Rod Hubbard | FBDD 2014 | Fragmentology - current topics in fragment based lead discovery

1

Fragmentology – current topics in fragment based lead discovery

Rod Hubbard

Vernalis Research & University of York

Zing, Punta Cana - July 2014

York

Cambridge

For slides – [email protected]

Old Slide

Updated

2

Fragmentology - agenda

• FBLD at Vernalis • Approach, successes, library

• Hit rates and selectivity • Experience across kinase projects

• Properties of kinase vs PPI hits

• Characterising 3D fragment space - sphericality

• WAC for detecting fragment binding

• Fragments as enzyme activators • Library optimised for SAR by catalog

• GlcNAcase story

• Comments on current state of FBLD

Punta Cana, July 2014

3








• GlcNAcase story



4

Optimise fragment Fragment to hit : Incorporate information from SAR by catalog and off-rate screening

-5

0

5

10

15

20

25

30

-50 0 50 100 150 200 250 300

RU

Resp

on

se

Tim e s

Cycle: 95 VER-00082099i 50 nM

Fitted Cycle: 95 VER-00082099i

Cycle: 96 VER-00082099i 500 n

Fitted Cycle: 96 VER-00082099i

Cycle: 97 VER-00082099i 5000

Fitted Cycle: 97 VER-00082099i -4

-2

0

2

4

6

8

-100 -50 0 50 100 150 200 250 300

RU

Resp

on

se

Tim e s

Cycle: 103 VER-00055030l 50 n

Fitted Cycle: 103 VER-00055030l

Cycle: 104 VER-00055030l 500


Cycle: 105 VER-00055030l 5000


Characterisation

X-ray or NMR guided model

The Vernalis process Hubbard et al (2007), CTMedChem, 7, 1568 Hubbard and Murray (2011), Methods Enzym, 493, 509

Target

Optimise fragment

Hits

Competitive NMR screen Fragment Library

~ 1400 compounds Ave MW 194

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

NH2

N

NH2

O

OEt

Virtual screen; literature; library (HTS) screen

Screen by SPR, DSF, biochem assay, WAC, Xray

Drug?

Old Slide

5

SBDD and FBLD output at Vernalis

• Candidates • Hsp90 - phase II candidate and oral backup - Novartis

• Fragment to lead; rapid structure-based optimisation

• Chk1 – pre-clinical candidate and oral backup

• Fragment growth; structure-based design

• Bcl-2 – protein-protein interaction – Phase I – Servier (Novartis)

• NMR to generate binding models; biophysics to characterise properties

• A2a – gpcr pharmacology – Phase II

• Ligand-based modelling

• FAAH – completed Phase I

• Modelling to incorporate fragment ideas to generate candidate

• Undisclosed from collaborations


6

SBDD and FBLD output at Vernalis

• Exploring target biology (“validation”) • PDPK1 – merging fragments with literature compounds

• Pak4 and STK33 – fragment exploration to test literature

• Pim1 – fragment exploration to understand selectivity

• PDHK1 – ORS to identify selective leads from fragments

• Tankyrase – fragment screen by X-ray; off-rate screening

• Undisclosed from collaborations • selective, cell-penetrant, in vivo tolerated inhibitors to

assess target biology in suitable disease models

• combining fragments with HTS hits to dissect key binding motifs and mine corporate collections


7

Vernalis structures

Ribosome projects

30S

50S

A-site

MDM2

GRP94

PDPK1 Chk1

DNA

gyrase

HSP90b HSP90a

CDK2

Aurora

Akt

Jak Catenin

GTPase

site

Pin1

FAS

Hsp70

P11

HSC70/Bag

Xiap

Pak4

Pim1

PPI-1

Bcl-2

>4000 complexes determined

Grp78

Kinase C PDK2

Bcl-xL

Adenosine

Kinase Target E

Jnk3

PDK3 / E2

Kinase H Kinase J

Target J

Target G Target I tankyrase

Kinase A

Target B

Updated

8

Fragment libraries

• The initial Vernalis library from 2004

Library design - JCICS, 44, 2157

87,133 from ACD

100 < MW < 250;

