Embed Size (px)
Citation preview
1
Current perspectives in fragment‐based drug discovery
Roderick E Hubbard Vernalis (R&D) Ltd, Cambridge
YSBL & HYMS, Univ of York, UK
ELRIG, September 2012
For a copy of slides –
2
Pre-Clinical Clinical Trials
Drug Discovery
Discovery
I II III
Medicine
Target Hit IDH2Ls
Lead Optimisation
3
•
Structural Biology•
Understanding target mechanism and biology
•
Structure‐Based Discovery •
Hit identification ‐
virtual screening, fragments
•
Structure‐Based Design•
Hits to Leads ‐
binding mode, chemotypes, properties
•
Lead optimisation ‐
affinity, specificity, ADME, p‐chem properties
Pre-Clinical Clinical Trials
Structure in Drug Discovery
Discovery
I II III
Medicine
Target Hit IDH2Ls
Lead Optimisation
4
•
Structural Biology•
Understanding target mechanism and biology
•
Structure‐Based Discovery •
Hit identification ‐
virtual screening,
fragments
•
Structure‐Based Design•
Hits to Leads ‐
binding mode, chemotypes, properties
•
Lead optimisation ‐
affinity, specificity, ADME, p‐chem properties
Pre-Clinical Clinical Trials
Structure in Drug Discovery
Discovery
I II III
Medicine
Target Hit IDH2Ls
Lead Optimisation
5
Overview
•
Summary of FBLD•
The methods
•
Some examples
•
The issues•
Fragments, chemical space, novelty and libraries
•
Fragment evolution•
Non‐conventional targets
•
Concluding remarks
6
Overview
•
Summary of FBLD•
The methods
•
Some examples
•
The issues•
Fragments, chemical space, novelty and libraries
•
Fragment evolution•
Non‐conventional targets
•
Concluding remarks
7
Why fragments?
•
Trying to find compounds that bind to target•
Compounds need to have required shape and chemistry
8
Why fragments?
•
Trying to find compounds that bind to target•
Compounds need to have required shape and chemistry
•
High Throughput Screening•
Compounds decorated in the wrong way
•
Particularly a problem with new target classes
9
Why fragments?
•
Hits from fragments•
Find small parts that bind
•
Then grow or merge fragments to create hit compound
10
Why fragments?
•
Hits from fragments•
Find small parts that bind
•
Then grow or merge fragments to create hit compound
•
Can also provide ideas•
Deconstruct HTS hits to optimise key interaction motifs
•
Provides ideas during Hit / lead optimisation•
Scaffold hopping
11
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)
12
Trends for new technologies
•
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
13
Trends for new technologies
•
Too rapid (often inexpert) deployment•
It doesn’t work ‐
disillusionment
Time
Expectation
Technology Trigger
Trough of Disillusionment
Inflated expectations
Analysis / terms initially proposed by Gartner group
14
Trends for new technologies
•
Too rapid (often inexpert) deployment•
It doesn’t work ‐
disillusionment
Time
Expectation
Technology Trigger
Trough of Disillusionment
Inflated expectations
Analysis / terms initially proposed by Gartner group
15
Trends for new technologies
•
Eventually, expertise grows•
Begin to understand how and where to apply methods
Time
Expectation
Technology Trigger
Inflated expectations
Trough of Disillusionment
Slope of Enlightenment
Analysis / terms initially proposed by Gartner group
16
Trends for new technologies
•
Learn how to integrate the methods into the process •
Add to productivity
Time
Expectation
Technology Trigger
Inflated expectations
Trough of Disillusionment
Slope of Enlightenment
Plateau of productivity
Analysis / terms initially proposed by Gartner group
17
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
…
•
Success has led to increased use•
Not sure which part of this curve we are on – in different organisations!!
Time
Expectation
Technology Trigger
Inflated expectations
Trough of Disillusionment
Slope of Enlightenment
Plateau of productivity
18
Why are fragments different?
