jonathan mason | fbdd 2014 | high end gpcr fbdd: driving hit discovery to candidate design with...
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Jonathan Mason | FBDD 2014 | High End GPCR FBDD: Driving Hit Discovery to Candidate Design with Protein Structure, Including Water Network EnergeticsTRANSCRIPT
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Jonathan S Mason
Senior Research Fellow CADD
Zing Fragment-Based Drug Discovery Conference
July 19-22, 2014 Punta Cana
© Heptares Therapeutics 2013
The HEPTARES name, the logo and STAR are trade marks of Heptares Therapeutics Ltd
High End GPCR FBDD: Driving Hit Discovery to Candidate Design with Protein Structure, Including
Water Network Energetics
Non-Confidential
Take Home Messages
Full FBDD/SBDD now possible for GPCRs and can be game-
changing
Waters are not optional – missing dimension in many
computational studies (protein + ligand + water)
Waters important for potency-binding (druggability)
- & may be key for selectivity & kinetics as well as potency
- & protein structure-function
- & possible now to generate & score networks rapidly
Each new target structure has “unexpected/predicted” elements - e.g. new Family B and C structures
A single ligand complex structure is generally not sufficient
- e.g. Insights from multiple orexin structures, including water-mediated
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Heptares Therapeutics
Exceptional pipeline of new medicines targeting G protein-coupled receptors
Multiple Phase 1 & Phase 2 clinical programs reporting data during 2014-16
Indications: Alzheimer’s, Schizophrenia, Diabetes, ADHD, Chronic Migraine
Leading structure- and fragment-based GPCR discovery platform
Proprietary StaR® technology enables small molecule & biologics discovery
Deals include: AstraZeneca, MedImmune, Cubist, MorphoSys, Takeda
Investors: Clarus Ventures, MVM, Novartis, Takeda, Stanley Foundation
Experienced team in UK (>70 staff) and USA / Boston
Unique SBDD Platform
StaR® technology: Small number of mutations that increase receptor thermal stability in the presence of a ligand in detergent receptor that preferentially exists in a single conformation (e.g. agonist or antagonist) with significantly increased levels of stability 1/3 known GPCR X-ray structures
Non-Confidential
Iterative process for
making StaRs
StaR approach delivers stabilised GPCRs
Pharmacology Correlates with
StaR Conformation
A2A inactive
StaR
→ folded protein → crystals
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Experimentally Enhanced Homology Models
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Heptares Corporate Deals
Heptares Product Pipeline
Clinical portfolio of GPCR drugs advancing internally and through new partnerships
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Heptares Technology Platform
Adenosine A2A
Antagonist for
Neurology
GPCR Structure Determination
Landmark structure reveals new
drug binding site in Family B GPCRs
GPCR Fragment-Based Design
FBDD of mGlu modulator; sub-
nanomolar affinity with superior PK
Controlling Receptor Kinetics
Drug kinetics by StaR® using SPR
related to X-ray crystal structures
Functional or blocking mAbs
generated using StaR® antigens
Stabilised Receptor (StaR®)
Increased stability
Antigens for mAb Discovery
Validated
pharmacology
Non-Confidential
10-30 Mutations to the Binding
site region
StaR Mutants screened on Biacore chips
Site directed mutagenesis
Congreve, M. et. al, J. Med. Chem., 2012, 55, 1898
StaR Proteins Allow Biophysical MappingTM of GPCR Binding Sites
Zhukov, A. et al., J. Med. Chem. 2011, 54, 4312.
