progressing fragments for challenging targets · 1 progressing fragments for challenging targets....

1

Progressing fragments for challenging targets

Roderick E Hubbard Vernalis (R&D) Ltd, Cambridge

YSBL & HYMS, Univ of York, UK

Fragments 2013

For slides –

[email protected]

2

Trends for new technologies

•

In the beginning – lots of excitement•

Which can lead to hype and over‐selling

Time

Expectation

Technology Trigger

Analysis / terms initially proposed by Gartner group (Murcko)

3


•

In the beginning – lots of excitement•

Which can lead to hype and over‐selling

Time

Expectation

Technology Trigger

Inflated expectations

Analysis / terms initially proposed by Gartner group

4


•

Too rapid (often inexpert) deployment•

It doesn’t work ‐

disillusionment

Time

Expectation

Technology Trigger

Trough of Disillusionment



5


•

Too rapid (often inexpert) deployment•

It doesn’t work ‐

disillusionment

Time

Expectation

Technology Trigger




6


•

Eventually, expertise grows•

Begin to understand how and where to apply methods

Time

Expectation

Technology Trigger



Slope of Enlightenment


7


•

Learn how to integrate the methods into the process •

Add to productivity

Time

Expectation

Technology Trigger




Plateau of productivity


8

Fragments

•

The ideas established in the molecular modelling

/ structural biology community during the 1980s and early 1990s

•

First reduced to practice by Abbott in SAR by NMR approach in mid 1990s•

Other pharmaceutical companies unable to replicate success

•

Approaches developed in small pharma

companies in late 1990s / early 2000s•

Astex, Vernalis, Plexxikon, SGX ….. and underground in large pharma

…

•

Underpinning concepts developed in the 2000s – complexity, ligand

efficiency

•

Success has led to increased use•

Different aspects of FBLD are on different parts of this curve•

And in different organisatiions

Time

Expectation

Technology Trigger





9

Fragments 2013 talk

•

Summary from 2009•

Where we were 4 years ago

•

New approaches / ideas for conventional targets•

Screening methods

•

Off‐rate screening for fragment to hit optimisation

•

How to approach non‐conventional targets•

What is a non‐conventional target?

•

Methods for determining binding mode•

Issues –

assays, plasticity, compound properties, 3D

•

Final remarks

10

Fragments 2013 talk

•



•


Screening methods

•


•



•


Issues –


•

Final remarks

11

SeeDs

process ‐

2008 Structural Exploitation of Experimental Drug Startpoints*

Fragment Library

Target

X-ray Structures

b ca

NMR CompetitiveBinding Experiment

ON

NHN

O

O

NH

N

NH

O

OH

O

NHN

NNH

NH

ONH

NNH

NNH

OHO

NNH

NNH

NH

N

NH

O

O

NH2

O

NHNH

Evolution

Validation Biacore

HSQC

Wet assay / Biacore

*Hubbard et al (2007), Curr Topics Med Chem, 7, 1568

Fragments 2009 talk

12

Finding fragments

•

Finding fragments that bind is not difficult•

A good way of assessing target “ligandability”

0%

1%

2%

3%

4%

5%

6%

7%

8%

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

D score

Cla

ss 1

hits

rate

s

Low hit rates (< 2%)High hit rates (> 2%)

Kinases(3-5% hit rate)

protein-protein interaction(0.4-3% hit rate)

Poor targets

1Calculate druggability

Chen & Hubbard (2009), JCAMD, 23, 603Fragments 2009 talk

Valid

ated

hit rate

Ligandability

calculated from structure (DScore)

13

Finding fragments

•

Finding fragments that bind is not difficult•

A good way of assessing target “ligandability”

•

Low hit rate can indicate difficult to progress•

See also Hajduk (2005) J Med Chem, 48, 2518

0%

1%

2%

3%

4%

5%

6%

7%

8%

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

D score

Cla

ss 1

hits

rate

s


Kinases(3-5% hit rate)


