Design of Fragment Screening Libraries
Peter W. Kenny
AstraZeneca, Alderley Park
IQPC Compound Libraries 2008
FBDD Essentials
Screen fragments
Synthetic
Elaboration
Target
Target & fragment hit
Target & lead
2D Protein-observe NMR: PTP1B
15N
ppm
1H ppm
V49 F30
W125
Y46/T154
Ligand Conc
(mM)
o 0
o 0.5
o 1.0
o 2.0
o 4.0
N
S
O
N
O
...
N
S
N
O
O
O
N
S
N
O
O
O
OMe
N
S
N
O
O
O
N
S
N
O
O
O
OMe
AZ10336676
3 mM
conformational lock
150 M
hydrophobic m-subst
130...
Why fragments?
• Leads are assembled from proven molecular
recognition elements
• Access to larger chemical space
• Abilit...
Ligand Efficiency (Bang For Buck)
Does molecule punch its weight?
• Scale pIC50 or DGº by molecular weight or number of he...
The Hann molecular complexity model
Hann et al [2001]: Molecular Complexity and Its Impact on the Probability of Finding L...
Overview of fragment based lead discovery
Target-based
compound selection
Analogues of known
binders
Generic screening
lib...
Scheme for fragment based lead optimisation
Fragment screening requirements
• Assay capable of reliably quantifying weak (~mM)
binding
• Library of compounds with low...
Screening Library Design Requirements
• Precise specification of substructure
– Count substructural elements (e.g. chlorin...
Measures of Diversity & Coverage
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
2-Dimensional representation of chemical space is used here...
Coverage & Diversity
Poor coverage of available
chemical space by small set of
mutually similar compounds
Reasonable cover...
Neighborhoods and library design
Sample
Availability
Molecular
Connectivity
Physical
Properties
screening samples Close analogs Ease of synthetic
elaborati...
NH
NN
H H
H H
O O
O
Me
NH
N
N H
H
H
H
O
O
O
Me
O
O
Degree of substitution as measure of molecular complexity
The prototypi...
Hits, non-hits & lipophilicity: Survival of the fattest*
Mean Std Err Std Dev
Hits 2.05 0.08 1.10
Non-Hits 1.35 0.03 1.24
...
20%
10%
30%
40%
50%
log(S/M)
Aqueous solubility:
Percentiles for measured log(S/M) as function of ClogP
Data set is partit...
Solubility in DMSO: Salts
Precipitate
observed
Precipitate
not observed
All samples
Adduct 525 29 554
Not Adduct 4440 89 4...
Acceptable diversity
And coverage?
Assemble library in
soluble form
Add layer to core
Incorporate layer
Yes
No
Select core...
The GFSL05 project
• Rationale
– Strategic requirement: Readily accessible source of compounds
for a range of fragment scr...
GFSL05: Design
• Molecular recognition considerations
– Requirement for at least one charged center or acceptably
strong h...
ClogP: Charged library compounds
ClogP: Neutral library compoundsNon-hydrogen atoms
GFSL05: Size and lipophilicity profile...
61
17
13
4 4
1
0
Breakdown of GFSL05 by charge type
Neutral
Anion Cation
Ionisation states are identified using AZ ionisat...
GFSL05: Numbers of neighbours within library as function of
similarity (Tanimoto coefficient; foyfi fingerprints)
0.90 0.8...
GFSL05: Numbers of available neighbours as function of similarity
(Tanimoto coefficient; foyfi fingerprints) and sample we...
A couple of questions to finish with…
• Is it helpful to think of leadlikeness in terms of the point at
which screening st...
GFSL05 Acknowlegements
Jeff Albert
Sam Blackburn
Niklas Blomberg
Roger Butlin
Alex Breeze
Gill Burgess
Jeremy Burrows
Lind...
Literature
General
• Erlanson et al, Fragment-Based Drug Discovery, J. Med. Chem., 2004, 47, 3463-3482.
• Congreve et al. ...
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Design of fragment screening libraries (IQPC 2008)

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These were the slides that I used for the 2008 IQPC compound libraries conference which was the first external lecture on fragment screening libraries.

