Transcript The Application of DiverseSolutions (DVS) in the

The Application of DiverseSolutions
(DVS) in the Establishment and
Validation of a Target Class-Directed
Chemistry Space
Eugene L. Stewart*, Peter J. Brown†, James A. Bentley§,
Timothy M. Willson‡
*Computational and Structural Sciences,
†Metabolic Center of Excellence for Drug Discovery,
‡Discovery Medicinal Chemistry
§Molecular Discovery Research Information Technology
GlaxoSmithKline, Five Moore Drive, Research Triangle Park, NC, 27709
A Set of Descriptors for NR Ligands
Topics
– An Introduction to Nuclear Receptors (NR) as a
System of Receptors
– The Process of NR Descriptor and Compound
Selection Using Those Descriptors
Descriptor Selection
Selection of NR Targeted Compound Collections
– Validation of Descriptors for NRs
– Use of an NR descriptor space in the determination
of the druggability of the NR super-family
– Results and Conclusions
A Set of Descriptors for NR Ligands
Topics
– An Introduction to Nuclear Receptors (NR) as a
System of Receptors
– The Process of NR Descriptor and Compound
Selection Using Those Descriptors
Descriptor Selection
Selection of NR Targeted Compound Collections
– Validation of Descriptors for NRs
– Use of an NR descriptor space in the determination
of the druggability of the NR super-family
– Results and Conclusions
Nuclear Receptor Signaling
OH
H
HO
I
I
CO2H
NH2
O
I
HO
OH
H
thyroid hormone
estradiol
CO2H
O
H
dihydrotestosterone
Nuclear
Receptor
retinoic acid
O
H
O
Nucleus
Cytoplasm
progesterone
H
OH
OH
HOO
O
OH
H
calcitriol
O
HO
O
OH
aldosterone
O
cortisol
HO
OH
Nuclear Hormone Receptors (NRs)
N
DNA
“Classical” Steroid Receptors
GR
MR
PR
AR
ER()
TR
RAR()
VDR
EcR
corticosterone
aldosterone
progesterone
DHT
estradiol
triiodothyronine
retinoic acid
1,25-(OH)2-D3
ecdysone
Ligand
C
“Orphan” Receptors
RXR (,)
PPAR (,)
LXR (,)
FXR
SF1 (,)
CAR
ROR (,)
RevErb (,)
HNF4 (,)
NGFIB (,)
PNR
TR2 (,)
COUP (,)
Tlx
ERR (,)
9-cis retinoic acid
fatty acids
oxysterols
bile acids
oxysterols
androstanes
cholesterol
heme
—
—
—
—
—
—
—
A Set of Descriptors for NR Ligands
Topics
– An Introduction to Nuclear Receptors (NR) as a
System of Receptors
– The Process of NR Descriptor and Compound
Selection Using Those Descriptors
Descriptor Selection
Selection of NR Targeted Compound Collections
– Validation of Descriptors for NRs
– Use of an NR descriptor space in the determination
of the druggability of the NR super-family
– Results and Conclusions
Methodology for Descriptor Analysis
Training Set
for Target
Class
Calculate Possible
Descriptors for
Training Set
Virtual Library
1) Combinatorial
2) Cmpds to be
Acquired
Existing Library
1) Corporate
2) Other
Yes
Form Descriptor
Space for Target Class
Virtual Screening:
1) Nearest Neighbor Analysis
2) Activity-seeded Cluster Analysis
Is Compound
Active for
Target?
Biological Screening
No
Eliminate Compound
No
Compounds
Representative of
Target Class Ligands
Is Library Virtual
Combinatorial
Library?
