# 1 the application of computational drug design to real life problems jan kelder molecular design...

# 1

The application of computational drug design to real life problems

Jan Kelder Molecular Design & Informatics

N.V. Organon

Bioinformatics IV CMBI Nijmegen:

Computational Drug Discovery

1 Juni 2006

# 2

Drug targets

Nuclear hormone receptors G-protein coupled receptors (GPCRs) Ion channel receptors Serine proteases Kinases and Phosphatases Phosphodiesterases and many more

# 3

Drug target families

2%

2%

46%

4%

4%

15%

5%

22% KinasesGPCRsIon channelsSer proteasesPhosphatasesCys proteasesNuclear receptorsOthers

A. L. Hopkins, Nature Rev. Drug Disc. 1, 727 - 730 (2002)

# 4

Drugs on the market by target families

1%

4%

6%

1%

47%

30%

7%

4%TransportersGPCRsIon channelsEnzymesDNAIntegrinsNuclear receptorsOthers

A. L. Hopkins, Nature Rev. Drug Disc. 1, 727 - 730 (2002)

# 5

Drug discovery

Molecular Modification Screening (MTS and HTS) Virtual Screening - 3D databases Structure-Based Drug Design

# 6

Molecular modification towards combined 5-HT2 and H1 antagonism

CH3

N

N

N

CH3

phenbenzamine (Antergan ®)

mianserin (Tolvon ®)

cyproheptadine (Periactin ®)

N

NCH3

CH3

H1 antagonist

H1 + 5-HT2 antagonistantidepressant

no antidepressant activity


H1 + 5-HT2 antagonist

# 7

Molecular modification

mianserin (Tolvon ®) cyproheptadine (Periactin ®)

# 8


# 9

Molecular modification towards combined 5-HT2 and H1 antagonism

CH3

NN

N

N

CH3tripelennamine (Azaron ®)

mirtazapine (Remeron ®)

cyproheptadine (Periactin ®)

N

NCH3

N

CH3

H1 antagonist

H1 + 5-HT2 antagonistantidepressant



H1 + 5-HT2 antagonist

# 10


CH3

NN

Nmianserin mirtazapineCH3

N

N

Noradrenalin (NA) Noradrenalin (NA)NA uptake blocker --------alpha-2 antagonist alpha-2 antagonistalpha-1 antagonist ---------

Serotonin (5-HT) Serotonin (5-HT)5-HT2A-2C antagonist 5-HT2A-2C antagonist

Histamine HistamineH1 antagonist H1 antagonist

# 11


# 12


mianserin mirtazapine

# 13

5-HT GPCR subtypes

5-HT7 HUMAN

5-HT1A HUMAN

5-HT1B HUMAN

5-HT1D HUMAN

5-HT1E HUMAN

5-HT1F HUMAN

5-HT5A HUMAN

5-HT5B MOUSE

5-HT4 HUMAN

5-HT6 HUMAN 5-HT2B HUMAN

5-HT2A HUMAN

5-HT2C HUMAN

# 14

Molecular modification towards selective 5-HT2C antagonism

CH3

O

N

N

CH3

H

(S)-(+)-mianserin (R)-(+)-Org 3363 Org 37415

Org GC 94 SDZ SER-082 (+)

CH3

N

N

H

CH3

N

N H

CH3

N

N H

H

H

CH3

N

HH

N

# 15

(R)-(+) Org 3363 and (+) - SDZ SER-082 :Two selective 5-HT2C antagonists

(R)-(+) Org 3363 (= Org 36743) SDZ SER-082 (+)

# 16

Fit of (R)-(+) Org 3363 and (+) enantiomer of SDZ SER-82

# 17

Drug discovery


# 18

HTS Compound library

HTS

HitOptimization

LeadOptimization

ConfirmedHit• validated activity / structure purified sample

In vitro optimizationon potency & selectivity

Lead•fulfill potency / selectivity criteriaand show activity in in vitro, ex vivo, or in vivo proof of principle model