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Design of fragment screening libraries (IQPC 2008)

  1. 1. Design of Fragment Screening Libraries Peter W. Kenny AstraZeneca, Alderley Park IQPC Compound Libraries 2008
  2. 2. FBDD Essentials Screen fragments Synthetic Elaboration Target Target & fragment hit Target & lead
  3. 3. 2D Protein-observe NMR: PTP1B 15N ppm 1H ppm V49 F30 W125 Y46/T154 Ligand Conc (mM) o 0 o 0.5 o 1.0 o 2.0 o 4.0 N S O N O O O Me L83 G277 G283 T263 A278 D48 Observation of protein resonances allows determination of Kd and can provides binding site information. These techniques require isotopically labelled protein and there are limits on the size of protein that can be studied. (Kevin Embrey)
  4. 4. N S N O O O N S N O O O OMe N S N O O O N S N O O O OMe AZ10336676 3 mM conformational lock 150 M hydrophobic m-subst 130 M AZ11548766 3 M PTP1B: Fragment elaboration P O O O F F P O O O F F 15M Inactive at 200M Elaboration by Hybridisation: Literature SAR was mapped onto the fragment AZ10336676 (green). Note overlay of aromatic rings of elaborated fragment AZ11548766 (blue) and difluorophosphonate (red). See Bioorg Med Chem Lett, 15, 2503-2507 (2005)
  5. 5. Why fragments? • Leads are assembled from proven molecular recognition elements • Access to larger chemical space • Ability to control resolution at which chemical space is sampled L
  6. 6. Ligand Efficiency (Bang For Buck) Does molecule punch its weight? • Scale pIC50 or DGº by molecular weight or number of heavy atoms as surrogate for molecular surface area – Rationale: Molecules interact by presenting molecular surfaces to each other. How effectively does a molecule make use of its molecular surface? • Fragment hits tend to have high ligand efficiency… – But then they need to! • Is high ligand efficiency indicative of hot spot on protein surface? Hopkins, Groom & Alex, Ligand efficiency: A useful metric for lead selection Drug Discov. Today 2004, 430-431
  7. 7. The Hann molecular complexity model Hann et al [2001]: Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 Success landscape
  8. 8. Overview of fragment based lead discovery Target-based compound selection Analogues of known binders Generic screening library Measure Kd or IC50 Screen Fragments Synthetic elaboration of hits SAR Protein Structures Milestone achieved! Proceed to next project
  9. 9. Scheme for fragment based lead optimisation
  10. 10. Fragment screening requirements • Assay capable of reliably quantifying weak (~mM) binding • Library of compounds with low molecular complexity and good aqueous solubility •
  11. 11. Screening Library Design Requirements • Precise specification of substructure – Count substructural elements (e.g. chlorine atoms; rotatable bonds; terminal atoms; reactive centres…) – Define generic atom types (e.g. anionic centers; hydrogen bond donors) • Meaningful measure of molecular similarity – Structural neighbours likely to show similar response in assay
  12. 12. Measures of Diversity & Coverage • • • • • • • • • • • • • • • 2-Dimensional representation of chemical space is used here to illustrate concepts of diversity and converage. Stars indicate compounds selected to sample this region of chemical space. In this representation, similar compounds are close together
  13. 13. Coverage & Diversity Poor coverage of available chemical space by small set of mutually similar compounds Reasonable coverage of available chemical space given small, diverse set of compounds Good coverage of available chemical space by appropriate number of compounds • • • • • •• • • • • • •
  14. 14. Neighborhoods and library design
  15. 15. Sample Availability Molecular Connectivity Physical Properties screening samples Close analogs Ease of synthetic elaboration Molecular complexity Ionisation Lipophilicity Solubility Molecular recognition elementsMolecular shape 3D Pharmacophore Privileged substructures Undesirable substructures Molecular size 3D Molecular Structure Fragment selection criteria
  16. 16. NH NN H H H H O O O Me NH N N H H H H O O O Me O O Degree of substitution as measure of molecular complexity The prototypical benzoic acid can be accommodated at both sites and, provided that binding can be observed, will deliver a hit against both targets (see Curr. Top. Med. Chem. 2007, 7, 1600-1629)
  17. 17. Hits, non-hits & lipophilicity: Survival of the fattest* Mean Std Err Std Dev Hits 2.05 0.08 1.10 Non-Hits 1.35 0.03 1.24 *Analysis of historic screening data & quote: Niklas Blomberg, AZ Molndal Comparison of ClogP for hits and non-hits from fragment screens run at AstraZeneca
  18. 18. 20% 10% 30% 40% 50% log(S/M) Aqueous solubility: Percentiles for measured log(S/M) as function of ClogP Data set is partitioned by ClogP into bins and the percentiles and mean ClogP is calculated for each. This way of plotting results is particularly appropriate when dynamic range for the measurement is low. Beware of similar plots where only the mean or median value is shown for the because this masks variation and makes weak relationships appear stronger than they actually are. (See Bioorg. Med. Chem. 2008, 16, 6611-6616). Measure solubility for neutral (at pH 7.4) fragments for which ClogP > 2.2
  19. 19. Solubility in DMSO: Salts Precipitate observed Precipitate not observed All samples Adduct 525 29 554 Not Adduct 4440 89 4529 All samples 4965 118 5083 Analysis of 5k solubilised samples showed that 5% of samples registered as ‘adduct’ (mainly salts) showed evidence of precipitation compared to 2% of the other samples
  20. 20. Acceptable diversity And coverage? Assemble library in soluble form Add layer to core Incorporate layer Yes No Select core Core and layer library design Compounds in a layer are selected to be diverse with respect to core compounds. The ‘outer’ layers typically contain compounds that are less attractive than the ‘inner’ layers. This approach to library design can be applied with Flush or BigPicker programs (Dave Cosgrove, AstraZeneca, Alderley Park) using molecular similarity measures calculated from molecular fingerprints. (See Curr. Top. Med. Chem. 2007, 7, 1600-1629).
