Lead-like Properties, High-throughput Screening and Combinatorial Library Design

1
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
2
Fastest - first and best
information
Kinetics Metabolism Enzymology
Potency Efficacy Selectivity
DESIGN AND SYNTHESIS
compounds
compounds
Lead HTS Combichem
3
Fisons History

• Early lit work - largely peptidic
• Approaches available to us
• solid phase ?
• Solution phase ?
• Singles or mixtures ?

4
Design Criteria

• Library Design Buzzwords and Concepts
• Diverse
• Universal !
• Pharmacophore mapping libraries
• focussed libraries

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Universal Library
Approach 1
Approach 2
Walters and Teague Tet Lett. 2000, 41, 2023
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Charnwood Universal Library
55,000 member library
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Early 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
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The Age of Lipinski
HTS
alerts

• HTS lead generation biases chemistry

9
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 ??

3000 hits 1e6 screen points
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Charnwood 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

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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

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
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Andrews Analysis Training set
Biotin

• Significant ,model incl by 2 outliers

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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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HTS screening Hits
Andrews predictions

• probabilities
• predicted
• p(lt10nM) 22
• obsd
• p(lt10nM) lte-8

HTS Obsd activities
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 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

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HtL 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
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GPCR Hit-to-Lead
Many analogues same or loss potency
Many analogues same potency

• Both series dropped -

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GPCR 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

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Difficult Project - 2 Renin Inhibitors
No renin inhibitor went passed PII all failed due
to poor bioavailability, high cost
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Process Lead Optimisation

• Optimisation Hypothesis

Lead-like
PDR
Outside drug space old Combi Library
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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
• 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)
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Leadlike Bang for your Bucks

• HTS screening environment
• Small leads probably need 1 charge _at_10mM

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100 lead - drug pairs
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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
lt250 and clogP lt2 1999 3000 added by
purchase. Synthesis added gt30000
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Early 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
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Mitsunobu Library
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Lead 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)
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Screening 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
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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
33
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