For slides – [email protected]
Progressing fragments for challenging targets
Roderick E Hubbard
Vernalis (R&D) Ltd, Cambridge
YSBL & HYMS, Univ of York, UK
Fragments 2013
1
Trends for new technologies
• In the beginning – lots of excitement
• Which can lead to hype and over‐selling
Expectation
Technology
Trigger
2
Time
Analysis / terms initially proposed by Gartner group (Murcko)
Trends for new technologies
• In the beginning – lots of excitement
• Which can lead to hype and over‐selling
Expectation
Inflated
expectations
Technology
Trigger
3
Time
Analysis / terms initially proposed by Gartner group
Trends for new technologies
• Too rapid (often inexpert) deployment
• It doesn’t work ‐ disillusionment
Expectation
Technology
Trigger
4
Inflated
expectations
Trough of
Disillusionment
Analysis / terms initially proposed by Gartner group
Time
Trends for new technologies
• Too rapid (often inexpert) deployment
• It doesn’t work ‐ disillusionment
Expectation
Technology
Trigger
5
Inflated
expectations
Trough of
Disillusionment
Analysis / terms initially proposed by Gartner group
Time
Trends for new technologies
• Eventually, expertise grows
• Begin to understand how and where to apply methods
Expectation
Inflated
expectations
Slope of
Enlightenment
Technology
Trigger
6
Trough of
Disillusionment
Analysis / terms initially proposed by Gartner group
Time
Trends for new technologies
• Learn how to integrate the methods into the process • Add to productivity
Expectation
Inflated
expectations
Slope of
Enlightenment
Technology
Trigger
7
Plateau of
productivity
Trough of
Disillusionment
Analysis / terms initially proposed by Gartner group
Time
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
Expectation
8
Technology
Trigger
Inflated
expectations
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
productivity
Time
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
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 2009 talk
SeeDs process ‐ 2008
Structural Exploitation of Experimental Drug Startpoints*
Target
abc
Biacore
Validation
X-ray Structures
HSQC
NMR Competitive
Binding Experiment
O
H2N
O
H
N
NH
O
NH
N
N
N
NH
NH
HN
Wet assay
/ Biacore
O
H
N
N
H
HO
N
NH
O
N
NH
N
N
N
NH
NH
H
N
O
HN
O
OH
N
Fragment Library
O
N
NH
O
O
11
*Hubbard et al (2007), Curr Topics Med Chem, 7, 1568
Evolution
Chen & Hubbard (2009), JCAMD, 23, 603
Fragments 2009 talk
Finding fragments
• Finding fragments that bind is not difficult
• A good way of assessing target “ligandability”
8%
Low hit rates (< 2%)
High hit rates (> 2%)
Class
1 hits rates
Validated hit rate
7%
6%
5%
protein-protein interaction
(0.4-3% hit rate)
4%
3%
2%
Kinases
(3-5% hit rate)
Poor targets
1%
0%
0.0
12
0.2
0.4
0.6
0.8
1.0
Ddruggability
sco re 1
Ligandability Calculate
calculated from structure (DScore)
1.2
1.4
Chen & Hubbard (2009), JCAMD, 23, 603
Fragments 2009 talk
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
8%
Low hit rates (< 2%)
High hit rates (> 2%)
Class
1 hits rates
Validated hit rate
7%
6%
5%
protein-protein interaction
(0.4-3% hit rate)
4%
3%
2%
Kinases
(3-5% hit rate)
Poor targets
1%
0%
0.0
13
0.2
0.4
0.6
0.8
1.0
Ddruggability
sco re 1
Ligandability Calculate
calculated from structure (DScore)
1.2
1.4
Fragments 2009 talk
Using fragments
• Growing fragments
14
Fragments 2009 talk
Using fragments
• Growing fragments – CHK1 example
Chk-1 IC50 >100µM
LE ~ 0.39
15
Fragments 2009 talk
Using fragments
• Growing fragments – CHK1 example
Chk-1 IC50 = 5µM
LE = 0.39
GI50 HCT116 >80µM
pH2AX (MEC) – inactive
16
Fragments 2009 talk
Using fragments
• Growing fragments – CHK1 example
Chk-1 IC50 = 0.2µM
LE = 0.33
GI50 HCT116 = 4µM
pH2AX (MEC) = 7µM
17
Fragments 2009 talk
Using fragments
• Growing fragments – CHK1 example
Chk-1 IC50 = 0.013µM
LE = 0.39
GI50 HCT116 = 1.8µM
pH2AX (MEC) =0.2µM
18
Series members further optimised
to identify Candidate V158411
Fragments 2009 talk
Using fragments
• Merging – HSP90 example
Screen
Known Ligands
Virtual Screening hits
Detailed Design
19
Fragments 2009 talk
HSP90 – BEP800 story
Fragment
Evolved fragment
NH2
NH2
N
N
N
OMe
O
N
H
N
S
O
VER-26734
FP IC50>5mM
VER-52959
FP IC50=535M
Brough et al (2009) J Med Chem 52,4794‐4809 Roughley et al (2012) Top Curr Chem 317, 61
VER-82576
G97
D93
NVP-BEP800
FP IC50=0.058M
KD = 0.9nM (SPR)
HCT116 GI50=0.161M
BT474 GI50=0.057M
H
N
F138
