From screening to molecular interactions: A short tour


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This was what I presented a the 2012 SancaMedChem course at USP São Carlos . The photograph in the title slide was taken in Asunción.

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From screening to molecular interactions: A short tour

  1. 1. From screening to molecular interactions: A short tour Peter W Kenny (
  2. 2. Some things that are hurting Pharma • Having to exploit targets that are weakly-linked to human disease • Inability to predict idiosyncratic toxicity • Inability to measure free (unbound) physiological concentrations of drug for remote targets (e.g. intracellular or within blood brain barrier) Dans la merde:
  3. 3. Screening and Chemical Space
  4. 4. 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
  5. 5. The neighborhood concept
  6. 6. Achtung! Spitfire! Hitting the target: The old way… Stuka on wikipedia
  7. 7. “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
  8. 8. HTS is so glamorous…
  9. 9. So, Maria, why do you think it is that the Russians are so much better than the Germans at tennis these days? Actually we started to beat them at their national sport almost 70 years ago and... .... as Uncle Joe was so fond of saying, quantity has a quality all of its own. … that even the stars of tennis have heard of it
  10. 10. • One measure of the power of an assay the weakness of the binding that can be detected and quantified Screening Assays
  11. 11. Looking for leads: An overview of screening Chemical Space Leads High throughput screening Virtual (directed) screening Hit to lead Fragment screening
  12. 12. A model for molecular complexity This model is equally relevant to conventional and fragment-based screening. See Hann, Leach & Harper J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 | Success landscape
  13. 13. 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 Blomberg et al JCAMD 2009, 23, 513-525 | | This way of thinking about molecular complexity is similar to the ‘needle’ concept introduced by Roche researchers. See Boehm et al J. Med. Chem. 2000, 43, 2664-2774 |
  14. 14. Hopkins, Groom & Alex, DDT 2004, 9, 430-431 Ligand Lipophilicity Efficiency LLE = pIC50 - ClogP Leeson & Springthorpe , NRDD 2007, 6, 881-890. Measured binding is scaled Measured binding is offset Binding Efficiency Measures Ligand Efficiency LE= DGº/NonHyd
  15. 15. FBDD Essentials Screen fragments Synthetic Elaboration Target Target & fragment hit Target & lead
  16. 16. Link Fragment Elaboration Tactics Merge Grow
  17. 17. P O O O F F P O O O F F 15M Inactive at 200M 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 M hydrophobic m-subst 130 M AZ11548766 3 M 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)
  18. 18. 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
  19. 19. Why fragments? • Access to larger chemical space • Counter the advantage of competitors’ large compound collections • Ligands are assembled from proven molecular recognition elements • Just a smart way to do Structure-Based Design • Control resolution at which chemical space is sampled
  20. 20. • Control of properties of compounds and materials by manipulation of molecular properties • Prediction-Driven or Hypothesis-Driven Molecular Design
  21. 21. (Descriptor-based) QSAR/QSPR: Some questions • How valid is methodology (especially for validation) when distribution of compounds in training/test space is highly non-uniform? • Do models predict activity or just locate neighbours? • Are ‘global’ models ensembles of local models? • How well do the methods handle ‘activity cliffs’? • How should we account for sizes of descriptor pools when comparing models?
  22. 22. Effect of bioisosteric replacement on plasma protein binding ? Date of Analysis N DlogFu SE SD %increase 2003 7 -0.64 0.09 0.23 0 2008 12 -0.60 0.06 0.20 0 Mining PPB database for carboxylate/tetrazole pairs suggested that bioisosteric replacement would lead to decrease in Fu so tetrazoles not synthesised. Birch et al, BMCL 2009, 19, 850-853
  23. 23. Molecular Interactions and Drug Action
  24. 24. Molecular Recognition • Functional behavior of molecules is determined by the interactions of its molecules with the different environments in which they exist • Mutual presentation of molecular surfaces • For association in water we need to match interaction potential to maximise affinity.
  25. 25. Direct interactions Indirect interactions ‘Non-classical’ e.g. heavy halogen Electrostatic e.g. hydrogen bonding Dispersion forces Steric clash Hydrophobic Conformational strain & entropy Non-covalent interactions A taxonomy of non-covalent interactions
  26. 26. Hydrogen Bonding Interactions between drug molecules in crystal lattice (Solubility, melting point polymorphism, crystallinity) Interactions between drug and water molecules (Solubility, distribution, permeability, potency, toxicity, efflux, metabolism) Interactions between drug molecules & (anti)target(s) (Potency, toxicity, efflux , metabolism, distribution) Hydrogen Bonding in Drug Discovery & Development Interactions between water molecules (Hydrophobic effect)
  27. 27. Cartoon representation of hydrophobic effect Polar Surface Binding Pocket
  28. 28. Cartoon representation of hydrophobic forces
  29. 29. Does octanol/water ‘see’ hydrogen bond donors? --0.06 -0.23 -0.24 --1.01 -0.66 Sangster lab database of octanol/water partition coefficients: --1.05
  30. 30. bond basicity  Plot of V/kJmol-1 against r/Å for pyridine on lone pair axis showing electrostatic potential minimum 1.2Å from nitrogen -300 -200 -100 0 V 0 1 2 3 4 5 r Electrostatic potential as function of position for acceptor V/kJmol-1 r/År/Å r
  31. 31. Fluorine: A weak hydrogen bond acceptor -0.122 -0.113 -0.071 -0.038
  32. 32. -0.054 -0.086 -0.091 -0.072 -0.104 -0.093 Hydrogen bonding of esters Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730
  33. 33. -0.316 -0.315 -0.296 -0.295 Bioisosteric relationship between carboxylic acids and tetrazoles Kenny, JCIM, 2009, 49, 1234-1244 -0.262 -0.261 -0.268 -0.268
  34. 34. H O H H O H H O H H O H N H O Effect of complex formation on predicted hydrogen bond acidity of water 1.2 (~ Alcohol) 2.0 (~ Phenol) 2.8 (~ 4-CF3Phenol) Kenny, JCIM, 2009, 49, 1234-1244
  35. 35. Summary • Screening as sampling chemical space – Fragments are thought to allow better sampling • Molecular design as a process of tuning interaction potential – Design can be hypothesis-driven or prediction- driven