Structure-based Drug Discovery - Welcome to CBSblicher/Courses/10_Structure-based_drug...Structure-based drug discovery Ph.D. Thomas M. Frimurer • “In Silico” screening and drug

1

Structure-based drug discovery

Ph.D. Thomas M. Frimurer

• “In Silico” screening and drug discovery

- Small molecule compound databases

- Molecular descriptors

- Compound filtering

• Pharmacophore perception technology- Ligand based drug design- Receptor based drug design

• Site Directed Drug Discovery- A knowledge based approach for hit and lead identification

Structure-based Drug Discovery

Drug Discovery & Development (DD&D)

Lock

Key

ProteinSmall molecule ligand

Protein ligandcomplex

+

in vivo efficacy


Preclinicaldevelopment

Lead optimization

Hit to LeadHit

IdentificationTarget

selection

Assay

In vitro screening

mleads

1Drug

DD&D: Expensive, time consuming, withnumerous bottlenecks

& low success rate

New Webster’s Dictionary:“drug any substance used

in the composition of

a medicine”

High Throughput Screenign (HTS)

Compoundlibrary

(105-107

molecules)

Variable 3-6 months 6 - 9 months 12-18 months 9-12 months


Target

In vitro screening

mleads

1Drug

“In Silico” hit and lead generation

Compoundlibrary

(105-107

molecules)

+

+

++

+

Virtual libraries1020 compounds

Focusedlibrary

102-103 comp. Hits/Lead compounds with

good drug like potential

Preclinicaldevelopment

Lead optimization

Hit to LeadHit

IdentificationTarget

selection

ADME: Adsorption, Distribution, Metabolism & Excretion

Drug like filters

ACD

Chemical Databases & Molecular Descriptors

• MW = 422.92• ClogP = 3.11• Polar Surface Area = 71 Å2

• Number of rotatable bonds = 8• Number of donor atoms = 2• Number of acceptor atoms = 6• Number of rings = 4• Etc.…

1D Descriptors• Molecular weight (MW)• ClogP• Polar Surface Area (PSA) • Number of Rotatable Bonds…

Virtuallibraries

Natural products

library

Publicly accessible

libraries (NCI)

Combinatoriallibraries

Drug libraryWDI

5 x 104

ProprietaryIn-house library

Chemical Libraries

MDDRCMD

2

WormBat


Virtuallibraries

Natural products

library

Publicly accessible

libraries (NCI)


Drug libraryWDI

5 x 104


Chemical Libraries

MDDRCMD

BAR CODE: 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1

2D Descriptors• Sub-structure fingerprints • Macro fingerprints • Pharmacophore Multiplets…

BAR CODE: 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1

N

NN

N

NN

ClNN

ClO


WormBat


Virtuallibraries

Natural products

library

Publicly accessible

libraries (NCI)


Drug libraryWDI

5 x 104


Chemical Libraries

MDDRCMD

BAR CODE: 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1

N O

O

ON

N O

N

Prosidian lead GPR119 ranked nr. 5 form the top

among 831.000 compounds

NN

ON

O

N O

O

Query ”Areana lead” GPR119

Ligand protein interaction forces

R4N+…....Cl- RNH3

+…....-O-C-R

O

RNH3+…....-O-C-R

O

CH3-CH=CH-CH2-CH2-R

R-CH2-CH2-CH2- CH- CH3

CH3

CH3-CH=CH-CH2-CH2-R


CH3

R - OH ….… O= R 2 NH ….... N<

Ionic bond (Salt bridge) Charge-charge interaction

RNH3+ O

R

Hδδδδ-

δδδδ+

Charge-dipole interaction

Hydrogen bond interaction

Hydrophobic interaction

O

O

δδδδ-

δδδδ-HN

HNδδδδ+

δδδδ+

Reinforced H-bondscharge-charge interaction

RNH3+

Cation-ππππ-aromaticinteraction

ππππ−−−−ππππ−−−−aromaticinteraction

Pharmacophore “a set of structural features in a

molecule that is recognized at a receptor site and is responsible for that molecule's biological activity"

Acceptor

Acidic

Aromatic

HydrophobicDonor

Excluded volume

WormBat


3D Descriptors• Conformations (multiple)• Pharmacophore features• Shape…

Virtuallibraries

Natural products

library

Publicly accessible

libraries (NCI)