Biophysical Map of binding site
Correlating binding data from multiple ligands with multiple mutant StaR proteins
Ligand-refined homology model and detection of protein-ligand binding modes
Correlation with crystallographic binding mode
Structure Kinetic & Activity Relationships
Fragments
BPM & X-ray structural data
for caffeine
Best-scoring binding mode
BPM refined X-ray structure
XAC ligand in A2AR
N
S
O
O
O
O
OH
Virtual screening
hits
HTL79 HTL638
N253
H250
N181L85
I66
Y271
H278
N253
H250
N181L85
I66
Y271
H278
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Non-Confidential
Common features of ligand binding sites can be recognised
Family B CRF1-structure showed much deeper binding, new Family C structures bridge A – B
Bortolato et al. BJP 2014
Non-Confidential
Family A, B, C, F binding sites
A
B
C
F
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‘High-end’ structure based drug design
Computational chemistry as an effective link between
high-quality structural, biophysical and
pharmacological data
Water network energetics including network
perturbation
New perspectives on druggability, selectivity and
kinetics using water relative free energies, 3D
physicochemical properties (GRID) and MD
simulations
Non-Confidential
Agenda
Heptares approach to GPCR X-ray structures
Biophysical techniques & fragment screening approaches applied to
GPCRs
Using structures to enable the design of optimized drug candidates for
difficult GPCR targets
The importance of water molecules & their energies in GPCR drug design
– from druggability analyses through potency & selectivity to kinetics
Insights from Structural Biology - Some new X-ray structures at Heptares
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Non-Confidential
GPCR Fragment Based Drug Discovery
Stabilised GPCRs can be used for fragment biophysical
screening similarly to enzyme targets by:
SPR
NMR – TINS
HCS (StaR proteins stable in 10% DMSO unlike wild type)
CE
Success with Family A, B & C
Identify orthosteric & allosteric ligand fragment hits,
particularly with label-free methods (e.g. TINS)
Non-Confidential
SPR kinetics /
stoichiometry
Thermal shift
CE
Radioligand binding
Fragment Screening Cascade
NMR/TINS
HCS
SPR
SAR
Structural model
BPM X-ray
Primary screening validated with • SPR • NMR • HCS • CE
Hits triaged by • SPR kinetics • SPR stoichiometry • Binding assays • Thermal shift
Hits validated by • SAR / analogues • X-ray / modelling • BPM / SDM
Primary Screening
Hit Confirmation
Hit Validation
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Selective fragments can be identified for each GPCR
- different to those found for enzymes
Rational starting points for design of selective or multi-target ligands
A2A: 531 fragments assayed 94 hits
8 1
81
70
579 fragments assayed
1400 fragments
assayed
7
0
0
Overlap of TINS hits
0
Beta AR
A2A
PDE
BACE Protease
531 fragments assayed
2
52
65
0
0
20
0
Fragment Screening – NMR/TINS
Immobilized protein
– only small amounts needed (~1mg)
Very sensitive:higher mM hits identified
(not found by SPR)
TINS = Target Immobilized NMR Screening
NMR (TINS) Screening (with ZoBio) - b1AR, A2A, GLP1
Non-Confidential
SPR Fragment Screening
A2A StaR
DPCPX
riboflavin
caffeine
theophylline
1-methylxanthine
3-methylxanthinexanthine
7-methylxanthine
hypoxanthineallopurinol
FADDPCPX
riboflavin
caffeine
theophylline
1-methylxanthine
3-methylxanthinexanthine
7-methylxanthine
hypoxanthineallopurinol
FAD
Highly stable surface – DPCPX controls
Inactive fragments
Weak (mM) fragments easily detected
More potent (uM) fragments
• Weakly binding fragments hits easily discriminated from inactives • Xanthines added to library as likely binders • Chip stable for days (David Myszka)
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SPR fragment screening
Analysis of hits
Hits are further analysed by
– Detection of non-specific binding on control surface (eg denatured
protein)
– Full concentration response curves
– Assessment of kinetics – fragments should be fast off
– Stoichiometry – fragments should ideally bind 1:1 with target
Non-Confidential
Vs. plots A2A and b1AR SPR screen of Maybridge
Fragment Library (500 compounds)
Standard
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A2A and b1AR Full Fragment Screen of Heptares
Fragments (>600 compounds)
Selective Hit
Caffeine
b1 Control
A2A Control
b1 Hits
A2A Hits
Hits binding to both receptors
Non-Confidential
Comparison with binding assay
(all A2A positives from HTL library)
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POC A2A known hits readily detectable by SPR
KD 0.5 mM Ki 0.03 mM
KD 5 mM Ki 0.1 mM
KD 16 mM Ki 5 mM
2.4% hit rate of A2A selective fragments
1.8% hit rate of b1AR selective fragments
A2A TINS screening identified allosteric compounds
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Capillary Electrophoresis (with Selcia)
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• Clear demonstration that a range of known fragments including caffeine can be detected
• Non-binding fragments such as aspirin do not compete for the probe ligand (DPCPX)
• Medium throughput method, allowing screening of ~1000 compounds per mg of protein
Non-Confidential 22
ChromaDepth glasses
put on
your
now
Thank you
Please
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b1AR: Structure-guided hit to lead to