N
S
H2N
N
K58
L107
O
Virtual Screening Hit
Cl
H2N
N
O
Cl
N
S
O
N
NH2
NH2
N
N
Cl
OEt
O
N
N
O
Cl
VER-41113
FP IC50=1.56M
20Virtual Screening Hit
HO
HO
VER-45616
FP IC50=0.9M
N
N
H
N
O
NVP-AUY922
Vernalis Phase II candidate (FBLD
/ SBDD derived)
NH2
Fragments 2009 talk
Pin1 Story
• Proline isomerase – persuasive biological rationale that key oncology target
• Structure available + D‐peptide tool compound
21
Fragments 2009 talk
O
Pin1 Story
OH
NH
H3 C
• Proline isomerase – persuasive biological rationale that key oncology target
• Fragments identified: fragment to hit evolution
• No correlation between biophysical and enzyme assays
22
Fragments 2009 talk
O
Pin1 Story
OH
NH
H3 C
• 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
23
Fragments 2009 talk
HO
O
O
Pin1 Story
OH
O
HN
N
NH
CH3
NH
Potter AJ et al, Bioorg Med Chem Lett. 2010; 20:586‐590
O
H3 C
• 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
24
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
25
Hubbard et al (2007), Curr Topics Med Chem, 7, 1568 Hubbard and Murray (2011), Methods Enzym, 493, 509
The Vernalis process
Target
Virtual screen; literature; library screen
Screen by SPR, DSF, WAC, biochem or binding assay, Xray
Hits
Competitive NMR screen
Fragment Library N
NH2
S
NH2
O
N
N
NH2
OMe
O
OEt
H2N
NH2
N
N
H
N
S
N
H
N
S
N
N
Cl
Cl
O
HO
N
N
N
O
NH2
N
Fragment to hit :
SAR by catalog
off‐rate screening
O
Cl
O
Drug?
Optimise fragment
O
N
N
H
Cl
HO
RU
RU
N
O
8
30
6
25
20
4
R esp o n se
N
R esp o n se
~ 1200 compounds
Ave MW 190
H2N
Characterisation 2
0
-2
26
Design, Build & Test
10
5
0
-4
-100
15
-5
-50
0
50
100
Tim e
150
200
250
300
s
-50
0
50
100
Time
150
200
250
300
s
X‐ray or NMR guided model
Some comments on fragment screening
Hubbard & Murray (2011), Meth Enzymology, 493, 509
• 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
27
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
28
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 !!
Expectation
29
Technology
Trigger
Inflated
expectations
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
productivity
Time
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
30
James Murray, Steve Roughley, Natalia Matassova
Exploiting the dissociation rate constant
k
KD = off
kon
koff
(s-1)
(M-1s-1)
PL P + L
kon
31
James Murray, Steve Roughley, Natalia Matassova
Exploiting the dissociation rate constant
k
KD = off
kon
(s-1)
(M-1s-1)
• 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)
32
James Murray, Steve Roughley, Natalia Matassova
Exploiting the dissociation rate constant
k
KD = off
kon
(s-1)
(M-1s-1)
• 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)
Ass
ocia
Resonance
Signal (RU)
tion
-k
on
Dissociation - koff
Kinetics
Concentration
Time (s)
• Surface plasmon resonance – a way to measure kinetics
33
• FOCUS ON THE Off‐RATE ‐ koff
James Murray, Steve Roughley, Natalia Matassova
Exploiting the dissociation rate constant
k
KD = off
kon
(s-1)
(M-1s-1)
• 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)
34
James Murray, Steve Roughley, Natalia Matassova
Off rate screening (ORS): Example
• Historical Hsp90: thienopyrimidines
• 200 nM – 5 M IC50 by FP assay
• Re‐prepared compounds by Suzuki reaction
R
O
N
H2N
DMF / H2O
O
N
N
S
O
NaHCO3
Pd(Ph3P)2Cl2
100 ºC Microwave
10 min
• Minimal work‐up
• Evaporate, partition
• Purity 50 – 80 % (LCMS)
• Screened by ORS
35
R
B(OH)2
Cl
H2N
N
S
O
James Murray, Steve Roughley, Natalia Matassova
Off rate screening (ORS): Example
• Historical Hsp90: thienopyrimidines
• 200 nM – 5 M IC50 by FP assay
• Re‐prepared compounds by Suzuki reaction
R
O
N
H2N
DMF / H2O
O
N
N
S
O
NaHCO3
Pd(Ph3P)2Cl2
100 ºC Microwave
10 min
• Minimal work‐up
• Evaporate, partition
• Purity 50 – 80 % (LCMS)
• Screened by ORS
36
R
B(OH)2
Cl
H2N
N
S
O
A: Pure starting material
B: Faux reaction
James Murray, Steve Roughley, Natalia Matassova
Off rate screening (ORS): Example
• Historical Hsp90: thienopyrimidines
• 200 nM – 5 M IC50 by FP assay
• Re‐prepared compounds by Suzuki reaction
R
O
N
H2N
DMF / H2O
O
N
N
S
O
NaHCO3
Pd(Ph3P)2Cl2
100 ºC Microwave
10 min
• Minimal work‐up
• Evaporate, partition
• Purity 50 – 80 % (LCMS)
• Screened by ORS
37
R
B(OH)2
Cl
H2N
N
S
O
A: Pure starting material
B: Faux reaction
C: Purified product
D: Crude reaction
Tankyrase ‐ Introduction
38
•
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 3UH4