unwanted functionality

7545

Library Size

Medicinal chemistry tractability; availability; cost

553

204

3,794

717

kinase pharmacophore

before 2D 3-point

fingerprint

novelty in

fingerprint vs

Library 1-3

Library I

2918

395

2D 3-point

pharmacophore

fingerprint

357 174 61

100 < MW < 250; unwanted functionality; wanted

functionality; solubility > 2mM

1.79M from rCat*

43,458

Library 2 Library 3 Library 4

Solubility; wanted

functionality;

tractability; availability;

cost

1028

Measure solubility; NMR spectrum; mass spec; stability

729

Library I Library 2 Library 3 Library 4

Manual

Cheminformatics

*rCat – Baurin et al (2004), JCICS, 44, 643


Old Slide

9

Fragment libraries

• The initial Vernalis library from 2004

• Subsequent evolution of the library driven by: • Regular QC and restocking

• Solubility, stability, availability of commercial compounds

• Adding fragments from in-house chemistry

• Consider all intermediates generated in-house

• Synthesise bespoke fragments from literature / target ideas

• Experience in evolving fragments

• The reality of synthetic tractability

• Added slightly larger and lipophilic compounds

• Current library at about 1350 fragments

Library design - JCICS, 44, 2157 *rCat – Baurin et al (2004), JCICS, 44, 643


10








• GlcNAcase story



11

Hit rates

• Since 2002, over 2050 different fragments have been screened against more than 30 targets • This has been an evolving fragment library

• Screened by ligand-observed NMR, biochemical assay, binding assay, Xray, SPR …..

• For consistency – will consider only the ligand-observed NMR screen results • 1291 fragments were not a hit against any of 19 targets

• No significant difference between hits and non-hits • HA, complexity, nRot ,etc, etc; perhaps SlogP

• Of these 19 targets • 9 are kinases

• 5 can be considered PPIs


This is for ligand-observed NMR screens

12

b c a

NMR competitive binding experiment

Fragment Library 1350 + fragments

Assayed in mixtures of 12

Target +

fragments

NMR experiment observes

fragment only if binding

Target +

fragments +

competitor ligand

Has competitor displaced the

fragment? => Specific binding

10 – 100 Hit

Fragments

Has the advantage can

monitor ligand and

protein in each

experiment

Ligand observed NMR –

no limit on MW of protein

Three ligand-observed

competitive NMR experiments –

a hit in all 3 is Class 1; in 2

experiments is Class2; 1 – Class 3

Old Slide

13

Hit rates for kinases at Vernalis

• Nine kinases have been screened in a competitive ligand-observed NMR experiment • (other kinases by biochemical or binding screen)

• Caveat – screened against evolving fragment library with changing instrumentation (cryoprobe / console) and operator (some subjectivity in defining hits)

• (only 565 fragments screened against all 9 targets)



14







15





• Kinase hit rate for all fragments – 12% • Kinase hit rate for the 174 fragments designed as a

kinase focussed sub-library – 8%



16

Selectivity

• For the hits from the 565 fragments that were screened against all 9 kinases • Number of fragments that were a hit for a certain

number of kinases (297 were not a hit for any)


96

66

41

26

14 14 8

2 0 0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9


e.g. – 14 fragments are hits against 6 different kinases

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Selectivity

• For the hits from the 565 fragments that were screened against all 9 kinases • Total number of hits for each kinase


5 28 102 55 37 51 42 32 42

AK CDK2 Kin1 Kin2 Jnk3 Kin3 Pak4 PDPK1 Stk33

AK

CDK2

Kin1

Kin2

Jnk3

Kin3

Pak4

PDPK1

Stk33


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Selectivity Punta Cana, July 2014

5 28 102 55 37 51 42 32 42


AK 2

CDK2 6

Kin1 30

Kin2 9

Jnk3 7

Kin3 20

Pak4 13

PDPK1 4

Stk33 8

• For the hits from the 565 fragments that were screened against all 9 kinases • Number of unique hits for each kinase


