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Drug discovery strategy final draft

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Drug discovery strategy final draft

  1. 1. Drug Discovery Anaeli Shockey Delaine M. Zayas-Bazán Nicollle A. Rosa Zuleika Velázquez Student Mentor: Mr. Carlos Castroda
  2. 2. Introduction • Food and Drug Administration (FDA) – Drug Discovery and Development – Clinical Trials – FDA Reviews and New Drug Application (NDA) – Manufacturing • Considered Parameters – Absorption – Distribution – Metabolism – Excretion • What is In silico drug discovery?
  3. 3. . Pharmacophore identification and Pharmacophore Model Generation (LigandScout) Identification of Top-hits and potential Lead Compounds. (Ranking of binding energies) Drug Discovery Strategy Primary Sequence Analysis; degree conservation (NCBI/Swiss-Prot) Biological Problem (Biomedically Relevant Condition or Process) Identification of optimal target (s) for drug development Identification of compounds that fulfill requirements of Pharmacophore model Filtering Small chemical compounds Databases Target Analysis Number, quality and distance of “hot spots’ 3D Structure www.pdb.org PyMol BioAssay Secondary Screening (AutoDock Vina) Primary Screening: Pharmacophore Model (ZINCPharmer) High Affinity Lead Compounds Further refinement of Pharmacophore Model FTMap and In Silico screening of chemical probes Therapeutically relevant protein targets Docking/screening of Filtered Databases B C D A
  4. 4. Identification of Top-hits and potential Lead Compounds. (Ranking of binding energies) Drug Discovery Strategy Identification of compounds that fulfill requirements of Pharmacophore model BioAssay Secondary Screening (AutoDock Vina) High Affinity Lead Compounds Further refinement of Pharmacophore Model Docking/ screening of Filtered Databases D Work Plan
  5. 5. Work Plan • Run the Docking/Screening (“AutoDock Vina”) – This is needed for the analysis of the top hits – It is achieved by the utilization of the program Auto Dock • Download Results and Ranking of Top Hits – For this, the programs CyberDuck and Excel were used. – The results were downloaded and then opened with Excel to sort by affinity – Select the drugs with the highest affinity – Look for information about the drugs and take the pictures
  6. 6. Work Plan • Analyze Interactions –Open Autodock and let the program analyze the results –Take pictures of the interactions
  7. 7. Name Affinity ZINC06716957 -11.4 ZINC14880002 -11.4 ZINC22940637 -10.6 CID_64143_Nelfinavir -10.5 Zinc14879987_Tipranavir -10.5 ZINC02570819 -10.4 ZINC00896717 -10.4 ZINC03951740 -10.4 zinc_3951740_lopinavir -10.4 ZINC22448696 -10.2 DMP -10 ZINC03914169 -10 ZINC52955754 -10 Identification of Top-hits Steps One and Two
  8. 8. Nilotinib • ZINC06716957 -11.4 • Tyrosine kinase inhibitor • Chronic myelogenous leukemia treatment • Hydrochloride monohydrate salt • Oral ingestion
  9. 9. Lopinavir • zinc_3951740_lopinavir -10.4 • Antiretroviral of the protease inhibitor class • Used with ritonavir (protease inhibitor) • Oral ingestion
  10. 10. Ergoloid • ZINC00896717 -10.4 • Mixture of methanesulfonate salts • Used to treat dementia and age-related cognitive impairments • Also used in a patients recovery after stroke • Oral ingestion and parenteral
  11. 11. Zafirlukast • ZINC14880002 -11.4 • Oral ingestion • Leukotriene receptor antagonist • Inhibits what causes inflammation in respiratory system
  12. 12. Images of the Drug’s Interactions with the amino acids of the Proteases of HIV
  13. 13. Images of the Drug’s Interactions with the amino acids of the Proteases of HIV • Due to technical difficulties with the program, the images of the other three drugs’ interactions with the amino acids of the proteases could not be presented. • This images composed the last step: the analysis of the results.
  14. 14. Conclusion • The fourteen drugs with the highest affinity were chosen. • These range from -10 to -11.4 • Nilotinib, Lopinavir, Ergoloid, and Zafirlukast were evaluated using AutoDock Vina, CyberDuck and Excel. • The drugs with the highest affinity to the HIV related protein are Zafirlukast and Nilotinib.

Hinweis der Redaktion

  • In silico drug discovery, which is basically the design or discovery of small molecules that are complementary in shape and charge to the bio-molecular target.
  • Having this technology allows us to know in advance how molecules will interact in an active site of a macromolecule

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