Development compound

ADMET

High Throughput Screening

# 19

High throughput screeningHigh throughput screening200,000200,000

Confirmed activesConfirmed actives100-500100-500

Retesting solid (+ LC-MS)Retesting solid (+ LC-MS)50-20050-200

Retesting Retesting

Purification/ResynthesisPurification/Resynthesis10-5010-50

Lead compoundsLead compounds0-200-20

High Throughput Screening

# 20

320 compounds/plateup to 150 plates/day

384-wells plate384-wells plate

Orally active LH agonist: Robot screening for LH receptor agonists

# 21

HTS on human luteinizing hormone receptor agonists

N

NS S

NH2

O

O

Confirmed hit: EC50 = 1.4 M

N

NS S

NH2

N

O

O

H

Lead compound Org 41841: EC50 = 0.03 M (= 30 nM)

N

NS S

NH2

N

O

N

H

H

O

N

O

Optimized compound Org 42599: EC50 = 3.1 nM

Not orally active Orally active

Orally active

# 22

LMW LH agonists: Org 42599 selected for development

# 23

Drug discovery


# 24

Decision Tree program

Oss

Rotterdam

Amsterdam

Groningen

Arnhem

Utrecht

Oss

Rotterdam

Amsterdam

Utrecht

Groningen

Arnhem

above

belowsea-level

# 25


Oss

Rotterdam

Amsterdam

Groningen

Arnhem

Utrecht

west ofUtrecht?

south ofGroningen?

south ofAmsterdam?

west ofArnhem?

yes

no

no

no

no

yes

yes

yes

above

belowsea-level

# 26

N

N XR4

R7

R6

R3

R2

R1

Y

• Synthesize first set of compounds based on LH agonist Lead Org 41841 (at least 50 compounds)

• Test LH receptor activity• Calculate Molecular Descriptors:

(molecular weight, lipophilicity, polar surface)• Build Decision Tree which separates active

from inactive compounds


CalculatedCalculatedmolecularmolecular

descriptorsdescriptors

activeactive

inactiveinactive

# 27

Decision Tree pEC50 > 7.5(n = 201)

23 actives (> 7.5) 23/23 correctly classified (100 %)

178 inactives (< 7.5) 173/178 correctly classified ( 97 %)

N

NX

R4

R7

R6

R3

R2R1

Y

P S R 6 >18.7

Inactive (14)

Inactive (3 )

Inactive (7 )

Inactive (12)

Inactive (137)

A ctive (26 )

A ctive (2)

P S R 6 >17.3

P S R 4 >2 .8

M W R 7 >30 .0

M W R 6 >87 .1

P S R 2 >1 .3

# 28

N

N XR4

R7

R6

R3

R2

R1

Y

• Select substitution site on molecular scaffold (R2 this time)• Design virtual library of compounds• Calculate Molecular Descriptors of all virtual compounds• Apply Decision Tree and predict active and inactive

compounds• Select, synthesize and test active compounds


# 29

Virtual library design

N

N

NH

S S

NH2

N

O

O

Br

Selection of amines based on availability (ACD database) and predicted potency

(pEC50 LH-CHO > 8.0; decision tree model derived from 250 analogues)

N

N

NH

S S

NH2

N

O

O

NR1

R2

HN

R1

R2 +

# 30

Virtual library design

ACDSelect

Reagents

N

N X

R1

R2

R3

R4

R6

R7

Y

Generate

Library

Predict

Actives

65predictedactives

65predictedactives

1934library

compounds

1934library

compounds

1934amines

1934amines

# 31

Virtual library for substitution at R2 derived from 1934 amines:

65 actives

1869 inactives

N

N XR4

R7

R6

R3

R2

R1

Y

# 32

Predicted LMW LH agonists

C lip A rt0

1

2

3

4

5

6

7

8

9

pEC50 CHO-LH

CMP01

CMP02

CMP03

CMP04

CMP05

CMP06

CMP07

CMP08

CMP09

CMP10

CMP11

CMP12

CMP13

CMP14

CMP1516/26 correctly predicted > 8.0 (62 %)23/26 correctly predicted > 7.5 (88 %)