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• For the hits from the 565 fragments that were screened against all 9 kinases • The number of fragments that hit two targets

• e.g. 12 fragments are hits for both JNK3 and PDPK1

Selectivity Punta Cana, July 2014

5 28 102 55 37 51 42 32 42


AK 2 0 2 0 0 1 1 0 0

CDK2 6 14 11 9 8 5 8 13

Kin1 30 35 19 18 21 20 29

Kin2 9 18 20 12 14 12

Jnk3 7 13 9 12 10

Kin3 20 12 7 7

Pak4 13 8 7

PDPK1 4 12

Stk33 8

e.g. 12 fragments are hits for

both JNK3 and PDPK1


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• GlcNAcase story



21

Properties of all fragments Punta Cana, July 2014

0

200

400

600

800

0 1 2 3 4 5 6 7 8 More

Fre

qu

en

cy

Hydrogen Bond Donors

0

200

400

600

800

1000

0 1 2 3 4 5 6 7 8 More

Fre

qu

en

cy

Hydrogen Bond Acceptors

1 51

202

466 494 422

315

59 27 11 0 0

200

400

600

6 8 10 12 14 16 18 20 22 24 More

Fre

qu

en

cy

Number of Heavy Atoms

14

958 957

116 3 0 0

500

1000

1500

0 1 2 3 4 More

Fre

qu

en

cy

Nring

0 3 11 77 272

591 785

280 29 0

0

500

1000

-4 -3 -2 -1 0 1 2 3 4 More

Fre

qu

en

cy

SlogP

180

457 534

478

235 118

33 9 3 1 0 0

200

400

600

0 1 2 3 4 5 6 7 8 9 More

Fre

qu

en

cy

Nrot

3.1 ± 1.3 1.3 ± 0.9

13.9 ± 3.0 1.0 ± 1.0

2.3 ± 1.5 1.6 ± 0.6


6 8 10 12 14 16 18 20 22 -4 -3 -2 -1 0 1 2 3

22

0 4

56

150 173

140

97

26 11 4 0 0 #N/A 0

50

100

150

200

6 8 10 12 14 16 18 20 22 24 26 More

Fre

qu

en

cy

Number of Heavy Atoms kinase hits

0 0 2 10 60

163

296

118 11 0

0

100

200

300

400

-4 -3 -2 -1 0 1 2 3 4 MoreFr

eq

ue

ncy

SLogP kinase hits

Properties of kinase hits Punta Cana, July 2014

1 51

202

466 494 422

315

59 27 11 0 0

200

400

600

6 8 10 12 14 16 18 20 22 24 More

Fre

qu

en

cy


0 3 11 77 272

591 785

280 29 0

0

500

1000

-4 -3 -2 -1 0 1 2 3 4 More

Fre

qu

en

cy

SlogP 13.9 ± 3.0 1.0 ± 1.0


14.1 ± 2.8

Heavy atom count reflects the library (we don’t have high HA count compounds)

