perspective of pharmaceutical molecular design
TRANSCRIPT
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
[𝐷𝑟𝑢𝑔 𝑿, 𝑡 ]𝑓𝑟𝑒𝑒
𝐾𝑑
Why is it drug discovery and not drug design?
In tissues
Free in
plasma
Bound to
plasma
protein
Dose of drug Eliminated drug
A simplified view of what happens to drugs
Pharmaceutical molecular design
• Control of behavior of compounds and materials by
manipulation of molecular properties
• Hypothesis-driven or prediction-driven
• Sampling of chemical space
– Does fragment-based screening allow better control of
sampling resolution?
Do1 Do2
Ac1
Kenny (2009) JCIM 49:1234-1244 DOI
Illustrating hypothesis-driven design
DNA Base Isosteres: Acceptor & Donor Definitions
Watson-Crick Donor & Acceptor Electrostatic Potentials for
Adenine IsosteresV
min
(Ac1)
Va (Do1)
Kenny (2009) JCIM 49:1234-1244 DOI
Choosing octanol was the first mistake...
Lipophilic & half ionised Hydrophilic & neutral
Introduction to partition coefficients
Polarity
NClogP ≤ 5 Acc ≤ 10; Don ≤5
An alternative view of the Rule of 5
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
Octanol/Water Alkane/Water
Octanol/water is not the only partitioning system
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
-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
DlogP
(corrected)
Vmin/(Hartree/electron)
DlogP
(corrected)
Vmin/(Hartree/electron)
N or ether OCarbonyl O
Prediction of contribution of acceptors to DlogP
DlogP = DlogP0 x exp(-kVmin)
Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
Basis for ClogPalk model
log
Pa
lk
MSA/Å2
Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOIKenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
𝐶𝑙𝑜𝑔𝑃𝑎𝑙𝑘 = 𝑙𝑜𝑔𝑃0 + 𝑠 ×𝑀𝑆𝐴 −
𝑖
∆𝑙𝑜𝑔𝑃𝐹𝐺,𝑖 −
𝑗
∆𝑙𝑜𝑔𝑃𝐼𝑛𝑡,𝑗
ClogPalk from perturbation of saturated hydrocarbon
logPalk predicted
for saturated
hydrocarbonPerturbation by
functional groups
Perturbation by
interactions
between
functional groups
Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
Performance of ClogPalk model
Hydrocortisone
Cortisone
(logPalk ClogPalk)/2
log
Pa
lk
Clo
gP
alk
AtropinePropanolol
Papavarlne
Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI
Another way to look at Structure activity relationships?
(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?
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
Neighborhoods and library design
Examples of relationships between structures
Tanimoto coefficient (foyfi) for structures is 0.90
Ester is methylated acid Amides are ‘reversed’
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
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
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
-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
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
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?
• Molecular design is not just about prediction so
how can we make hypothesis-driven design more
systematic?
• There is life beyond octanol/water (and atom-
centered charges) if we choose to look for it
• Even molecules can have meaningful relationships
Some stuff to think about
Spares follow
The lurking menace of correlation inflation
Kenny & Montanari (2013) JCAMD 27:1-13 DOI
Preparation of synthetic data for correlation
inflation study
Add Gaussian
noise (SD=10) to Y
Kenny & Montanari (2013) JCAMD 27:1-13 DOI
Correlation inflation by hiding variationSee 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
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 FlatlandSee Lovering, Bikker & Humblet (2009) JMC 52:6752-6756 DOI
Kenny & Montanari (2013) JCAMD 27:1-13 DOI