Poor targets


Chen & Hubbard (2009), JCAMD, 23, 603Fragments 2009 talk

Valid

ated

hit rate

Ligandability


14

Using fragments

•

Growing fragments

Fragments 2009 talk

15

Using fragments

•

Growing fragments – CHK1 example

Chk-1 IC50 >100µM

LE ~ 0.39

Fragments 2009 talk

16

Using fragments

•


Chk-1 IC50 = 5µM

LE = 0.39

GI50 HCT116 >80µM

pH2AX (MEC) – inactive

Fragments 2009 talk

17

Using fragments

•


Chk-1 IC50 = 0.2µM

LE = 0.33

GI50 HCT116 = 4µM

pH2AX (MEC) = 7µM

Fragments 2009 talk

18

Using fragments

•


Chk-1 IC50 = 0.013µM

LE = 0.39

GI50 HCT116 = 1.8µM

pH2AX (MEC) =0.2µM

Series members further optimised to identify Candidate V158411

Fragments 2009 talk

19

Using fragments

•

Merging – HSP90 example

Known LigandsVirtual Screening hitsScreen

Detailed Design

Fragments 2009 talk

20

N

N SNH2

O

NH

Cl

Cl

ON

NN

NH2

OOMe

VER-26734FP IC50 >5mM

NN

NH2

SNH

OVER-52959

FP IC50 =535M

Fragment Evolved fragment

HSP90 –

BEP800 story

N

N

N

NH2

Cl

ClN

VER-82576NVP-BEP800

FP IC50 =0.058MKD = 0.9nM (SPR)

HCT116 GI50 =0.161MBT474 GI50 =0.057M

NO

OH

OH

O

NH

N O

NVP-AUY922Vernalis Phase II candidate (FBLD

/ SBDD derived)

VER-41113FP IC50 =1.56M

Virtual Screening Hit

VER-45616FP IC50 =0.9M

SNNH2

NH2N

NH2

O

OEt

Virtual Screening Hit

Brough et al (2009) J Med Chem 52,4794‐4809 Roughley et al (2012) Top Curr

Chem 317, 61

D93 G97 K58

F138L107

Fragments 2009 talk

21

Pin1 Story

•

Proline

isomerase

–

persuasive biological rationale that key oncology target

•

Structure available + D‐peptide tool compound

Fragments 2009 talk

22

Pin1 Story

•

Proline

isomerase

–


•

Fragments identified: fragment to hit evolution•

No correlation between biophysical and enzyme assays

NH

OH

O

H3C

Fragments 2009 talk

23

Pin1 Story

•

Proline

isomerase

–


•

Fragments identified: fragment to hit evolution

•

Issue with over‐binding – multiple copies of fragment binding to the protein – SPR and Xray

NH

OH

O

H3C

Fragments 2009 talk

24

Pin1 Story

•

Proline

isomerase

–


•

Fragments identified: fragment to hit evolution

•

Issue with over‐binding – multiple copies of fragment binding to the protein – SPR and Xray

•

Designed 3D fragment –

progressed multiple series < 100nM on target showing cellular activity

Potter AJ et al, Bioorg

Med Chem

Lett. 2010; 20:586‐590

N

NH

HN

O

O

HO

O

CH3NH

OH

O

H3C

Fragments 2009 talk

25

Fragments 2013 talk

•



•


Screening methods

•


•



•


Issues –


•

Final remarks

26

Optimise fragment

Fragment to hit :SAR by catalogoff‐rate screening

-5

0

510

15

20

25

30

-50 0 50 100 150 200 250 300

RU

Res

pons

e

Time s

-4

-2

0

2

4

6

8

-100 -50 0 50 100 150 200 250 300

RU

Res

pons

e

Tim e s

Characterisation

X‐ray or NMR guided model

The Vernalis processHubbard et al (2007), Curr

Topics Med Chem, 7, 1568 Hubbard and Murray (2011), Methods Enzym, 493, 509

Target

Optimise fragment

Hits

Competitive NMR screenFragment Library

~ 1200 compoundsAve MW 190

Design, Build & Test

N

N SNH2

O

NH

Cl

Cl

ON

NO

OH

OH

O

NH

N O

NN

NH2

OOMe

NN

NH2

SNH

O

N

N

N

NH2

Cl

ClN

SNNH2

NH2N

NH2

O

OEt

Virtual screen; literature; library screen

Screen by SPR, DSF,

WAC, biochem

or

binding assay, Xray

Drug?