Drug libraryWDI

5 x 104


Chemical Libraries

MDDRCMD

Drug libraryWDI

5 x 104


MDDRCMD

WormBat


3D Descriptors• Conformations (multiple)• Pharmacophore features• Shape…


1D Descriptors• Molecular weight (MW)• ClogP• Polar Surface Area (PSA) • Number of Rotatable Bonds…

Virtuallibraries

Natural products

library

Publicly accessible

libraries (NCI)


Chemical Libraries

DD&D: More than lock & key

Lock

Key

Protein

Ligand


+

in vivo efficacy

AbsorptionDistribution

MetabolismExcretion

“In Silico” prediction of ADME/T helps to avoid bad drug candidates

3

Compound Filtering

Compound is less likely absorbed when:

• > 5 H Bond Donors (expressed as sum of OH's NH's)• M.W. > 500• LogP > 5 (MlogP > 4.15)

• > 10 H Bond acceptors (expressed as sum of N's and O's)

Basic Filtering based on Lipinski’s rule of 5:

Ref.: C.A. Lipinski et al, Adv. Drug Del. Rev., 1997, 23, 3-25.

More elaborate filtering based on physiochemical

properties and advanced classification methods

Statistical methods (Multivariate Regression) Decision Trees

Hidden Markov Models (HMM) Neural Networks (NN)

etc ..

MDDR105CMD

5 x 103

“Random”

compounds

Drug-like

compounds

Non drug-like

Improving the odds in discriminating drug-like form non drug-like compounds

Neural Network

Drug-like

Drug libraryDrug libraryWDI

5 x 104







Integration of In-Silico Strategies

Receptor based drug design

• Pharmacophore perception • Structure Based Focusing• High Throughput Docking• De Novo Design• Fragment based approach• etc ..

…

Ligand-based drug design

• Pharmacophore perception • Shape & electrostatic properties• Lead/Scaffold hopping• Molecular connectivity's• QSAR• ADME/T

…


Lock

Key

Protein

Ligand


+

in vivo efficacy

Ligand Based Drug Design

N

N

N

O

O

F

2 * Aromat / hydrophob1 * Acceptor1 * Steric fearture1 * Basic siteBond path constrains

Small molecule ligand

3D pharmacophore

4

Receptor Based Drug Design

Positive site

Donor site

Acceptor siteHydrophobic site

Fraction of “Accepted” compounds

Receptor based drug design

Receptor binding site features







Hit / Leadgeneration

Target validation& target selection

Leadoptimization

A knowledge-based,efficient approach for

Hit and Lead Generation

Pharmacophore and SAR drivenLead Optimization

SD3 Site-Directed Drug Discovery®

CDHit / Leadgeneration

Generate and screen“target-tailored”

mini-libraries

Know-how on 7TM receptor - ligand

interactions


Leadoptimization CD

SD3 Site-Directed Drug Discovery®

Drug discovery in 7TM receptors

Pharmacophore(search tool) for in silico

screening of large (107)

chemical libraries

SD3 The Process

In-vitro screeningHits

Relatedtargets

AssociatedLigands

Pharmacophore(search tool) for in silico screening

In-houselibrary

107

compounds

7TM models

Tailoredlibraries

Lead

Hit / Leadgeneration


Leadoptimization CD

Generate and screen target-tailored mini-libraries

Know How on 7TM receptor-ligand interactions

I.

II.

III.

3IV.

V.3

VI.

VII.A

A

V L

L

H

G

L

A

S

L

LG

L

V

E

N

G

S

D

L

L

A

S

AS

L

P

F

F

T

Y

F

L A

V

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NM

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A

A A

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YH

V

F

S

L

L

E

A

G

L

P

FV

TS

L

A

F

F

N

S V

AN

P

V

Putative G protein-coupled receptor GPR44

TM-III TM-IV TM-V TM-VI TM-VIIHSFFMF NY AKFAF TWYHS LTF

HSFFMFNYAKFAFTWYHSLTF

Residues facing the ”ligand-binding pocket”

