(Direct Reading Seminar)

Department of Pharmaceutical Chemistry

Faculty of Pharmacy

University of Ibadan

Pharmacophore Mapping in Drug Development

MBACHU Chinedu C.

Matric No: 172725

Definitions of PharmacophoreFeatures

Rational drug designVirtual Screening

Classification of Pharmacophore Drug Design

Docking ProcessApplications of Pharmacophore Models

Case StudyConclusion References

Acknowledgement

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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 pharmacophore models.

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

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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)

Drug Design

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The process of finding drug by design.

Based on what the drug targeting?

Metabolic or Signaling pathway

Specific for disease or pathology.

Drugs

Bind to active site & Work.

“A substances used in

the diagnosis,

treatment or

prevention of disease.

Overview of Pharmacophore-based Drug Design

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Activity data

Search compound

library for actives

Generate

pharmacophore

Test activity

Buy or synthesise ‘hits’

pharmacophore.org

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.

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

Virtual screening

AR Leach, VJ Gillet, An Introduction to Cheminformatics

OUR TARGET

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classification

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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

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

Flexibility in docking

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Systematic search Monte Carlo methods (MC) Molecular Dynamics (MD) Simulated Annealing (SA) Genetic Algorithms (GA)

Available in packages:AutoDock (MC,GA,SA)GOLD (GA)Sybyl (MD)

DOCKFlexXGOLDAutoDOCKHammerhead FLOG

Docking programs

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

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APPLICATIONS

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CASE STUDY

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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

FTase enzyme

Virtual Lead Identification of Farnesyltransferase

Inhibitors Based on Ligand and Structure-Based

Pharmacophore Techniques

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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

of FTase enzyme to optimize hit selection

2D structures

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Features pharmacophore

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Ranked

Higher

Fitness

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Best Inhibitor

Mapping of Pharm-3A with 2ZIS-NH8903

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Structure-Based hypothesis

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Mapping of Pharm-3A over Pharm-B

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Structure-based fitness

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Same with Pharm-3A

Mapping of 2ZIS-NH8903 on Pharm-

3B

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Steps in Graphics

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Conclusion

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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

References

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Elumalai P, Liu HL, Zhao JH. et al. Pharmacophore modeling, virtual screening and

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.

References

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Lindsley CW, Zhao Z, Leister WH. et al. Allosteric Akt (PKB) inhibitors: discovery and

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.

Acknowledgement

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Dr B.B. Samuel

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THANK YOU FOR LISTENING