# 33

3D-database pharmacophore searches 5-HT2C and 5-HT2A antagonists

X-ray structure of mesulergine

# 34

3D query derived from mesulergine

6-Membered aromatic ringat a distance of 5.18 Angstromof a basic N atom (type 14D)with a tolerance of 1.0

Two aliphatic carbon atomsconnected to the basic N atom

Exclusion sphere placed in the direction where the basic N atomcan be protonated at a distanceof 7.0 Angstrom with a radius of5.3 Angstrom

A second exclusion sphere is placedat a distance of 7.0 Angstrom of thebasic N atom in the direction of theN-CH3 bond with a radius of 4.5Angstrom

# 35

3D-database pharmacophore searches 5-HT2C and 5-HT2A antagonists

Chembase:

79716 3D structures

9229 hits (11.6 % of 79716)

1500 hits available for testing

979 hits used for testing after elimination of 521 compounds tested before on 5-HT2C receptor binding

113 5-HT2C ligands found (11.5 % of 979)

211 5-HT2A ligands found (21.5 % of 979)

# 36

Comparison between MTS screen and 3D-database pharmacophore search

Chembase:

Mesulergine (5-HT2C) Ketanserin (5-HT2A) # hits > 95 % competition # hits > 95 % comp.

MTS screening 49 (4.9 %) 83 (8.3 %) (1000 compounds)

3D pharmacophore 113 (11.5 %) 211 (21.5 %)screening (979 compounds)

3D pharmacophore 283 (18.9 %) 470 (31.3 %)screening(1500 compounds) 3.9 x 3.8 x

# 37

Results 3D-database pharmacophore searches 5-HT2C antagonists

NCH3

HH

H

Org 9283

Two compounds were selected that showedalready interesting 5-HT2C antagonisticpotency and selectivity (Org 9283 and Org 20659)

Org 9283 has been chosen as the lead compoundfor developing selective 5-HT2C antagonists aspotential antidepressants/anxiolytics

