Pharmocophores are set of functional group constraints responsible for the pharmacological activity of a compound.

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
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3. Definitions and features
Features
• Hydrophobic centroids
•Aromatic rings
• Hydrogen bond
acceptors HBA or
• Hydrogen bond donor
HBD
•Cation and
•Anions
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 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
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• 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
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 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
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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
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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
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12. classification
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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
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 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)
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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…
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 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
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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
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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
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of FTase enzyme to optimize hit selection

21. 2D structures
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22. Features pharmacophore
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Ranked
Higher

23. Fitness
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Best Inhibitor

24. Mapping of Pharm-3A with 2ZIS-NH8903
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25. Structure-Based hypothesis
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26. Mapping of Pharm-3A over Pharm-B
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27. Structure-based fitness
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Same with Pharm-3A

28. Mapping of 2ZIS-NH8903 on Pharm-
3B
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29. Steps in Graphics
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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
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31. References
Elumalai P, Liu HL, Zhao JH. et al. Pharmacophore modeling, virtual screening and
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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
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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
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THANK YOU
FOR LISTENING