I'm a molecule designer... get me out of here!

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Presented at Vertex and Novartis during July 2013 trip to Cambridge MA. A correlation inflation teaser followed by alkane/water logP and SAR/SPR based on relationships between structures. The photograph in the title slide was taken in Asunción.

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I'm a molecule designer... get me out of here!

  1. 1. I’m a molecule designer…. Get me out of here! Peter W Kenny (pwk.pub.2008@gmail.com)
  2. 2. Some things that are hurting Pharma • Having to exploit targets that are poorly-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 on far side of blood brain barrier) Dans la merde : http://fbdd-lit.blogspot.com/2011/09/dans-la-merde.html
  3. 3. Molecular Design • Control of behavior of compounds by manipulation of molecular properties • Hypothesis-driven or prediction-driven • Sampling of chemical space – Does fragment-based screening allow better control of sampling resolution?
  4. 4. Achtung! Spitfire! Prediction-driven design: Ju 87 Stuka Stuka on wikipedia
  5. 5. “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 Hypothesis-driven design: B52 Stratofortress B52 on wikipedia
  6. 6. Do1 Do2 Ac1 Kenny (2009) JCIM 49:1234-1244 DOI Illustrating hypothesis-driven design DNA Base Isosteres: Acceptor & Donor Definitions
  7. 7. Watson-Crick Donor & Acceptor Electrostatic Potentials for Adenine Isosteres Vmin(Ac1) Va (Do1) Kenny (2009) JCIM 49:1234-1244 DOI
  8. 8. The lurking menace of correlation inflation Kenny & Montanari (2013) JCAMD 27:1-13 DOI
  9. 9. r N 1202 R 0.247 ( 95% CI: 0.193 | 0.299) N 8 R 0.972 ( 95% CI: 0.846 | 0.995) Correlation Inflation in Flatland See Lovering, Bikker & Humblet (2009) JMC 52:6752-6756 DOI Kenny & Montanari (2013) JCAMD 27:1-13 DOI
  10. 10. Choosing octanol was the first mistake...
  11. 11. Polarity N ClogP ≤ 5 Acc ≤ 10; Don ≤5 An alternative view of the Rule of 5
  12. 12. 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: http://logkow.cisti.nrc.ca/logkow/index.jsp --1.05
  13. 13. Octanol/Water Alkane/Water Octanol/water is not the only partitioning system
  14. 14. logPoct = 2.1 logPalk = 1.9 DlogP = 0.2 logPoct = 1.5 logPalk = -0.8 DlogP = 2.3 logPoct = 2.5 logPalk = -1.8 DlogP = 4.3 Differences in octanol/water and alkane/water logP values reflect hydrogen bonding between solute and octanol Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
  15. 15. DlogP = 0.5 PSA/ Å2 = 48 Polar Surface Area is not predictive of hydrogen bond strength DlogP = 4.3 PSA/ Å2 = 22 Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
  16. 16. -0.054 -0.086 -0.091 -0.072 -0.104 -0.093 Hydrogen bonding of esters Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
  17. 17. DlogP (corrected) Vmin/(Hartree/electron) DlogP (corrected) Vmin/(Hartree/electron) N or ether O Carbonyl O Prediction of contribution of acceptors to DlogP DlogP = DlogP0 x exp(-kVmin) Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
  18. 18. Basis for ClogPalk model logPalk MSA/Å2 Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOIKenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
  19. 19. 𝐶𝑙𝑜𝑔𝑃𝑎𝑙𝑘 = 𝑙𝑜𝑔𝑃0 + 𝑠 × 𝑀𝑆𝐴 − 𝑖 ∆𝑙𝑜𝑔𝑃𝐹𝐺,𝑖 − 𝑗 ∆𝑙𝑜𝑔𝑃𝐼𝑛𝑡,𝑗 ClogPalk from perturbation of saturated hydrocarbon logPalk predicted for saturated hydrocarbon Perturbation by functional groups Perturbation by interactions between functional groups Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
  20. 20. Performance of ClogPalk model Hydrocortisone Cortisone (logPalk  ClogPalk)/2 logPalkClogPalk Atropine Propanolol Papavarlne Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
  21. 21. Another way to look at SAR?
  22. 22. (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? • Are models predicting activity or locating neighbours? • To what extent are ‘global’ models just ensembles of local models? • How well do the methods handle ‘activity cliffs’? • How should we account for sizes of descriptor pools when comparing model performance?
  23. 23. 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
  24. 24. Neighborhoods and library design
  25. 25. Examples of relationships between structures Tanimoto coefficient (foyfi) for structures is 0.90 Ester is methylated acid Amides are ‘reversed’
  26. 26. Leatherface molecular editor From chain saw to Matched Molecular Pairs c-[A;!R] bnd 1 2 c-Br cul 2 hyd 1 1 [nX2]1c([OH])cccc1 hyd 1 1 hyd 3 -1 bnd 2 3 2 Kenny & Sadowski Structure modification in chemical databases, Methods and Principles in Medicinal Chemistry (Chemoinformatics in Drug Discovery 2005, 23, 271-285 DOI
  27. 27. Glycogen Phosphorylase inhibitors: Series comparison DpIC50 DlogFu DlogS 0.38 (0.06) -0.30 (0.06) -0.29 (0.13) DpIC50 DlogFu DlogS 0.21 (0.06) 0.13 (0.04) 0.20 (0.09) DpIC50 DlogFu DlogS 0.29 (0.07) -0.42 (0.08) -0.62 (0.13) Standard errors in mean values in parenthesis; see Birch et al (2009) BMCL 19:850-853 DOI
  28. 28. 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 were not synthesised. Birch et al (2009) BMCL 19:850-853 DOI
  29. 29. -0.316 -0.315 -0.296 -0.295 Bioisosterism: Carboxylate & tetrazole -0.262 -0.261 -0.268 -0.268 Kenny (2009) JCIM 49:1234-1244 DOI
  30. 30. Amide N DlogS SE SD %Increase Acyclic (aliphatic amine) 109 0.59 0.07 0.71 76 Cyclic 9 0.18 0.15 0.47 44 Benzanilides 9 1.49 0.25 0.76 100 Effect of amide N-methylation on aqueous solubility is dependent on substructural context Birch et al (2009) BMCL 19:850-853 DOI
  31. 31. Relationships between structures Discover new bioisosteres & scaffolds Prediction of activity & properties Recognise extreme data Direct prediction (e.g. look up substituent effects) Indirect prediction (e.g. apply correction to existing model) Bad measurement or interesting effect?
  32. 32. MUDO Molecule Editor • SMIRKS-based re-write of Leatherface using OEChem • Can process 3D structures (e.g. form covalent bond between protein and ligand) • Identification of matched molecular pairs is much easier than with Leatherface • Submitted (source code in supplemental information): – Kenny, Montanari, Propopczyk, Sala, Rodrigues Sartori (2013) Automated molecule editing in molecular design. JCAMD (submitted)
  33. 33. Stuff to think about • Data can be massaged and correlations can be enhanced but it won’t extract us from ‘la merde’ • There is life beyond octanol/water (and atom- centered charges) if we choose to look for it • Even molecules can have meaningful relationships

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