crystallography progression
KD = 16 mM
LE = 0.41
N
NH
F
FF
Ki = 500 nM
LE = 0.61
N
NH
b1AR & b2AR structures used to guide selections
1. Warne, T. et al. Nature 2011, 469, 241
2. Rasmussen, S. G. F. et al. Nature 2007, 450, 383.
Fragments bind in a consistent and efficiently way to receptor (crystal co-complex structures)
• High affinity, high solubility
fragments
• Two molecules of
complementary structure
selected for co-crystallisation
• Structures of both solved in
b1AR from single crystals
• – 3.25 Å and 2.7 Å
Ki = 68 nM
LE = 0.65
N
NH
NH
N
N
NH
Ki = 224 nM
LE = 0.53
Docking and design in a protein-ligand binding model led to suggestion to introduce donor atom
• Receptor fragment screening
by SPR of a StaR construct
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b1-AR
Chemical Structure b1 pKia LE cLogP
b LLE
c
N
N
NH
5.20 0.44 1.59 3.61
N
NH
5.87 0.67 1.11 4.76
N
NH
ClCl
7.07 0.69 3.03 4.04
N
N
NH
6.65 0.53 2.05 4.60
N
N
NH
5.80 0.47 1.44 4.36
N
N
N
NH
SMe
S
6.70 0.43 3.69 3.01
N
NH
NH
7.17 0.65 1.03 6.14
b1AR FBDD
Non-Confidential
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N
NH
NH2
3 A3 pKi = 6.4
NN
NH
1 H4 pKi = 7.0
N N
N
N
NH2
2 H4 pKi = 8.2
N
N
NH
F3C
4 b2AR pKD = 7.8
ONH
ONH
5 H1 pKi = 8.2 6 H1 pKi = 6.1
Other GPCR Family A FBDD
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CXCR4 Chemokine Receptor Antagonist
Hit 2 Lead
39/808 hits from diversity
screening (4.8%)
Follow up gives clear SAR
Rapid identification of
potent hit series
Hit Series
IC50 = 10 nM (LE = 0.34)
Good solubility
MWT ~300, cLogP 1.3, PSA ~76
N
NH
NH
NH
N
NH
NH
NH
Example fragment hit
IC50 = 150 mM (LE = 0.47)
Good solubility
MWT 144, cLogP 1.3, PSA ~39
Non-Confidential
High Concentration Screening
Lipid Receptor Agonist StaR
Agonist StaR binds known agonists with
higher affinity compared to wild-type
receptor
10% DMSO has no effect on ligand-
binding to StaR in membranes, unlike
wild-type receptor
Enables screening of fragment library at
high concentration (100 mM) which would
be impossible with wild-type receptor
Approx 6% hit rate (84 / 1419 fragments
inhibit > 30% binding n = 2)
2 3 4 5 6 7 8 9
2
3
4
5
6
7
8
9
pKI, Wild Type
pK
I, S
taR
0 20 40 60 80 100
0
20
40
60
80
100
% inhibition of specific binding (n = 2)
% in
hib
itio
n o
f s
pe
cif
ic b
ind
ing
(n
= 1
)
StaR
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2
0
1000
2000
3000
4000
1%
5%
10%
[DMSO]
Log [Agonist] (M)
Bo
un
d lig
an
d (
cp
m)
Wild-type
-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1
0
5000
10000
15000
20000
25000
Log [Agonist] (M)
Bo
un
d lig
an
d (
cp
m)
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High Concentration Screening: mGlu5 and mGlu2
First example of stabilisation of a family C receptor
mGlu5 stabilisation carried out with a negative
allosteric modulator (NAM) with binding site in the
transmembrane region of the receptor
Very dramatic increase in expression with the StaR
StaR has significantly higher DMSO tolerance
Bespoke Family C fragment set yielded tractable hits
for mGlu5 and mGlu2 (6-8% hit rate, >30% cut off)
mGlu5
Initial fragment hit
Rapid progress to advanced lead