  21. 21. The GFSL05 project • Rationale – Strategic requirement: Readily accessible source of compounds for a range of fragment screening applications – Tactical objective: Assemble 20k structurally diverse compounds with properties that are appropriate for fragment screening as 100mM DMSO stocks • Design overview – Core and layer design applied with successively more permissive filters (substructural, neighborhood, properties) – Bias compound selection to cover unsampled chemical space
  22. 22. GFSL05: Design • Molecular recognition considerations – Requirement for at least one charged center or acceptably strong hydrogen bonding donor or acceptor • Substructural requirements defined as SMARTS – Progressively more permissive filters to apply core and layer design – Restrict numbers of non-hydrogen atoms (size) and terminal atoms (complexity) – Filters to remove undesirable functional groups (acyl chloride) and to restrict numbers of others (nitro, chloro) – ‘Prototypical reaction products’ for easy follow up • Control of lipophilicity (ClogP) dependent on ionisation state – Solubility measurement for more lipophilic neutrals • Tanimoto coefficient calculated using foyfi fingerprints (Dave Cosgrove) as primary similarity measure – Requirement for neighbour availability in core and layer design
  23. 23. ClogP: Charged library compounds ClogP: Neutral library compoundsNon-hydrogen atoms GFSL05: Size and lipophilicity profiles Rotatable bonds
  24. 24. 61 17 13 4 4 1 0 Breakdown of GFSL05 by charge type Neutral Anion Cation Ionisation states are identified using AZ ionisation and tautomer model. Multiple forms are generated for acids and bases where pKa is thought to be close to physiological pH (see Kenny & Sadowski Methods and Principles in Medicinal Chemistry 2005, 23, 271-285)
  25. 25. GFSL05: Numbers of neighbours within library as function of similarity (Tanimoto coefficient; foyfi fingerprints) 0.90 0.85 0.80
  26. 26. GFSL05: Numbers of available neighbours as function of similarity (Tanimoto coefficient; foyfi fingerprints) and sample weight >10mg >20mg 0.90 0.85 0.80 0.90 0.85 0.80
  27. 27. A couple of questions to finish with… • Is it helpful to think of leadlikeness in terms of the point at which screening stops and synthesis begins? • Does a screening technology that allows millimolar binding of a compound to be characterized reliably make that compound more leadlike?
  28. 28. GFSL05 Acknowlegements Jeff Albert Sam Blackburn Niklas Blomberg Roger Butlin Alex Breeze Gill Burgess Jeremy Burrows Lindsey Cook Dave Cosgrove Al Dossetter Phil Edwards Kevin Embrey Thomas Fex Rutger Folmer Richard Gallagher Andrew Grant Ed Griffen James Haigh Neil Hales Richard Kilburn Jin Li Sorel Muresan Paul Owen Steve St-Gallay Adam Shapiro Ellen Simkiss Kin Tam Daniel Taylor Dave Timms Tony Wilkinson
  29. 29. Literature General • Erlanson et al, Fragment-Based Drug Discovery, J. Med. Chem., 2004, 47, 3463-3482. • Congreve et al. Recent Developments in Fragment-Based Drug Discovery, J. Med. Chem., 2008 51, 3661–3680. • Albert et al, An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca's drug discovery programmes. Curr. Top. Med. Chem. 2007, 7, 1600-1629 • Hann et al Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 • Shuker et al, Discovering High Afinity Ligands for Proteins: SAR by NMR, Science, 1996, 274 1531-1534). Screening Libraries • Schuffenhauer et al, Library Design for Fragment Based Screening, Curr. Top. Med. Chem. 2005, 5, 751-762. • Baurin et al, Design and Characterization of Libraries of Molecular Fragments for Use in NMR Screening against Protein Targets, J. Chem. Inf. Comput. Sci., 2004, 44, 2157- 2166 • Colclough et al, High throughput solubility determination with application to selection of compounds for fragment screening. Bioorg, Med. Chem. 2008, 16, 6611-6616. • Kenny & Sadowski, Structure modification in chemical databases. Methods and Principles in Medicinal Chemistry 2005, 23, 271-285.
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