Eugene L. Stewart*, Peter J. Brown † , James A. Bentley § , Timothy M. Willson ‡

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 TechnologyGlaxoSmithKline, 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



System of Receptors– The Process of NR Descriptor and Compound

Selection Using Those DescriptorsDescriptor Selection





Nuclear Receptor Signaling

NucleusCytoplasm

dihydrotestosterone

estradiol

progesterone

aldosteronecortisol

calcitriol

thyroid hormone

retinoic acid

HO OH

OHO

O

OHO

O

OHO

H

O

O

H

OH

OHH

OH

HO

H

O

CO2HHO I

II

NH2

OHHO

H OH

CO2H

NuclearReceptor

“Classical” Steroid Receptors “Orphan” Receptors

GR corticosteroneMR aldosteronePR progesteroneAR DHTER() estradiolTR triiodothyronineRAR() retinoic acidVDR 1,25-(OH)2-D3

EcR ecdysone

RXR (,) 9-cis retinoic acidPPAR (,) fatty acidsLXR (,) oxysterolsFXR bile acidsSF1 (,) oxysterols CAR androstanesROR (,) cholesterolRevErb (,) hemeHNF4 (,) —NGFIB (,) —PNR —TR2 (,) —COUP (,) —Tlx —ERR (,) —

CN DNADNA LigandLigand

Nuclear Hormone Receptors (NRs)



System of Receptors







Methodology for Descriptor Analysis

Training Setfor Target

Class

Calculate PossibleDescriptors for

Training Set

Form DescriptorSpace for Target Class

Is Library VirtualCombinatorial

Library?

NoIs CompoundActive for

Target?Biological Screening

Yes

Eliminate Compound

No

Virtual Library1) Combinatorial2) Cmpds to be Acquired

Existing Library1) Corporate2) Other

Virtual Screening:1) Nearest Neighbor Analysis2) Activity-seeded Cluster Analysis

CompoundsRepresentative of

Target Class Ligands

Yes

Biased TargetedArray Selection

Synthesize BiasedLibrary

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

Set of Quality TargetLigand Descriptors

Training Set ofTarget Ligands

Drug-like Molecules(WDI or MDDR)

Drug-like Molecules(WDI or MDDR)

Training Set ofTarget Ligands

Reality of Targeted Compound Selection

“Classical” Steroid Receptors “Orphan” Receptors

GR corticosteroneMR aldosteronePR progesteroneAR DHTER() estradiolTR triiodothyronineRAR() retinoic acidVDR 1,25-(OH)2-D3

EcR ecdysone

RXR (,) 9-cis retinoic acidPPAR (,) fatty acidsLXR (,) oxysterolsFXR bile acidsSF1 (,) oxysterols CAR androstanes ROR (,) cholesterolRevErb (,) hemeHNF4 (,) —NGFIB (,) —PNR —TR2 (,) —COUP (,) —Tlx —ERR (,) —

907 knownNR ligandsfrom WDI

CN DNADNA LigandLigand

Nuclear Hormone Receptors (NRs)

Targeted Descriptor Selection for NRs

NR900(907 cmpds)

WDI(42,608 cmpds)

WDI(42,608 cmpds)

NR900(907 cmpds)

DiverseSolutions (DVS)

Apply Basis Setof Descriptors

52 Standard 2D and3D BCUT Metrics

SAVOL MolecularVolume

5 Descriptors measure:1) Charge2) Polarizability3) Molecular Shape & Size

Select Descriptors for aDescriptor Space such that:1) Maximize dimensionality2) Minimize axes correlation3) Separate WDI and NR900

DiverseSolutions selected the following descriptors as axes to define 5D NR descriptor space:

BCUT: diagonal = Gasteiger-Huckel chargesoff-diagonal = inverse atomic distance

BCUT: diagonal = H-bond donor abilityoff-diagonal = Burden’s numbers

BCUT: diagonal = tabulated polarizabilitiesoff-diagonal = Burden’s numbers

BCUT: diagonal = tabulated polarizabilitiesoff-diagonal = inverse atomic distance

SAVOL molecular volume

NR Descriptor Selection

World Drug IndexNR900

Normalized BCUT lowest eignevalueDiagonal: Gasteiger-Huckel ChargesOff-diagonal: inverse distance

Nor

mal

ized

SA

VO

LM

olec

ular

Vol

ume



System of Receptors









Class


Training Set



Library?



Yes

Eliminate Compound

No






Yes



Virtual Screening with NR Descriptor Space

VirtualScreen

Descriptor A

Des

crip

tor

B

Descriptor A

Des

crip

tor

B

CompoundDatabase

Calculate NR Descriptorsand Apply Descriptor Space Select Compounds

This database could be:• Corporate collection• Virtual libraries• Compounds to be purchased

The virtual screen may consist of oneof the following:• A nearest neighbor analysis• A set of clusters defined by the training set

Locate database compounds in theneighborhood of the training setcompounds

Biological ScreenBiological screening of the selectedcompounds has two purposes:• Find progressable hits to be followed up through chemistry• Gain more knowledge about the characteristics of NR ligands



System of Receptors




– Validation of Descriptors for NRs– Use of an NR descriptor space in the determination

of the druggability of the NR super-family




Class


Training Set



Library?



Yes

Eliminate Compound

No






Yes



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 set24,000 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

Targeted Screening Validation

Comparative Screening Results

Two receptors yielded hits from both sets which enabled a comparison of hit rates



Class


Training Set



Library?



Yes

Eliminate Compound

No






Yes



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




System of Receptors







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 M 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* Receptors with no hits– COUP-TF1– COUP-TF2– COUP-TF3– DAX– GCNF– LRH1– PNR– RevErbA– ROR– ROR– ROR– SHP– SF1– TLX– TR2– TR4

* Hits with structure-activity across a small series of analogs

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

Chemical Tractability compiled from GSK screening results

ARARCARCARERERPRPR

LXRLXRPPARPPAR

PXRPXR

GRGRMR MR RARRARRXRRXRFXRFXRTRTR

LRH1LRH1ERRERRSF1SF1RORROR

RevErbRevErb

TLXTLXSHPSHP

TR2/4TR2/4GCNFGCNFNGFIBNGFIBCOUPCOUPHNF4HNF4PNRPNRDAXDAX

HIGH

LOW

Acknowledgements

NR Chemistry– Sharon Boggs– Peter Brown– Richard Caldwell– Esther Chao– Jon Collins– Patrick Maloney– Barry Shearer– Phil Turnbull

Informatics– Deborah Jones-Hertzog

CASS– Mike Cory– Felix DeAnda

NR Screening/Biology – Richard Buckholz– Steve Blanchard– Lisa Miller– Linda Moore– Derek Parks– Mike Watson– Bruce Wisely

Compound Acquisition– David Langley

Compound Services– Brenda Ray

Eugene L. Stewart*, Peter J. Brown † , James A. Bentley § , Timothy M. Willson ‡

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Transcript of Eugene L. Stewart*, Peter J. Brown † , James A. Bentley § , Timothy M. Willson ‡