27

Some comments on fragment screening

•

For “good”

target sites (many enzymes):•

If assays configured correctly•

Same hits identified by ligand

observed NMR and SPR

•

validated hits tend to give crystal structures

•

Careful QC of fragment library –

attention to assay conditions

•

There can be lots of false negatives from screening by X‐ray•

Requires suitable crystal system

•

“Wet”

assays can work sometimes•

But high concentrations can confound the assay

•

Thermal melt methods unreliable

•

For non‐conventional targets (such as protein‐protein):•

Many issues•

Overbinding, problems due to properties of compounds and target

•

Cross‐validate binding by different techniques

Hubbard & Murray (2011), Meth Enzymology, 493, 509

28

Leads generated for many Targets

•

Disclosed targets include:•

Kinases: CDK2, Chk1, PDPK1, PDHK1, Pim1, STK33, Pak4

•

ATPases: DNA gyrase, Grp78, HSP70 and HSP90•

protein‐protein interaction targets: Pin1 and Bcl‐2

•

FAAH and tankyrase

•

Undisclosed targets include:•

kinases

with unusual binding sites

•

protein‐protein interaction targets•

novel classes of enzymes in large multi‐domain complexes

29

Summary – fragments and conventional targets

•

Fragment screen to assess targets

•

Fragments alongside HTS and knowledge‐based•

You always find something new

•

Fragments to hits for conventional targets is relatively straightforward

•

(when crystal structures / good models available)•

The main issues are organisational and cultural•

Discuss !!

Time

Expectation

Technology Trigger





30

Fragments 2013 talk

•



•


Screening methods

•


•



•


Issues –


•

Final remarks

31

Exploiting the dissociation rate constantkoff (s-1)kon (M-1s-1)KD =

James Murray, Steve Roughley, Natalia Matassova

PL P + Lkoff

kon

32

Exploiting the dissociation rate constant

•

Cannot improve association rate constant above ~10‐8

M‐1s‐1

•

No point in drug discovery, too many membranes in the way!

•

Dissociation rate constant can be infinite (ie

covalent!)

•

We have seen that it is the key driver of potency•

As have others (review Copeland, Future Med. Chem. 3(12), 2011)

koff (s-1)kon (M-1s-1)KD =


33


•


M‐1s‐1

•

No point in drug discovery, too many membranes in the way!

•


covalent!)

•



•

Surface plasmon

resonance – a way to measure kinetics•

FOCUS ON THE Off‐RATE ‐

koff

ResonanceSignal (RU)

Dissociation - koff

Asso

ciat

ion

- kon

Kinetics

ConcentrationTime (s)

KineticsKinetics

Time (s)Time (s)Time (s)Time (s)Time (s)Concentration

Time (s)Concentration

Time (s)



34


•


M‐1s‐1

•

No point in drug discovery, too many membranes in the way

•


covalent)

•



•

Independent of concentration•

Exploit this to assess unpurified

reactions, off‐rate screening (ORS)



35

•

Historical Hsp90: thienopyrimidines•

200 nM – 5 M IC50

by FP assay

•

Re‐prepared compounds by Suzuki reaction

•

Minimal work‐up•

Evaporate, partition

•

Purity 50 – 80 % (LCMS)

•

Screened by ORS

N

N S

Cl

NH2O

ON

N SNH2O

O

RB(OH)2

R

DMF / H2O

NaHCO3

Pd(Ph3P)2Cl2100 ºC Microwave 10 min

Off rate screening (ORS): ExampleJames Murray, Steve Roughley, Natalia Matassova

36

•


200 nM – 5 M IC50

by FP assay

•


•



•


•

Screened by ORS

N

N S

Cl

NH2O

ON

N SNH2O

O

RB(OH)2

R

DMF / H2O

NaHCO3



A: Pure starting materialB: Faux reaction

37

•


200 nM – 5 M IC50

by FP assay

•


•



•


•

Screened by ORS

N

N S

Cl

NH2O

ON

N SNH2O

O

RB(OH)2

R

DMF / H2O

NaHCO3



A: Pure starting materialB: Faux reactionC: Purified productD: Crude reaction

38

3UH4Novartis (2012)

Tankyrase

‐

Introduction

•

Axin

is targeted for turnover through poly‐D‐ribose labelling

by Tankyrase. This removes axin, which has a

role in stabilising

‐catenin

•

Inhibition of Tankyrase

has been proposed to enhance the degradation of ‐catenin, an indirect way of affecting

the WNT‐pathway

•

Crystal structure of tankyrase

available in 2007

2RF5 Structural Genomics Consortium (2007)

