Title: Lead-like Properties, High-throughput Screening and Combinatorial Library Design
1Lead-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
2Fastest - first and best
information
Kinetics Metabolism Enzymology
Potency Efficacy Selectivity
DESIGN AND SYNTHESIS
compounds
compounds
Lead HTS Combichem
3Fisons History
- Early lit work - largely peptidic
- Approaches available to us
- solid phase ?
- Solution phase ?
- Singles or mixtures ?
4Design Criteria
- Library Design Buzzwords and Concepts
- Diverse
- Universal !
- Pharmacophore mapping libraries
- focussed libraries
5Universal Library
Approach 1
Approach 2
Walters and Teague Tet Lett. 2000, 41, 2023
6Charnwood Universal Library
55,000 member library
7Early GPCR Library
Distribution of ACDlogPs in PDR and
GPCR Libraries
25
20
PDR ACDlogP
15
GPCR ACDlogP
occur
10
5
0
1
4
7
-5
-2
10
13
16
ACDlogP
Distribution of Ns and Os in PDR and
GPCR Libraries
40
35
30
25
20
Count
Ns Os PDR
15
Ns and Os GPCR
10
5
0
0
4
8
12
16
20
Ns and Os
8The Age of Lipinski
HTS
alerts
- HTS lead generation biases chemistry
9Design 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.
10Our 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
11Charnwood Confirmed HTS Hits
3000 hits 1e6 screen points
- In gt 1e6 screen tests - not 1 nM hit
- probability of a nM hit lt 1e-6
- But hits are already drug-like size
12Bang 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 11.5nN1.2n N 8.2n CO2- 10n PO4-
2.5n OH 3.4 n CO 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
13 Andrews Analysis Training set
Biotin
- Significant ,model incl by 2 outliers
14Andrews - 2
15Andrews - Coloured by Charge
- Multiply charged compounds overpredicted
- oral targets 0,1 charge
16Final Model - 0,1 charges
17HTS screening Hits
Andrews predictions
- probabilities
- predicted
- p(lt10nM) 22
- obsd
- p(lt10nM) lte-8
HTS Obsd activities
Many hits underperform
18HTS 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 -
19HtL Examples - GPCR Project
IC50 0.55 mM Mwt 350 clogP 3.7
IC50 4.6 mM Mwt 268 ClogP 3.4
IC50 0.18 mM Mwt 380 ClogP 4.5
20GPCR Hit-to-Lead
Many analogues same or loss potency
Many analogues same potency
21GPCR Hit-to-Lead
IC50 4.6 mM Mwt 268 ClogP 3.4
IC50 0.02 mM Mwt 336 ClogP 5.3 (-lt)
- Rapid Hit-to-Lead optimisation
- clear SAR
- drug-like series with good DMPK
- patentable
22Difficult Project - 2 Renin Inhibitors
No renin inhibitor went passed PII all failed due
to poor bioavailability, high cost
23Process Lead Optimisation
Lead-like
PDR
Outside drug space old Combi Library
24Bang 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
- filtered by est Mwt - and 0,1 charge
DG (kcal/mol) -14 - 0.7n DOF (n 1-8) 0.75 n
Csp2sp3 (n4-18) 11.5n N (n0,1) 1.2n N
(n0-4) 2.5n OH (n0,1) 3.4 n CO (n0-2)
1.1 n O,S (n0-2) 1.3n hal (n0,1)
25Leadlike Bang for your Bucks
- HTS screening environment
- Small leads probably need 1 charge _at_10mM
26100 lead - drug pairs
27Lead-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
lt250 and clogP lt2 1999 3000 added by
purchase. Synthesis added gt30000
28Early GPCR Library
Distribution of ACDlogPs in PDR and
GPCR Libraries
25
20
PDR ACDlogP
15
GPCR ACDlogP
occur
10
5
0
1
4
7
-5
-2
10
13
16
ACDlogP
Distribution of Ns and Os in PDR and
GPCR Libraries
40
35
30
25
20
Count
Ns Os PDR
15
Ns and Os GPCR
10
5
0
0
4
8
12
16
20
Ns and Os
29Mitsunobu Library
30Lead Continiuum -
Drug-like
Leadlike
HtL problems ? Topical target ?
350
Mwt gt500
Mwt lt200
HTS screening
Non-HTS
Shapes (Vertex ) Needles(Roche) MULBITS(GSK) Cryst
allead(Abbott)
31Screening File Split
- Step taken by some companies - drivers
- logical conclusion of leadlike paradigm
- cost/feasibility some HTS technologies
Screening file
Bad - topical/desperate file
Good oral file
32Summary
- 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
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