1 1 0 0 1 | 1 0 0 0 0 | 1

HYDROPHOBICCH3-CH=CH-CH2-CH2-R


CH3

CH3-CH=CH-CH2-CH2-R


CH3

AROMATICPOLAR

R-OH……..O=R2NH…..…N<

- CHARGE

R-COO- …… +N

+ CHARGER3NH+..…. -O

VI:16 -YY

(simplified example)

Binding pocket turned into

”pseudo-sequence”

… turned into physicochemical

”barcode”

Physicochemical analysis to identify “pocket-related” receptors

Highly knowledge-based process:• which residues ?• their relative importance ? • which physicochemical descriptors ?• antagonism / agonism

5

Identification of “pocket-related” receptors with drug-like ligands

Target receptorwith no or limited small

molecule ligand(s)

I.

II.

III.

3IV.

V.3

VI.

VII.A

A

V L

L

H

G

L

A

S

L

LG

L

V

E

N

G

S

D

L

L

A

S

AS

L

P

F

F

T

Y

F

L A

V

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NM

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A

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V

F

S

L

L

E

A

G

L

P

FV

TS

L

A

F

F

N

S V

AN

P

V


Physicochemical analysisof binding pockets

Allows for 7TM “target jumping”

“Pocket-relatedreceptor(s)”

with known drug-like ligand(s)

7TMs with small

molecule ligands

7TMs with no

useful ligands

Ligand

information.

Pocket

information

+ mutationalanalysis etc. ?

I.

II.

III.

3IV.

V.3

VI.

VII.A

A

V L

L

H

G

L

A

S

L

LG

L

V

E

N

G

S

D

L

L

A

S

AS

L

P

F

F

T

Y

F

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V

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NM

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VL

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DT

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L

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A

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A

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V

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L

L

A

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FV

TS

L

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N

S V

AN

P

V

Small“Target-tailored”

Mini libraries

Small“Target-tailored”

Mini libraries

Target receptor(with no or limited small

molecule ligand)

“Pocket-related”receptor(s)with drug-like

ligand(s)

Pharmacophore“search tool” for in silico screening of

large (107) chemical libraries

Target sitefeatures Ligand

features

Pharmacophore based on both target receptor pocket

and small molecule recognition (in related receptors)

- Developed against 20+ GPCR targets

(in-house)

Library

In-house compound library

• Highly enriched GPCR library (40.000)

Kinases

Metalloproteases

Methyltransferases

Ion channels

..

• Focused libraries (10.000)

- Continuesly improved and enriched

In-house compound library database

(in-house)

Library

Maintained and pre-computed in-house

• Commercial stock-available library (107)

• 50.000 compounds

Databases collected from 30+ companies- Libraries being updated two to three times a year- Compounds can be acquired on a weekly basis

In-silico screening, pharmacophore perception, docking, lead hopping

development of target focused libraries (charry picking) on

a regular bases

Chem.Series A

Chem.Series C

Chem.Series D

Chem.Series B

Chem.Series F

Chem.Series ESAR-rich

Hit series

Improve “drug-like” properties

Hits from107

cmps.

Hitexpansion

Refinequery

E.g. agonismvs.

antagonism

In house(in-house)

In house(in-house)

Library #1Library #1 Library #2Library #2 Library #3Library #3 Library #4Library #4 Library # NLibrary # N

Hits

Chemical tractability?

Strategy for generating iterative series of 7TM “target-tailored” mini-libraries to identify hits

In house(in-house)

In house(in-house)

Scaffoldjumping

+

Pharmacophoresimproved

throughout process

More/better“back-up ”series

High contentReceptor

assays

Self Organizing Map (SOM) - “cluster algorithm” based on neural networks and artificial

learning“. Dots (i.e. 7TM receptor binding pockets) close to each other are “similar”

CRTH2

AT1

AT2

I.

II.

III.

3IV.

V.3

VI.

VII.A

A

V L

L

H

G

L

A

S

L

LG

L

V

E

N

G

S

D

L

L

A

S

AS

L

P

F

F

T

Y

F

L A

V

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NM

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A

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F

A

L

C

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V

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S

L

L

E

A

G

L

P

FV

TS

L

A

F

F

N

S V

AN

P

V




CRTH2

I.

II.

III.

3IV.

V.3

VI.

