Fragment screening library workshop (IQPC 2008)


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I also ran a workshop on selection of compounds for fragment screening just before the 2008 IQPC compound library conference and these are the slides I used.

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Fragment screening library workshop (IQPC 2008)

  1. 1. Design of compound libraries for fragment screening IQPC Compound Libraries 2008, Workshop D Peter W. Kenny AstraZeneca, Alderley Park
  2. 2. Workshop outline • Introduction to fragment based drug discovery (FBDD) • Diversity, coverage and library design • Fragment selection criteria • An example: GFSL05 (AstraZeneca generic fragment screening library) • Exercises
  3. 3. Introduction to fragment based drug discovery (FBDD)
  4. 4. FBDD Essentials Screen fragments Synthetic Elaboration Target Target & fragment hit Target & lead
  5. 5. Why fragments? • Leads are assembled from proven molecular recognition elements • Access to larger chemical space • Ability to control resolution at which chemical space is sampled. L
  6. 6. Fragment screening requirements • Assay capable of reliably quantifying weak (~mM) binding • Library of compounds with low molecular complexity and good aqueous solubility •
  7. 7. 2D Protein-observe NMR: PTP1B 15N ppm 1H ppm V49 F30 W125 Y46/T154 Ligand Conc (mM) o 0 o 0.5 o 1.0 o 2.0 o 4.0 N S O N O O O Me L83 G277 G283 T263 A278 D48 Observation of protein resonances allows determination of Kd and can provides binding site information. These techniques require isotopically labelled protein and there are limits on the size of protein that can be studied. (Kevin Embrey)
  8. 8. 1D Ligand-observe NMR Ligand in buffer Ligand and target protein After saturation with potent inhibitor Isotopically labelled protein is not required when observing ligand resonances and there are no restrictions on protein molecular weight. However competition experiments are necessary to quantify binding (Rutger Folmer).
  9. 9. Measurement of fragment binding by SPR [Inhibitor] uM 0 0 0.2 0.4 0.6 0.8 1 0.001 0.01 0.1 1 10 100 1000 In these experiments, protein is first allowed to bind to ligand (target definition compound) that has been immobilised on sensor chip (Biacore). Test compounds binding competitvely with respect to TDC effectively draw protein off sensor and strength of binding can be quantified (Wendy VanScyoc). Figure shows ~200 MW fragment binding with similar affinities (102 mM &145 mM) to different forms of target protein
  10. 10. -6 -5 -4 -3 -2 -10 0 10 20 30 40 50 60 70 80 90 Log Untitled Untitled log [compound]/M %inhibition IC50 = 371 mM Biochemical assay run at high concentration Inhibition of target enzyme by ~200 MW fragment. When using a biochemical assay at high concentration it is necessary to check for non-specific binding and other potential artifacts. It is also possible to assess solubility under assay conditions. Compounds identified by biochemical assays are inhibitory which may not always be the case when using affinity methods. (Adam Shapiro).
  11. 11. Crystal Structure of AZ10336676 bound to PTP1B WPD Loop F182 Catalytic Loop C215 Y46 Q266 Crystallographic detection of fragment binding reveals binding mode but does not allow affinity to be quantified. Crystallography can be challenging with weakly bound inhibitors (Andrew Pannifer & Jon Read)
  12. 12. N S N O O O N S N O O O OMe N S N O O O N S N O O O OMe AZ10336676 3 mM conformational lock 150 mM hydrophobic m-subst 130 mM AZ11548766 3 mM PTP1B: Fragment elaboration P O O O F F P O O O F F 15mM Inactive at 200mM Elaboration by Hybridisation: Literature SAR was mapped onto the fragment AZ10336676 (green). Note overlay of aromatic rings of elaborated fragment AZ11548766 (blue) and difluorophosphonate (red). See Bioorg Med Chem Lett, 15, 2503-2507 (2005)
  13. 13. The Hann molecular complexity model Hann et al [2001]: Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 Success landscape
  14. 14. Ligand Efficiency (Bang For Buck) Does molecule punch its weight? • Scale pIC50 or DGº by molecular weight or number of heavy atoms as surrogate for molecular surface area – Rationale: Molecules interact by presenting molecular surfaces to each other. How effectively does a molecule make use of its molecular surface? • Fragment hits tend to have high ligand efficiency… – But then they need to! • Is high ligand efficiency indicative of hot spot on protein surface A. L. Hopkins, C. R. Groom, A. Alex, Ligand efficiency: A useful metric for lead selection, Drug Discov. Today 2004, 430-431.