compounds in less than 1 year
Fragment Based Drug Discovery
for mGlu5
Non-Confidential
HTL compound 1
Initial fragment hit
Jan 2012
HTL compound 2
SAR / SBDD
Mar 2012
HTL compound 36
Improved ADME profile
Jul 2012
HTL compound 43
Initial lead molecule
Aug 2012
PK
optimisation
HTL compound 98
Excellent rat PK
HTL compound 101
No P450 issues, different
PK profile
HTL compound 74
Excellent rat PK
Rapid progress to advanced lead compounds in less than 1 year
Fragment Based Drug Discovery for mGlu5
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Comparison of Heptares GPCR Fragment Hits with
Enzyme Targets
Target Method Hit LE Target Method Hit LE
A2A TINS NMR 0.56 Protein kinase B
X-ray soaking
0.47
A2A
SPR 0.53 DPPIV HCS 0.46
Family A aminergic
SPR 0.41 Thrombin X-ray
soaking 0.40
Family A peptidergic
SPR 0.31 BACE SPR ~0.3
Family A lipid HCS agonist 0.55* HSP90 NMR 0.53
Family A Chemokine
CXCR4
HCS antagonist
0.47 PDE4 HCS 0.46
GPCRs highly comparable to enzyme targets in terms of quality of hits, when StaR proteins used for screening
* StaR assay
Non-Confidential
Agenda
Heptares approach to GPCR X-ray structures
Biophysical techniques & fragment screening approaches applied to GPCRs
Using structures to enable the design of optimized drug candidates for
difficult GPCR targets
The importance of water molecules & their energies in GPCR drug design
– from druggability analyses through potency & selectivity to kinetics
Insights from Structural Biology - Some newly solved X-ray structures from
Heptares
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Non-Confidential
Techniques to Aid GCPR Structure Based Design
A1R pocket
boundary
A2AR pocket
boundary
lipophilic hotspots
in yellow (C1 probe)
3,5-disubstitution increases
affinity & selectivity
BPM reveals
novel binding mode: ligand sits
deep in ‘ribose pocket’
Biophysical Mapping
Water network energetics
GRID hotspots & surfaces
Non-Confidential
Does SBDD make a difference?
X-ray structures of a broad range of M1 agonist ligands
have been solved
Med Chem design heavily guided by protein-ligand
crystallography
Flexibility/induced fit in 1-2Å range at various sites in cavity
(main chain and side chain)
Quite similar ligands bind with the common substructure
atoms not overlapping
Enabling power of SBDD, caution for pharmacophore-
based approaches
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Non-Confidential
Agenda
Heptares approach to GPCR X-ray structures
Biophysical techniques & fragment screening approaches applied to GPCRs
Using structures to enable the design of optimized drug candidates for difficult
GPCR targets
The importance of water molecules & their energies in GPCR drug design
– from druggability analyses through potency & selectivity to kinetics
Insights from Structural Biology - Some new X-ray structures at Heptares
Non-Confidential
Why Water?
Water molecules play an essential role in the structure and function of
biological systems
-appear to play a key role in the GPCR structures available to date, perhaps due
to the deep pockets present in the GPCR transmembrane binding sites?