SlogP slightly higher for hits

1.2 ± 0.9

6 8 10 12 14 16 18 20 22

6 8 10 12 14 16 18 20 22

-4 -3 -2 -1 0 1 2 3

-4 -3 -2 -1 0 1 2 3

23

1 51

202

466 494 422

315

59 27 11 0 0

200

400

600

6 8 10 12 14 16 18 20 22 24 More

Fre

qu

en

cy


0 4

56

150 173

140

97

26 11 4 0 0 #N/A 0

50

100

150

200

6 8 10 12 14 16 18 20 22 24 26 More

Fre

qu

en

cy

Number of Heavy Atoms kinase hits

0 0 2 10 60

163

296

118 11 0

0

100

200

300

400

-4 -3 -2 -1 0 1 2 3 4 MoreFr

eq

ue

ncy

SLogP kinase hits

Properties of kinase and PPI hits Punta Cana, July 2014

0 3 11 77 272

591 785

280 29 0

0

500

1000

-4 -3 -2 -1 0 1 2 3 4 More

Fre

qu

en

cy

SlogP 13.9 ± 3.0 1.0 ± 1.0


14.1 ± 2.8 1.2 ± 0.9

0 4

23

68 76

68

50

10 9 4 0 0 0

20

40

60

80

6 8 10 12 14 16 18 20 22 24 26 More

Fre

qu

en

cy

Number of Heavy Atoms PPI hits

0 0 2 2 17

64

138

79

10 0

50

100

150

-4 -3 -2 -1 0 1 2 3 More

Fre

qu

en

cy

SlogP PPI hits 14.3 ± 3.1 1.4 ± 0.9

SlogP even higher for PPI hits

6 8 10 12 14 16 18 20 22

6 8 10 12 14 16 18 20 22

-4 -3 -2 -1 0 1 2 3

-4 -3 -2 -1 0 1 2 3

6 8 10 12 14 16 18 20 22 -4 -3 -2 -1 0 1 2 3

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Kinase fragments that are hits for a PPI

• There are 661 fragments that are hits against any kinase

• There are 312 fragments that are hits against any PPI

• Interestingly the number of fragments that are hits against both kinase and PPIs is 214; this seems like a higher percentage than you would think, by chance.

• Thinking about this further ……


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• Characterising 3D fragment space – sphericality



• GlcNAcase story



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All fragments in library Punta Cana, July 2014

Rod

Disc

Sphere

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Kinase fragments Punta Cana, July 2014

Rod

Disc

Sphere

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PPI fragments Punta Cana, July 2014

Rod

Disc

Sphere

29

All fragments in library Punta Cana, July 2014

Increasing

sphericality (S) –

from 0 to 1

Roughley and Hubbard submitted

Rod

Disc

Sphere

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PPI

Sphericality of fragments and hits

• Consider just those with S > 0.02

• Plot is the percentage of hits with a particular sphericality (removed 0-0.02) • Kinase hits marginally less spherical

• PPI hits more reflective of overall library


0

1

2

3

4

5

6

7

8

0 0.1 0.2 0.3 0.4 0.5 0.6

All

kinase

31

More details on Sphericality

• Consider accessible conformations

• All non-H atoms converted to C • Heavy atoms such as Br distort the PMI

• Br is 7 times the mass of C; vdw only 1.7 x

• Use original bond lengths so most of volume retained

• Consider what coverage a library provides

• Count occupancy of S bins – an S index which can be used to characterise / compare libraries


Roughley and Hubbard submitted

32

Correlation of S with other measures Punta Cana, July 2014

Nrot HAC NAr NSat Fsp3

VolsurfG VolsurfR VolsurfS Volsurf V PBF

Sph

eric

alit

y Roughley and Hubbard submitted

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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

• Probably don’t have the compounds in our libraries yet to answer the question about relevance • Progressed PPI hits (cf Astex as well) have more sp3

character in the binding scaffold

• The challenge will be synthetic tractability


Updated

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• Characterising 3D fragment space – sphericality



• GlcNAcase story



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Screening fragment libraries

• Different experimental approaches have different strengths and limitations


Affinity 10mM 1mM 100mM 10mM 1mM

Fragments MW 110-250

Scaffolds MW 250-350

Lead Compounds

X-Ray crystallography

Ligand-observed NMR

Surface Plasmon Resonance (SPR)

Enzyme / binding assays (HCS)

Isothermal Titration Calorimetry (ITC)

Hit Compound MW 250-500

Protein-observed NMR

Differential scanning fluorimetry (DSF / TSA)

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

Mass spectrometry (MS)

Weak Affinity Chromatography (WAC)

Updated

36

New ideas in fragment screening

• Need for rapid, cheap, robust, generic method for detecting weak binding

• Currently investigating weak affinity chromatography • collaboration with Ohlson, Linneaus Univ (Elinor Melby)

• Immobilise target to silica beads in capillary column

• Flow over (mixture of) ligands

• Detect with UV; identify with mass spec

• Retention time related to affinity

Duong-Thi M et al (2011), Anal Biochem

Old Slide

37

WAC for HSP90

• 111 fragments screened by WAC, NMR, SPR, FP and Tm shift • Selected fragments also by ITC

