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In memory of
Rich Green 1947 - 2001
An Outstanding Medicinal Chemist and Colleague
“Making Lead Discovey less complex?”
Mike Hann, Andrew Leach & Gavin Harper.
Computational Chemistry and Informatics Unit
GlaxoSmithKline Medicines Research Centre
Gunnels Wood Rd
Stevenage
SG1 2NY
email [email protected]
Subtitle: Molecular Recognition versus the
gambling game that we play in using
HTS and libraries to discover new
leads
Libraries - have they been successful
at revolutionising the drug discovery
business?
 Despite some successes, it is clear that the high throughput

synthesis of libraries and the HTS screening paradigms
have not delivered the results that were initially anticipated.
Why
– immaturity of the technology,
– the inability to make the right types of molecules with the
technology
– lack of understanding of what the right types of molecule
to make actually are
 drug likeness, Lipinski,etc
An additional reason exemplified by a very
simple model of Molecular Recognition
 Define a linear pattern of +’s and -’s to represent the recognition




features of a binding site
Vary the Length/Complexity of a linear Binding site as +’s and -’s
Vary the Length/Complexity of a linear Ligand up to that of the
Binding site
Calculate probabilities of number of matches as ligand complexity
varies.
Example for binding site of 9 features:
Feature Position
Binding site features
Ligand mode 1
Ligand mode 2
1 2 3 4 5 6 7 8 9
- - + - + - - + + + + + -
Probabilities of ligands of varying complexity
(i.e. number of features) matching a binding site of
complexity 12
As the ligand/receptor match becomes
more complex the probability of any
given molecule matching falls to zero.
i.e. there are many more ways of getting
it wrong than right!
1
0.9
Probability
0.8
0.7
Match any
1 matches
2 matches
3 matches
4 matches
5 matches
6 matches
7 matches
8 matches
9 matches
10 matches
11 matches
0.6
0.5
0.4
0.3
0.2
0.1
0
2
3
4
5
6
7
8
9
Complexity of Ligand
10
11
12
Probaility
Probaility
The effect of potency
1
0.9
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
2
2
3
3

4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
Ligand Complexity
Ligand Complexity
P (useful event) = P(measure binding) x P(ligand matches)
Probability
of of
measuring
binding
Probability
measuring
binding
Probability
Probability
of of
matching
matching
just
just
one
one
way
way
Probability
matching
one way
Probability
of of
useful
event just
(unique
mode)
Probaility
Too simple.
Low probability of
1
measuring
affinity
0.9
even
if there is a
0.8
unique
mode
0.7
Optimal.
But where is it
for any given
system?
Too complex.
Low probability of
finding lead even if
it has high affinity
0.6
0.5
0.4
0.3
0.2
0.1
0
2
3
4
5
6
7
8
9
10
Ligand Complexity
Probability of useful event (unique mode)
11
12
Limitations of the model
 Linear representation of complex events
 No chance for mismatches - ie harsh model
 No flexibility
 only + and - considered
 But the characteristics of any model will be the
same
1
0.9
Probaility
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2
3
4
5
6
7
8
Ligand Complexity
9
10
11
12
P (useful event) = P(measure binding) x P(ligand matches)
 Real data to support this hypothesis!!
Leads vs Drugs



Data taken from W. Sneader’s book “Drug Prototypes and their exploitation”
Converted to Daylight Database and then profiled with ADEPT
480 drug case histories in the following plots
Sneader Lead Sneader Drug WDI
Change in MW on going from Lead to Drug
for 470 drugs
400
Change in MW in going from
Sneader Leader to Drugs
300
200
100
Average MW
increase = 42
0
0
100
200
300
400
500
-100
-200
-300
MW of Sneader Drugs
600
700
800
ADEPT plots for WDI &
a variety of GW libraries
WDI
WDI
WDI
WDI
WDI
WDI
 Molecules in libraries are still even more complex
than WDI drugs, let alone Sneader Leads
In terms of numbers
Average property values for the Sneader lead set, average change
on going to Sneader drug set and percentage change.