Structural Genomics Consortium (2007)
Novartis (2012)
Alba Macias, Chris Graham and team
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 •
39
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
Tankyrase ‐ Hit identification
Crystals
Soaked fragments in pools of 8
Data collection (up to
1.6Å resolution)
Streamlined structure solution
62 hits from 1563
fragments
Characterised by SPR and TSA
40
Alba Macias, Chris Graham and team
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
41
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
DCA – 2q8h
Pfizer – 2bu7
E2 / L2 site
AZD‐7545 – 2q8g
Novartis – 2bu5
42
ATP site – 2q8i
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
Fragment bound to PDHK1
VER‐00236029
• Can identify PDHK selective compounds
PDHK1
• And HSP90 selective compounds
HSP90
VER‐00236030
PDHK1
HSP90
43
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
44
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
45
Non‐conventional targets
46
Non‐conventional targets
• Can find fragments that bind
• Orthogonal biophysical methods can validate and characterise fragment binding
47
Non‐conventional targets
• 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
48
Non‐conventional targets
• Can find fragments that bind
• Evolution requires robust model of fragment binding
• 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
49
Non‐conventional targets
• Can find fragments that bind
• Evolution requires robust model of fragment binding
• 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
Protein/ligand
Filtered
NOESY
- Intermolecular
NOEs
50
Unfiltered
NOESY
- All NOEs
Non‐conventional targets
• Can find fragments that bind
• Evolution requires robust model of fragment binding
• 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
• Have developed leads from fragments using NMR models
• High affinity ligands give X‐ray structures that confirm model
51
Video of Bcl‐2 plasticity
52
Bcl‐2 ‐ ligandability
8%
Low hit rates (< 2%)
High hit rates (> 2%)
Class
1 hits rates
Validated hit rate
7%
6%
5%
protein-protein interaction
(0.4-3% hit rate)
4%
3%
2%
1%
0%
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Ligandability
calculated from structure (DScore)
druggability
1 Calculate
Dscore
• changes dramatically as ligands explore available pockets / flexibility
53
Geneste, Murray, Davidson, leDiguarher et al
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
1400
Tumour volumes (mm3)
Median +/- InterQuartile Range
1200
1000
800
Compound 3 50 mg/kg
600
Compound 3 100 mg/kg
Compound 4 50 mg/kg
Compound 4 100 mg/kg
400
ABT-263 50 mg/kg
ABT-263 100 mg/kg
Control (untreated)
200
54
0
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Time after tumour inoculation (days)
Treatment schedule (per os)
(Twice a week)
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
Expectation
55
Technology
Trigger
Inflated
expectations
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
productivity
Time
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
56
Updated from Chen & Hubbard (2009), JCAMD, 23, 603
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
8%
Low hit rates (< 2%)
High hit rates (> 2%)
Class
1 hits rates
Validated hit rate
7%
6%
5%
protein-protein interaction
targets – varying hit rates
4%
3%
2%
Kinases
high hit rate
Poor targets
1%
0%
0.0
57
0.2
0.4
0.6
0.8
1.0
Ddruggability
sco re 1
Ligandability Calculate
calculated from structure (DScore)
1.2
1.4
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
58
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
rod
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0
59
0.1
sphere
Vernalis
0.2
0.3
0.4
0.5
disc
0.6
0.7
0.8
0.9
1
rod
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0
sphere
York fragments
0.1
0.2
0.3
0.4
0.5
disc
0.6
0.7
0.8
0.9
1
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
60
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
61
• But hopefully better
End
• References in the slides acknowledge who did the work
• FBLD conference
•
•
•
•
•
•
62
2008 – San Diego
2009 – York
2010 – Philadelphia
2012 – San Francisco
2014 – Basle http://www.fbldconference.org
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
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• Aim to establish additional collaborations during 2013 / 14