  15. 15. Overview of fragment based lead discovery Target-based compound selection Analogues of known binders Generic screening library Measure Kd or IC50 Screen Fragments Synthetic elaboration of hits SAR Protein Structures Milestone achieved! Proceed to next project
  16. 16. Scheme for fragment based lead optimisation
  17. 17. Literature General • Erlanson et al, Fragment-Based Drug Discovery, J. Med. Chem., 2004, 47, 3463-3482. • Congreve et al. Recent Developments in Fragment-Based Drug Discovery, J. Med. Chem., 2008 51, 3661–3680. • Albert et al, An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca's drug discovery programmes. Curr. Top. Med. Chem. 2007, 7, 1600-1629 • Hann et al Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 • Shuker et al, Discovering High Afinity Ligands for Proteins: SAR by NMR, Science, 1996, 274 1531-1534). Screening Libraries • Schuffenhauer et al, Library Design for Fragment Based Screening, Curr. Top. Med. Chem. 2005, 5, 751-762. • Baurin et al, Design and Characterization of Libraries of Molecular Fragments for Use in NMR Screening against Protein Targets, J. Chem. Inf. Comput. Sci., 2004, 44, 2157- 2166 • Colclough et al, High throughput solubility determination with application to selection of compounds for fragment screening. Bioorg, Med. Chem. 2008, 16, 6611-6616. • Kenny & Sadowski, Structure modification in chemical databases. Methods and Principles in Medicinal Chemistry 2005, 23, 271-285.
  18. 18. Diversity, coverage and library design
  19. 19. Screening Library Design Requirements • Precise specification of substructure – Count substructural elements (e.g. chlorine atoms; rotatable bonds; terminal atoms; reactive centres…) – Define generic atom types (e.g. anionic centers; hydrogen bond donors) • Meaningful measure of molecular similarity – Structural neighbours likely to show similar response in assay
  20. 20. Measures of diversity & coverage • • • • • • • • • • • • • • • 2-Dimensional representation of chemical space is used here to illustrate concepts of diversity and converage. Stars indicate compounds selected to sample this region of chemical space. In this representation, similar compounds are close together
  21. 21. Coverage & Diversity Poor coverage of available chemical space by small set of mutually similar compounds Reasonable coverage of available chemical space given small, diverse set of compounds Good coverage of available chemical space by appropriate number of compounds • • • • • •• • • • • • •
  22. 22. Neighborhoods and library design
  23. 23. Acceptable diversity And coverage? Assemble library in soluble form Add layer to core Incorporate layer Yes No Select core Core and layer library design Compounds in a layer are selected to be diverse with respect to core compounds. The ‘outer’ layers typically contain compounds that are less attractive than the ‘inner’ layers. This approach to library design can be applied with Flush or BigPicker programs (David Cosgrove, AstraZeneca, Alderley Park) using molecular similarity measures calculated from molecular fingerprints. (See Curr. Top. Med. Chem. 2007, 7, 1600-1629).
