department author lead-like properties, high- throughput screening and combinatorial library design...
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DepartmentAuthor
Lead-like Properties, High-throughput Screening and Combinatorial Library Design
Andy Davis, Simon Teague, Tudor Oprea, John Steele, Paul Leeson
Teague, Leeson, Oprea, Davis, Angew Chem 1999, 38, 3743
DepartmentAuthor
Fastest - first and best
Target HTSHit
EvaluationHit toLead
LeadOptimisation
DESIGN AND
SYNTHESIS
Potency Efficacy
Selectivity
compounds
information
Kinetics Metabolism Enzymology
compounds
LeadHTS + Combichem
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Fisons History
• Early lit work - largely peptidic
• Approaches available to us• solid phase ?
• Solution phase ?
• Singles or mixtures ?
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Design Criteria
• Library Design Buzzwords and Concepts• “Diverse“
• “Universal !”
• Pharmacophore mapping libraries
• focussed libraries
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“Universal” Library
SNH2
R
R160CO2H
Solidphase
SHNH
R160
O
R40X
Solutionphase
SNH
R160
O
R40
i) Solid phase R80CO2H
ii) Cleave
NH2R N
HR
80
O
Br
STEP 2
NuNH
R80
O
Nu
STEP 1
Approach 1
Approach 2
Walters and Teague Tet Lett. 2000, 41, 2023
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Charnwood “Universal” Library
Distribution of ACDlogP's in Universal Library vs PDR Drugs
0
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15
20
-5 -2 1 4 7
10
13
16
ACDlogPs
% C
ou
nt
PDR ACDlogP's
% Universal logP's
Distribution of MWt in Universal Library vs PDR Drugs
0
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25
10
0
25
0
40
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55
0
70
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85
0
10
00
MWt
%C
ou
nt
PDR MWt
% universal MWt
55,000 member library
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Distribution of Ns and Os in PDR and GPCR Libraries
0
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0 4 8
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16
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Ns and Os
%C
ou
nt
% Ns Os PDR
% Ns and Os GPCR
Distribution of donors in PDR and GPCR Libraries
0
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25
30
35
0 1 2 3 4 5 6
Mo
re
donors
% C
ou
nt
%dons PDR
%dons GPCR
Distribution of ACDlogPs in PDR and GPCR Libraries
0
5
10
15
20
25
-5 -2 1 4 7
10 13 16
ACDlogP
% o
ccu
r PDR ACDlogP
GPCR ACDlogP
Distribution of Mwt in PDR and GPCR Libraries
0
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301
00
20
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10
00
Mwt
% O
cc
ur
PDR MWt
GPCR Mwt
Early GPCR Library
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The Age of Lipinski
• HTS lead generation biases chemistry
alertsHTS
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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
• focussed libraries etc etc.
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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 ??
0
5
10
15
20
4.5 5
5.5 6
6.5 7
7.5 8
pIC50
%
cou
nt
3000 hits 1e6 screen points
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Charnwood Confirmed HTS Hits
• In > 1e6 screen tests - not 1 nM hit• probability of a nM hit < 1e-6
• But hits are already drug-like size
0
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20
4.5 5
5.5 6
6.5 7
7.5 8
pIC50
%
cou
nt
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150
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450
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750
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950
MWt
%
cou
nt
3000 hits 1e6 screen points
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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
G (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 GHB = 0.5-1.5 kcal/mol Glipo = 0.7 kcal/mol -CH3
Grot= 0.4 - 1.4 kcal/mol
Williams etal Chemtracts, 1994, 7, 133
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y = 1.1993x - 2.4771R2 = 0.3168
-10
-5
0
5
10
15
20
25
0 5 10 15 20
obsd pKi
An
dre
ws
pK
i
Andrews Analysis Training set
• Significant ,model incl by 2 outliers
Biotin
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Andrews - 2
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Andrews - Coloured by Charge
• Multiply charged compounds overpredicted• oral targets 0,1 charge
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Final Model - 0,1 charges
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0
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90