•
A fragment is just a small weak hit
•
Requires assay(s) that can detect binding reliably
•
Methods for evolving fragments (libraries and/or design)
•
Design of library includes constraints of assay / evolution
•
Requires structure to get hits on scale of assay•
to generate SAR that drives medicinal chemistry
•
Track the ligand
efficiency –
binding energy per heavy atom
Affinity10mM 1mM 100M 10M 1M
Fragments MW 110-250
Scaffolds MW 250-350
Lead Compounds
Hit Compound MW 250-500
19
Screening fragment libraries
Affinity10mM 1mM 100M 10M 1M
Fragments MW 110-250
Scaffolds MW 250-350
Lead Compounds
X-Ray crystallography
Ligand-observed NMR
Surface Plasmon Resonance (SPR)
Enzyme / binding assays (HCS)
•
Different experimental approaches have different strengths and limitations
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)
20
Biophysical Methods
•
For the equilibrium
PL(aq) P(aq) + L(aq)
Equilibrium constant Kd
= koff
/kon
G°
= ‐RTlnKd
= H°‐TS°kon
koff
21
Biophysical Methods
•
For the equilibrium
•
Can measure kinetics – SPR
PL(aq) P(aq) + L(aq)
Equilibrium constant Kd
= koff
/kon
G°
= ‐RTlnKd
= H°‐TS°kon
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)
22
Setting up SPR
•
Challenge is to immobilise protein on the chip
•
Various strategies•
Biotinylation
of free NH2
groups => streptavidin
chip•
His tagged protein => Ni chip
Green – Single His Tagged Pin1
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
-400 -300 -200 -100 0 100 200 300 4
Adjusted sensorgramRU
Res
pons
e (0
= C
aptu
re 2
sta
rt)
Time (0 = Sample 1 start)
Sample injection
Protein injection
23
Setting up SPR
•
Challenge is to immobilise protein on the chip
•
Various strategies•
Biotinylation
of free NH2
groups => strepatavidin
chip•
His tagged protein => Ni chip
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
-400 -300 -200 -100 0 100 200 300 4
Adjusted sensorgramRU
Res
pons
e (0
= C
aptu
re 2
sta
rt)
Time (0 = Sample 1 start)
Sample injectionGreen – Single His Tagged Pin1
Red – Double His Tagged Pin1- 12 aa spacer
Protein injection
See also Fischer et al (2011) Anal Chem, 83:1800 for double‐his tag protocols applied to SiaP
protein
24
b ca
NMR competitive binding experiment
Fragment Library1200 + 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
Unlabelled target – no size limit
+
25
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,
biochem
assay, Xray
Drug?
26
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Pay attention to quality of the library –
solubility / aggregation etc
•
Same hits identified by ligand
observed NMR and SPR
•
High percentage of validated hits give crystal structures
Hubbard & Murray (2011), Meth Enzymology, 493, 509
27
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray
Hubbard & Murray (2011), Meth Enzymology, 493, 509
28
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray•
Sometimes, multiple soaks to obtain crystal structure of fragment bound
•
Suggests not a robust way to screen for fragments•
However, if you have crystals, can be the only way (see later)
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6
SoaksStructures
Hubbard & Murray (2011), Meth Enzymology, 493, 509
29
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray•
Requires suitable crystal system – and is a lot of hard work
•
“Wet”
assays can work sometimes•
But high concentrations (HCS) can confound the assay
Hubbard & Murray (2011), Meth Enzymology, 493, 509
30
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray•
Requires suitable crystal system – and is a lot of hard work
•
“Wet”
assays can work sometimes•
But high concentrations (HCS) can confound the assay
Hubbard & Murray (2011), Meth Enzymology, 493, 509
0
10
20
30
40
50
60
70
80
90
HSP
70
PIN
1
PPI-2
PPI-1
HSP
90
KIN
ASE-
1
KIN
ASE-
2
KIN
ASE-
3
FAAH
•
Assayed up to 35 NMR hits in biochemical assay for some targets
•
Concentration limited by DMSO tolerance
•
400µM for kinase; 2mM for others
•
Wide variability
•
