Pharmacophore mapping in Drug Development
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The document discusses pharmacophore mapping and its significance in drug development, highlighting features such as hydrophobic groups and hydrogen bond donors/acceptors. It explores rational drug design, including virtual screening and docking processes, to identify effective compounds that bind to specific biological targets. A case study on farnesyltransferase inhibitors illustrates the application of pharmacophore techniques in discovering potential anticancer drugs.
Pharmacophore mapping in Drug Development
- 1. Pharmacophore Mapping in Drug Development MBACHU Chinedu C. Matric No: 172725 (Direct Reading Seminar) Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Ibadan
- 2. Definitions of Pharmacophore Features Rational drug design Virtual Screening Classification of Pharmacophore Drug Design Docking Process Applications of Pharmacophore Models Case Study Conclusion References Acknowledgement 2
- 3. Definitions and features Features • Hydrophobic centroids •Aromatic rings • Hydrogen bond acceptors HBA or • Hydrogen bond donor HBD •Cation and •Anions 3 A pharmacophore is an abstract description of molecular features which are necessary for molecular recognition of a ligand by a biological macromolecule. A pharmacophore is a representation of generalized molecular features including; 3D (hydrophobic groups, charged/ionizable groups, hydrogen bond donors/acceptors), 2D (substructures), and 1D (physical or biological) properties that are considered to be responsible for a desired biological activity Pharmacophore Mapping is the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3D
- 4. Simply 4 • Two somewhat distinct usages: • That substructure of a molecule that is responsible for its pharmacological activity (c.f. chromophore) • A set of geometrical constraints between specific functional groups that enable the molecule to have biological activity Bojarski, Curr. Top. Med. Chem. 2006, 6, 2005.
- 5. Rational Drug Design 5 Use knowledge of protein or ligand structures Does not rely on trial-and-error or screening Computer-aided drug design (CADD) now plays an important role in rational design Structure-based drug design Uses protein structure directly CADD: Protein-ligand docking Ligand-based drug design Derive information from ligand structures Protein structure not always available 40% of all prescription pharmaceuticals target GPCRs Protein structure has large degree of flexibility Structure deforms to accommodate ligands or gross movements occur on binding CADD: Pharmacophore approach, Quantitative structure-activity relationship (QSAR)
- 6. Drug Design The process of finding drug by design. Based on what the drug targeting? Metabolic or Signaling pathway 6 Specific for disease or pathology. Drugs Bind to active site & Work. “A substances used in the diagnosis, treatment or prevention of disease.
- 7. Overview of Pharmacophore-based Drug Design 7 Activity data Generate pharmacophore Search compound library for actives Test activity Buy or synthesise ‘hits’ pharmacophore.org
- 8. Virtual Screening Computational technique. Producing large libraries of compound that docked in to the binding site using computer programme. The goal is finding interesting new scaffolds rather than many hits. Low hits rate are clearly very preferable.
- 9. Virtual screening Virtual screening is the computational or in silico analogue of biological screening The aim is to score, rank or filter a set of structures using one or more computational procedures It can be used to help decide which compounds to screen (experimentally) which libraries to synthesise which compounds to purchase from an external company to analyse the results of an experiment, such as a HTS run
- 10. Virtual screening AR Leach, VJ Gillet, An Introduction to Cheminformatics
- 11. OUR TARGET 11
- 13. 13 Docking process Descriptions of the receptor 3D structure, binding site and ligand Sampling of the configuration space of the binding complex Evaluating free energy of binding for scoring Local/global minimum Ensemble of protein structures and/or mutiple ligands Multiple binding configurations for a single protein structcture and a ligand
- 14. Protein-ligand docking 14 A Structure-Based Drug Design (SBDD) method “structure” means “using protein structure” Computational method that mimics the binding of a ligand to a protein Given... • Predicts... • The pose of the molecule in the binding site • The binding affinity or a score representing the strength of binding Image credit: Charaka Goonatilake, Glen Group, University of Cambridge. http://www-ucc. ch.cam.ac.uk/research/cg369-research.html
- 15. Flexibility in docking Systematic search Monte Carlo methods (MC) Molecular Dynamics (MD) Simulated Annealing (SA) Genetic Algorithms (GA) 15 Available in packages: AutoDock (MC,GA,SA) GOLD (GA) Sybyl (MD) Docking programs DOCK FlexX GOLD AutoDOCK Hammerhead FLOG
