GPCR fragment-based design and a novel structure- based … and Mason_tcm18-223174.pdf ·...
Transcript of GPCR fragment-based design and a novel structure- based … and Mason_tcm18-223174.pdf ·...
John Christopher & Jonathan Mason
4th RSC/SCI Symposium on GPCRs in Medicinal Chemistry.
Windlesham September 2012
GPCR fragment-based design and a novel structure-
based perspective on druggability
Non-Confidential
Heptares Therapeutics
Breakthrough medicines targeting previously undruggable GPCRs
$40M from leading venture investors since 2009
Major R&D partnerships with multiple large pharma companies
Structure-Based Drug Design for GPCRs, enabled for the first time by StaRs
(Stabilised Receptors)
Leading GPCR capability in industry, integrating chemistry & structural biology
Engine creating both small molecule NCEs and antibody therapeutics
Exceptional pipeline of investigational medicines for serious diseases
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Non-Confidential
GPCR Discovery Preclinical Phase 1 Phase 2 Indication(s)
Muscarinic M1 Alzheimer’s Disease, Schizophrenia
Orexin 1 Binge Eating, Nicotine Addiction
Dual Orexin 1/2 Chronic Insomnia
GLP-1 Type 2 Diabetes (First Oral NCE)
GPR39 Type 2 Diabetes (Disease Modifying)
mGlu5 Autism, Dyskinesia, Depression
CGRP Migraine Treatment & Prophylaxis
Adenosine A2A / Progress Confidential CNS Disorders
Progress Confidential CNS Disorders
Progress Confidential Undisclosed
Progress Confidential Antibody Therapeutics
Progress Confidential Undisclosed
Heptares Product Pipeline
MedImmune
Non-Confidential
Outline of Presentation
Fragment Screening Methods
Fragment H2L examples
A Water perspective...
– Druggability
– SAR & Kinetics
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SPR kinetics /
stoichiometry
Radioligand binding
Thermal shift
CE
StaRs enable Fragment Screening
NMR
HCS
SPR
SAR
Structural model
BPMX-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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SPR Fragment Screening POC
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 (mM)
fragments
Weakly binding fragments hits easily discriminated from inactives
Xanthines added to library as likely binders
Chip stable for days Congreve, M. et al, Methods Enzymol. 2011, 493, 115
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Vs. plots
-10
10
30
50
70
-10 10 30 50 70
A2A
b1A
R
Adenosine A2A and b1 Adrenergic Receptor (b1 AR)
SPR screen
b1 selective
hits
A2A selective
hits
Non-selective
hits
Standard
Similar results for screening Heptares
fragment library against A2A and b1AR
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NMR Screening: b1AR (with ZoBio)
TINS = Target Immobilized NMR Screening:
Immobilized protein – only small amounts needed (~1mg)
Very sensitive method: mM hits identified (not found by SPR)
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Congreve, M. et al, Methods Enzymol. 2011, 493, 115
Non-Confidential
High Concentration Screening
Lipid Receptor Agonist StaR
Agonist StaR binds known agonists with higher
affinity compared to wild-type receptor (binding
assay radioligand usage significantly reduced).
10% DMSO has a minimal effect on ligand-binding
to the StaR in membranes, unlike wild-type.
Enables high concentration (100 mM) fragment
screening which is not feasible with wild-type.
Approx 6% hit rate (84 / 1419 fragments inhibit
binding > 30%, n = 2).
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)
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
)
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Outline of Presentation
Fragment Screening Methods
Fragment H2L examples
A Water perspective...
– Druggability
– SAR & Kinetics
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Hit to lead example 1:
CXCR4 Chemokine Receptor Antagonist
39 hits (4.8%) from 808 molecules
(fragments + targeted screening set)
Follow up gives clear SAR
Rapid identification of potent hit
series with considerably better
profile than gold standard
Hit Series Exemplar
Ki = 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
Ki = 150 mM (LE = 0.47)
Good solubility
MWT 144, cLogP 1.3, PSA 39
PlerixaforDosed s.c.
