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Computer-Aided Discovery of New HIV-1 Integrase Inhibitors

This project aims to find new inhibitors for HIV-1 integrase using bioinformatics and computer-aided drug discovery techniques. The project duration is from April 1, 2005 to March 31, 2008.

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Computer-Aided Discovery of New HIV-1 Integrase Inhibitors

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  1. Computer-Aided Discovery of New HIV-1 Integrase Inhibitors • (ISTC/BTEP Project # 3197/111) • Vladimir Poroikov • Laboratory for Structure-Function Based Drug Design, • Institute of Biomedical Chemistry of Rus. Acad. Med. Sci.

  2. Slovenja: The Land of Many Dreams

  3. HIV/AIDS as a Global Threat Acquired immunodeficiency syndrome (AIDS), which is caused by HIV, is an immunosuppressive disease that results in life-threatening opportunistic infections and malignancies. First reported in 1981 in the United States, AIDS has become a major worldwide epidemic. The United Nations Program on AIDS (UNAIDS) estimates that at the end of 2002 nearly 42 million will have died of AIDS. During 2002, about 3 million people became infected. AIDS is presently the leading cause of death in Africa and the fourth leading cause of death worldwide. Cos P. et al. J. Nat. Prod., 2004,67, 284-293.

  4. HIV-1 Replication Cycle HAART – Highly Active AntiRetroviral Therapy

  5. Problems with the Current Therapy: - Adverse/Toxic effects. - High cost of treatment. - Multiple drug resistance.

  6. 5’ACTGGAA 3’TGACCTT TAGCAGT 3’ ATCGTCA 5’ U3 R U5 gag pol env U3 R U5 3’-end processing U3 R U5 U3 Cellular DNA U5 Post-integration processing U3 U5 U3 U5 Mechanism of HIV-1 DNA integration into a cellular DNA integrase HIV-1 DNA integrase cytoplasm nucleus 5’ACTGGAA 3’ ACCTT TAGCA 3’ ATCGTCA 5’ U3 R U5 gagpolenv U3 U5 R Strand transfer

  7. HIV-1 Integrase as Anti-HIV Target • HIV-1 integrase: • Catalyzes one of the crucial step of HIV replication. • Has no cellular analogs. • All retroviral integrases have a conservative structure. Is a prospective target for treating HIV infection and preventing AIDS.

  8. ISTC/BTEP Project # 3197/111 The purpose of the project is to find new efficient inhibitors of HIV-1 integrase on the basis of the latest technologies in bioinformatics and computer-aided drug discovery. Duration: April 1, 2005 – March 31, 2008

  9. First Approval of HIV-1 Integrase Inhibitor

  10. Problems with Finding of HIV-1 Integrase Inhibitors • Viral strains resistant to HIV-1 integrase inhibitors have been already identified. • Conformation of integrase is rather flexible, it is stabilized in the pre-integration complex. • Three-dimensional structure of full-length integrase as well as the structure of integrase complex with viral DNA are not known.

  11. Participating Institutions Institute of Biomedical Chemistry of RAMS (IBMC), Moscow (leading organization – computer-aided drug discovery) Institute of Organic Chemistry of RAS (IOC), Moscow (chemical synthesis of potential compounds) Institute of Physical-Chemical Biology of MSU (IPCB), Moscow (testing of potential compounds in vitro) National Cancer Institute, NIH, Frederick, MD (molecular modelling, testing in cell culture)

  12. ISTC/BTEP Project # 3197/111 HIV/AIDS Computer-assisted discovery of new HIV-1 integrase inhibitors Svyatoslav Shevelev IOC RAS (FWS) Marina Gottikh IPCB MSU Vladimir Poroikov IBMC RAMS Marc Nicklaus NCI/NIH

  13. PASS: Prediction of Activity Spectra for Substances

  14. What is the Biological Activity Spectrum? Biological Activity Spectrum is the “intrinsic” property of the compound that reflects all kinds of its biological activity, which can be found in the compound’s interaction with biological entity. Poroikov V. and Filimonov D. In: Predictive Toxicology. Ed. by Christoph Helma. Taylor & Francis, 2005,459-478.

  15. Biological Activity Spectrum Represents 3300 kinds of biological activity (PASS 2007), including: 374 pharmacotherapeutic effects, e.g. Alzheimer's disease treatment Anabolic Analgesic Angiogenesis inhibitor Angiogenesis stimulant Antiarrhythmic Antiarrhythmic Class III Antiarthritic Antibacterial . . .

  16. Biological Activity Spectrum Represents 3300 kinds of biological activity (PASS 2007), including: 2755 biochemical mechanisms, e.g. 5 Alpha reductase inhibitor 5 Hydroxytryptamine 1 agonist 5 Hydroxytryptamine 1A antagonist 5 Hydroxytryptamine 1B agonist 5 Lipoxygenase inhibitor 5-Phytase inhibitor 6 Phosphofructokinase inhibitor Acetaldehyde dehydrogenase inhibitor Acetate kinase inhibitor Acetate-CoA ligase inhibitor . . .