39

Tankyrase

‐

Introduction

•

Axin

is targeted for turnover through poly‐D‐ribose labelling

by Tankyrase. This removes axin, which has a

role in stabilising

‐catenin

•

Inhibition of Tankyrase

is therefore proposed to enhance the degradation of ‐catenin, an indirect way of affecting the WNT‐pathway

•

Crystal structure of tankyrase

available in 2007

•

At Vernalis:•

Initially – low levels of protein production – insufficient

material for fragment screen by NMR•

Able to produce large numbers of apo‐crystals that

preliminary trials showed were suitable for ligand

soaking

Alba Macias, Chris Graham and team

40

Tankyrase

‐

Hit identification

Crystals

Soaked fragments in pools of 8

Data collection (up to1.6Å

resolution)

Streamlined structure solution

Characterised by SPR and TSA

62 hits from 1563fragments


41

Tankyrase

‐

Fragment to hit evolution

•

The most attractive fragments were not suitable for rapid chemistry

•

Designed a modified fragment

•

Off‐rate screening libraries identified vectors and substituents•

Crystals soaked directly with reaction mixtures

•

Lead Series driven using combinations of tools•

Computational chemistry

•

X‐ray crystallography•

SPR (ORS), DSF

•

Medicinal Chemistry

•

Properties•

5 nM

vs

TNKS2, high ligand

efficiency (0.60)

•

Affects PD markers in cells (stabilises Axin2, inhibits WNT pathway)

•

Tools to probe the biology


42

PDHK – an unusal

kinase

•

GHKL family protein (like HSP90)

•

Other companies have targetted

for diabetes•

AZ and Novartis in the 1990s – various allosteric

sites

•

Vernalis investigated as a cancer metabolism target•

Off‐rate screening to selectively evolve a fragment

E2 / L2 site

AZD‐7545 – 2q8g

Novartis –

2bu5

DCA – 2q8h

ATP site – 2q8i

Pfizer – 2bu7

43

PDHK project

•

Initial fragment hit for ATP site – also binds to HSP90

•

Structure shows which vector(s) to explore

•

Parallel libraries synthesised and screened by SPR•

Using the off‐rate screening method – changes in koff

•

One SPR channel with HSP90, one with PDHK

•

Can identify PDHK selective compounds•

And HSP90 selective compounds

Fragment bound

to PDHK1

HSP90

HSP90

PDHK1

PDHK1

VER‐00236029

VER‐00236030

44

Fragments 2013 talk

•



•


Screening methods

•


•



•


Issues –


•

Final remarks

45

Fragments 2013 talk

•



•


Screening methods

•


•



•


Issues –


•

Final remarks

46

Non‐conventional targets

47


•

Can find fragments that bind•

Orthogonal biophysical methods can validate and

characterise

fragment binding

48


•

Can find fragments that bind

•

Evolution requires robust model of fragment binding

•

Best model is from X‐ray structure•

But sometimes high affinity ligand

required for structure

49


•


•


•

Best model is from X‐ray structure

•

NMR methods can provide sufficient quality of model•

Experiments can be filtered to reveal just the interactions

between protein and ligand

Unfiltered NOESY

- All NOEs

50


•


•


•


•



between protein and ligand

Unfiltered NOESY

- All NOEs

Filtered NOESY

- Intermolecular NOEs

Protein/ligand

51


•


•


•


•



between protein and ligand•

Have developed leads from fragments using NMR models

•

High affinity ligands give X‐ray structures that confirm model

52

Video of Bcl‐2 plasticity

53

Bcl‐2 ‐

ligandability

•

changes dramatically as ligands explore available pockets / flexibility

0%

1%

2%

3%

4%

5%

6%

7%

8%

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Dscore

Cla

ss 1

hits

rate

s



1 Calculate druggability

Valid

ated

hit rate

Ligandability


54

Selective Bcl‐2 inhibitor program

•

Structure‐guided optimisation has generated selective Bcl‐2 inhibitors

•

MW < 780; > 100‐fold selective over other BH3

domains•

Sub‐10 nM efficacy in cell models of AML

•

In vivo, rapid and strong apoptotic response in RS4;11 xenograft models, both iv and oral

•

Platelet sparing (cf ABT‐263)•

Key was biophysical assays to assess cell penetration

and compound aggregation

Time after tumour inoculation (days)

