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Molecular design: One step back and two paths forward

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I presented this at the RACI Biomolecular on the Beach conference in December 2011. 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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Molecular design: One step back and two paths forward

  1. 1. Molecular Design: One step back and two paths forward Peter W Kenny (pwk.pub.2008@gmail.com)
  2. 2. Some things that are hurting Pharma • Having to exploit targets that are less well-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: http://fbdd-lit.blogspot.com/2011/09/dans-la-merde.html
  3. 3. Keep an eye out for creative data analysis
  4. 4. Add Normally-distributed noise Data set A Data set B Points plotted at constant increment Equal numbers of points for each value of x Preparation of data sets
  5. 5. r2 = 0.99 RMSE = 0.36 Data set A: Fit median value of Y to X An example of this approach to plotting data can be seen in Leeson & Springthorpe, The influence of drug-like concepts on decision-making in medicinal chemistry. Nat. Rev. Drug Discov. 2007, 7, 881-890.
  6. 6. Low Medium High Data set B: Use value of X to split into three equally-sized groups and show mean and associated confidence interval for each An example of this approach to analysing data can be seen in: Gleeson, Generation of a Set of Simple, Interpretable ADMET Rules of Thumb. J. Med. Chem. 2008, 51, 817-834.
  7. 7. What data set A really looks like Fit to original data N=11000; r2 = 0.09 ; RMSE = 9.95 Fit to transformed data N=11; r2 = 0.99 ; RMSE = 0.36 Percentile plot (see Colclough et al BMC 2008, 16, 6611-6616) 90% 75% 50% 25% 10% Residual plot for fit to original data
  8. 8. Fit to original data N=10000; r2 = 0.08 ; RMSE = 10.0) Residual plot for fit to original data Low Medium High What data set B really looks like Mean values of Y and (barely visible) confidence intervals shown with standard deviations x
  9. 9. Octanol was the first mistake...
  10. 10. Lipophilic & half ionised Hydrophilic Introduction to partition coefficients
  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, J. Med. Chem. 2008, 51, 3720-3730
  15. 15. DlogP = 0.5 PSA/ Å2 = 48 Polar Surface Area is not predictive of hydrogen bond strength Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730 DlogP = 4.3 PSA/ Å2 = 22
  16. 16. 1.0 1.1 0.8 1.3 1.7 0.8 1.5 Measured values of DlogP Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730 1.6 1.1
  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 Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730 DlogP = DlogP0 x exp(-kVmin)
  18. 18. logPoct = 0.89 predicted logPalk = -4.2 PSA/Å2 = 53 logPoct = 1.58 predicted logPalk = -1.4 PSA/Å2 = 65 Lipophilicity/polarity of Morphine & Heroin Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730
  19. 19. logPhxdlogPoct log(Cbrain/Cblood) DlogP Prediction of blood/brain partitioning R2 = 0.66 RMSE = 0.54 R2 = 0.82 RMSE = 0.39 R2 = 0.88 RMSE = 0.32 Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730
  20. 20. Difficulties in measuring logPalk: Many compounds poorly soluble in alkanes Self-association masks polarity
  21. 21. Alkane/water partition coefficients: Where next? General access to logPalk likely to require predictive models for some time Carefully measure logPalk for structurally diverse compounds Solvation models: logPalk easier to measure than ΔG(gaq)
  22. 22. Another way to look at SAR
  23. 23. (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? • 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?
  24. 24. 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
  25. 25. Neighborhoods and library design
  26. 26. Examples of relationships between structures Tanimoto coefficient (foyfi) for structures is 0.90 Ester is methyl-substituted acid Amides are ‘reversed’
  27. 27. 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.
  28. 28. 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 shown in parenthesis; see Birch et al, BMCL 2009, 19, 850-853
  29. 29. 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
  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, BMCL 2009, 19, 850-853
  31. 31. Relationships between structures Discover new bioisosteres 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. Conclusions • Data can be massaged and correlations can be enhanced but it won’t extract us from ‘la merde’ • There is life beyond octanol/water if we choose to look for it • Even molecules can have meaningful relationships
  33. 33. Selected references • Seiler (1974) Interconversion of lipophilicities from hydrocarbon/water systems into the octanol/water system. Eur. J. Med. Chem. 9, 473–479. • Toulmin, Wood & Kenny (2008) Toward Prediction of Alkane/Water Partition Coefficients. J. Med. Chem. 51, 3720-3730. http://dx.doi.org/10.1021/jm701549s • Kenny & Sadowskii (2005) Structure modification in chemical databases. Methods and Principles in Medicinal Chemistry 23(Chemoinformatics in Drug Discovery), 271-285 http://dx.doi.org/10.1002/3527603743.ch11 • Leach et al (2006) Matched Molecular Pairs as a Guide in the Optimization of Pharmaceutical Properties; a Study of Aqueous Solubility, Plasma Protein Binding and Oral Exposure,. J. Med. Chem. 49, 6672-6682. http://dx.doi.org/10.1021/jm0605233 • Birch et al (2009) Matched molecular pair analysis of activity and properties of glycogen phosphorylase inhibitors. Bioorg. Med. Chem. Lett. 19, 850-853. http://dx.doi.org/10.1016/j.bmcl.2008.12.003 • Wassermann, Wawer & Bajorath (2010) Activity Landscape Representations for Structure−Activity Relationship Analysis. J. Med. Chem. 53, 8209-8223. http://dx.doi.org/10.1021/jm100933w Alkane/water partition coefficents Relationships between structures

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