Lead-like Properties, Highthroughput Screening and
Combinatorial Library Design
Andy Davis, Simon Teague, Tudor Oprea, John
Steele, Paul Leeson
Teague, Leeson, Oprea, Davis, Angew Chem 1999, 38, 3743
Department
Author
Fastest - first and best
Target
HTS
Hit
Evaluation
Hit to
Lead
Lead
Optimisation
information
Potency
Efficacy
Selectivity
Kinetics
Metabolism
Enzymology
DESIGN
AND
SYNTHESIS
compounds
compounds
Lead
HTS + Combichem
Department
Author
Fisons History
Combinatorial chemistry in SciFinder
800
count
700
600
500
400
CC +lib design
Series1
300
200
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
100
0
year
• Early lit work - largely peptidic
• Approaches available to us
Department
Author
•
solid phase ?
•
Solution phase ?
•
Singles or mixtures ?
Design Criteria
• Library Design Buzzwords and Concepts
•
“Diverse“
•
“Universal !”
•
Pharmacophore mapping libraries
•
focussed libraries
Department
Author
“Universal” Library
H
N
160
R
Approach 1
SH
O
R40X
R160CO2H
Solution
phase
Solid
phase
H
N
160
R
H2N
R
S
40
S
R
O
Approach 2
STEP 1
R
H2N
i) Solid phase
ii) Cleave
R80CO2H
H
N
80
R
STEP 2
Nu
Br
O
Walters and Teague Tet Lett. 2000, 41, 2023
Department
Author
H
N
80
R
O
Nu
Charnwood “Universal” Library
Distribution of MWt in Universal
Library vs PDR Drugs
Distribution of ACDlogP's in
Universal Library vs PDR Drugs
25
15
10
PDR ACDlogP's
% Universal logP's
5
%Count
20
15
PDR MWt
10
% universal MWt
5
1000
850
700
550
400
250
16
13
10
7
4
1
-2
ACDlogPs
100
0
0
-5
% Count
20
MWt
55,000 member library
Department
Author
Early GPCR Library
Distribution of ACDlogPs in PDR and
GPCR Libraries
30
25
25
20
20
15
PDR MWt
10
GPCR Mwt
5
% occur
PDR ACDlogP
15
GPCR ACDlogP
10
5
1000
16
13
7
10
Mwt
4
1
0
-5
900
800
700
600
500
400
300
200
100
0
-2
% Occur
Distribution of Mwt in PDR and GPCR
Libraries
ACDlogP
Distribution of Ns and Os in PDR and
GPCR Libraries
Distribution of donors in PDR and
GPCR Libraries
30
% Ns Os PDR
% Ns and Os GPCR
Author
20
%dons PDR
15
%dons GPCR
10
6
More
donors
5
4
3
2
1
0
0
20
16
12
8
4
5
Ns and Os
Department
% Count
25
0
%Count
35
40
35
30
25
20
15
10
5
0
The Age of Lipinski
HTS
alerts
• HTS lead generation biases chemistry
Department
Author
Design Criteria
• Library Design Buzzwords and Concepts
•
“Diverse“
•
“Universal”
•
Pharmacophore mapping libraries
•
Drug-like properties
– Lipinski etal Adv Drug Del. Rev. 1997, 23, 3-25
– Sadowski, J. Med. Chem, 1998. 41, 3325.
– Ajay etal, J.Med.Chem, 1998, 41, 3314
•
Department
Author
focussed libraries etc etc.
Our experiences ??
• by 1998
•
75%+ screening bank Combi derived
•
applied current design criteria
•
focussed upon “drug-like libraries”
• we are looking for drug-like potency •
do we find it ??
3000 hits 1e6 screen points
20
%
count
15
10
5
Department
Author
pIC50
8
7.5
7
6.5
6
5.5
5
4.5
0
Charnwood Confirmed HTS Hits
20
15
15
• In > 1e6 screen tests - not 1 nM hit
•
probability of a nM hit < 1e-6
• But hits are already drug-like size
Department
Author
MWt
950
850
750
650
550
pIC50
450
8
7.5
7
6.5
0
6
0
5.5
5
5
5
350
10
250
10
150
%
count
20
4.5
%
count
3000 hits 1e6 screen points
Bang for your Buck
• Andrews analysis (J Med Chem 1984, 27, 1648.)