  24. 24. Fragment selection criteria
  25. 25. Sample Availability Molecular Connectivity Physical Properties screening samples Close analogs Ease of synthetic elaboration Molecular complexity Ionisation Lipophilicity Solubility Molecular recognition elementsMolecular shape 3D Pharmacophore Privileged substructures Undesirable substructures Molecular size 3D Molecular Structure Fragment selection criteria
  26. 26. NH NN H H H H O O O Me NH N N H H H H O O O Me O O Degree of substitution as measure of molecular complexity The prototypical benzoic acid can be accommodated at both sites and, provided that binding can be observed, will deliver a hit against both targets (see Curr. Top. Med. Chem. 2007, 7, 1600-1629)
  27. 27. Hits, non-hits & lipophilicity: Survival of the fattest* Mean Std Err Std Dev Hits 2.05 0.08 1.10 Non-Hits 1.35 0.03 1.24 *Analysis of historic screening data & quote: Niklas Blomberg, AZ Molndal Comparison of ClogP for hits and non-hits from fragment screens run at AstraZeneca
  28. 28. 20% 10% 30% 40% 50% log(S/M) Aqueous solubility: Percentiles for measured log(S/M) as function of ClogP Data set is partitioned by ClogP into bins and the percentiles and mean ClogP is calculated for each. This way of plotting results is particularly appropriate when dynamic range for the measurement is low. Beware of similar plots where only the mean or median value is shown for the because this masks variation and makes weak relationships appear stronger than they actually are. (See Bioorg. Med. Chem. 2008, 16, 6611-6616). Measure solubility for neutral (at pH 7.4) fragments for which ClogP > 2.2
  29. 29. Solubility in DMSO: Salts Precipitate observed Precipitate not observed All samples Adduct 525 29 554 Not Adduct 4440 89 4529 All samples 4965 118 5083 Analysis of 5K solubilised samples showed that 5% of samples registered as ‘adduct’ (mainly salts) showed evidence of precipitation compared to 2% of the other samples
  30. 30. # # Generic fragment screening library # # SMARTS for restriction of substitution in fragments # # restrict_subs_1.smt # #------------------------------------------------------------- # Some general size restrictions to set tone of search # Hev [A,a] 5-20 Arom a 5-12 Term [A;D1]-[A,a] 0-2 Fuse [c,A;R2] 0-2 #------------------------------------------------------------- # Specific atom types: Explicit specification of what is # permitted in molecule. If it's not allowed it's verboten! # CH2 [C;H2;!R] 0-2 O1 [OD2] 0-2 O2 [OH] 0-2 O3 O=C[OH] 0-1 O4 O=C[NX3] 0-2 O5 O=c[n&X3,o&X2] 0-2 O6 O=c1aa[n&X3,o&X2]cc1 0-2 O7 O=S 0-2 TerAm [N;!+;X3]([CX4])([CX4])[CX4] 0-2 N1 [N,n;!+;X3] 0-2 N2 [n;X2] 0-3 N3 [n;H;!+] 0-1 N4 [N;X3;!H0;!+] 0-2 S1 S(c)[C&X4,c] 0-1 CO C(=O)[N,O&H] 0-2 SO S(=O)=O 0-1 ArOS [o,s] 0-1 # Specific requirements # Atoms providing polar interaction Interact1 [$TerAm,$N2,$N3,$N4] * Interact2 [$O2,$O3,$O4,$O5,$O6,$O7] * Interact [$Interact1,$Interact2] 1-4 # # Benzene ring Benzene c1ccccc1 6-12 #------------------------------------------------------------- # # Decrapping SMARTS: Don't want these # AtmOK1 [c,$CH2,$O1,$O2,$O3,$O4,$O5,$O6,$O7] * AtmOK2 [$N1,$N2,$N3,$N4,$TerAmin,$S1] * AtmOK3 [$CO,$SO,$ArOS,C&H3,F,Cl] * CrpAtm [A,a;!$AtmOK1;!$AtmOK2;!$AtmOK3] 0 Cation [A,a;+] 0 ReactHal [F,Cl,Br,I][C&X4,$(c[nX2]),$(C=O),N,O,S] 0 SulfEster S(=O)O[CX4] 0 NAcyl NC=O * NN1 [N;!$NAcyl]-[N;!$NAcyl] 0 NN2 [N,n]-N 0 NO [N,n;!$NAcyl]-O 0 AcycEst C(=O)O[a,A] 0 Anhydrid O=[C,c][o,O][C,c]=O 0 Formyl [CH]=O 0 Keto O=C(C)C 0 Quinon O=c1ccc(=O)cc1 0 Phenol [OH]c 0 Anilin1 [NH2]c1ccccc1 0 Anilin2 [NH]([CH3])c1ccccc1 0 Het2sp3c [O,N,n,S]-;!@[CX4]-[O,N,n,S] 0 # # Groups to restrict: Not so bad in very small numbers Amino [NH2] 0-1 Chloro [Cl] 0-1 Hydroxyl [OH] 0-1 # # Combinations of groups to be restricted AmHydrox [$Amino,$Hydroxyl] 0-1 Example of SMARTS used to select fragments
