Design of fragment screening libraries (Feb 2010 version)

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I have lectured on design of fragment screening libraries a number of times and, to be honest, my material is getting a bit dated. This presentation is from Feb 2010 when I was visiting CSIRO and the …

I have lectured on design of fragment screening libraries a number of times and, to be honest, my material is getting a bit dated. This presentation is from Feb 2010 when I was visiting CSIRO and the photo in the title slide was taken in Tierra del Fuego.

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  • 1. Design of Fragment Screening Libraries Peter W. Kenny (
  • 2. FBDD Essentials Screen fragments Synthetic Elaboration Target Target & fragment hit Target & lead
  • 3. Why fragments? • Ligands are assembled from proven molecular recognition elements • Access to larger chemical space • Ability to control resolution at which chemical space is sampled L
  • 4. 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)
  • 5. 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).
  • 6. -6 -5 -4 -3 -2 -10 0 10 20 30 40 50 60 70 80 90 log [compound]/M %inhibition IC50 = 371 mM Biochemical assay run at high concentration Inhibition of target enzyme by ~200 Da 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. (Shapiro, Walkup & Keating J. Biomol. Screen. 2009, 4, 1008 -1016)
  • 7. 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 tethered to 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). Fragment (~200 Da) binding with similar affinities (102 mM &145 mM) to different forms of target protein
  • 8. P O O O F F P O O O F F 15mM Inactive at 200mM 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 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)
  • 9. Binding Efficiency Measures LE = DGº/NonHyd Hopkins, Groom & Alex, DDT 2004, 9, 430-431 BEI = pIC50/(MW/kDa) Abad-Zapaftero & Metz, DDT 2005, 10, 430-431 LLE = pIC50 - ClogP Leeson & Springthorpe , NRDD 2007, 6, 881-890. Scale Measured Binding Offset Measured Binding
  • 10. 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
  • 11. 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
  • 12. Scheme for fragment based lead optimisation
  • 13. Fragment screening requirements • Assay capable of reliably quantifying weak (~mM) binding • Library of compounds with low molecular complexity and good aqueous solubility •
  • 14. Achtung! Spitfire! Hitting the target: The old way… Stuka on wikipedia
  • 15. “Why can’t we pray for something good, like a tighter bombing pattern, for example? Couldn’t we pray for a tighter bombing pattern?” , Heller, Catch 22, 1961 …and the new B52 on wikipedia
  • 16. Measures of Diversity & Coverage • • • • • • • • • • • • • • • 2-Dimensional representation of chemical space is used here to illustrate concepts of diversity and coverage. Stars indicate compounds selected to sample this region of chemical space. In this representation, similar compounds are close together
  • 17. Coverage, Diversity & Library Design •• • • • •• • • • • • •
  • 18. Neighborhoods and library design
  • 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. 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
  • 21. 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)
  • 22. 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
  • 23. 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
  • 24. Solubility in DMSO: Salts Precipitate not observed Precipitate 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
  • 25. 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 (Dave Cosgrove, AstraZeneca, Alderley Park) using molecular similarity measures calculated from molecular fingerprints. (See Curr. Top. Med. Chem. 2007, 7, 1600-1629).
  • 26. 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
  • 27. At least they didn’t make you coordinate the generic fragment screening library project We should never have listened to HR OK it’s Dien Bien Phu or you can coordinate the generic fragment screening library project. A personal view of coordinating GFSL05…
  • 28. GFSL05: Overview • 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
  • 29. APGNMR07: Overview • General – 1200 Compounds – Derived in part form existing AZ NMR libraries and GFSL05 – Molecules smaller than those in GFSL05 – 200mM in d6-DMSO • Partitioning of library – Groups (200) of 6 compounds defined – Allows screening of mixtures of 6 or 12 – Acid:Base:Neutral = 1:1:4
  • 30. ClogP: Charged library compounds ClogP: Neutral library compoundsNon-hydrogen atoms GFSL05: Size and lipophilicity profiles Rotatable bonds
  • 31. GFSL05 APGNMR07 Lipophilicity profiles for fragment libraries
  • 32. 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 Kenny & Sadowski Methods and Principles in Medicinal Chemistry 2005, 23, 271-285)
  • 33. Library Property Ionized N Mean SD SE GFSL05 Non-H atoms No 12284 15.64 3.42 0.03 Yes 8058 16.04 3.58 0.04 ClogP No 12284 1.248 1.056 0.010 Yes 8058 1.658 1.337 0.015 APGNMR07 Non-H atoms No 800 13.66 2.18 0.08 Yes 400 13.91 2.14 0.11 ClogP No 800 1.528 0.978 0.035 Yes 400 1.718 1.006 0.050 Summary statistics for fragment libraries
  • 34. GFSL05: Numbers of neighbours within library as function of similarity (Tanimoto coefficient; foyfi fingerprints) 0.90 0.85 0.80
  • 35. 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
  • 36. A couple of questions to finish with… • Is it helpful to think of leadlikeness in terms of the point at which screening stops and synthesis begins? • Does a screening technology that allows millimolar binding of a compound to be characterized reliably make that compound more leadlike?
  • 37. 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 • Blomberg et al, Design of compound libraries for fragment screening, JCAMD 2009, 23, 513-525. • 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.
  • 38. FBDD Blogs Practical Fragments: FBDD Literature: (these will both lead you to LinkedIn & facebook groups)