Displacement of waters from a binding site is a key component of ligand
binding, with significant binding energy, and thus potency, often from the
entropic gain of the displacement
But all waters are not equal…
- Burying an ”unhappy” water [i.e. entropically and/or enthalpically worse
than bulk sovent] may affect both potency and kinetics
- Pertubation of the remaining waters will also affect binding
Opportunity to provide new insights into druggability, to drive SAR,
selectivity and find solutions to many SAR issues (predicition of ”magic methyl”,
SAR not explainable by direct ligand-protein interactions...) + kinetics
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b-ionone binding pocket
L3.40, P5.50, W6.48
Hydrophobic hindering
mechanism L3.43, F6.44,
M6.40
Water pocket N1.50
L2.46, D2.50 S7.45, N7.49, P7.50
Ionic lock R3.50, E6.30
Rhodopsin Activation
W6.48
D2.50
F6.44
Y7.53
S7.45
N7.49 M6.40
L3.43
b-ionone movement L3.40, P5.50, W6.48
Occupy vacated hydrophobic pocket
Opening of the Hydrophobic
hindering mechanism
Unstable water channel N1.50
L2.46, D2.50 S7.45, N7.49, P7.50
Ionic lock broken
Interaction Y7.53 Y5.58
isomerisation to the all-trans retinal form
W6.48
D2.50
F6.44
Y5.58
Y7.53
S7.45
N7.49
M6.40
L3.43
Water Channel
Tehan et al. Pharmacol Ther. 2014 S0163-7258(14)00032-1
Waters in GPCR Protein Flexibility and Activation
Bortolato et al. BJP 2014
Y5.58
GPCR Hydrophobic Core and Activation
Hydrophobic Core common feature across subfamilies
Non-Confidential
Receptor Activation and Agonist Design
Cluster of GPCR Core Residues Control Activation
Conserved “Hydrophobic Hindering Mechanism”
I3.40
I6.40
F6.44
L3.43
TM3 TM6
Beta-2, rhodopsin, A2a et al
L3.43 gripped by F6.44 & I6.40 - Holds TM3 & TM6 in
inactive state
Mutations => constitutive activity
Inverse agonists impede release of 3.43 so freezing
receptor in inactive state
Agonists promote binding-site helical movements and
steric clashes that trigger breakage of cluster
Extracellular
Cytoplasmic
F6.44
I3.43
M6.40
H2O
Extracellular
Cytoplasmic
b2 AR
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The New Wave: WaterMap (Schrödinger) + GRID/WaterFLAP (Mol. Discovery)
GRID Map contoured at:
C1= (lipophilic) probe:
Yellow -2.5 kcal/mol
Water (H-bonding) probe:
Green -6.0 kcal/mol
Surface defined by CH3 probe:
Grey 1.0 kcal/mol
S1
S4
Drug-like properties from GRID
Factor Xa
The myth: Basic S1 for
serine proteases (factor Xa)
The structure
that broke the myth
The clinical
candidate
Potency, selectivity from waters
CRY probe = C1= + DRY
Waters in GPCR Protein Ligand Binding and Kinetics
Non-Confidential
Factor Xa
Update: New WaterFLAP/GRID based protocol
o GRID/WaterFLAP (Molecular Discovery) based protocol - adds waters iteratively taking explicity into account other waters already added + ligand if present - with short MD optimization; o Scored using GRID-based OH2 + CRY + ENTropy
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Water network computation reproduces
high-resolution X-ray water network
Technical details:
o Build a map of water
occupancy seeded with
GRID/WaterFLAP
1. - MD simulation with
GROMACS 200ps
- Energies (chemical potentials)
assigned using CRY/H2O/ENT
GRID potentials
- Approx 2 hours on a cpu.
.