  31. 31. An example: GFSL05 (AstraZeneca generic fragment screening library)
  32. 32. The GFSL05 project • Rationale – Strategic requirement: Readily accessible source of compounds for a range of fragment screening applications – Tactical objective: Assemble 20K structurally diverse compounds with properties that are appropriate for fragment screening as 100mM DMSO stocks • Design overview – Core and layer design applied with successively more permissive filters (substructural, neighborhood, properties) – Bias compound selection to cover unsampled chemical space
  33. 33. GFSL05: Design • Molecular recognition considerations – Requirement for at least one charged center or acceptably strong hydrogen bonding donor or acceptor • Substructural requirements defined as SMARTS – Progressively more permissive filters to apply core and layer design – Restrict numbers of non-hydrogen atoms (size) and terminal atoms (complexity) – Filters to remove undesirable functional groups (acyl chloride) and to restrict numbers of others (nitro, chloro) – ‘Prototypical reaction products’ for easy follow up • Control of lipophilicity (ClogP) dependent on ionisation state – Solubility measurement for more lipophilic neutrals • Tanimoto coefficient calculated using foyfi fingerprints (Dave Cosgrove) as primary similarity measure – Requirement for neighbour availability in core and layer design
  34. 34. ClogP: Charged library compounds ClogP: Neutral library compoundsNon-hydrogen atoms GFSL05: Size and lipophilicity profiles Rotatable bonds
  35. 35. 61 17 13 4 4 1 0 Breakdown of GFSL05 by charge type Neutral Anion Cation Ionisation states are identified using AZ ionisation and tautomer model. Multiple forms are generated for acids and bases where pKa is thought to be close to physiological pH (see Methods and Principles in Medicinal Chemistry 2005, 23, 271-285)
  36. 36. GFSL05: Numbers of neighbours within library as function of similarity (Tanimoto coefficient; foyfi fingerprints) 0.90 0.85 0.80
  37. 37. GFSL05: Numbers of available neighbours as function of similarity (Tanimoto coefficient; foyfi fingerprints) and sample weight >10mg >20mg 0.90 0.85 0.80 0.90 0.85 0.80
  38. 38. Exercises
  39. 39. Exercise 1: Directed library using crystal structural information You are selecting fragments for screening against an enzyme target. You have available the crystal structure of a complex with a stable substrate analog, further access to crystallography and a robust biochemical assay. • What advantages and disadvantages do you see in using a biochemical assay • How would you select the compounds in the screening library? • How would you follow up hits from the primary screen?
  40. 40. Exercise 2: Generic library for screening by X-ray crystallography You are selecting a single generic set of fragments for screening against multiple, unrelated targets using X-ray crystallography. • How might the requirements of crystallography differ from those of other technologies for detecting binding? • How would you select the library compounds? • How would you partition the screening library into mixtures for screening?