• Green is hit; pink is non-hit • Blank cells means not determined

• Good consistency across methods • FP and Tm give many false negatives

• WAC advantage is amount of protein and speed

• Currently investigating for other proteins • Key is preparation of the column

ITC X-ray Tm FP SPR NMR WAC

Old Slide Meiby et al (2013) Anal Chem 85(14):6756-6

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• GlcNAcase story



39

York fragment library - 2011

unwanted functionality Input Supplier

Library

HA filter

Schulz et al (2011), JCAMD, 25, 611-620

Fragments List Non-Fragments

Identify diverse fragment

with largest number of

similar Non-Fragments

Remove fragment from

Fragments List

Remove Nearest Neighbours

from Non-Fragments

Fragment Set

A fragment library that maximally represents available chemicals

Useful for SAR by Catalog

Old Slide

40








• GlcNAcase story



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O-GlcNAc is an essential PTM

• O-linked β-D-N-acetylglucosamine – post-translational modification at serine and threonine hydroxyls – a nucleocytoplasmic modification

• Abundant and essential for life in metazoans – complex biology

• Implicated in numerous disease processes - diabetes, cancer, neurodegeneration

• Large and growing list of O-GlcNAcylated proteins

• Only two modifying enzymes OGT and OGA

• Mechanisms of substrate and site-specific regulation not yet understood


Darby, Landstrom, Davies et al submitted

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Bacterial orthologue – BtGH84

• Family 84 glycoside hydrolase

• 40% identity with human OGA domain

• Crystal structure available

• Low micromolar (class-wide unselective) inhibitors developed previously • tool compounds

• Fragment screen of 91 fragments • Ligand observed NMR

• PUGNac as competitor

• Investigated both competitive and non-competitive inhibitors


PUGNAc Thiamet G


43

Fragment hits

• High hit rate – 24%; two classes of hits • 18 competitive wit hPUGNAc

• 4 non-competitive with PUGNAc

• Hits “validated” in activity assay



44

BtGH84 is activated by a fragment

• Non-competitive fragment M1E05 enhances BtGH84 activity

• Confirmed with alternative substrate, many controls

• Activation is concentration dependent

• Increases the Vmaxapp and reduces the Km

app - 2.5 mM to 1.3 mM



M1E05

45

BtGH84 is activated by a fragment

• Non-competitive fragment M1E05 enhances BtGH84 activity

• Confirmed with alternative substrate, many controls

• Activation is concentration dependent

• Increases the Vmaxapp and reduces the Km

app - 2.5 mM to 1.3 mM

• Assay, NMR and ITC confirm that M1E05 affects PUGNAc binding


M1E05


46

Thienopyrimidine analogues


M1E05


47

Thienopyrimidine analogues

• So far – no crystal structures of these bound

• (solubility issues)

• STD competition assay showed S054 competes for same site

• Non-essential activator kinetics suggests mechanism

• Compounds stabilise folding of loop required for catalysis

• Have a negative effect on Tm in thermal shift assay

• Better compounds required for chemical biology

• But interesting possibilities in industrial biotechnology / bioprocessing



48








• GlcNAcase story



49

Technology cycle

• Rather “management consultant” but a useful reminder

• Drug discovery has “suffered” such cycles for many methods

• Structure-based design

• Combinatorial chemistry

• Genomics and post-genomic technologies

• etc

• etc

• Key is gaining sufficient expertise and learning how and when to apply the techniques


Time

Expectation

Technology

Trigger

Inflated

expectations

Trough of

Disillusionment

Slope of

Enlightenment

Plateau of

productivity

Bezdek initially, then Gartner group (Murcko for pharma)

J. Bezdek, IEEE Transactions on Fuzzy Systems. Feb. 1993, pp. 1-6 Old Slide

50

Typical timelines - kinase

• Crystal structure and fragment screen in 4 months • More than 120 fragment hits (NMR screen)