Yes
Synthesize Biased
Library
Biased Targeted
Array Selection
Theory of Targeted Compound Selection
Universe of Compounds
(Virtual or Real)
Universe of Compounds
(Virtual or Real)
Target Ligands
Target Receptor Ligands
Chemistries/Compound Collections
Chemistries/Compound Collections
Reality of Targeted Compound Selection
Training Set of
Target Ligands
Set of Quality Target
Ligand Descriptors
Training Set of
Target Ligands
Drug-like Molecules
(WDI or MDDR)
Drug-like Molecules
(WDI or MDDR)
Nuclear Hormone Receptors (NRs)
DNA
N
“Classical” Steroid Receptors
GR
MR
PR
AR
ER()
TR
RAR()
VDR
EcR
corticosterone
aldosterone
progesterone
DHT
estradiol
triiodothyronine
retinoic acid
1,25-(OH)2-D3
ecdysone
907 known
NR ligands
from WDI
Ligand
C
“Orphan” Receptors
RXR (,)
PPAR (,)
LXR (,)
FXR
SF1 (,)
CAR
ROR (,)
RevErb (,)
HNF4 (,)
NGFIB (,)
PNR
TR2 (,)
COUP (,)
Tlx
ERR (,)
9-cis retinoic acid
fatty acids
oxysterols
bile acids
oxysterols
androstanes
cholesterol
heme
—
—
—
—
—
—
—
Targeted Descriptor Selection for NRs
DiverseSolutions (DVS)
NR900
(907 cmpds)
52 Standard 2D and
3D BCUT Metrics
Apply Basis Set
of Descriptors
SAVOL Molecular
Volume
Select Descriptors for a
Descriptor Space such that:
1) Maximize dimensionality
2) Minimize axes correlation
3) Separate WDI and NR900
WDI
(42,608 cmpds)
NR900
(907 cmpds)
5 Descriptors measure:
1) Charge
2) Polarizability
3) Molecular Shape &
Size
WDI
(42,608 cmpds)
NR Descriptor Selection
DiverseSolutions selected the following descriptors as axes
to define 5D NR descriptor space:
BCUT: diagonal = Gasteiger-Huckel charges
off-diagonal = inverse atomic distance
BCUT: diagonal = H-bond donor ability
off-diagonal = Burden’s numbers
BCUT: diagonal = tabulated polarizabilities
off-diagonal = Burden’s numbers
BCUT: diagonal = tabulated polarizabilities
off-diagonal = inverse atomic distance
SAVOL molecular volume
Normalized SAVOL
Molecular Volume
World Drug Index
NR900
Normalized BCUT lowest eignevalue
Diagonal: Gasteiger-Huckel Charges
Off-diagonal: inverse distance
A Set of Descriptors for NR Ligands
Topics
– An Introduction to Nuclear Receptors (NR) as a
System of Receptors
– The Process of NR Descriptor and Compound
Selection Using Those Descriptors
Descriptor Selection
Selection of NR Targeted Compound Collections
– Validation of Descriptors for NRs
– Use of an NR descriptor space in the determination
of the druggability of the NR super-family
– Results and Conclusions
Methodology for Descriptor Analysis
Training Set
for Target
Class
Calculate Possible
Descriptors for
Training Set
Virtual Library
1) Combinatorial
2) Cmpds to be
Acquired
Existing Library
1) Corporate
2) Other
Yes
Form Descriptor
Space for Target Class
Virtual Screening:
1) Nearest Neighbor Analysis
2) Activity-seeded Cluster Analysis
Is Compound
Active for
Target?
Biological Screening
No
Eliminate Compound
No
Compounds
Representative of
Target Class Ligands
Is Library Virtual
Combinatorial
Library?
Yes
Synthesize Biased
Library
Biased Targeted
Array Selection
This database could be:
• Corporate collection
• Virtual libraries
Compound
• Compounds to be purchased
Biological screening of the selected
Database
compounds has two purposes:
• Find progressable hits to be followed
up throughNR
chemistry
Calculate
Descriptors
• and
GainApply
more Descriptor
knowledgeSpace
about the
characteristics of NR ligands
The virtual screen may consist of one
of the following:
• A nearest neighbor analysis
Virtual
• A set of clusters defined by the
Screen
training set
Locate database compounds in the
neighborhood of the training set
compounds
Descriptor A
Biological Screen
Select Compounds
Descriptor B
Descriptor B
Virtual Screening with NR Descriptor Space
Descriptor A
A Set of Descriptors for NR Ligands
Topics
– An Introduction to Nuclear Receptors (NR) as a
System of Receptors
– The Process of NR Descriptor and Compound
Selection Using Those Descriptors
Descriptor Selection
Selection of NR Targeted Compound Collections
– Validation of Descriptors for NRs
– Use of an NR descriptor space in the determination
of the druggability of the NR super-family
– Results and Conclusions
Methodology for Descriptor Analysis
Training Set
for Target
Class
Calculate Possible
Descriptors for
Training Set
Virtual Library
1) Combinatorial
2) Cmpds to be
Acquired
Existing Library
1) Corporate
2) Other
Yes
Form Descriptor
Space for Target Class
Virtual Screening:
1) Nearest Neighbor Analysis
2) Activity-seeded Cluster Analysis
Is Compound
Active for
Target?
Biological Screening
No
Eliminate Compound
No
Compounds
Representative of
Target Class Ligands
Is Library Virtual
Combinatorial
Library?
Yes
Synthesize Biased
Library
Biased Targeted
Array Selection
Targeted Screening Validation
Question: How do we test this strategy?