Tum

our

volu

mes

(mm

3 )M

edia

n +/

- Int

erQ

uart

ile R

ange

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 390

200

400

600

800

1000

1200

1400

Control (untreated)

Compound 3 50 mg/kgCompound 3 100 mg/kgCompound 4 50 mg/kgCompound 4 100 mg/kg

Treatment schedule (per os)(Twice a week)

ABT-263 50 mg/kgABT-263 100 mg/kg

Geneste, Murray, Davidson, leDiguarher et al

55

Summary ‐

non‐conventional targets

•

For non‐conventional targets•

And where structures are hard to obtain

•

It takes time –

and not yet clear how often fragments can be successful

•

Integration with biophysical methods can be key•

Also – a commitment to the long haul

Time

Expectation

Technology Trigger





56

Fragments 2013 talk

•



•


Screening methods

•


•



•


Issues –


•

Final remarks

57

Finding fragments ‐

issues

•

For some targets, it can take time to configure a robust assay

•

For fragment screening but also hit progression•

Protein construct, assay conditions, etc, etc

0%

1%

2%

3%

4%

5%

6%

7%

8%

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

D score

Cla

ss 1

hits

rate

s


Kinaseshigh hit rate

protein-protein interactiontargets – varying hit rates

Poor targets


Updated from Chen & Hubbard (2009), JCAMD, 23, 603

Valid

ated

hit rate

Ligandability


58

3D fragments

•

Most of the compounds in fragment libraries are commercially available small molecules

•

Medicinal chemist emphasis on chemical tractability•

Some use privileged fragments from existing drugs

•

Most of the fragments are flat heterocycles•

This is fine for some targets (kinases, ATPases)

•

Perhaps limiting for other (new) target classes

•

Some initiatives underway to introduce more 3D fragments

•

The challenge will be synthetic tractability•

A York initiative

59

York 3D fragments

•

Peter O’Brien has developed chemistry for adding lithiated

N‐Boc

heterocycles

2

(formed from 1) to

heterocyclic, symmetrical ketones

•

The resulting fragments have distinctive 3D shapes, presenting useful looking

pharmacophores•

Shape measured by principle moments

0.50.550.6

0.650.7

0.750.8

0.850.9

0.951

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

York fragmentsrod sphere

disc0.5

0.550.6

0.650.7

0.750.8

0.850.9

0.951

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Vernalisrod sphere

disc

60

York 3D fragments

•

Peter O’Brien has developed chemistry for adding lithiated

N‐Boc

heterocycles

2 (formed from 1) to

heterocyclic, symmetrical ketones

•

The resulting fragments and lead‐like compounds have distinctive 3D shapes, presenting useful

looking pharmacophores

•

Project underway to explore the chemistry•

Generate 500 member library

•

See how it performs against various targets

61

Concluding remarks

•

Straight forward to find fragments for most sites on most proteins•

Opportunities for new “3D”

fragments?

•

The challenge is knowing what to do with the fragments•

Off‐rate screening allows exploration of vectors

•

Evolving fragments in absence of structure?

•

For conventional targets•

Lots of starting points; opportunity for “good”

medicinal chemistry

•

Issue in some organisations is integration with medicinal chemistry

•

For non‐conventional targets•

Provides starting points when other techniques fail

•

Close integration with biophysics is crucial; takes time and commitment

•

Not necessarily faster – patience required•

But hopefully better

62

End

•

References in the slides acknowledge who did the work

•

FBLD conference•

2008 – San Diego

•

2009 – York•

2010 –

Philadelphia

•

2012 – San Francisco•

2014 – Basle

•

http://www.fbldconference.org

63

Vernalis Research Overview•

Approximately 60 staff in research•

Based in Cambridge, UK (Granta

Park)

•

Recognised for innovation and delivery in structure and fragment‐based drug discovery

•

Structure‐based drug discovery since 1997•

Distinctive expertise combining X‐ray, NMR, ITC and SPR to

enable drug discovery against established and novel, challenging targets

•

Portfolio of discovery projects•

Six development candidates generated in the past six years

•

Protein structure, fragments and modelling integrated with medicinal chemistry

•

Internal Vernalis projects in oncology•

Collaborations across all therapeutic areas•

e.g. oncology, neurodegeneration, anti‐infectives

•

Aim to establish additional collaborations during 2013 / 14

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