•
scoring without a protein
– analysed 200 good ligands for their receptor
– assume all interactions are optimally made
– apply fn group counts = regression vs potency
DG (kcal/mol) = -14 -0.7n DOF + 0.7 n Csp2 + 0.8 n Csp3
+11.5nN++1.2n N +8.2n CO2- + 10n PO4- + 2.5n OH
+ 3.4 n C=O +1.1 n O,S +1.3n hal
D Williams DGHB = 0.5-1.5 kcal/mol
DGlipo = 0.7 kcal/mol -CH3
DGrot= 0.4 - 1.4 kcal/mol
Williams etal Chemtracts, 1994, 7, 133
Department
Author
Andrews Analysis Training set
25
y = 1.1993x - 2.4771
R2 = 0.3168
Andrews pKi
20
15
10
Biotin
5
0
-5
-10
0
5
10
15
obsd pKi
• Significant ,model incl by 2 outliers
Department
Author
20
Andrews - 2
Department
Author
Andrews - Coloured by Charge
• Multiply charged compounds overpredicted
•
Department
Author
oral targets 0,1 charge
Final Model - 0,1 charges
Department
Author
HTS screening Hits
Andrews predictions
16
14
12
10
• probabilities
y /% 8
6
•
4
predicted
– p(<10nM) = 22%
2
0
-2
0
2
4
6
8
10
pK i
12
14
16
18 >18
obsd
– p(<10nM) <e-8%
HTS Obsd activities
90
•
80
70
60
y /%
Many hits underperform
50
40
30
20
10
0
-2
0
Department
Author
2
4
6
8 10
pIC 50
12
14
16
18 >18
HTS Screening Hits
• Drug-like hits
– potency of many underperform
– binding via weak non-specific interactions
– not all interactions made or very suboptimal
– would explain “flat SAR” in Hit-to-Lead activities
– small mM leads easier to optimise than large mM
• “easy” and “difficult” hit-to-lead projects
•
easy to increase Mwt/logP - increase potency
– easy to demonstrate SAR, increase potency 10x
•
difficult because of flat SAR
– difficult to reduce Mwt and logP maintaining potency
–
Department
Author
HtL Examples - GPCR Project
acid
R
S
CONH2
N
IC50 = 0.55 mM
Mwt 350
clogP 3.7
IC50 = 4.6 mM
Mwt 268
ClogP 3.4
R
H
N
O
IC50 = 0.18 mM
Mwt 380
ClogP = 4.5
Department
Author
OH
S
O
GPCR Hit-to-Lead
R
S
CONH2
Many analogues
same or loss potency
Many analogues
same potency
R
H
N
O
OH
S
O
• Both series dropped Department
Author
GPCR Hit-to-Lead
acid
acid
N
Cl
Cl
IC50 = 4.6 mM
Mwt 268
ClogP 3.4
IC50 = 0.02 mM
Mwt 336
ClogP 5.3 (:-<)
• Rapid Hit-to-Lead optimisation
Department
Author
•
clear SAR
•
drug-like series with good DMPK
•
patentable
“Difficult” Project - 2 Renin Inhibitors
MWt Distribution of PDR Drugs and Renin
Inhibitors
25
% Count
20
15
PDR MWt
10
renin
5
1200
1100
1000
900
800
700
600
500
400
300
200
100
0
MWt
No renin inhibitor went passed PII
all failed due to poor bioavailability, high cost
Department
Author
22
Process Lead Optimisation
• Optimisation Hypothesis
25
20
Lead-like
PDR
Outside drug space
old Combi Library
15
y/%
10
b
5
0
100 200 300 400 500 600 700
Mr
Department
Author
Bang for your Buck - 2
Would a lead-like compound “hit” in HTS ?