0 4 8 12 16 20 24 months

51


• Extensive fragment to lead campaign • Crystal structures of >80 fragment complexes

• Identified novel binding modes in more than 2 series

• Five lead series identified within 12 months • “conventional” structure-guided fragment evolution


0 4 8 12 16 20 24 months

52


• Extensive fragment to lead campaign • Crystal structures of >80 fragment complexes

• Identified novel binding modes in more than 2 series

• Five lead series identified within 12 months • “conventional” structure-guided fragment evolution


0 4 8 12 16 20 24 months

log (affinity)

10 nM

10 µM

100µM

1µM

100 nM

Time (months) 2 4 6

HTS

Fragment

53


• Lead optimisation on two series • In vivo active, highly potent and selective compounds

identified within 24 months


0 4 8 12 16 20 24 months

54

Typical timelines – PPI

• Project initiated • No progressable hits from HTS

• Fragment screen at Vernalis

• Often requires adaptation of methods • Crystal structures not possible with fragments bound

• Models generated from limited NMR information

• New assays to track compound properties

• Intensive exploration of fragment SAR

• Iteration to develop assays to identify tool compounds


0 1 2 3 4 5 6 years

55


• For a particular project • Initial lead series identified within 2 years

• Insufficient ligand efficiency – designed new scaffolds


0 1 2 3 4 5 6 years

56


• Lead series identified after a further year • Adequate ligand efficiency and potential for optimisation

• Robust model of SAR from NMR guided models


0 1 2 3 4 5 6 years

57


• Biophysics and structure-guided optimisation led to candidate after further 2.5 years • Once inhibitors less than about 100nM then crystal

structure possible

• This is representative of more than one project at Vernalis • Requires a commitment to the long haul


0 1 2 3 4 5 6 years

58

Technology cycle

• For conventional targets

• Highly productive – gives lots of choice; main issues are organisational

• If structure-enabled – then detailed structure-based design possible

• Opportunities to mine HTS hits; you always learn something from fragments

• For non-conventional targets

• (and where structures are hard to obtain)

• It takes time – and not yet clear how often fragments can be successful

• The assays are often a real barrier for new classes of target • Catch 22 – need tool compound to validate to find tool compound

• Often – not clear what the target actually is (partners, PTM state etc)

• Integration with biophysical methods can be key; pragmatic application of the most appropriate methods to recognise and solve problems


Time

Expectation

Technology

Trigger

Inflated

expectations

Trough of

Disillusionment

Slope of

Enlightenment

Plateau of

productivity

Bezdek initially, then Gartner group (Murcko for pharma)

J. Bezdek, IEEE Transactions on Fuzzy Systems. Feb. 1993, pp. 1-6 Old Slide

59

End

• References in the slides acknowledge who did the work

• FBLD conference

• 2014 – September 21st -24th - Basle

• http://www.fdld2014.org

• Deadline for poster abstracts - end of August or until full

• Registration limited to 200 attendees – it is filling up

For slides – [email protected] Punta Cana, July 2014

http://www.fdld2014.org/

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Vernalis Research Overview

• Approximately 65 staff in research

• Based in Cambridge, UK (Granta Park)

• Structure-based drug discovery since 1997

• Distinctive expertise combines X-ray, NMR, ITC and SPR to enable drug discovery against established and novel targets

• A growing reputation for solving problems in early drug discovery against challenging targets (PPIs, novel classes)

• Portfolio of discovery projects

• Six development candidates generated in the past four years

• Cell active lead compounds generated against 13 targets

• Internal Vernalis projects in oncology and anti-infectives

• Collaborations across all therapeutic areas

• e.g. oncology, neurodegeneration, anti-infectives

• Novartis, GSK, Lundbeck, Genentech, Asahi Kasei, Servier

• Aim to establish additional collaborations through 2014/15

Rod Hubbard | FBDD 2014 | Fragmentology - current topics in fragment based lead discovery

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Transcript of Rod Hubbard | FBDD 2014 | Fragmentology - current topics in fragment based lead discovery