MWT = 502
cLogP = -0.2
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Hit to lead example 2:
b1AR antagonists
N
NH
F
FF
KD = 16 mM
Caffeine
b1
Control
A2A
Control
b1 Hits
A2A Hits
A2A
b1A
R
Typical results for „well behaved‟ hits
SPR screening
with A2A as
counter screen
Several related hits
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NH OH
O
OH
NH
O
Analoguepurchasing
SPR hit
KD = 16 mM
LE = 0.41
IC50 = 7 mM
Binding Assay
LE = 0.50
b1AR: Structure-guided hit to lead progression
Analoguepurchasing
N
NH
F
FF
Warne, T. et al.Nature 2011, 469, 241
IC50 = 58 nM
Binding Assay
LE = 0.66
MW <250, Highly soluble
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b1AR: Fragment X-ray crystallography
• Protein-Fragment crystal co-crystal complexes solved to high resolution.
• Novel chemical matter, breaking the mould of existing chemistry.
- Lack aminoalcohol motif
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11 GPCRs (Family A, B & C) screened in 14 assays
GPCRs are highly comparable to enzyme targets in terms of quality of hits,
when StaR proteins are used for screening.
* StaR assay
Comparison of Heptares GPCR Fragment
Hits with Enzyme Targets
Target Method Hit LE
Adenosine A2A TINS NMR 0.56
Adenosine A2A SPR 0.53
Family A aminergic SPR 0.41
Family A peptidergic SPR 0.31
Family A lipid HCS 0.55*
CXCR4 HCS 0.47
Target Method Hit LE
Protein kinase B X-ray soak 0.47
DPPIV HCS 0.46
Thrombin X-ray soak 0.40
BACE SPR ~0.30
HSP90 NMR 0.53
PDE4 HCS 0.46
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Outline of Presentation
Fragment Screening Methods
Fragment H2L examples
A Water perspective...
– Druggability
– SAR & Kinetics
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GPCR structural information can be revolutionary – previously we were
biased by ligand + analogs data
Beware of biases in how we see and “force-fit” data
Courtesy of Arthur Doweyko,
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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 and find
solutions to many SAR issues (predicition of ”magic methyl”, SAR not explainable
by direct ligand-protein interactions...)
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Water: The new wave...
New computational approaches now available that can create water
networks (with or without a ligand present)
+ energetically differentiate ”happy” and ”unhappy” waters
Favourable binding sites contain multiple ”unhappy” waters, with a cluster
being particularly favourable ( higher affinity/LE fragment hits etc...)
- in an environment compatible with a drug-like molecule (mixture of
hydrophobic & hydrophilic hotspots rule of 5 etc properties)
OH
Ser
OH
Ser
OH
Ser
... e.g. not all serines are the same
All pockets are equal, but somepockets are more equal than others
Non-Confidential
The New Wave: WaterMap (Schrödinger) + GRID (Mol. Discovery)
Abel et al.
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GRID Map contoured at:
Aromatic CH (lipophilic) probe: Yellow
-2.5 kcal/mol
Water probe: Green -6.0 kcal/mol
Surface defined by CH3 probe: Grey
1.0 kcal/mol
1. MD simulation and clustering technique to build a map of water occupancy in the factor Xa active site2. Chemical potentials assigned to the water sites using the inhomogenous solvation theory
very ‘unhappy’ water
‘unhappy’ water
bulk-like water
‘happy’ water
Vacuum bubble
Bulk solvent
DGWaterMap coded:
Factor Xa
S1
S4
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A New Structure-Based Perspective on Druggability– 3D GRID physicochemical + Water energetics
GRID (Molecular Discovery) used to identify binding site complementary
properties – energetic analysis of binding site with ligand functional groups:
focus on hotspots for lipophilic probe (C1=) + H-bonding (H2O probe)
- new combined hydrophobic (DRY) and lipophilic (C1=) probe : CRY
WaterMap (Schrödinger) & SZMAP (Open Eye) used to generate a full
network of waters in binding site with free energy estimation (vs bulk solvent –
neutral atom) including a breakdown into entropic & enthalpic contributions
Combined analysis enables a new druggability assessment:
- fragment/ligand potency, properties, shape requirements…
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Waters that are calculated to have a significant positive free
energy (e.g. relative to being in bulk solvent) are termed „unhappy‟.