• Andrews analysis of leads
•
estimated pKi for “leadlike” ligand
•
15,000 “random” drugs designed
•
random numbers of “features bounded by oral drugs
DG (kcal/mol) = -14 - 0.7n DOF (n = 1-8) + 0.75 n Csp2+sp3 (n=4-18)
+ 11.5n N+ (n=0,1) + 1.2n N (n=0-4) + 2.5n OH (n=0,1) + 3.4 n C=O (n=0-2)
+ 1.1 n O,S (n=0-2) + 1.3n hal (n=0,1)
filtered by est Mwt - and 0,1 charge
Department
Author
Leadlike Bang for your Bucks
Distribution of Andrews predicted pKi for
neutral and basic leads Mwt <300
700
600
Count
500
400
N+
300
N
200
100
0
-9
-7
-5
-3
-1
1 3 5 7
predicted pKi
• HTS screening environment
•
Department
Author
Small leads probably need 1 charge @10mM
9
11 13 15
100 lead - drug pairs
Av. 302
Md. 315
15
Frequency
Frequency
16
14
12
10
8
6
4
2
0
Av. 391
Md. 384
20
10
5
0
MW
129 182 234 286 339 391 444 496 548
156 208 260 312 364 416 468 520 572 624
14
Av. 8.3
Md. 8.4
10
10
8
6
4
8
6
4
2
2
0
Frequency
12
10
4
5
6
7
8
9 10 11 12 13 14 15 16
Av. 1.57
Md. 2.11
n=58
3
CLogP
Frequency
12
10
8
5
6
7
8
9
10 11 12 13 14 15 16
Av. 2.73
Md. 2.54
n=70
6
4
0
-3.8 -2.8 -1.8 -0.8 0.3
Av. 0.72
Md. 0.72
n=57
1.3
2.3
3.3
4.3
-2.3 -1.8-1.3 -0.8 -0.3 0.3 0.8 1.3 1.8 2.3 2.8 3.3 3.8 4.3 4.8 5.3 5.8
5.3
LogD7.4
8
6
4
14
12
10
Av. 1.69
Md. 1.69
n=71
8
6
4
2
2
0
0
Department
4
2
- 7.5- 6.5- 5.5- 4.5- 3.5 - 2.5- 1.5- 0.5 0.5 1.5 2.5 3.5 4.5 5.5
Author
0
CMR
Frequency
Frequency
3
7
6
5
4
3
2
1
0
Av. 10.6
Md. 10.2
12
Frequency
Frequency
12
- 7.5- 6.5- 5.5- 4.5- 3.5- 2.5- 1.5- 0.5 0.5 1.5 2.5 3.5 4.5 5.5
Lead-like Profile
• Mwt 200-350
•
optimisation adds ca. 100
• logP 1-3
•
optimisation may increase by 1-2 logunits
• single charge
•
positive charge preferred
•
secondary or tertiary amine
1998:
less than 600 solid compounds with mwt <250 and clogP <2
1999:
3000 added by purchase. Synthesis added >30000
Department
Author
Early GPCR Library
Distribution of ACDlogPs in PDR and
GPCR Libraries
30
25
25
20
20
15
PDR MWt
10
GPCR Mwt
5
% occur
PDR ACDlogP
15
GPCR ACDlogP
10
5
1000
16
13
7
10
Mwt
4
1
0
-5
900
800
700
600
500
400
300
200
100
0
-2
% Occur
Distribution of Mwt in PDR and GPCR
Libraries
ACDlogP
Distribution of Ns and Os in PDR and
GPCR Libraries
Distribution of donors in PDR and
GPCR Libraries
30
% Ns Os PDR
% Ns and Os GPCR
Author
20
%dons PDR
15
%dons GPCR
10
6
More
donors
5
4
3
2
1
0
0
20
16
12
8
4
5
Ns and Os
Department
% Count
25
0
%Count
35
40
35
30
25
20
15
10
5
0
60
55
50
45
40
35
30
25
20
15
10
5
0
45
40
35
PDR99
leadlike
count %
count %
Mitsunobu Library
30
25
PDR99
20
leadlike
15
10
5
0
0
2
4
6
8
0
10
4
8
12
16
20
24
NsOs
Donors
15
40
35
25
pdr99
20
leadlike
15
count %
count %
30
10
PDR99
leadlike
5
10
5
0
0
100 200 300 400 500 600 700 800 900
Mwt
Department
Author
-5
-2.5
0
2.5
5
ACDlogP
7.5
10
Lead Continiuum Leadlike Drug-like
Mwt <200
350
HtL problems ?
Mwt >500
Topical target ?
Non-HTS
Shapes (Vertex )
Needles(Roche)
MULBITS(GSK)
Crystallead(Abbott)
Department
Author
HTS screening
Screening File Split
• Step taken by some companies - drivers
•
logical conclusion of leadlike paradigm
•
cost/feasibility some HTS technologies
Screening file
MWt- Distribution
of Screening File
Bad
topical/desperate
file
Good oral file
25
20
20
15
15
Count
25
AR-C MWt%
Department
Author
MWt
0
00
10
90
0
80
0
70
0
60
0
50
0
40
0
30
0
0
10
0
00
10
90
0
80
0
70
0
60
0
50
40
30
20
0
0
0
0
0
5
0
5
MWt
AR-C
10
20
10
10
Count
MWt Distribution of Screening File
Summary
• HTS
•
starting points are crucial to speed throughout process
•
screening file should reflect what chemists can easily
work upon
•
ideally we all want to find drugs in our screening file
– but generally a HTS finds only leads not drugs
•
file-size isnt everything = quality is equally important
• Libraries
•
Many approaches - targeted libraries v successful
– kinase libraries - 4x hit rate - screening file
•
libraries should reflect what you wish to find
– leads not drugs
Teague, Leeson, Oprea, Davis, Angew Chem 1999, 38, 3743
Department
Author
Department
Author