Perspective of pharmaceutical molecular design

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Perspective of pharmaceutical molecular design

  1. 1. A perspective of pharmaceutical molecular design Peter W Kenny (pwk.pub.2008@gmail.com)
  2. 2. Some things that make drug discovery difficult β€’ Having to exploit targets that are weakly-linked to human disease β€’ Inability to predict idiosyncratic toxicity β€’ 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. [π·π‘Ÿπ‘’π‘” 𝑿, 𝑑 ] π‘“π‘Ÿπ‘’π‘’ 𝐾 𝑑 Why is it drug discovery and not drug design?
  4. 4. In tissues Free in plasma Bound to plasma protein Dose of drug Eliminated drug A simplified view of what happens to drugs
  5. 5. Pharmaceutical molecular design β€’ Control of behavior of compounds and materials by manipulation of molecular properties β€’ Hypothesis-driven or prediction-driven β€’ Sampling of chemical space – Does fragment-based screening allow better control of sampling resolution?
  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. Choosing octanol was the first mistake...
  9. 9. Lipophilic & half ionised Hydrophilic & neutral Introduction to partition coefficients
  10. 10. Polarity N ClogP ≀ 5 Acc ≀ 10; Don ≀5 An alternative view of the Rule of 5
  11. 11. Does octanol/water β€˜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
  12. 12. Octanol/Water Alkane/Water Octanol/water is not the only partitioning system
  13. 13. 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
  14. 14. -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
  15. 15. 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
  16. 16. Basis for ClogPalk model logPalk MSA/Γ…2 Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOIKenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
  17. 17. πΆπ‘™π‘œπ‘”π‘ƒπ‘Žπ‘™π‘˜ = π‘™π‘œπ‘”π‘ƒ0 + 𝑠 Γ— 𝑀𝑆𝐴 βˆ’ 𝑖 βˆ†π‘™π‘œπ‘”π‘ƒπΉπΊ,𝑖 βˆ’ 𝑗 βˆ†π‘™π‘œπ‘”π‘ƒπΌπ‘›π‘‘,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
  18. 18. Performance of ClogPalk model Hydrocortisone Cortisone (logPalk  ClogPalk)/2 logPalkο€­ClogPalk Atropine Propanolol Papavarlne Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
  19. 19. Another way to look at Structure activity relationships?
  20. 20. (Descriptor-based) QSAR/QSPR: Some questions β€’ How valid is methodology (especially for validation) when distribution of compounds in training/test space is highly non-uniform? β€’ Are models predicting activity or locating neighbours? β€’ To what extent are β€˜global’ models just ensembles of local models? β€’ How well do the methods handle β€˜activity cliffs’? β€’ How should we account for sizes of descriptor pools when comparing model performance?
  21. 21. Measures of Diversity & Coverage β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ β€’ 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
  22. 22. Neighborhoods and library design
  23. 23. Examples of relationships between structures Tanimoto coefficient (foyfi) for structures is 0.90 Ester is methylated acid Amides are β€˜reversed’
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. -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
  28. 28. 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
  29. 29. 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?
  30. 30. β€’ Molecular design is not just about prediction so how can we make hypothesis-driven design more systematic? β€’ There is life beyond octanol/water (and atom- centered charges) if we choose to look for it β€’ Even molecules can have meaningful relationships Some stuff to think about
  31. 31. Spares follow
  32. 32. The lurking menace of correlation inflation Kenny & Montanari (2013) JCAMD 27:1-13 DOI
  33. 33. Preparation of synthetic data for correlation inflation study Add Gaussian noise (SD=10) to Y Kenny & Montanari (2013) JCAMD 27:1-13 DOI
  34. 34. Correlation inflation by hiding variation See Hopkins, Mason & Overington (2006) Curr Opin Struct Biol 16:127-136 DOI Leeson & Springthorpe (2007) NRDD 6:881-890 DOI Data is naturally binned (X is an integer) and mean value of Y is calculated for each value of X. In some studies, averaged data is only presented graphically and it is left to the reader to judge the strength of the correlation. R = 0.34 R = 0.30 R = 0.31 R = 0.67 R = 0.93 R = 0.996
  35. 35. 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

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