Many hits would be missed by HCS
31
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray•
Requires suitable crystal system – and is a lot of hard work
•
“Wet”
assays can work sometimes•
But high concentrations can confound the assay
•
Calorimetry
(ITC) not yet for screening•
But a robust way of validating / confirming / quantifying binding
Hubbard & Murray (2011), Meth Enzymology, 493, 509
32
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray•
Requires suitable crystal system and it is a lot of hard work
•
“Wet”
assays can work sometimes•
But high concentrations can confound the assay
•
Calorimetry
(ITC) not yet for screening •
Thermal melt methods •
Differential scanning fluorimetry
(DSF) or Thermal Shift Analysis (TSA)
•
Heat the protein in presence of fluorescence dye and measure Tm – look
for a shift on ligand
binding
•
For a number of targets we have assessed validated NMR hits (for
which a
crystal structure) in DSF•
8 to 23% confirmation rate
•
Not a robust way to screen for fragments (in our hands)•
Can be useful to screen for cryptic (allosteric) sites
Temp
Fluo
rese
nce
NH
O
O
OH
CH3 CH3
ON
O
NH CH3
NClOH
OH
OH
Hubbard & Murray (2011), Meth Enzymology, 493, 509
33
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray•
Requires suitable crystal system and it is a lot of hard work
•
“Wet”
assays can work sometimes•
But high concentrations can confound the assay
•
Calorimetry
(ITC) not yet for screening •
Thermal melt methods unreliable•
Weak fragments can bind without stabilizing protein
•
Can find cryptic / allosteric
sites (sometimes)
Hubbard & Murray (2011), Meth Enzymology, 493, 509
34
Vernalis experience
•
For “good”
active sites (many enzymes):•
If assays configured correctly•
Same hits identified by ligand
observed NMR and SPR
•
Lots of false negatives from screening by X‐ray•
Requires suitable crystal system and it is a lot of hard work
•
“Wet”
assays can work sometimes•
But high concentrations can confound the assay
•
Calorimetry
(ITC) not yet for screening •
Thermal melt methods unreliable•
Weak fragments can bind without stabilizing protein
•
Can find cryptic / allosteric
sites (sometimes)
•
For non‐conventional sites (such as protein‐protein):•
Many issues•
Overbinding, problems due to compound properties
•
Cross‐validate binding by different techniques
•
Need for faster, more sensitive, less resource intensive methods
Hubbard & Murray (2011), Meth Enzymology, 493, 509
35
Overview
•
Summary of FBLD•
The methods
•
Some examples
•
The issues•
Fragments, chemical space, novelty and libraries
•
Fragment evolution•
Non‐conventional targets
•
Concluding remarks
36
Using 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, 603
37
Using 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
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, 603
38
Using fragments
•
Finding fragments that bind is not difficult
•
The challenge is knowing what to do with the hits•
Link, grow or merge
Known LigandsVirtual Screening hits
Screen
Detailed Design
Screen
Screen 1 Optimise 1 Screen 2 Optimise 2 LinkLINK
GROW
MERGE
39
Using fragments
•
Finding fragments that bind is not difficult
•
The challenge is knowing what to do with the hits•
Link,
grow
or merge
ScreenGROW
40
Using fragments
•
Finding fragments that bind is not difficult•
A good way of assessing target “ligandability”
•
The challenge is knowing what to do with the hits•
Growing – CHK1 example
Chk-1 IC50 >100µM
LE ~ 0.39
41
Using fragments
•
Finding fragments that bind is not difficult•
A good way of assessing target “ligandability”
•
The challenge is knowing what to do with the hits•
Growing – CHK1 example
Chk-1 IC50 = 5µM
LE = 0.39
GI50 HCT116 >80µM
pH2AX (MEC) – inactive
42
Using fragments
•
Finding fragments that bind is not difficult•
A good way of assessing target “ligandability”
•
The challenge is knowing what to do with the hits•
Growing – CHK1 example
Chk-1 IC50 = 0.2µM
LE = 0.33
GI50 HCT116 = 4µM
pH2AX (MEC) = 7µM
