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

About This Presentation
Title:

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

Description:

Andy Davis, Simon Teague, Tudor Oprea, John Steele, Paul Leeson ... Walters and Teague Tet Lett. 2000, 41, 2023. Department. Author. Charnwood 'Universal' Library ... – PowerPoint PPT presentation

Number of Views:173
Avg rating:3.0/5.0
Slides: 34
Provided by: astraz5
Category:

less

Transcript and Presenter's Notes

Title: 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

5
Universal Library
Approach 1
Approach 2
Walters and Teague Tet Lett. 2000, 41, 2023
6
Charnwood Universal Library
55,000 member library
7
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
8
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.

10
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
11
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

12
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
13
Andrews Analysis Training set
Biotin
  • Significant ,model incl by 2 outliers

14
Andrews - 2
15
Andrews - Coloured by Charge
  • Multiply charged compounds overpredicted
  • oral targets 0,1 charge

16
Final Model - 0,1 charges
17
HTS screening Hits
Andrews predictions
  • probabilities
  • predicted
  • p(lt10nM) 22
  • obsd
  • p(lt10nM) lte-8

HTS Obsd activities
Many hits underperform
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

19
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
20
GPCR Hit-to-Lead
Many analogues same or loss potency
Many analogues same potency
  • Both series dropped -

21
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

22
Difficult Project - 2 Renin Inhibitors
No renin inhibitor went passed PII all failed due
to poor bioavailability, high cost
23
Process Lead Optimisation
  • Optimisation Hypothesis

Lead-like
PDR
Outside drug space old Combi Library
24
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)
25
Leadlike Bang for your Bucks
  • HTS screening environment
  • Small leads probably need 1 charge _at_10mM

26
100 lead - drug pairs
27
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
28
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
29
Mitsunobu Library
30
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)
31
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
32
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
(No Transcript)
Write a Comment
User Comments (0)
About PowerShow.com