WO 98377EP 98-201462

5

5.5

6

6.5

7

7.5

8

8.5

9

5-HT2C 5-HT2A 5-HT2B 5-HT1A

SDZ SER-082

mianserin

Org 3363

Org 37415

Org 9283

mesulergine

# 38

Drug discovery


# 39

Protein Data Bank

20946 structures

17752 X-ray structures 3194 NMR structures

# 40

Drug targets


# 41

NR4A2-NOT

NR4A3-NOR1

NR4A1-NGFI

NR5A1-SF1

NR5A2-FTF

NR6A1-GCNF

NR2F1-COTF

NR2F2-ARP1

NR2F6-EAR2

NR2E3-PNR

NR2B1-RRXA NR2B2-RRXB

NR2A2-HN4G

NR2E1-TLX

NR2C1-TR2-11

NR2C2-TR4

NR2B3-RRXG

NR2A1-HNF4

NR0B1-DAX1NR0B2-SHP

NR1C1-PPAR

NR1C2-PPAS

NR1C3-PPAT

NR1D1-EAR1

NR1D2-BD73

NR1I3-CAR

NR1H2-NER

NR1H3-LXR

NR1H4-FAR

NR1I1-VDR

NR1B3-RRG1

NR1F3-RORG

NR1F2-RORB

NR1F1-ROR1NR1A2-THB1

NR1A1-THA1NR1I2-PXR

NR1B2-RRB2NR1B1-RRA1

NR3C1-GCR

NR3C4-ANDR

NR3C3-PRGRNR3A1-ESTR

NR3A2-ERBT

NR3B1-ERR1

NR3B2-ERR2

NR3C2-MCR

NR3B2-ERR3

Hormone receptors

Dimerisation

Lipid metabolism

Drug metabolism

Cholesterol metabolism

Cell growth

Development

48 nuclearreceptors

# 42

NR4A2-NOT

NR5A2-FTF

NR2B1-RRXA NR2B2-RRXBNR2A1-HNF4

NR1C1-PPAR

NR1C2-PPAS

NR1C3-PPAT

NR1H2-NER

NR1H3-LXR

NR1H4-FAR

NR1I1-VDR

NR1B3-RRG1NR1F2-RORB

NR1F1-ROR1NR1A2-THB1

NR1I2-PXR

NR1B1-RRA1

NR3C1-GCR

NR3C4-ANDR

NR3C3-PRGRNR3A1-ESTR

NR3A2-ERBT

NR3B2-ERR3

25 X-rays LBD

NR3C2-MCR

# 43

DCA/B FE

FE

FE

DAX1

Heterodimers:

CAR, RXR, RAR, TR,

PPAR, HNF4, ER

Heterodimers:

SF1

Drug targets: Nuclear hormone receptors (typical and atypical)

LBDDBD

# 44

ER nuclear receptor domains

AB

D

F

E

C

LBD

DBD

DCA/B FE

# 45

Ligand binding domains (LBD) nuclear hormone receptors

Progesterone receptor (PR) 1A28, 1E3K Androgen receptor (AR) 1I37, 1I38, 1E3G Estrogen receptor (ER) 1A52, 1ERE, 1ERR, 1QKM 1QKN, 1QKT, 1QKU, 3ERD 3ERT, 1G50, 1HJ1 Glucocorticoid receptor (GR) 1M2Z, 1NHZ, 1P93 Mineralocorticoid receptor (MR) 1Y9R, 1YA3 Vitamin D3 receptor (VDR) 1DB1, 1IE8, 1IE9 Retinoic acid receptor (RAR) 1EXA, 1EXX, 1FCX, 1FCY 1FCZ, 2LBD, 3LBD, 4LBD Retinoid X receptor (RXR) 1LBD, 1FBY, 1G1U, 1G5Y 1DKF, 1FM6, 1FM9 Peroxisome proliferator-activated rec. 1K74, 1K7L, 1KKQ, 1PRG (PPAR) 2PRG, 3PRG, 4PRG,

1GWX 2GWX, 3GWX

# 46

Steroid hormone receptors

O

H

H

H

O

ProgesteroneTestosteroneDihydrotestosteroneEstradiolAldosteroneCorticosteroneCalcitrioletc

H-bond donor HD1 ----

---- H-bond donor HD2

Progesterone

# 47

LBD nuclear progesterone receptor in complex with progesterone

PDB code 1A28

P.B. Sigler and S.P. Williams , Nature 393, 392 - 396 (1998)

Q

R

T

# 48

Synthetic Steroidal Progestogens

O

O

progesterone (1933)

ethisterone (1938)O

OHCH

northisterone (1956)O

OHCH

O

OHCH

norethynodrel (1957)

OHCH

lynestrenol (1962)

O

CHOH

norgestrel (1966)

CHCH2

OH

desogestrel (1981)

O

CHOH

gestodene (1987)

norgestimate (1986)

CH

N

OAc

OH

# 49

Synthetic Steroidal Progestogens

drospirenone (2000)

O

O

Oetonogestrel (1999)

O

CHCH2

OH

# 50

LBD nuclear progesterone receptor in complex with etonogestrel (model)

PDB code 1A28

Q

R

T

# 51

LBD nuclear androgen receptor in complex with dihydrotestosterone

PDB code 1I37

J.S. Sack et al. , Proc. Nat. Acad. Sci. USA 98, 4904 - 4909 (2001)

Q

R

T

# 52

Non-steroidal androgens

NH

O2N

OH

H

H

Kaken (WO 0127086)

NH

O

CF3

O

N

CF3

Ligand (WO 00116139)

NH

O

CF3

NC

SOH

NH

CH2R OR = Cl, H

Univ. of Tennessee

# 53

LBD nuclear androgen receptor in complex with Kaken compound (MD simulation)

# 54

Kaken compound (MD minimum) + dihydrotestosterone (DHT)