-2 0 2 4 6 8 10 12 14 16 18 >18pIC 50
y /%
0
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4
6
8
10
12
14
16
-2 0 2 4 6 8 10 12 14 16 18 >18pK i
y /%
Andrews predictions
HTS Obsd activities
HTS screening Hits
• probabilities• predicted
– p(<10nM) = 22%
• obsd– p(<10nM) <e-8%
Many hits underperform
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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 M leads easier to optimise than large M
• “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
–
DepartmentAuthor
HtL Examples - GPCR Project
N
CONH2SR
SOH
O
NH
O
R
acid
IC50 = 4.6 MMwt 268ClogP 3.4
IC50 = 0.55 MMwt 350clogP 3.7
IC50 = 0.18 MMwt 380ClogP = 4.5
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GPCR Hit-to-Lead
Many analoguessame or loss potency
CONH2SR
SOH
O
NH
O
R
Many analoguessame potency
• Both series dropped -
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GPCR Hit-to-Lead
• Rapid Hit-to-Lead optimisation• clear SAR
• drug-like series with good DMPK
• patentable
N
acid
Cl
Cl
acid
IC50 = 4.6 MMwt 268ClogP 3.4
IC50 = 0.02 MMwt 336ClogP 5.3 (:-<)
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22
MWt Distribution of PDR Drugs and Renin Inhibitors
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251
00
20
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10
00
110
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00
MWt
% C
ou
nt
PDR MWt
renin
“Difficult” Project - 2 Renin Inhibitors
No renin inhibitor went passed PII
all failed due to poor bioavailability, high cost
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0
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20
25
100 200 300 400 500 600 700
M r
y / %
Process Lead Optimisation
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25
100 200 300 400 500 600 700
M r
y / % b
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25
100 200 300 400 500 600 700
M r
y / %
PDROutside drug space old Combi Library
Lead-like
• Optimisation Hypothesis
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Bang for your Buck - 2Would 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
filtered by est Mwt - and 0,1 charge
G (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)
DepartmentAuthor
Leadlike Bang for your Bucks
• HTS screening environment• Small leads probably need 1 charge @10M
Distribution of Andrews predicted pKi for neutral and basic leads Mwt <300
0
100
200
300
400
500
600
700
-9 -7 -5 -3 -1 1 3 5 7 9 11 13 15predicted pKi
Co
un
t
N+
N
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100 lead - drug pairs
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1998: less than 600 solid compounds with mwt <250 and clogP <2
1999: 3000 added by purchase. Synthesis added >30000
1998: less than 600 solid compounds with mwt <250 and clogP <2
1999: 3000 added by purchase. Synthesis added >30000
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
DepartmentAuthor
Distribution of Ns and Os in PDR and GPCR Libraries
0
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40
0 4 8
12
16
20
Ns and Os
%C
ou
nt
% Ns Os PDR
% Ns and Os GPCR
Distribution of donors in PDR and GPCR Libraries
0
5
10
15
20
25
30
35
0 1 2 3 4 5 6
Mo
re
donors
% C
ou
nt
%dons PDR
%dons GPCR
Distribution of ACDlogPs in PDR and GPCR Libraries
0
5
10
15
20
25
-5 -2 1 4 7
10 13 16
ACDlogP
% o
ccu
r PDR ACDlogP
GPCR ACDlogP
Distribution of Mwt in PDR and GPCR Libraries
0
5
10
15
20
25
301
00
20
0
30
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0
10
00
Mwt
% O
cc
ur
PDR MWt
GPCR Mwt
Early GPCR Library
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0
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10
15
-5 -2.5 0 2.5 5 7.5 10
ACDlogP
cou
nt % PDR99
leadlike
Mitsunobu Library
05
1015202530354045505560
0 2 4 6 8 10
Donors
cou
nt % PDR99
leadlike
0
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45
0 4 8 12 16 20 24
NsOs
cou
nt % PDR99
leadlike
0
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100 200 300 400 500 600 700 800 900
Mwt
cou
nt % pdr99
leadlike
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Lead Continiuum -
350Mwt >500 Mwt <200
Drug-likeLeadlike HtL problems ?
Topical target ?
HTS screeningNon-HTS
Shapes (Vertex )Needles(Roche)MULBITS(GSK)Crystallead(Abbott)
DepartmentAuthor
Screening File Split• Step taken by some companies - drivers
• logical conclusion of leadlike paradigm
• cost/feasibility some HTS technologies
Screening file
Good oral file Bad - topical/desperate file
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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
DepartmentAuthor