- There should be a particularly good free energy gain from
displacing these waters; they are only in the site as creating a
vacuum is even more energetically unfavourable.
- The number and the relative position of the predicted „unhappy‟
water clusters reveal hotspots for small molecule ligand
binding
A New Structure-Based Perspective on Druggability– 3D GRID physicochemical + Water energetics
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Can we generate a water network in a protein-ligand complex similar
to that experimentally determined in high resolution X-ray structures ?
- e.g. A2A at 1.8Å (4EIY)
Water Networks: Validation
Excellent correlation
+ we also know the relative energies
of the waters, and can re-evaluate
with different ligands
- a significant advance
Can we now explain previously
unexplained SAR (in terms of direct
ligand-protein interactions) etc
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Binding Site Analysis for 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 „Unhappy‟ water = >2.5 kcal vs bulk solvent (vacuum usually worse)
The link with protein active site druggability is not only to the total number of
„unhappy‟ waters in each binding pocket but also to their arrangement and
proximity (connectivity) to other „unhappy‟ waters.
-Two representative protease enzyme
targets factor Xa and BACE1 have key
„unhappy‟ waters displaced by the ligand
that are not connected
- extreme case in factor Xa where they are
in subpockets almost 10 Å apart, giving a
lower value of 0–1 connections;
- In GPCRs such as A2A the predicted
„unhappy‟ waters have a favourable
more globular arrangement with an
average 2-3 other adjacent „unhappy‟
waters at < 3 Å
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β1- (from StaR) & β2- (from T4L insertion) Adrenergic Receptors
β1 antag
β2 antag
β1 agonist
β2 agonist
Inactive Active
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Druggable regions with clusters of „unhappy‟ waters (WaterMap) with GRID showing concurrent
lipophilic & H-bond hotspots
Dopamine D3 Histamine H1Muscarinic M2
Druggability : Dopamine, Histamine & Muscarinic GPCRs
New insights from structural biology into the druggability of G protein-coupled receptors. Mason, Bortolato, Congreve, Marshall. Trends Pharmacol Sci. 2012;33(5):249-60.
OH
O
O
N+
H
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What about more recent structures?
S1P1 Lipid GPCR Opioid kappa Receptor
NHO
N+
P
O-
O
O-
H
HH
OH
N+
NH
O NH
H
OH
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Kinases are more like GPCRs for druggability in terms of regions of connected „unhappy‟ waters
c-Abl DFG-in c-Abl DFG-out
New insights from structural biology into the druggability of G protein-coupled receptors. Mason, Bortolato, Congreve, Marshall. Trends Pharmacol Sci. 2012;33(5):249-60.
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These sparsely distributed ‘unhappy’ waters are distributed in different subpockets, meaning that fairly ‘angular’ ligands are needed, with a requirement for a precise shape and conformation; this could explain why random screening (HTS) often fails. ranked lower in overall druggability
Less druggable enzyme targets do not have large clusters
of „unhappy‟ waters
The myth:Basic S1 for serine
proteases (factor Xa)
The structure that broke the myth
The clinical candidate
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Less Druggable Sites Still Tractable to SBDD
GPCR Compared to Protease
CXCR4 has a more challenging site:
Structure shows the “hot-spot” for binding to be
less deep / druggable (lipophilic, less buried
ionic interaction)
BACE has delocalised druggable site
– hot-spots linked by SBDD
New insights from structural biology into the druggability of G protein-coupled receptors. Mason, Bortolato, Congreve, Marshall. Trends Pharmacol Sci. 2012;33(5):249-60.
