The Application of DiverseSolutions DVS in the Establishment and Validation of a Target Class-Directed Chemistry Space

Slide 1: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

Slide 2: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

Slide 3: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

Slide 7: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

Slide 8:Methodology for Descriptor Analysis

Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

Slide 9:Theory of Targeted Compound Selection

Target Ligands

Slide 10:Reality of Targeted Compound Selection

Slide 11:Descriptor Selection for Targeted Design

Select training set of compounds known to be active and �drug-like� for a given target(s) Use other inactive, drug-like molecules as a �background� for comparison with actives Calculate as many descriptors as possible (and appropriate) for both active and inactive compounds Generate descriptor space for all compounds from a subset of the calculated descriptors

Slide 13:Targeted Descriptor Selection for NRs

DiverseSolutions (DVS)

Slide 14: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

NR Descriptor Selection

Slide 16: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

NR Descriptor Selection

Slide 17:Descriptor Discrimination of NR Ligands

Slide 18: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

Slide 19:Methodology for Descriptor Analysis

Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

Slide 20:Virtual Screening with NR Descriptor Space

Compound Database This database could be: Corporate collection Virtual libraries Compounds to be purchased The virtual screen may consist of one of the following: A nearest neighbor analysis A set of clusters defined by the training set Locate database compounds in the neighborhood of the training set compounds Biological screening of the selected compounds has two purposes: Find progressable hits to be followed up through chemistry Gain more knowledge about the characteristics of NR ligands

Slide 21: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

Slide 22:Methodology for Descriptor Analysis

Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

Slide 23: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

Slide 24: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

Slide 25:Comparative Screening Results

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

Slide 26:Methodology for Descriptor Analysis

Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

Slide 27: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

Slide 28: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

Slide 29: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

Slide 30:10K 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 PPARg LXRa

Slide 31:Receptor Screens

Selected 16 orphan NRs not previously screened at GSK in cells COUP-TF1, COUP-TF2, COUP-TF3, DAX, GCNF, PNR, LRH1, RevErbAa, RORa, RORb, RORg, 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

Slide 32:Results to Date

Receptors with hits* Receptors with no hits COUP-TF1 COUP-TF2 COUP-TF3 DAX GCNF LRH1 PNR RevErbAa RORa RORb RORg SHP SF1 TLX TR2 TR4 * Hits with structure-activity across a small series of analogs

Slide 33:Conclusion

Remaining orphan receptors show low chemical tractability in LBD-Gal4 format Some hits identified in cell-free FRET assays LRH1 SF1 RORa RevErbAa 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

Slide 34:Triangle of Tractability

Chemical Tractability compiled from GSK screening results AR CAR ER PR LXR PPAR PXR GR MR RAR RXR FXR TR LRH1 ERR SF1 ROR RevErb TLX SHP TR2/4 GCNF NGFIB COUP HNF4 PNR DAX H I G H L O W

Slide 35: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