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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,

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

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  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

  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

  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

  4. Nuclear Receptor Signaling thyroid hormone estradiol dihydrotestosterone Nuclear Receptor retinoic acid Nucleus Cytoplasm progesterone calcitriol aldosterone cortisol

  5. DNA Ligand N C Nuclear Hormone Receptors (NRs) “Classical” Steroid Receptors “Orphan” Receptors RXR (,) 9-cis retinoic acid PPAR (,) fatty acids LXR (,) oxysterols FXR bile acids SF1 (,) oxysterols CAR androstanes ROR (,) cholesterol RevErb (,) heme HNF4 (,) — NGFIB (,) — PNR — TR2 (,) — COUP (,) — Tlx — ERR (,) — GR corticosterone MR aldosterone PR progesterone AR DHT ER() estradiol TR triiodothyronine RAR() retinoic acid VDR 1,25-(OH)2-D3 EcR ecdysone

  6. 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

  7. Methodology for Descriptor Analysis Calculate Possible Descriptors for Training Set Virtual Library 1) Combinatorial 2) Cmpds to be Acquired Form Descriptor Space for Target Class Existing Library 1) Corporate 2) Other Virtual Screening: 1) Nearest Neighbor Analysis 2) Activity-seeded Cluster Analysis Compounds Representative of Target Class Ligands Yes Biological Screening No No Yes Synthesize Biased Library Biased Targeted Array Selection Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

  8. Theory of Targeted Compound Selection Universe of Compounds (Virtual or Real) Universe of Compounds (Virtual or Real) Target Receptor Ligands Chemistries/Compound Collections Chemistries/Compound Collections Target Ligands

  9. 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)

  10. DNA Ligand N C 907 known NR ligands from WDI Nuclear Hormone Receptors (NRs) “Classical” Steroid Receptors “Orphan” Receptors RXR (,) 9-cis retinoic acid PPAR (,) fatty acids LXR (,) oxysterols FXR bile acids SF1 (,) oxysterols CAR androstanes ROR (,) cholesterol RevErb (,) heme HNF4 (,) — NGFIB (,) — PNR — TR2 (,) — COUP (,) — Tlx — ERR (,) — GR corticosterone MR aldosterone PR progesterone AR DHT ER() estradiol TR triiodothyronine RAR() retinoic acid VDR 1,25-(OH)2-D3 EcR ecdysone

  11. Targeted Descriptor Selection for NRs 52 Standard 2D and 3D BCUT Metrics NR900 (907 cmpds) Apply Basis Set of Descriptors SAVOL Molecular Volume NR900 (907 cmpds) Select Descriptors for a Descriptor Space such that: 1) Maximize dimensionality 2) Minimize axes correlation 3) Separate WDI and NR900 5 Descriptors measure: 1) Charge 2) Polarizability 3) Molecular Shape & Size WDI (42,608 cmpds) WDI (42,608 cmpds) DiverseSolutions (DVS)

  12. 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

  13. World Drug Index NR900 Normalized SAVOL Molecular Volume Normalized BCUT lowest eignevalue Diagonal: Gasteiger-Huckel Charges Off-diagonal: inverse distance

  14. 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

  15. Methodology for Descriptor Analysis Calculate Possible Descriptors for Training Set Virtual Library 1) Combinatorial 2) Cmpds to be Acquired Form Descriptor Space for Target Class Existing Library 1) Corporate 2) Other Virtual Screening: 1) Nearest Neighbor Analysis 2) Activity-seeded Cluster Analysis Compounds Representative of Target Class Ligands Yes Biological Screening No No Yes Synthesize Biased Library Biased Targeted Array Selection Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

  16. Virtual Screening with NR Descriptor Space Biological Screen Calculate NR Descriptors and Apply Descriptor Space Select Compounds Virtual Screen Descriptor B Descriptor B Descriptor A Descriptor A • This database could be: • Corporate collection • Virtual libraries • Compounds to be purchased Compound Database • 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 • 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

  17. 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

  18. Methodology for Descriptor Analysis Calculate Possible Descriptors for Training Set Virtual Library 1) Combinatorial 2) Cmpds to be Acquired Form Descriptor Space for Target Class Existing Library 1) Corporate 2) Other Virtual Screening: 1) Nearest Neighbor Analysis 2) Activity-seeded Cluster Analysis Compounds Representative of Target Class Ligands Yes Biological Screening No No Yes Synthesize Biased Library Biased Targeted Array Selection Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

  19. 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

  20. 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

  21. Two receptors yielded hits from both sets which enabled a comparison of hit rates Comparative Screening Results

  22. Methodology for Descriptor Analysis Calculate Possible Descriptors for Training Set Virtual Library 1) Combinatorial 2) Cmpds to be Acquired Form Descriptor Space for Target Class Existing Library 1) Corporate 2) Other Virtual Screening: 1) Nearest Neighbor Analysis 2) Activity-seeded Cluster Analysis Compounds Representative of Target Class Ligands Yes Biological Screening No No Yes Synthesize Biased Library Biased Targeted Array Selection Training Set for Target Class Is Library Virtual Combinatorial Library? Is Compound Active for Target? Eliminate Compound

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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 Results to Date * Hits with structure-activity across a small series of analogs

  29. 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

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

  31. 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 Acknowledgements • 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

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