or 2. WaterMAP (MD) to optimize
& evaluate energies
A2A at 1.8Å Liu, W. et. al, Science, 2012, 337:232
Excellent correlation + we also know the relative energies of the waters, and
can re-evaluate with different ligands - a significant advance
Non-Confidential
Binding Site Analysis for Ligandability / Druggability
Druggability – ligandability with properties of an oral drug
Use a combined analysis of ‘unhappy’ waters & binding site
preference (lipophilic/hydrophobic)
Analyse numbers, connectivity & distribution of ‘unhappy’ waters
Analyse environment (lipophilic etc) & entropy ‘Unhappy’ water = >2 kcal vs bulk solvent (vacuum usually worse) MD/WaterMap
or combined OH2/CRY/ENTropy GRID/WaterFLAP
Druggabllity linked to both number and proximity of “unhappy” waters and the
mixed concurrent hydrophobic-hydrophilic properties preferred for a ligand
X-ray structures show that potent efficient ligands displace the “unhappy”
waters – observed for all GPCR structures
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Druggable regions with clusters of ‘unhappy’ waters (WaterMap) with GRID showing adjacent/concurrent lipophilic & H-bond hotspots
Dopamine D3 Histamine H1 Muscarinic M2
OH
O
O
N+
H
Druggability : Dopamine, Histamine & Muscarinic GPCRs
= hydrophilic hotspot
= hydrophobic hotspot Predicted water energy
Low DG High DG
Bulk-like
Mason et al. TIPS 2012, 249-60
Non-Confidential
Druggability : Chemokine GPCRs
κ opioid receptor m opioid receptor δ opioid receptor
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Waters & GRID hotspots important for Adenosine A2A
antagonist triziazine & chromone series
Used GRID hotspots to enable first full GPCR
SBDD to give a clinical candidate for A2A
The highly ligand efficient designed structures
displace a cluster of unhappy waters deep in the
pocket (missed by previous ZM-like ligands from HTS etc)
Mason, J.S. et al., In Silico Pharm, 2014, 1:23
Non-Confidential
Alternative series from virtual screening utilizes other lipophilic hotspot region - Lipophilic hotspots key for GPCR design
NN
N
N
NH2
ON
S
O
O
O
Andrews, S.P. et. al, Med. Chem. Commun., 2014, epub
o High LE 0.5
Chromone VS series A1 selective from
lipophilic (vs H-bonding) groups
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Water can explain chromone ‘magic methyl’
33x more potent
Mason et al. In Silico Pharm. 2013 Andrews et al. Med Chem Comm 2014
MD shows ligand moves to avoid a
”dewetted” / high energy water situation
Non-Confidential
Agenda
Update: Heptares approach to GPCR X-ray structures
Update:Using structures to enable the design of optimized drug candidates
for difficult GPCR targets
The importance of water molecules & their energies in GPCR drug design
– from druggability analyses through potency & selectivity to kinetics
Insights from Structural Biology - Some new X-ray structures at
Heptares
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Non-Confidential 65
Courtesy of Arthur Doweyko
Structural information can be revolutionary – previously we were
biased by ligand + analogs data
Beware of biases in how we see and “force-fit” data
Beware of force-fitting ligands
(pharmacophores) without taking
water into account
Non-Confidential
o Suvorexant (Merck) DORA - EMPA - SB-334,867 (+ Heptares ligands)
o Sit high in orexin 1 / 2 binding site
o Suvorexant in hydrophobic collapse conformation as predicted
o Many polar interactions water mediated
o Ligand pharmacophore-based overlays do not correspond to experimental
structure-based overlay
Family A GPCR Structures
– Mutliple ligand structures key
NNO
N
O
N
N
N
Cl
O
N
NH NH
NN
O
S
O
ON
N
O
N
O
N
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Family B GPCR Structures
50-receptor Family B includes many important
drug targets
– GLP-1, PTH, Glucagon, CGRP
CRF-1 (anxiety/depression) 7TM domain solved
at high resolution with bound antagonist – Major differences to Family A
– Useful modelling of Family B enabled for the first time
Fundamental advance opening up new avenues
for drug design
– Driver for in-house GLP-1 agonist programme
Also Glucagon structure from Srevens/Scripps,
but no bound igand visible
Non-Confidential
CRF1 Antagonist StaR Structure – First Family B GPCR
Trans-membrane
helical bundle forms a chalice-like structure
Family B GPCR Structures
Antagonist binding site is located in
the intracellular half of the receptor
CP-376395
FamA
ligands
13-23Å
NH
N O
Binds in a predominantly
hydrophobic pocket:
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Radar plot of Heptares mGlu5
PCC properties. A perfect
compound hits the bull’s eye.
