Fragment screening library workshop (IQPC 2008)

transcript

Design of compound libraries for fragment screening

IQPC Compound Libraries 2008, Workshop D

Peter W. Kenny

AstraZeneca, Alderley Park

Workshop outline

• Introduction to fragment based drug discovery (FBDD)

• Diversity, coverage and library design

• Fragment selection criteria

• An example: GFSL05 (AstraZeneca generic fragment screening library)

• Exercises

Introduction to fragment based drug

discovery (FBDD)

FBDD Essentials

Screen fragments

Synthetic

Elaboration

Target

Target & fragment hit

Target & lead

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.

Fragment screening requirements

• Assay capable of reliably quantifying weak (~mM) binding

• Library of compounds with low molecular complexity and good aqueous solubility

2D Protein-observe NMR: PTP1B

1H ppm

V49 F30

Y46/T154

Ligand Conc

G283T263

Observation of protein resonances allowsdetermination of Kd and can provides binding siteinformation. These techniques require isotopicallylabelled protein and there are limits on the size ofprotein that can be studied. (Kevin Embrey)

1D Ligand-observe NMR

Ligand in buffer

Ligand and target protein

After saturation with potent inhibitor

Isotopically labelled protein is not required whenobserving ligand resonances and there are norestrictions on protein molecular weight. Howevercompetition experiments are necessary to quantifybinding (Rutger Folmer).

Measurement of fragment binding by SPR

[Inhibitor] uM

0.001 0.01 0.1 1 10 100 1000

In these experiments, protein is first allowed to bind to ligand (target definition compound) that hasbeen immobilised on sensor chip (Biacore). Test compounds binding competitvely with respect to TDCeffectively draw protein off sensor and strength of binding can be quantified (Wendy VanScyoc).

Figure shows ~200 MW fragment binding

with similar affinities (102 mM &145 mM)

to different forms of target protein

-6 -5 -4 -3 -2-10

Log Untitled

log [compound]/M

IC50 = 371 mM

Biochemical assay run at high concentration

Inhibition of target enzyme by ~200 MWfragment. When using a biochemical assayat high concentration it is necessary tocheck for non-specific binding and otherpotential artifacts. It is also possible toassess solubility under assay conditions.Compounds identified by biochemical assaysare inhibitory which may not always be thecase when using affinity methods. (AdamShapiro).

Crystal Structure of AZ10336676 bound to PTP1B

WPD Loop

Catalytic

Crystallographic detection of fragment binding revealsbinding mode but does not allow affinity to be quantified.Crystallography can be challenging with weakly boundinhibitors (Andrew Pannifer & Jon Read)

AZ103366763 mM

conformational lock

150 mM

hydrophobic m-subst

130 mM

AZ11548766

PTP1B: Fragment elaboration

Inactive at 200mM

Elaboration by Hybridisation: Literature SAR was mappedonto the fragment AZ10336676 (green). Note overlay ofaromatic rings of elaborated fragment AZ11548766 (blue)and difluorophosphonate (red). See Bioorg Med Chem Lett,15, 2503-2507 (2005)

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

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

A. L. Hopkins, C. R. Groom, A. Alex, Ligand efficiency: A useful metric for lead selection,

Drug Discov. Today 2004, 430-431.

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

SARProtein

Structures

Milestone achieved!Proceed to next

project

Scheme for fragment based lead optimisation

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., 200851, 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 forDrug 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 Usein NMR Screening against Protein Targets, J. Chem. Inf. Comput. Sci., 2004, 44, 2157-2166

• Colclough et al, High throughput solubility determination with application to selectionof compounds for fragment screening. Bioorg, Med. Chem. 2008, 16, 6611-6616.

• Kenny & Sadowski, Structure modification in chemical databases. Methods andPrinciples in Medicinal Chemistry 2005, 23, 271-285.

Diversity, coverage and library design

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

Measures of diversity & coverage

•••

••

2-Dimensional representation of chemical space is used here to illustrate concepts ofdiversity and converage. Stars indicate compounds selected to sample this region ofchemical space. In this representation, similar compounds are close together

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

• •

• ••

•• •• •• •

Neighborhoods and library design

Acceptable diversity

And coverage?

Assemble library in

soluble form

Add layer to core

Incorporate layer

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 (David

Cosgrove, AstraZeneca, Alderley Park) using molecular similarity measures calculated

from molecular fingerprints. (See Curr. Top. Med. Chem. 2007, 7, 1600-1629).