VII.A

A

V L

L

H

G

L

A

S

L

LG

L

V

E

N

G

S

D

L

L

A

S

AS

L

P

F

F

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V

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V

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S

L

L

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A

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TS

L

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N

SV

AN

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V




AT1 / AT2

Target receptor(with no known small

molecule ligand)

“Pocket-relatedreceptor(s)”with drug-like

ligand(s)

7TMs with smallmolecule ligands

7TMs with nouseful ligands

Ligandinformation.

Pocketinformation

Random sequences (blue)7TM receptor binding pockets (yellow)

“Target Jumping” Exemplified withCRTH2 / AT1

6

AT1 receptorCRTH2 receptor

I.

II.

III.

3IV.

V.3

VI.

VII.M

I

P T

L

Y

S

I

I

F

V

VG

I

F

G

N

S

A

D

L

C

F

L

LT

L

P

L

W

A

V

Y

T A

M

C

KI

AS

A

S

V

S

F

NL

YA

S

V

F

LI

W

L

L

A

GL

AS

L

P

A

I

I

HR

NV

P

I

G

L

G

L

T

NI

L

G

F

L

F

PF

L

L F

F

F

F

S

W

I

P

HQ

I

F

T

F

L

V

A

M

P

IT

IC

I

A

Y

F

N

N C

LN

P

L

Positivecharged

Hydrophobic sub-pocket

Polarsub-pocket

Hydrophobic sub-pocket

I.

II.

III.

3IV.

V.3

VI.

VII.A

A

V L

L

H

G

L

A

S

L

LG

L

V

E

N

G

S

D

L

L

A

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AS

L

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TS

L

A

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F

N

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AN

P

V

Positivecharged

Hydrophobic sub-pocket Hydrophobic

sub-pocket

Polarsub-pocket

• Unrelated by traditional phylogenetics

• Related by binding pocket properties

CRTH2 and AT1 binding pocketrelationships

I.

II.

III.

3IV.

V.3

VI.

VII.A

A

V L

L

H

G

L

A

S

L

LG

L

V

E

N

G

S

D

L

L

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CRTH2 AT1/AT2

Target sitefeatures AT Ligand

features

Generate and screen target-tailoredmini-library

10 % hit rate < 10µµµµM

• In silico screening

1.2 million compds

• Compound retrieval~600 compounds~40 known AT ligands

Pharmacophore

1 10 100 1000 10000 100000 10000000

1

2

3

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1 10 100 1000 10000 100000 10000000

1

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15

%

100

90

80

70

60

50

40

30

20

10

0

0 0.001 0.01 0.1 1 10 100 %

Number ofscreened

compounds

Number of active

compounds

1 10 100 1000 10000 100000 10000000

1

2

3

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1 10 100 1000 10000 100000 10000000

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1 10 100 1000 10000 100000 10000000

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4 activesIn top 20

AT1 Ligand Query

AT1 / AT2 Ligand Query

ran

do

m

op

tim

um

Combined AT1 ligand and CRTH2 pocket

information

- provides an enriched Hit rate

Target pocket informationtells you which ligand features

are really important

x 100

Affinity onCRTH2receptor(target)

Selectivity vs. related targets

10 mM

10 nM

Hits identified by Pharmacophore

KnownAT ligands

O

Br

COOH

N

NH

N

N

N

OOH NH

NNN

O

candesartan

Affinity on related AT1 and AT2 receptors

pIC

50

(C

RT

H2

)

Receptor relationships translates into ligand relationships

LosartanAT1 ligand

CRTH2 Pharmacophore hit

Diverse structural classes

Similar Pharmacophore Properties

Conclusions & Outlooks

• “In Silico” DESIGN AND SCREENING are helpful tools for efficient drug design and development;

• VIRTUAL SCREENING can help to speed-up the DD&D process and save funds allocated for real HTS;

• Computational Aided Drug Discovery (CADD) can guide organic chemistry synthesis efforts (e.g. “In Silico” combinatorial libraries);

• VIRTUAL SCREENING helps to cherry-pick ligands and offers binding mode analysis against different targets;

Documents

Structure-based Drug Discovery - Welcome to CBSblicher/Courses/10_Structure-based_drug...Structure-based drug discovery Ph.D. Thomas M. Frimurer • “In Silico” screening and drug