43
Using fragments
•
Finding fragments that bind is not difficult•
A good way of assessing target “ligandability”
•
The challenge is knowing what to do with the hits•
Growing – CHK1 example
Chk-1 IC50 = 0.013µM
LE = 0.39
GI50 HCT116 = 1.8µM
pH2AX (MEC) =0.2µM
Series members further optimised to identify Candidate V158411
44
Using fragments: Hsp90
NNH
OH
OH
O
O
NO
OH
OH
O
NH
N O
OO
OH
OH
RBT-8667 (VER-63579)FP IC50 = 0.28MGI50 = 6M
VER-52296FP IC50 = 0.009MGI50 = 0.014M
RBT-26617 (VER-26617)FP IC50 = ~ 1mM
Starting fragment Hit from SAR by Catalogue (also MTS and VS)
• GI50 in HCT116 colon cell line
FP IC50 = 0.28MGI50 = 6M
FP IC50 = 0.009MGI50 = 0.014M
FP IC50 = ~1mM
D93 G97K58
F138
L107
rCatN
O
O
O
O
Phase II Candidate(Novartis)
D93G97
K58
F138
L107
Brough et al (2008) J Med Chem 51,196‐218Roughley et al (2012) Top Curr
Chem
, 317, 61
45
Overview
•
Summary of FBLD•
The methods
•
Some examples
•
The issues•
Fragments, chemical space, novelty and libraries
•
Fragment evolution•
Non‐conventional targets
•
Concluding remarks
46
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
•
Pin1 example
47
Pin1 Story
•
Proline
isomerase
–
persuasive biological rationale that key oncology target
•
Structure available + D‐peptide tool compound
48
Pin1 Story
•
Proline
isomerase
–
persuasive biological rationale that key oncology target
•
Fragments identified: fragment to hit evolution•
No correlation between biophysical and enzyme assays
NH
OH
O
H3C
49
Pin1 Story
•
Proline
isomerase
–
persuasive biological rationale that key oncology target
•
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
50
Pin1 Story
•
Proline
isomerase
–
persuasive biological rationale that key oncology target
•
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
51
3D fragments
•
Proline
isomerase
–
persuasive biological rationale that key oncology target
•
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
•
Some initiatives underway to introduce more 3D fragments
•
The challenge will be synthetic tractability•
Watch this space
Potter AJ et al, Bioorg
Med Chem
Lett. 2010; 20:586‐590
N
NH
HN
O
O
HO
O
CH3NH
OH
O
H3C
52
Overview
•
Summary of FBLD•
The methods
•
Some examples
•
The issues•
Fragments, chemical space, novelty and libraries
•
Fragment evolution•
Non‐conventional targets
•
Concluding remarks
53
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 =
James Murray, Steve Roughley
54
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)
•
Independent of concentration•
Exploit this to assess unpurified
reactions, off‐rate screening (ORS)
koff (s-1)kon (M-1s-1)KD =
James Murray, Steve Roughley
55
•
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
A: Pure starting materialB: Faux reactionC: Purified productD: Crude reaction
56
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)
57
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
58
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
Alba Macias, Chris Graham and team
59
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
Alba Macias, Chris Graham and team
60
Overview
•
Summary of FBLD•
The methods
•
Some examples
•
The issues•
Fragments, chemical space, novelty and libraries
•
Fragment evolution•
Non‐conventional targets
•
Concluding remarks
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 options and starting points
•
Provides opportunity for “good”
medicinal chemistry
•
For non‐conventional targets•
Provides starting points when other techniques fail
•
Close support from biophysics is crucial
•
Not necessarily faster•
But hopefully better
62
End
•
References in the slides acknowledge who did the work
FBLD2012:
Fragment Based Lead DiscoverySan Francisco: 23-26 Sep 2012
(http://www.fbldconference.org)
For a copy of slides –
63
Vernalis 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 projects in oncology•
Collaborations with large and small pharma
across all therapeutic areas
•
Aim to establish additional collaborations during 2012 / 13
