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Transcript of UCT Oct 2014
Molecular design for drug discovery
Peter W Kenny
http://fbdd-lit.blogspot.com
Outline of presentation
• Some thoughts on molecular design
• Design of compound libraries for screening
• Relationships between structures as framework for
analysing biological activity and physicochemical
properties
Some things that make drug design difficult
• Having to exploit targets that are weakly-linked to
human disease
• Poor understanding and prediction of 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
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
TEP = [𝐷𝑟𝑢𝑔 𝑿,𝑡 ]𝑓𝑟𝑒𝑒
𝐾𝑑
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
Kenny, Leitão & Montanari JCAMD 2014 28:699-710 DOI
Property-based design as search for ‘sweet spot’
Green and red lines represent probability of achieving ‘satisfactory’ affinity and
‘satisfactory’ ADMET characteristics respectively. The blue line shows the product of
these probabilities and characterizes the ‘sweet spot’. This way of thinking about the
‘sweet spot’ has similarities with Hann molecular complexity model
Kenny & Montanari, JCAMD 2013 27:1-13 DOI
Hypothesis-driven Molecular Design
• Ask good questions with informative compounds and
relevant assays
• Framework for establishing structure-activity
relationships (SARs) as efficiently as possible
• Molecular interactions provide natural framework in
which to pose design hypotheses
Kenny JCIM 2009 49:1234-1244 DOI Hypothesis-driven design versus prediction driven molecular design
Linusson et al JMC 2000 43:1320-1328 DOI Statistical molecular design
Bissantz, Kuhn & Stahl JMC 2010 53:5061-5084 DOI Medicinal chemist’s guide to molecular interactions
Do1 Do2
Ac1
Kenny JCIM 2009 49:1234-1244 DOI
Illustrating hypothesis-driven design
Adenine bioisosteres
Ac2
Watson-Crick Donor & Acceptor Electrostatic Potentials for
Adenine Isosteres
Vm
in(A
c1)
Va (Do1)
Kenny JCIM 2009 49:1234-1244 DOI
PTP1B (Diabetes/Obesity): Fragment elaboration
Literature SAR was mapped onto intial fragment hit (green). Note overlay of
aromatic rings of elaborated fragment (blue) and difluorophosphonate (red).
Black et al BMCL 2005 15:2503-2507 DOI
Inactive at 200mM
15 mM
3000 mM3 mM
150 mM
(Conformational lock)
130 mM
(3-Phenyl substituent)
“Why can’t we pray for something good, like a tighter bombing pattern, for example? Couldn’t we pray for a tighter bombing pattern?” , Heller, Catch 22, 1961
Design of compound libraries for screening
(a view from Hanoi with additional insight from Heller)
Measures of Diversity & Coverage
•• •
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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
Coverage, Diversity & Library Design
••
• ••
•• •• •• •
•
Acceptable diversity
And coverage?
Assemble library in
soluble form
Add layer to core
Incorporate layer
Yes
No
Select core
Core and layer library design
Compounds in a layer are selected to be diverse with respect to core compounds. The ‘outer’ layers
typically contain compounds that are less attractive than the ‘inner’ layers. This approach to library
design can be applied with Flush or BigPicker programs (Dave Cosgrove, AstraZeneca, Alderley
Park) using molecular similarity measures calculated from molecular fingerprints.
Blomberg et al JCAMD 2009 23:513-525 DOI
Sample
AvailabilityMolecular
Connectivity
Physical
Properties
screening samples Close analogs Ease of synthetic
elaboration
Molecular
complexity
Ionisation Lipophilicity
Solubility
Molecular
recognition
elementsMolecular shape
3D Pharmacophore
Privileged
substructures
Undesirable
substructures
Molecular
size
3D Molecular
Structure
Fragment selection criteria
Why I don’t use the rule of 3: http://fbdd-lit.blogspot.com/2011/01/rule-of-three-considered-harmful.html
Library design for phenotypic screening
•• •
•
••
•
•
•
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•
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•
Chemical fingerprints typically used to calculate molecular similarity while biological fingerprints
can be used directly (sampling of actives from different assays)
Bio
log
y (
assa
y r
esu
lts)
Chemistry (structures)
Another way to look at structure-activity relationships?
Leatherface molecule 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
MUDO Molecule Editor
• SMIRKS-based re-write of Leatherface
using OEChem
• Can also process 3D structures (e.g.
form covalent bond between protein and
ligand)
• Identification of matched molecular pairs
much simpler than with Leatherface
Kenny, Montanari, Propopczyk, Sala, Sartori JCAMD 2013 27:655-664 DOI
K777 docked (green) covalently to
Cruzain with crystallographic ligand
Examples of relationships between structures
Tanimoto coefficient (foyfi) for structures is 0.90
Ester is methylated acid Amides are ‘reversed’
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
Birch et al BMCL 2009 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 BMCL 2009 19:850-853 DOI
-0.316
-0.315
-0.296
-0.295
Bioisosterism: Carboxylate & tetrazole
-0.262
-0.261
-0.268
-0.268
Kenny JCIM 2009 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 BMCL 2009 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 and efficient?
• Screening library design as optimization of
bombing patterns
• Even molecules can have meaningful relationships
Stuff to think about
Spare slides follow…
(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?
Fragment-based lead discovery: Generalised workflow
Target-based compound selection
Analogues of known binders
Generic screening library
Measure
Kd or IC50
Screen
Fragments
Synthetic elaboration
of hits
SARProtein
Structures
Milestone achieved!Proceed to next
project
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
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
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