Presented at ResearResearch Center for Molecular Medicine of the Austrian Academy of Sciencesch Center for Molecular Medicine of the Austrian Academy of Sciences (CeMM) in July 2014 to a non-chemist audience. Not sure how it worked but it was an enjoyable visit and nobody fell asleep in the talk.

1. Aspects of pharmaceutical molecular design
Peter Kenny

2. Outline of presentation
• General aspects of molecular design and introducing
pharmaceutical molecular design
• How to enhance your correlations in one easy lesson
• Introduction to partition coefficients
• Using relationships between structures to mine
activity and properties of compounds

3. Some things that make drug discovery difficult
• 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 on far side of blood brain barrier)
Dans la merde : http://fbdd-lit.blogspot.com/2011/09/dans-la-merde.html

4. In tissues
Free in
plasma
Bound to
plasma
protein
Dose of drug
Eliminated drug
A simplified view of what happens to drugs after dosing

5. TEP =
[𝐷𝑟𝑢𝑔 𝑿,𝑡 ] 𝑓𝑟𝑒𝑒
𝐾 𝑑
Target engagement potential (TEP)
A basis for pharmaceutical molecular design?
Design objectives
• Low Kd for target(s)
• High (hopefully undetectable) Kd for antitargets
• Ability to control [Drug(X,t)]free

6. Pharmaceutical molecular design
• Control of behavior of compounds and materials by
manipulation of molecular properties
• Sampling of chemical space
– Does fragment-based screening allow better control of
sampling resolution?

7. Hypothesis-Driven
Framework in which to
assemble SAR/SPR as
efficiently as possible
Prediction-Driven
Assume that we can build
predictive models with
required degree of
accuracy
Molecular Design

8. Do1 Do2
Ac1
Kenny (2009) JCIM 49:1234-1244 DOI
Illustrating hypothesis-driven design with
DNA base isosteres: H bond acceptor & donor
definitions

9. Watson-Crick Donor & Acceptor Electrostatic Potentials for
Adenine Isosteres
Vmin(Ac1)
Va (Do1)

10. The lurking menace of correlation inflation
Kenny & Montanari (2013) JCAMD 27:1-13 DOI

11. Preparation of synthetic data for correlation
inflation study
Add Gaussian
noise (SD=10) to Y
Kenny & Montanari (2013) JCAMD 27:1-13 DOI

12. 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

13. 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

14. Choosing octanol was the first mistake...

15. Why are we interested in partition coefficients?
• Definition and quantification of lipophilicity
• For modelling permeability (e.g. blood brain barrier)
• Insight into hydrophobic effect (molecular recognition)
• ‘Compound quality’ (bad stuff like poor solubility and
metabolic instability associated with high lipophilicity)

16. Lipophilic & half ionised Hydrophilic & neutral
Introduction to partition coefficients

17. Polarity
N
ClogP ≤ 5 Acc ≤ 10; Don ≤5
An alternative view of the Rule of 5

18. 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

19. Octanol/Water Alkane/Water
Octanol/water is not the only partitioning system

20. 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

21. -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

22. 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

23. 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

24. 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

25. Another way to look at Structure Activity Relationships?

26. (Descriptor-based) QSAR/QSPR
• How likely is it that we will have sufficient data for useful activity
model before project delivers?
• 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 ensembles of local models?
• How well do methods handle ‘activity cliffs’?
• How should we account for sizes of descriptor pools when
comparing model performance?

27. 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

28. Neighborhoods and library design

29. Examples of relationships between structures
Tanimoto coefficient (foyfi) for structures is 0.90
Ester is methylated acid Amides are ‘reversed’

30. 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

31. 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

32. 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

33. -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

34. 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

35. 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?

36. • Molecular design is not just about prediction so
how can we make hypothesis-driven design more
systematic?
• Inflated correlations will not extract us from ‘la
merde’ even if we can get a journal to print them
• There is life beyond octanol/water (and atom-
centered charges) if we only choose to look for it
• Even molecules can have meaningful relationships
Some stuff to think about