drug design edited1

8/8/2019 Drug Design Edited1

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Virtual Screening Discovery of New

Acetylcholinesterase Inhibitors

Mr.Wichitsak sukhano 535150054-3

Advisor : Dr. Chantana Boonyarat


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Contents

Drug discovery

Virtual screening in drug discovery

Types

Virtual screening process Virtual Screening Discovery of New

Acetylcholinesterase Inhibitors

CHERM Database

Database


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Most drug molecules inhibit the activity of a specificprotein by blocking its active site

Examples of proteins targeted by drugs:

HIV protease (Ritonavir, Viracept,)

Dihydrofolate reductase (trimethoprim,methotrexate)

Drug Discovery process

Targetdiscovery

Lead

Identification

Pre-

clinicaltest

Lead

Optimization

MarketEntry

ClinicalTrials


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Discovering Lead Compounds

?

VS/HTS


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Computer aided drug design

Why does it bind

in this way?

How can we maketighter binding,

more specific

compounds?


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Advantages of virtual screening

hit or success rate can be 10-30%,

whereas in HTS it is usually < 0.1%

avoids the cost of assaying compounds

that do not fit the binding site

provides a clear structural hypothesis for

ligand binding mode and interactions with

the protein


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Ligand-based virtual screening

The 3D structure of the biological target is unknown and a set of

geometric rules and/or physical-chemical properties (pharmacophore

model) obtained by QSAR studies are used to screen the database.

Structure-based virtual screening

It involves molecular dockingcalculations between each molecule to test

and the biological target (usually a protein). To evaluate the affinity a

scoring function is applied. The 3D structure of the target must be known.

Virtual Screening


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Ligand-based virtual screening

3D-QSAR method that search for relationship between the biological activity of aset of compounds (with specified alignment) and their three-dimensional

physicochemical properties (so-called molecular fields).


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Structure-based virtual screening

Ligand Receptor

+

Ligand receptor complex

Docking software

The complex quality is evaluated bythe score.


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Automated docking methods

the scoring (or energy) function

the strategy used to search for the lowest

score


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Common Docking Tools

MethodLigand flexibility

sampling

Scoring functionSuitability for

large- VS

Dock Incremental buildForce field or

contact scoreHigh

FlexX Incremental build Empirical score High

SlideConformational

ensembles

Empirical score High

FredConformational

ensembles

Gaussian score or

empirical scoresHigh

Gold Genetic algorithm Empirical score Low

Glide Exhaustive search Empirical score Low

AutoDock Genetic algorithm Force field Low

LigandFit Monte Carlo Empirical score Low

ICM

Pseudo-Brownian

sampling local

minimization

Mixed force field

and empirical

score

Low

QXP Monte Carlo Force field Low


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MolecularDocking


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Virtual Screening

: Computational or in silico analog of biological screening

Score, rank, and/orfilter aset of structures using

oneor more computational

ACDWDINCI

Ranking

HITS

X-rayCrystallography

NMRhomology modeling


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Process - Target based VS

Database Preparation

In silico libraryStarting Database of Compounds

ACD, WDI, NCI, inhouse collection

Post Processing

Molecular Docking:Docking tools- sampling algorithms- ligand flexibility- scoring function

Docking Analysis - screeningRank by Scoring Function

Target Preparation

Target-protein

receptor, enzyme

Testing Lead optimization

Add H

Add charges

Check ionization &

tuatomer


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Database Preparations

Filtering

reduce number of compounds, common filtering protocols Lipinskis rule of five ( solubility & permeability )

MW, logP, ... Physical filters ( number of rotatable bonds) ADMET ( stability & toxicity - some functional groups)Expandingincrease various forms of each cpds to cover relevant states(e.g. ionized forms, tautomers, isomers, conformers) 3D structure generation --> 3D- co-ordinates

assign ionization all forms of tautomeric states enantiomers of chiral centers assign partial charges geometric isomers


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DATABASE PREPARATION


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Chemical filters

ilters remove structures not wanted in a succession of screening meth

ubstructure filters to eliminate molecules known to have problems

Filters


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Pharmacokinetic filters

Poor absorption or permeation is more

likely when:

MW > 500 LogP>5

More than 5 H-bond donors (sum of OH and

NH groups) More than 10 H-bond acceptors (sum of N

and O atoms)

Lipinski rule of 5


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Other finding

70% of drug-like molecules have:

Between 0 and 2 H-bond donors

Between 2 and 9 H-bond acceptors

Between 2 and 8 rotatable bonds

Between 1 and 4 rings


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Polar surface area

Amount of molecular surface due to polar

atoms (N and O plus attached hydrogens)

Especially good for prediction of oral

absorption and brain penetration

Polar surface are greater than 140 square

Angstroms has been associated with poorabsorption


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Drugs discovery from VS

Selective COX-2 inhibitor NSAIDs

Enfuvirtide, a peptide HIV entry

inhibitor

Zanamivir, an antiviral drug

Isentress, HIV Integrase inhibitor


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Virtual Screening Discovery

of New Acetylcholinesterase

Inhibitors from CERMN

Chemical Library


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The 3 D acetylcholinesterase (AChE)


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Acetylcholine (ACh)

first neurotransmitteridentified (Otto Loewi,1921)

important role in movement:

causes muscle contraction

also found in brain:important role in attention,learning & memory


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ACh with Alzheimers disease

The cholinergic hypothesis, theyprolong the effects of the endogenouslyreleased neurotransmitter, acetylcholine

(ACh), by inhibiting the enzymeacetylcholin-esterase, thereby improvingthe cognitive abilities of early stage AD

patients.