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Conserved mechanism suggests orthosteric agonists
should be attainable for any GPCR
Inactive Active
Druggable A2A Antagonist site Druggable A2A Agonist site
Non-Confidential
First GPCR Candidate Wholly Derived from SBDD
Superior adenosine A2A antagonists
Entirely novel chemotypes
Candidate licensed to Shire
Treatment of multiple neuro disorders
Radical binding mode with highly
optimised receptor interactions
Features– Non-furan, non-xanthine
– Very low molecular weight
– Relatively polar
Benefits– Attractive safety profile
– Improved oral bioavailability and PK
– Excellent in vivo efficacy
Candidate series (blue) binds very efficiently to receptor
Langmead, C. et al. (2012) J. Med. Chem. Mar 8;55(5):1904-9Congreve, M. et al. (2012) J. Med. Chem. Mar 8;55(5):1898-903
Hal
operid
ol
Istrad
efyl
line
Pre
laden
ant
HTL
0
20
40
60
80
1001h post-dose
2h post-dose
Effect of istradefylline, preladenant or HTL(1 mg/kg, PO) on haloperidol-induced catalepsy in rats
% 0
.8 m
g/k
g h
alo
peri
do
l re
sp
on
se
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Using GRID hotspots enabled the first full GPCR SBDD
to give a clinical candidate for the Adenosine A2a
A2A antag
ZM24138
New insights from structural biology into the druggability of G protein-coupled receptors. Mason, Bortolato, Congreve, Marshall. Trends Pharmacol Sci. 2012;33(5):249-60.
The highly ligand efficient designed structures displaces a cluster
of unhappy waters deep in the pocket, missed by previous ZM-like
ligands (from HTS etc)
Non-Confidential33
Biophysical MappingTM uses StaRs to map the binding
sites of GPCRs & conf
10-30 Mutations to theBinding site region
Mutant StaRs screened on Biacore chips
Detection of binding of different ligands to each
mutant StaR
Biophysical Map
of binding site
Ligand refined homology model and prediction of protein-ligand binding modes
Correlating binding data from multiple ligands with multiple
StaR proteins
Zhukov, A. et al., J. Med. Chem. 2011, 54, 4312.
Site directed mutagenesis
on StaR®
Non-Confidential
Experimentally Enhanced Homology Models
Biophysical
Mapping
Existing structures
Virtual screeningFragment screening
Further X-ray
structures
Knowledge of conformations
Reported variants
StaR mutagenesisFirst
homology
model
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Multiple generations of
Experimentally Enhanced
Homology Models
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Structure-Based Lead Optimisation
BP
- Structure can enable small ligand efficient
changes to modulate selectivity & potency
• Models of A2A, refined using the biophysical mapping, & of A1 used -
dockings prioritized from large virtual libraries (1,2,4-triazines)
– Used GRID hotspots to design compounds that efficiently displaced the
“unhappy “ waters deep in the site
– Used GRID surfaces of A2 vs A1 to probe for subltle differences to efficiently
design improved selectivity
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Waters & GPCR SBDD / Druggability
• New perspective on druggability possible by analysis of binding site using water free energies & 3D physicochemical properties (GRID)
• Looking at pertubation of all waters, including those remaining, is looking promising
• Multiple approaches possible, with combined approaches often useful
- for apo structure generate water network with WaterMap or GRID- for complexes generate initial complete water network with SZMAP
& optimize with WaterMap- binding site preferences from GRID analysis with functional groups- scoring (displacement and perturbation) by all 3 methods:
(WaterMap, SZMAP, GRID/CRY probe)
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Heptares Therapeutics Ltd.Computational Chemistry
Protein Engineering
Structural Sciences
Pharmacology
Medicinal Chemistry
Management
MRC LMB, CambridgeRichard Henderson
Chris Tate
Guillaume Lebon
Tony Warne
Jessica Li
Jenny Miller
Paul Scherrer InstituteGebhard Schertler
U. of Leiden / ZoBioGregg Siegal
Francis Figaroa
Johan Hollander
Eiso AB
BiofocusChris Richardson
Acknowledgements
U. of Utah / Biosensor ToolsDavid Myszka
Rebecca Rich