Design of Fragment Screening Libraries

Peter W. Kenny (pwk.pub.2008@gmail.com)

FBDD Essentials

Screen fragments

Synthetic

Elaboration

Target

Target & fragment hit

Target & lead

Why fragments?

• Ligands are assembled from proven molecular recognition elements

• Access to larger chemical space

• Ability to control resolution at which chemical space is sampled

L

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

NS

O

N

OO

O

Me

L83

G277

G283T263

A278

D48

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).

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

0

10

20

30

40

50

60

70

80

90

log [compound]/M

% in

hib

itio

n

IC50 = 371 mM

Biochemical assay run at high concentration

Inhibition of target enzyme by ~200 Da fragment. When using a biochemical assay at highconcentration it is necessary to check for non-specific binding and other potential artifacts. It isalso possible to assess solubility under assay conditions. Compounds identified by biochemicalassays are inhibitory which may not always be the case when using affinity methods. (Shapiro,Walkup & Keating J. Biomol. Screen. 2009, 4, 1008 -1016)

Measurement of fragment binding by SPR

[Inhibitor] uM

00

0.2

0.4

0.6

0.8

1

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 tethered to sensor chip (Biacore). Test compounds binding competitvely with respect to TDCeffectively draw protein off sensor and strength of binding can be quantified (Wendy VanScyoc).

Fragment (~200 Da) binding with similar affinities (102 mM &145 mM) to different

forms of target protein

PO

O

O

FF

PO

O

O

FF

15mM

Inactive at 200mMN

S

N

OO

O

NS

N

OO

O

OMe

NS

N

OO

O

NS

N

OO

O

OMe

AZ103366763 mM

conformational lock

150 mM

hydrophobic m-subst

130 mM

AZ11548766

3 mM

PTP1B: Fragment elaboration

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)

Binding Efficiency

Measures

LE = DGº/NonHydHopkins, Groom & Alex, DDT 2004, 9, 430-431

BEI = pIC50/(MW/kDa)Abad-Zapaftero & Metz, DDT 2005, 10, 430-431

LLE = pIC50 - ClogPLeeson & Springthorpe , NRDD 2007, 6, 881-890.

Scale Measured

Binding Offset Measured

Binding

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

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

Fragment screening requirements

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

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

•

Achtung!Spitfire!

Hitting the target: The old way…

Stuka on wikipedia

“Why can’t we pray for something good, like a tighter bombing pattern, for example? Couldn’t we pray for a tighter bombing pattern?” , Heller, Catch 22, 1961

…and the new

B52 on wikipedia

Measures of Diversity & Coverage

•• •

•

••

•

•

•

••

•

••

•

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

Coverage, Diversity & Library Design

••

• ••

•• •• •• •

•

Neighborhoods 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

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

size

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

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

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 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

not observed

Precipitate

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

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).

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

At least they didn’t make you

coordinate the generic fragment screening library

project

We should never have listened to HR

OK it’s Dien Bien Phu or you can coordinate the generic fragment screening library project.

A personal view of coordinating GFSL05…

GFSL05: Overview

• 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

APGNMR07: Overview

• General

– 1200 Compounds

– Derived in part form existing AZ NMR libraries and GFSL05

– Molecules smaller than those in GFSL05

– 200mM in d6-DMSO

• Partitioning of library

– Groups (200) of 6 compounds defined

– Allows screening of mixtures of 6 or 12

– Acid:Base:Neutral = 1:1:4

ClogP: Charged library compounds

ClogP: Neutral library compoundsNon-hydrogen atoms

GFSL05: Size and lipophilicity profiles

Rotatable bonds

GFSL05

APGNMR07

Lipophilicity profiles for fragment libraries

61

1713

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)

Library Property Ionized N Mean SD SE

GFSL05

Non-H atoms

No 12284 15.64 3.42 0.03

Yes 8058 16.04 3.58 0.04

ClogP

No 12284 1.248 1.056 0.010

Yes 8058 1.658 1.337 0.015

APGNMR07

Non-H atoms

No 800 13.66 2.18 0.08

Yes 400 13.91 2.14 0.11

ClogP

No 800 1.528 0.978 0.035

Yes 400 1.718 1.006 0.050

Summary statistics for fragment libraries

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

>10mg

>20mg

0.90 0.85 0.80

0.90 0.85 0.80

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?

LiteratureGeneral

• 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

• Blomberg et al, Design of compound libraries for fragment screening, JCAMD 2009,23, 513-525.

• 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.

FBDD Blogs

Practical Fragments: http://practicalfragments.blogspot.com

FBDD Literature: http://fbdd-lit.blogspot.com

(these will both lead you to LinkedIn & facebook groups)