- 16. The perfect scoring function will… 16 Accurately calculate the binding affinity Will allow actives to be identified in a virtual screen Be able to rank actives in terms of affinity Score the poses of an active higher than poses of an inactive Will rank actives higher than inactives in a virtual screen Score the correct pose of the active higher than an incorrect pose of the active Will allow the correct pose of the active to be identified Broadly speaking, scoring functions can be divided into the following classes: Forcefield-based Based on terms from molecular mechanics forcefields GoldScore, DOCK, AutoDock Empirical Parameterised against experimental binding affinities ChemScore, PLP, Glide SP/XP Knowledge-based potentials Based on statistical analysis of observed pairwise distributions PMF, DrugScore, ASP
- 17. 17
- 18. APPLICATIONS 18
- 19. CASE STUDY Virtual Lead Identification of Farnesyltransferase Inhibitors Based on Ligand and Structure-Based Pharmacophore Techniques Ftase is an essential enzyme in the Ras signaling pathway associated with cancer Thus, designing inhibitors for this enzyme might lead to the discovery of compounds with effective anticancer activity pharmacophore hypotheses were generated using structure-based and ligand-based approaches built in Discovery Studio v3.1. Knowing the presence of the zinc feature is essential for inhibitor’s binding to the active site of 19 FTase enzyme
- 20. further customization was applied to include this feature in the generated pharmacophore hypotheses Thorough validation using ROC analysis and ligand pharmacophore mapping The hypotheses were used to screen 3D databases to identify possible hits high ranked hits that showed sufficient ability to bind the zinc feature in active site, were further refined by applying drug-like criteria (Lipiniski’s “rule of five” and ADMET filters) Finally, the two candidate compounds ZINC39323901 and ZINC01034774 were allowed to dock using CDOCKER and GOLD in the active site 20 of FTase enzyme to optimize hit selection
- 21. 2D structures 21
- 22. Features pharmacophore 22 Ranked Higher
- 23. Fitness 23 Best Inhibitor
- 24. Mapping of Pharm-3A with 2ZIS-NH8903 24
- 26. Mapping of Pharm-3A over Pharm-B 26
- 27. Structure-based fitness 27 Same with Pharm-3A
- 28. Mapping of 2ZIS-NH8903 on Pharm- 3B 28
- 29. Steps in Graphics 29
- 30. Conclusion The pharmacophore concept is a successful and well-known approach for drug design (both ligand and structure based) as well as for virtual screening Pharmacophoric mapping is a promising concept in the development of drug within shorter time and limited resources when compared with the conventional drug development process 30
- 31. References Elumalai P, Liu HL, Zhao JH. et al. Pharmacophore modeling, virtual screening and 31 docking studies to identify novel HNMT inhibitors. J TAIWAN INST CHEM. 2012 doi:10.1016/j.jtice.2012.01.004. Gu¨ner, O.F. 2000 Pharmacophore Perception Development and Use in Drug Design, International University Line Langer, T. and Hoffmann, R.D. 2006 Pharmacophores and Pharmacophore Searches,Wiley VCH. Kirchmair, J. et al. (2005) Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space sub- sampling algorithms. J. Chem. Inf. Model. 45, 422–430. Kirchmair, J. et al. 2006 Comparative performance assessment of the conformational model generators Omega and Catalyst: a large scale survey on the retrieval of protein-bound ligand conformations. J. Chem. Inf. Model. 46,422–430. Kubinyi, H. 2006 Success stories of computer-aided design. In ComputerApplications in Pharmaceutical Research and Development(Ekins,S.,ed.),pp.377–424, Wiley- Interscience, New York.
- 32. References Lindsley CW, Zhao Z, Leister WH. et al. Allosteric Akt (PKB) inhibitors: discovery and 32 SAR of isozyme selective inhibitors. BIOORG MED CHEM LETT. 2005;15:761-764 Patel, Y. et al. 2002 A comparison of the pharmacophore identification programs: catalyst, DISCO and GASP. J. Comput. Aid. Mol. Des. 16, 653 681. Qosay A. A., Haneen A. A. et al,; Virtual Lead Identification of Farnesyltransferase Inhibitors Based on Ligand and Structure-Based Pharmacophore Techniques; Pharmaceuticals 27 May 2013 6, 700-715; doi:10.3390/ph6060700 Wermuth,C.G.andLanger,T.1993 Pharmacophore identification In 3D-QSAR in Drug Design. Theory, Methods and Applications (Kubinyi, H., ed.), pp. 117136, ESCOM. Wolber, G. and Langer, T. 2005 LigandScout: 3D pharmacophores derived from protein boundlig and sand their use as virtual screening filters. J.Chem. Inf. Model. 45, 160– 169. Wolber, G. and Dornhofer, A. A.2006 Efficient over lay of small organic molecules using 3D pharmacophores. J. Comput. Aid. Mol. Des.20, 773–788. Wolber, G. and Kosara, R. 2006 Pharmacophores from macromolecular complexes with Ligand Scout. In Pharmacophores and Pharmacophore Searches,(vol.32) (Langer, T. and Hoffmann, R.D., eds) pp. 131–150, Wiley-VCH.
- 33. Acknowledgement Dr B.B. Samuel 33
- 34. 34 THANK YOU FOR LISTENING