Av #
arom

arom
%
Av

ClogP ClogP
%
Av
CMR

CMR
%
1.3
0.2**
15
1.9
0.5**
26
7.6
1.0**
14.5
Av #
HBA

HBA
%
Av #
HBD

HBD
%
2.2
.3**
14
.85
-.05+
(4)
Av
MW

MW
%
Av
MV

MV
%
272
42.0**
15
289
38.0**
13
Av #

heavy heavy
19.
%
3.0**
16
Av #

Rot B Rot B
%
3.5
.9**
23
Astra Zeneca data similar using hand picked data from literature
AZ increases typically even larger (because of data picking?)
1
0.8
0.7
Probaility
Catch 22
problem
0.9
0.6
0.5
0.4
0.3
0.2
0.1
0
2
3
4
5
6
7
8
Ligand Complexity
9
10
11
12
 We are dealing with probabilities so increasing the


number of samples assayed will increase the number of
hits (=HTS).
We have been increasing the number of samples by
making big libraries (=combichem)
And to make big libraries you have to have many points of
diversity
Which leads to greater complexity

 Which decreases the probability of a given molecule
being a hit
Catch 21
Concentration as the escape route
 Screen less complex molecules to find more hits


– Less potent but higher chance of getting on to the success
landscape
– Opportunity for medicinal chemists to then optimise by adding
back complexity and properties
Need for it to be the right sort of molecules
– the Mulbits (Multiple Bits) approach
– Mulbits are molecules of MW < 150 and highly soluble.
– Screen at up to 1mM
Extreme example from 5 years ago - Thrombin:
– Screen preselected (in silico) basic mulbits in a Proflavin
displacement assay specific
– known to be be specific for P1 pocket.
Thrombin Mulbit to “drug”
N
N
N
NH
NH2
NH2
2-Amino Imidazole (5mM), as the
sulphate, showed 30% displacement
of Proflavin (18µM) from
Thrombin (10µM)
(cf Benzamidine (at 5mM)
shows 70% displacement) under
similar conditions
Absorbance at 466nM relative
to that at 444nM was used as
the measure of amount of
proflavin displaced
O
O
O
H
S
N
N
H O
Thrombin IC50 = 4µM
(15 min pre-incubation; for assay
conditions see reference 23)
N
Related Literature examples of
Mulbits type methods
 Needles method in use at Roche
 .Boehm, H-J.; et al Novel Inhibitors of DNA Gyrase: 3D Structure
Based Biased Needle Screening, Hit Validation by Biophysical
Methods, and 3D Guided Optimization. A Promising Alternative to
Random Screening. J. Med. Chem., 2000, 43 (14), 2664 -2674.
 NMR by SAR method in use at Abbott
 Hajduk, P. J.; Meadows, R. P.; Fesik, S. W.. Discovering high-affinity ligands
for proteins.

Science, 1997, 278(5337), 497-499.
Ellman method at Sunesis
 Maly, D. J.; Choong, I. C.; Ellman, J. A.. Combinatorial target-guided
ligand assembly: identification of potent subtype-selective c-Src
inhibitors. Proc. Natl. Acad. Sci. U. S. A., 2000, 97(6), 2419-2424.
In conclusion
 Lipinski etc does not go far enough in directing
us to leads.
 We have provided a model which explains why.
 “Everything should be made as simple as
possible but no simpler.” Einstein
– Simple is a relative not absolute term
 where is that optimal peak in the plot for each target?
– Simple does not mean easy!!
Thanks:
Rich Green, Giampa Bravi, Andy Brewster, Robin Carr, Miles Congreve, Darren Green,
Brian Evans, Albert Jaxa-Chamiec, Duncan Judd, Xiao Lewell, Mika Lindvall,
Steve McKeown, Adrian Pipe, Nigel Ramsden, Derek Reynolds, Barry Ross,
Nigel Watson, Steve Watson, Malcolm Weir, John Bradshaw, Colin Grey,
Vipal Patel, Sue Bethell, Charlie Nichols, Chun-wa Chun and Terry Haley