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Action Potential

Na+

Ca2+

Acetylcholinesterase

AChE: No inhibition

Presynaptic neuronPostsynaptic target

Muscarinic

Receptor

ACH

ACH

Choline Acetate

ACH

ACH

ACH

ACH

ACHACH

ACH

ACH

ACH


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Action Potential

Na+

Ca2+

Acetylcholinesterase

AChE: Inhibition by drugs

Presynaptic neuronPostsynaptic target

Muscarinic

Receptor

ACH

ACH

ACH

ACH

ACH

ACH

ACHACH

ACH

ACH

ACH

ACH

ACH

ACH

ACH

ACH

ACH


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Types of cholinesterases

Acetylcholinesterasex Located in synapses

x Substrate selectivity:

ACH

Plasma cholinesterase

(Butyrylcholinesterase)x

Located in plasma(non-neuronal)

x Substrate selectivity:

ACH

Succinylcholine

Local anesthetics(procaine)


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General structure of AChE


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The knowledge of the 3D structure of AChE

understanding its remarkable

catalytic efficacy

rational drug design

developing therapeutic

approaches to OP

intoxication.


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Acetylcholinesterase (AchE)

Catalytic triad- Glutamate (Glu327)

- Serine (Ser 200)

- Histidine (His 440)

Peripheral anionic site (PAS)

-Tyrosine (Tyr 70)

-Trptophan (Trp 279)

Zaheer-ul-Haq et al,(2010)


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Acetylcholinesterase (AchE)

Peripheral anionic site (PAS)

The mouth of the gorge

Contains two aromatic amino acid Tyrosine

Trptophan


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N

O

H

O

N

N

H

O

(a)

(b)(d)

(c)

phe 297

phe 295

acyl pocket

(a)

(c)

Esteratic site

Anionic site


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H. Dvir et al. / Chemico-Biological Interactions xxx (2010) xxxxxx


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Pheriperalsite

Active site

Gorge

D il


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N

O

O O

HN

O

NH

HN

NH

O

Donepezil

(S,S)-(-)-bis(10)-hupyridone

Inhibit AChE activity

Protect neuron from beta-amyloid toxicity


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Method

Virtual Screening by Docking.(Gold)

Pharmacophore-Based Virtual Screening.

(Catalyst) In vitro Tests of AChE Biological Activity.

(method of Ellman et al.)


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Virtual Screening by Docking

Target PDB

-1EVE (AChE: Donepezil complex)

-All water molecules were deleted Database

- CERMN database

- 2D to 3D by ChemAxon Package


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Docking

Gold program ; docking conformations

-A genetic algorithm (conformational

spaces&binding mode)

-population size 100

-number of subpopulation 5

-Size of niche 2

-maximum number of genetic applications100000

-selection pressure 1.1


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Docking

Migration 5% of time

Crossover and mutation 95/95

Fitness function ; GoldScore andChemScore (predict affinity)


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In vitro Tests of AChE Biological

Activity Ellman method

AChE ; lyophilized electric eel (enz)

5 min

Measure absorbance at 412 nm

ACh iodide (Substrate)

AChE + DTNB+ Test cpds


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Docking result

6,626 81 24 4

Gold program In vitro test

Structure-based screening


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Pharmacophore-Based Virtual Screening

3D Pharmacophor PDB-1EVE (Donepezil)

-1H22 (Hupyridone)

Catalyst ; definition pharmacophoraligment(superimpose)

- 3D spatial arrangements of chemical 2molecule

-3D by tolerances sphere

Principal&MaxOmitFeat set 2, 0

Feature misses and complelte misses kept 11


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Superposition error, check superposition,

tolerance factor weights assign set 2

PP select function of structure

characteristic


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Pharmacophore results

6,62640 29 16 10

Ligan-based screening

Catalyst

7 pharmacophores

Divided into 3 gr In vitro test

10 cpds > 80%


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PP effeiency better SBS (34% successrate)

Both screening show 3,5

Cpd 3 is the best inh. IC50 45+-10 nM Confirm with score 61% 27th is the best

Cpd 5 is IC50 514+-149nM, is 3rd best of

docking score (58%)and 5th best of pp Both of them interx cas/pas (close position

donepezil)


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SANTOS ET AL.


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Santos et al.

Compound

3

Compound

5


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Virtual Screening Discovery of

New Acetylcholinesterase

Inhibitors By ADAM&EVE

Effi i t M th d f Hi h Th h t Vi t l


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Efficient Method for High-Throughput Virtual

Screening Based on Flexible

Docking

Virtual Screening tests

- Target :TC AChE ;1ACL

- Databases : ACD (110,000 cpds),

MAYBRIDGE (47,000 cpds)

- Molecular docking : ADAM&EVE In vitro test bioactivity : Ellman method

Mizutani and Itai.

CONVERTER


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Journal of Medicinal Chemistry, 2004, Vol. 47, No. 20

Remove H2OAdd charge

Add H

vdW

ElectrostaticAMBER

CONVERTER

EVE

Hits criteria

Gasteiger

charge

HIT C it i


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HITs Criteria


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Results

1

3

35

cpds

114 cpds

Hits

157,000 cpds

Docking

ADA

M&EVE

In vitro

test

Ranking


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Conclusion

speed - reduce hit to drug timeline from 15 yrsfrom hit identification to approved drug

cost

quality of drug candidate for clinical phase

decrease attrition rate (90%) in clinical phase

**complimentary approach to exp. HTS to increasesuccess rate

drug design edited1

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