 H Angstrom C 2H C 2H 7 (IC nm) 8 (IC50 9 M) Ashton, M.A. et al, Drug. Disco. Today, 1, 71 (1996)

13 2D Pharmacophores Use pharmacophore concept, but not 3D distances or arrangements Encodepairs of pharmacophorepointspoints separatedby a certain number of bonds (typically from1 to ~10) These are used to produce a vector, or fingerprint, which can then be used to compute molecular similarity (cf in 2D chemical database systems such as ISIS) Advantage Don t have to worry about active conformations Disadvantage May be less selective that 3D methods (i.e. may need to screen more molecules)

14 Reduced Graphs A powerful 2D pharmacophore description CB1 antagonist example H Aromatic ring Aromatic Ring Acceptor Aliphatic Ring Acceptor Donor / Acceptor Aromatic ring Harper, G. et al, J. Chem. Inf. Comput. Sci., 44(6), 2145 (2004)

15 CB1 Antagonists t H Me C H H H Pfizer 2D FP: RG: Solvay 2D FP: RG: 1 Astra 2D FP: RG: 1 H Bayer H S F F H Va. Commonwealth Solvay H Me H F S F Eli Lilly Aventis Merck U. Conneticut

16 Reaching Beyond the Fog in HTS Data driven analysis finds motifs, Reduced Graphs, frameworks etc that are enriched in the active population The Reduced Graph Descriptor in Virtual Screening and Data-Driven ustering of High-Throughput Screening Data. G. Harper, G. S. Bravi, S. D. Pickett, J. Hussain, and D. V. S. Green. J. Chem. Inf. Comput. Sci., 44(6), (2004)

17 GSK is Highly Selective All new compounds for potential purchase are filtered through three phases: i) focus on lead-like physicochemical properties ii) removal of molecules with undesirable functional groups iii) requirement for adding diversity to existing set Commercially available compounds from selected suppliers Filter on lead like properties (clogp<3, mw<360) Remove undesirable functional groups Diversity selection to avoid over sampling areas of chemical space Identified for priority purchase by med chem Check supplier availability Pass GSK QC 4,600,000 (100%) 530,000 (11%) 130,000 (2.8%) 42,000 (0.9%) 26,000 (0.6%) 18,000 (0.4%) ~16,000 (0.35%) Enter GSK collection If molecules don t meet our purchase criteria we don t buy them!

18 Are you looking for a probe, a tool, a publication. or a drug? 537 compounds 115 pass GSK filters only 71 with no issues!

19 Drug discovery and computational chemistry Target ID Lead Discovery Lead ptimisation Preclinical Development PC Full Development Registration Target Selection Candidate Selection Product Differentiation Market Entry Predictive ADMET Molecular Diversity HTS analysis Array Design Molecular Similarity (Q)SAR analysis Structure Based Design Ligand based Design Attrition analysis/risk assessment Structural Genomics Sequence homology

20 BioDig Why? Common problem in medicinal chemistry.. Key Issue: High intrinsic clearance CF 3 S Hets from Phenyl Why don t you change it for an oxadiazole it worked for us in Prog X. Can we systematically mine existing data to find structural changes that lead to better clearance? Standing on the shoulders of giants Isaac ewton

21 Mining our data How? How do we analyse our SAR? ne way is to look for pairs of compounds which only differ by a single change The change in activity can then be attributed to the structural change

22 BioDig ADME database A database has been created where we have collected together the compound pairs (and associated structural changes) in our ADMEdata Property umber of compounds umber of matched molecular pairs umber of transforms earance 47k 8M 6M Solubility 170k 96M 82M Log D 158k 89M 68M herg 175k 98M 76M PP Binding 188k 70M 55M P450 3A4 240k 161M 136M P450 2D6 231k 150M 128M Represents a huge amount of knowledge to tap into

23 BioDig ADME The information in the database can be used to answer other questions Substructure X: What possible replacements havebeen tried and which improve clearance? 100 PRFILE_CLUSTER - 22 PRFILE_CLUSTER - 23 PRFILE_CLUSTER PRFILE_CLUSTER - 28 PRFILE_CLUSTER - 31 PRFILE_CLUSTER PRFILE_CLUSTER - 33 PRFILE_CLUSTER - 34 PRFILE_CLUSTER Binned ACTIVITY_CHAGE

24 Real example.. What happens to intrinsic clearance when you make the following structural change? Pull out the clearance data for compound pairs with this structural t change..

25 Intrinsic earance Data.. Change in intrinsic clearance (log) can reduce intrinsic clearance otice the shoulder on lefthand side of the 20 distribution umber of compounds Binned ACTIVITY_CHAGE

26 Intrinsic earance Data Cmpd 1 Property Cmpd clearance MW clog P RIGVAL1 logp clearance

27 Drug discovery and computational chemistry Target ID Lead Discovery Lead ptimisation Preclinical Development PC Full Development Registration Target Selection Candidate Selection Product Differentiation Market Entry Predictive ADMET Molecular Diversity HTS analysis Array Design Molecular Similarity (Q)SAR analysis Structure Based Design Ligand based Design Attrition analysis/risk assessment Structural Genomics Sequence homology

28 Predictive modelling A predictive model quantitatively relates a number of descriptors (variable factors that are likely to influence future behaviour or results) to an outcome. In marketing, for example, a customer's s gender, age, and purchase history (descriptors) might predict the likelihood of a future sale (outcome). In drug discovery, descriptors tend to be derived from chemical structure, and outcomes are in vitro or in vivo phenomena the goal is to predict behaviour before synthesis modelscan be built fromexperimental data too: e.g. prediction of %F from solubility, permeability and clearance data QSAR has been practiced since the 1960s and can trace its ancestry back to 1863!