  17. Biological Activity Spectrum Represents 3300 kinds of biological activity (PASS 2007), including: 50 adverse effects & toxicity, e.g. Arrhythmogenic Carcinogenic Cardiotoxic Cytotoxic DNA damaging Embryotoxic Eye irritation, corrasive Hematotoxic Hyperglycemic . . .

  18. Biological Activity Spectrum Represents 3300 kinds of biological activity (PASS 2007), including: 121 metabolic terms, e.g. CYP2 substrate CYP24 substrate CYP27 substrate CYP2A substrate CYP2A1 substrate CYP2A10 substrate CYP2A3 substrate CYP2A6 substrate . . .

  19. Pa Pi for Activity: 0.853 0.020 Anxiolytic 0.694 0.035 Sedative . . . How Biological Activity Spectrum Is Predicted? Structure of new compound Estimating the probability that it has a particular biological activity Anxiolytic Sedative 5HT1A Inhibitor Carcinogen . . . Predicted biological activity spectrum

  20. Some Examples ofPASS INet (www.ibmc.msk.ru/PASS) Predictions, Confirmed by the Experiments Geronikaki A. et al. Prediction of biological activity via Internet. Medicinal chemist's point of view. SAR & QSAR Environ. Res., 2007, 19, 27-38 .

  21. Former Collaboration (CRDF Grant RC1-2064) • Lab. Med. Chem., Lab. Str.-Funct. Based • NCI, NIH Drug Des., IBMC, RAMS • Computer-assisted mechanism-of-action analysis of large databases including 250,000 chemical compounds • registered by NCI

  22. PASS Predictions Searchable in NCI DB Browser (http://cactus.nci.nih.gov) More than 64 million PASS predictions included. More than 700 activities available. Predictions separately searchable by probabilities of activity and inactivity. Both types combinable by logical AND. Predictions searchable by probability ranges (in subintervals of 0.0 – 1.0). PASS searches combinable with any other search criteria.

  23. Based on PASS predictions, a fraction of “active” compounds can be increased significantly: Poroikov et al. J. Chem. Inf. Comput. Sci., 2003, 43, 228-236.

  24. 15 Medicine 10 5 years 3D-TI Conference, Dec. 10-11, 2007 Idea Creating New Medicines Is a High Risk Journey

  25. General Scheme of Search for New HIV-1 Inhibitors Improvement of PASS Training Set Molecular Modelling (Target Based Design) Development of New Synthetic Routes, Chemical Synthesis Computer Screening of Diverse Databases O P T I M I Z A T I O N In Vitro Testing Hits Testing in Cell Culture Leads

  26. HIV-1 IN Inhibitors Database Prepared for Input to PASS Training Set

  27. 3D Model of HIV-1 Integrase (Karki R. et al. JCAMD, 2004, 18: 739.

  28. Example of HIV-1 Inhibitors Pharmacophore

  29. Optimization of the Specialized PASS Training Set 2006 2008 2205 compounds 260 compounds - Publications (only with Mg2+) - Tested in NCI - Tested in IPCB • Publications • Patents • NIAID HIV Therapeutics Database

  30. From Hits to Leads: Structure Optimization GS 9137 MK-0518 IOCh-18-47 IC50: 3’-P = 0.2 M, ST = 20 M IOCh-18-76 IC50: 3’-P = 80 M, ST = 80 M IOCh-18-74 IC50: 3’-P = 0.3 M, ST = 0.5 M

  31. Strand Transfer Inhibition by Compounds IOCh-18-47, IOCh-18-74 and IOCh-18-92

  32. 3’-Processing Inhibition by Compounds IOCh-18-47, IOCh-18-74 and IOCh-18-92

  33. Summary of the Results • 198 compounds were selected as hits, synthesized (or purchased from vendors of commercially available samples) • 176 compounds were tested in vitro on inhibition for strand transfer and 32 compounds were tested on inhibition for 3’-processing. • 15 compounds were identified as HIV-1 integrase inhibiting agents with IC50 values in the micromolar and sub-micromolar range. • For 4 most active compounds results were further confirmed by in vitro testing at NCI. • The discovered compounds belong to the chemical series where this activity was unknown (NCEs).

  34. Some Prospects for a Near Future (BII Supported?) • Synthesis and biological testing of additional rationally designed derivatives from the same chemical series, to increase potency and decrease toxicity. • Detailed study the mechanism of binding, specificity, etc. for this classes of compounds. • Preparation and submission of patent(s) . • Negotiations with pharmaceutical companies about possibilities of commercialization.

  35. Acknowledgements IBMC Tamara Fedoronchuk Dmitry Filimonov Tatyana Gloriozova Dmitry Druzhilovsky Alexey Lagunin Alexander Shkrob Alexander Veselovsky IOC Svyatoslav Shevelev & Associates IPCB Marina Gottikh & Associates NCI Marc Nicklaus & Associates Winay Pattak & Associates Elena Shilova Antonina Boudunova Financial support: ISTC/BTEP Project # 3197/111

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