Fragment selection criteria

Sample

AvailabilityMolecular

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

3D Molecular

Structure

Fragment selection criteria

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)

Hits, non-hits & lipophilicity: Survival of the fattest*

Mean Std Err Std Dev

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

20%10%

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 ofplotting results is particularly appropriate when dynamic range for the measurement is low. Beware of similar plotswhere only the mean or median value is shown for the because this masks variation and makes weak relationshipsappear 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

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

# Generic fragment screening library

# SMARTS for restriction of substitution in fragments

# restrict_subs_1.smt

#-------------------------------------------------------------

# Some general size restrictions to set tone of search

Hev [A,a] 5-20

Arom a 5-12

Term [A;D1]-[A,a] 0-2

Fuse [c,A;R2] 0-2

#-------------------------------------------------------------

# Specific atom types: Explicit specification of what is

# permitted in molecule. If it's not allowed it's verboten!

CH2 [C;H2;!R] 0-2

O1 [OD2] 0-2

O2 [OH] 0-2

O3 O=C[OH] 0-1

O4 O=C[NX3] 0-2

O5 O=c[n&X3,o&X2] 0-2

O6 O=c1aa[n&X3,o&X2]cc1 0-2

O7 O=S 0-2

TerAm [N;!+;X3]([CX4])([CX4])[CX4] 0-2

N1 [N,n;!+;X3] 0-2

N2 [n;X2] 0-3

N3 [n;H;!+] 0-1

N4 [N;X3;!H0;!+] 0-2

S1 S(c)[C&X4,c] 0-1

CO C(=O)[N,O&H] 0-2

SO S(=O)=O 0-1

ArOS [o,s] 0-1

# Specific requirements

# Atoms providing polar interaction

Interact1 [$TerAm,$N2,$N3,$N4] *

Interact2 [$O2,$O3,$O4,$O5,$O6,$O7] *

Interact [$Interact1,$Interact2] 1-4

# Benzene ring

Benzene c1ccccc1 6-12

#-------------------------------------------------------------

# Decrapping SMARTS: Don't want these

AtmOK1 [c,$CH2,$O1,$O2,$O3,$O4,$O5,$O6,$O7] *

AtmOK2 [$N1,$N2,$N3,$N4,$TerAmin,$S1] *

AtmOK3 [$CO,$SO,$ArOS,C&H3,F,Cl] *

CrpAtm [A,a;!$AtmOK1;!$AtmOK2;!$AtmOK3] 0

Cation [A,a;+] 0

ReactHal [F,Cl,Br,I][C&X4,$(c[nX2]),$(C=O),N,O,S] 0

SulfEster S(=O)O[CX4] 0

NAcyl NC=O *

NN1 [N;!$NAcyl]-[N;!$NAcyl] 0

NN2 [N,n]-N 0

NO [N,n;!$NAcyl]-O 0

AcycEst C(=O)O[a,A] 0

Anhydrid O=[C,c][o,O][C,c]=O 0

Formyl [CH]=O 0

Keto O=C(C)C 0

Quinon O=c1ccc(=O)cc1 0

Phenol [OH]c 0

Anilin1 [NH2]c1ccccc1 0

Anilin2 [NH]([CH3])c1ccccc1 0

Het2sp3c [O,N,n,S]-;!@[CX4]-[O,N,n,S] 0

# Groups to restrict: Not so bad in very small numbers

Amino [NH2] 0-1

Chloro [Cl] 0-1

Hydroxyl [OH] 0-1

# Combinations of groups to be restricted

AmHydrox [$Amino,$Hydroxyl] 0-1

Example of SMARTS used to select fragments

An example: GFSL05 (AstraZeneca

generic fragment screening library)

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

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

ClogP: Charged library compounds

ClogP: Neutral library compoundsNon-hydrogen atoms

GFSL05: Size and lipophilicity profiles

Rotatable bonds

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 Methods and Principles

in Medicinal Chemistry 2005, 23, 271-285)

GFSL05: Numbers of neighbours within library as function of

similarity (Tanimoto coefficient; foyfi fingerprints)

0.90 0.85 0.80

GFSL05: Numbers of available neighbours as function of similarity

(Tanimoto coefficient; foyfi fingerprints) and sample weight

0.90 0.85 0.80

Exercises

Exercise 1:

Directed library using crystal structural information

You are selecting fragments for screening against anenzyme target. You have available the crystalstructure of a complex with a stable substrate analog,further access to crystallography and a robustbiochemical assay.

• What advantages and disadvantages do you see in using a biochemical assay

• How would you select the compounds in the screening library?

• How would you follow up hits from the primary screen?

Exercise 2:

Generic library for screening by X-ray

crystallography

You are selecting a single generic set of fragments forscreening against multiple, unrelated targets using X-raycrystallography.

• How might the requirements of crystallography differfrom those of other technologies for detectingbinding?

• How would you select the library compounds?

• How would you partition the screening library intomixtures for screening?

Fragment screening library workshop (IQPC 2008)

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