29 Why Predict? A multi objective optimisation process Lead Potency X Safety Drug X Absorption PC2 Solubility Metabolic stability PC1 Predictive QSAR: Faster way to navigate the route via prediction and knowledge Traditional Way: Sequential Process, Costly, Lengthy

30 Drivers for Change

31 Molecular fields Shape fingerprint Reduced Graphs A P D/A Ar D Reference Shapes 3D Pharmacophores H Shape H H 2 3 point H 2 Pharmacophores Fragments D fingerprints Atom pairs and paths D fingerprints

32 Statistics A bewildering variety of statistical methods are available Mainly driven by data mining & prediction needs in other industries: marketing, finance, telecom, insurance etc multiple linear regression, logistic regression, K nearest neighbours, PLS, linear discriminant analysis, decision trees, neural networks, Support Vector Machines and many, many more

33 Virtual Screening Experiment Enrichment Binary Kernel Discrimination D Shape based based similarity D pharmacophore based PLS model %picked riginal slide provided by Paul Bamborough

34 Drivers for change in predictive modelling Too many models, too little time Many new parameters being added to address attrition in drug discovery Data volumes and rate of production QSAR specialists ~ constant (or declining!) Modelling cannot produce up to date, hand crafted models for every required end point

35 Drivers for change in predictive modelling Modelling tools come from different sources Internal, external, scripts, etc. Data types, descriptors, file formats, etc. There is no holy grail method Model and descriptor performance is variable Individual expertise typically in one or two tools/techniques Data preparation typically takes more time than model building Results in local expertise, global questions

36 Drivers for change in predictive modelling Consumption by non specialists ili profiling of virtual compounds design tools etc Build >deployment needs to be low effort Build >deployment needs to be low effort Blind use of models model being used by someone who does not have mothers love

37 Implementation

38 QSAR Workbench

39 Drug discovery and computational chemistry Target ID Lead Discovery Lead ptimisation Preclinical Development PC Full Development Registration Target Selection Candidate Selection Product Differentiation Market Entry Predictive ADMET Molecular Diversity HTS analysis Array Design Molecular Similarity (Q)SAR analysis Structure Based Design Ligand based Design Attrition analysis/risk assessment Structural Genomics Sequence homology

40 Drug Receptor Interactions ΔG= ΔH TΔS ΔS rt ΔS int ΔH DW ΔHRW S = Entropy, H = enthalpy, rt = rotational/translational, int = internal, DW = drug<->water, RW = Receptor<->water, vib = vibrational, w = water, DR = drug<->receptor ΔG ΔS vib ΔS w ΔH DR

41 Caveats All methods have approximations Many techniques compute ETHALPIES Biological measurements yield FREE EERGIES Enivironmental effects (solvent, ph, receptor) often ignored

42 What does a molecule look like to another molecule? H 2 0 H H

43 The electrostatic potential and electric field generated by a monofunctional chorismate mutase engineered from the P protein of E. coli (Structure from A Lee, P. A. Karplus, B. Ganem and J. ardy, Dept of Chemistry, Cornell Univ.; electrostatic calculations by Daniel Ripoll, CSERG). An imaginary plane intersecting the protein through the two active site regions is colored according to the values of the electrostatic potential (proceeding from low to high in the sequence: dark blue, light blue, green, black, yellow, red). The electric field lines were initiated from a circle contained in the plane, and the direction of the field is indicated by the blue and red lines.

44 Theoretical considerations when working with Protein Structures What you see on a screen is not real Proteins move Hydrogens are often assigned not observed How is solvation being treated? especially when looking to make h bonds Am I taking entropy into account? How are vdw energies being calculated? remember r 12! Ligand conformational energies are as important as protein ligand interaction energies Drug discovery is a multi objective Drug discovery is a multi objective optimisation process

45 HIV protease example from DuPont Merck (1994) Aim: find small molecule alternative to peptide based inhibitors P1 pocket Protein backbone H H P1 pocket Pharmacophore definition P Å P1 R H H H Water mediated interaction H R Peptide mimetic with diol motif to mimic transition state of enzyme catalysed reaction, and hydrophobic groups to fill the P1 pockets Å Å Hbond donor/acceptor - H - H Catalytic aspartyl acids Lam et al., Science, 263, (1994) 707 citations (google scholar)

46 3D database search and initial design P1 3565Å Superimposes on Å key water molecule! P Å Hbond donor/acceptor Incorporation of water molecule into small molecule design: 3D database hit Peptide SAR: Diol better than mono ol Knowledge base: Urea better h bond acceptor than ketone

47 3. Synthesis of lead compounds Computational modelling Predicts precise stereochemistry required Lead ptimisation DMP323 Supremely elegant design from peptide > organic small molecule but not enough to make a drug DMPK issues due to poor chemical properties 18 years later what would you do different? (remember Multi bjective ptimisation)

48 Questions