Answer: Compare the results of screening our
NR targeted sets with a random or diverse set
of compounds
– Selected a NR targeted set using NR descriptors
8,000 compound selected from GSK liquid collection
– Selected a representative, diverse set
24,000 compounds selected as a diverse set of solids and
liquids from all GSK sites
11% of this set is contained in NR Space
Targeted Screening Validation
Screened both the diverse and targeted set
against a panel of 6 orphan NR assays
Compared curve data for diverse vs targeted
compounds
Considered only those compounds with a
pEC50 > 6.0 as hits
Only two screens generated curve data that
was comparable under this criteria for both sets
Comparative Screening Results
0.5
Two receptors yielded hits from
both sets which enabled a
comparison of hit rates
0.45
Hit Rate (%)
0.4
0.35
0.3
0.25
Diverse Set
Targeted Set
Targeted Libs
0.2
0.15
0.1
0.05
0
Orphan NR1 Orphan NR2
Methodology for Descriptor Analysis
Training Set
for Target
Class
Calculate Possible
Descriptors for
Training Set
Virtual Library
1) Combinatorial
2) Cmpds to be
Acquired
Existing Library
1) Corporate
2) Other
Yes
Form Descriptor
Space for Target Class
Virtual Screening:
1) Nearest Neighbor Analysis
2) Activity-seeded Cluster Analysis
Is Compound
Active for
Target?
Biological Screening
No
Eliminate Compound
No
Compounds
Representative of
Target Class Ligands
Is Library Virtual
Combinatorial
Library?
Yes
Synthesize Biased
Library
Biased Targeted
Array Selection
A Set of Descriptors for NR Ligands
Results and Conclusions
– By utilizing a targeted approach to library design and
compound selection, we have improved our hit rates in
orphan NR assays by 2-fold over random or diverse
compound selection
– NR targeted collections that are in the range of 40 60% effective give good coverage of an NR descriptor
space while still exploring “uncharted” regions of that
space
– Screening compound collections with better coverage
of NR descriptor space results in improved hit rates
A Set of Descriptors for NR Ligands
Topics
– An Introduction to Nuclear Receptors (NR) as a
System of Receptors
– The Process of NR Descriptor and Compound
Selection Using Those Descriptors
Descriptor Selection
Selection of NR Targeted Compound Collections
– Validation of Descriptors for NRs
– Use of an NR descriptor space in the determination
of the druggability of the NR super-family
– Results and Conclusions
Druggability of The Nuclear Receptome
How many of the remaining orphan receptors
are chemically tractable?
Data from GSK ligand screening experiment
– 16 orphan receptors
– 10,000 compounds
– Cell-based assay
– LBD-Gal4 chimera format
10K NR Probe Set
Selected using molecular descriptors derived from
known NR chemotypes
– Analyzed >23,000 public and proprietary NR ligands
– Activity-seeded clusters to maximize chemical diversity
– Set composed of 5000 externally purchased compounds and
5000 GSK proprietary compounds
– Low overlap with GSK screening collection (1.5 million
compounds)
Multiple hits identified on control receptors
– PPAR
– LXR
Receptor Screens
Selected 16 orphan NRs not previously screened at GSK in cells
– COUP-TF1, COUP-TF2, COUP-TF3, DAX, GCNF, PNR, LRH1,
RevErbA, ROR, ROR, ROR, SHP, SF1, TLX, TR2, TR4
Screen format
– LBD-Gal4 chimeras and UAS-tk-Luc reporter in BacMam viruses1
– Transduced multiple cell types with BacMam viruses
– Selected cells with optimal receptor expression to allow identification of
agonists and inverse agonists
– Screened the 10K probe set at 1.0 mM in duplicate
Followed up all hits (however weak) with chemical analog synthesis
Ran experiment over 18 month period
– Total budget = 2 conventional HTS
1M.
Boudjelal et al Biotecnol. Annu. Rev. 2005 11 1387
Results to Date
Receptors with hits*
* Hits with structure-activity
across a small series of
analogs
Receptors with no hits
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
COUP-TF1
COUP-TF2
COUP-TF3
DAX
GCNF
LRH1
PNR
RevErbA
ROR
ROR
ROR
SHP
SF1
TLX
TR2
TR4
Conclusion
Remaining orphan receptors show low chemical tractability in
LBD-Gal4 format
Some hits identified in cell-free FRET assays
–
–
–
–
LRH1
SF1
ROR
RevErbA
However demonstration of robust cellular activity has been
difficult
– Many of the receptors are constitutively active
– Some are constitutive repressors
– LBD-Gal4 chimera may not be the optimal assay format
Triangle of Tractability
TLX
GR
H
I
G
H
AR
PXR
MR
LXR CAR RAR
PPAR ER
RXR
PR
FXR
TR
SHP
LRH1
TR2/4
ERR
GCNF
SF1
NGFIB
ROR
COUP
RevErb HNF4
PNR
DAX
Chemical Tractability compiled from GSK screening
results
L
O
W
Acknowledgements
NR Chemistry
– Sharon Boggs
– Peter Brown
– Richard Caldwell
– Esther Chao
– Jon Collins
– Patrick Maloney
– Barry Shearer
– Phil Turnbull
Compound Acquisition
– David Langley
Compound Services
– Brenda Ray
NR Screening/Biology
– Richard Buckholz
– Steve Blanchard
– Lisa Miller
– Linda Moore
– Derek Parks
– Mike Watson
– Bruce Wisely
Informatics
– Deborah Jones-Hertzog
CASS
– Mike Cory
– Felix DeAnda