New mechanism for broad range of neurological and psychiatric diseases
Clinically validated in Autism, Dyskinesia, Depression/Anxiety, Migraine
Heptares leadership in SBDD for mGlu Family C (8 important GPCRs)
High resolution X-ray structures of mGlu5 in complex with multiple ligands
Creation of novel allosteric modulators using antagonist StaR® proteins
Highly differentiated next-generation drugs with best-in-class properties
Advantages: superior control over PK:PD; enhanced oral BA; cleaner chemistry
lacking toxicity alerts / sites of metabolism; excellent affinity & potency
mGlu5 Antagonists & Family C GPCRs: Neurological Diseases
Heptares have X-ray structures of
mGlu5 – defines drug binding
Heptares agent: QD/low dose with
tight PK:PD relationship to avoid
Cmax-driven AEs seen in competitors
Family C GPCR Structures
Non-Confidential
mGlu5 bound to mavoglurant at 2.6Å resolution
TM1
TM2
TM3
TM4
TM5
TM6
TM7
TM1
TM2 TM3
TM4
TM5
TM6
TM7
mavoglurant
ECL1 ECL2
ICL1
ICL3 C-ter
N-ter
N-ter
ECL1
ECL2
ECL3
ECL3
OMIT (before inclusion) FO-FC = 2.0s
• Outward kink in TM7 propagated by highly conserved (>90%) Pro/Lys motif • Ligand visible / unambiguous
• Conserved S-S – ECL2 – top of TM3 • ICL1 = short a-helical structure • Continuous density for ICL3 / ECL1 / ECL3
Family C GPCR Structures
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• Conserved S-S – ECL2 – TM3 anchors ECL2 shallow across top of receptor
• Additional network of interactions ECL2-TM1 / TM2 ECL1 / TM3 Gln6473.32 (92% conserved)
Closed entrance consistent with native ligands binding to orthosteric site in ECD
Restricted entrance to allosteric pocket
Non-Confidential
mavoglurant
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Overall very similar – 0.98Å rmsd across Cα in TMD ECL2 of mGlu1 does not cross top of receptor as in mGlu5 FITM binds higher (overlays with carazolol from beta), much more analogous to class A orthosteric positions
Comparison of mGlu1 (purple) vs mGlu5 (green)
Confidential
Summary – GPCR FBDD/SBDD
Multiple new X-ray structures have fully enabled structure based drug
discovery for GPCRs including FBDD
Virtual screening and fragment screening provide improved approaches to
lead generation (SPR, TINS, HCS on StaR proteins)
Compounds from FBDD/SBDD have optimised profiles compared to those
derived from high throughput screening
FBDD/SBDD can address many previous issues in GPCR drug discovery – Selectivity
– Lack of chemical starting points
– Limited chemistry which includes toxicophores
– Receptor kinetics
Demonstrated that SBDD can be used across multiple GPCR families to
generate optimised and novel drug candidates
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Conclusions – Water & Lipophilic Hotspots
o Solvent is as important as the ligand and the protein!
o Water network modeling/energetics and network perturbation is
essential to understand ligand binding (& kinetics)
• Believe it is important to take in account in an explicit way the perturbation
of the remaining water network resulting from ligand binding as well as
displaced waters
• Residence time - unhappy waters (trapped, being formed...)
o New perspective on druggability possible by analysis of binding site
using water free energies & 3D physicochemical properties (GRID) in
particular lipophilic hotspots
Confidential
Class A
Class C
Class B
Acknowledgements
Computational Chemistry
Andrea Bortolato
Dahlia Weis
Ben Tehan
Francesca Deflorian
Crystallography & Biophysics
Rob Cooke
João Dias (M1)
Andy Doré (mGlu5, A2A)
Andrei Zhukov (Biacore)
Chemistry
Miles Congreve
Giles Brown (M1)
John Christopher (mGlu5)
Malcolm Weir (CEO) Fiona Marshall (CSO)
Protein Engineering
Ali Jazayeri
Nathan Robertson (A2A, M1)
Jay Patel (mGlu5)
Giselle Wiggin (mGlu5)
Protein Expression
James Errey (mGlu5)
Maria Serrano-Vega (mGlu5)
Markus Koglin (M1)
Kris Okrasa (M1, mGlu5)
Pharmacology
Alastair Brown
Ed Hurrell (M1)
Kirstie Bennett (mGlu5)
Molecular Discovery
(GRID/ WaterFLAP)
Massimo Baroni
Gabriel Cruciani
Simon Cross
Schrödinger
(WaterMap)
Woody Sherman
Thijs Beuming
Robert Abel