Introduction to Research ,Need for research, Need for design of Experiments, ...
Partition Coefficients Reveal Insights for Drug Discovery
1. Partition coefficients and drug discovery
(on tour in rural Wisconsin)
Peter W Kenny
http://fbdd-lit.blogspot.com | http://www.slideshare.net/pwkenny
2. Molecular Design
• Control of behavior of compounds and materials by
manipulation of molecular properties
• Hypothesis-driven or prediction-driven
• Sampling of chemical space
– For example, does fragment-based screening allow better
control of sampling resolution?
Kenny, Montanari, Propopczyk, Sala, Sartori (2013) JCAMD 27:655-664 DOI
Kenny JCIM 2009 49:1234-1244 DOI
5. Partition coefficients in drug discovery
• Octanol/water is default partitioning system
• Model for cell membrane (permeability)
• Generic measure of (aqueous) desolvation for
modelling actiivity, solubility…
9. 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 (ΔlogP) 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
10. -0.054
-0.086
-0.091
-0.072
-0.104 -0.093
Connection between lipophilicity and hydrogen bonding
Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
DlogP = 0.5
DlogP = 1.3
Minimized electrostatic potential (Vmin) values (atomic
units) are predictive of hydrogen bond basicity
12. 1.0 1.1 0.8 1.3 1.7
0.8 1.5
What do these measured values of DlogP tell us?
Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730
1.6 1.1
13. 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
14. 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
16. Examples of structural relationships between compounds
Tanimoto coefficient (foyfi) for structures is 0.90
Ester is methylated acid Amides are ‘reversed’
17. Hypothesis-driven molecular design and relationships between
structures as framework for analysing activity and properties
?
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 . Tetrazoles were not synthesised even though their logP values are expected to
be 0.3 to 0.4 units lower than for corresponding carboxylic acids.
Birch et al (2009) BMCL19:850-853 DOI
18. 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
19. Structural relationships between compounds
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?
20. • Partition coefficients are not quite as boring as you
thought that they were
• There is life beyond octanol/water (and atom-
centered charges) if we choose to look for it
• Even molecules can have meaningful relationships
Stuff to think about