# 55

Homology modelling

In case no experimental 3D structure of the LBD of a nuclear receptor is available homology modelling can be tried

Template selection Sequence alignment between target and template Model building Optimization of the model Validation Ligand docking

# 56

Homology model LBD nuclear vitamin D3 receptor vs. experimental structure Homology model (green)

LBD VDR based on LBD PPAR

X-ray structure VDR (blue) Alignment:

D. R. Boer et al , Thesis University of Utrecht (2001)

33 % similarity for residues 131 - 42753 % similarity for residues 226 - 427

# 57

LBDs nuclear vitamin D3 receptor and PR in complex with calcitriol and progesterone

PDB code 1DB1PDB code 1A28

# 58

Drug targets


# 59

Bovine rhodopsin X-ray model

K. Palczewski et al., Science 289, 739 - 745 (2000)

PDB code 1F88

# 60

X-ray models GPCR and G-protein

ß

# 61

Ligand binding domains (LBD) G-protein coupled receptors(GPCRs)

Follicle Stimulating Hormone (FSH) receptor Luteinizing Hormone (LH) receptor Thyroid Stimulating Hormone (TSH) receptor Serotonin (5-HT) receptors and many more

# 62

TRANSMEMBRANE REGION hLH receptor + Org 41841 (MD)

# 63

Lead optimization LH agonistLH receptor homology model

# 64

TRANSMEMBRANE REGION hLH receptor + Org 42599 (MD)

# 65

LO LMW LH agonists

# 67

Luteinizing hormone (LH) + LMW LH agonist Org 41841

# 68

LH receptor activation

LHRTM domain

EC domain LH/hCG

LMW LH agonist

# 69

LFR/FLR chimeric receptors

transient transfection LFR/FLR.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

LHR-LH

LHR-FSH

FSHR-LH

FSHR-FSH

LFR-LH

LFR-FSH

FLR-LH

FLR-FSH

fold

incr

ease

0

0.1

1

10

100

Chimeric receptors respond as expected

mU/ml

# 70

LTR/TLR chimeric receptors

Org 42599 binds in TM domain of LH receptor

transient transfection LTR/TLR - ORG42599H.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

LHR TSHR LTR TLR

fold

incr

ease

0

-11

-9

-7

-6

M

# 71

Ligand binding domains (LBD) G-protein coupled receptors(GPCRs

Follicle Stimulating Hormone receptor Luteinizing Hormone receptor Thyroid Stimulating Hormone receptor Serotonin (5-HT) receptors and many more

# 72

Serotonin GPCRs

OH

NH

NH3+

AcetylcholineNoradrenalinAdrenalinDopaminSerotoninHistaminOpioidetc

Acidic residue Asp ---- ---- H-bond acceptor Ser/Thr

Serotonin

# 73

5-HT2C GPCR transmembrane model based on bacteriorhodopsin

# 74

Mutation studies 5-HT2C receptor

serotonin 8.0 6.5 5.3

Org 35018 7.6 6.4 5.5

OH

NH

NH2

NH

NH2

OH

S

NH2

Wildtype S219A F327A

5-HT2C mutant mutant

receptor receptor receptor

pKi pKi pKi

tryptamine 6.9 6.6 5.2

# 75

5-HT2C GPCR model based on bovine rhodopsin

D

S

# 76

5-HT GPCRs: Structure based query

TM3: CxxxxxxDxxxxxxxxxxxxxxxxDRY

TM5: xxxxxxxxSxxxFxxPxx TM5: xxxxxxxxTxxxFxxPxx

# 77

5-HT GPCRs

1869 GPCRs

879 GPCRs (unique)

71 GPCRs

12 5-HT GPCRs

59 GPCRs 3 new

Structure based query

# 78

Conclusions

The PDB forms a rich source of experimental structures that expands rapidly

Homology modeling is useful in cases where experimental structures are not yet available and good templates exist

Knowledge of how ligands bind to proteins can be utilised to suggest annotations of unknown protein sequences

# 1 the application of computational drug design to real life problems jan kelder molecular design...

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