Computational decision support for drug design

Apr 04/AMJ

Computational decision support for drug design

Profiling of small molecule compound libraries

Anne Marie Munk Jørgensen

Apr 04/AMJ

Lundbeck

Lundbeck’s Vision is to become the world leader in psychiatry and neurology

Focus solely on treatment of diseases in the central nervous system (CNS)

•depression•Psychoses•Migraine•Alzheimer•Sleep disorders

5000 people worldwide – app 800 in R & D

Apr 04/AMJ

Outline

o What is a small molecule drug?

o How can computational methods help during the drug discovery phase?

• Library profiling: overall characterisation of a large pool of structures.

• Prediction of more specific characteristics like biological activity and ADME properties

• Privileged structures….

Apr 04/AMJ

A small molecule drug

… is a compound (ligand) which binds to a protein, often a receptor and in this way either initiates a process (agonists) or inhibits the natural signal transmitters in binding (antagonists)

The structure/conformation of the ligand is complementary to the space defined by the proteins active site

The binding is caused by favourable interactions between the ligand and the side chains of the amino acids in the active site. (Electrostatic interactions, hydrogen bonds, hydrophobic contacts…)

The ligand binds in a low energy conformation < 3 kcal/mol

Apr 04/AMJ

Binding site complementarity

H-bond donatingH-bond acceptingHydrophobicFlo98, Colin McMartin.J.Comp-Aided Mol. Design,V.11, pp 333-44 (1997)

HIV-Portease inhibitorJACS,V.16,pp847 (1994)

Apr 04/AMJ

Example of ligand binding

1UVT, TrombinInhibitor

Apr 04/AMJ

No vacancy!

Apr 04/AMJ

Molecular factors

Conformation

Electronic distribution

Ionization

Intramolecular interactions

Intermolecular forces

Solubility,Partitioning

Carrupt P-A., Testa B., Gailard P.Boyd D.B., Lipkowitz K.B., Reviews in Computational Chemistry, Vol. 11, 1997, pp. 241-304.

Apr 04/AMJ

Compound library profiling

• 10 years ago: Diversity + HTS• Now: very high focus on how

biologically relevant the screening collection is.

• Computational methods to predict drug likeness, CNS likeness….

High throughput is not enough … to get high output…..

Apr 04/AMJ

Compound analysis

Chemical intuitionIdeal

50.000 Structures:

Apr 04/AMJ

Choosing the right descriptors is difficult

Wolfgang Sauer, SMI 2004

Apr 04/AMJ

How we describe the structures in the computer

o Calculate a number of phys chem descriptors, like molecular weight, nhba, nhbd, logP, SASA…..

o Describe the structures by keys….

Apr 04/AMJ

Lipinski statistics

References

(1) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev JID - 8710523 1997, 23, 3-25.

Drug Like 1 CNS Like, present work, 90% limit.

MW < 500 149.4 – 446.6

# hydrogen acceptors < 10 1 - 5

# hydrogen donors < 5 0 - 3

logP < 5 -0.3 – 4.9

# rotatable bonds NR 0 – 8.4

Rule of 5

Apr 04/AMJ

Diversity and "Chemical Space"

PCA

Apr 04/AMJ

Chemical space navigator

Global Positioning System (GPS)

Chem GPS (Oprea & Gottfries, J. Comb. Chem

2001) We want to define the CNS ”world” – the space which is biologically relevant when considering CNS drugs

Apr 04/AMJ

CNS model

12 descriptors 3 components,R2X=0.71

CNS ”World”

CNS drug spaceBlue dots define::

Apr 04/AMJ

CNS ”world” sub classes

O

O

O

O

NN

O O

O

NO

N

O

N

NN

N O

O

O

O

BrH

H

Chiral

Apr 04/AMJ

Model used to predict CNS-likeness

N

N N

O O

O

I

I

I

O O

O

O

O

O

N

NN

NN

N

O

O

O

OS

N

N

S

O

N

N

O

O

O

O O

O

OO

N

N

O

O

N

N

N

O

O

N

N

O

O

O

O

H

H

H H

Chiral

O

N

N

F

Apr 04/AMJ

Structural clustering based on keys

0.349 1

1 38 3 6 13 19 26 31

clust_benzo (order)

N

N O

O

ClCl

N

N O

OCl

N

N

Cl O

O

O

…01000100110001….

C=O C=C

C-N

Similarity by Tanimoto:

Tc= Bc/(B1 + B2 – Bc)

Apr 04/AMJ

Structural analysis

o Clustering

o Virtual screening – looking for structural similar compounds in a large pool of structures…..

o Analysis of known drugs/ cns drugs some rings or scaffolds are very popular:

N

S

N

N

Apr 04/AMJ

I have talked about overall profiling of a largenumber of compounds…… in terms of CNS-likeness

… now I will turn to talk about predictionof more specific characteristics like biological activity and ADME properties…..

Quantitative Structure Activity Relationship

or

Quantitative Structure Property Relationship

Apr 04/AMJ

In house QSAR study

-0,5

0

0,5

1

1,5

2

2,5

0 1000 2000 3000 4000

IC50

Sig

ma

P/p

i

sigmaP

pi

N

N

O

O

S

R

Correlation between Glyt-1 inhibitor activity and sigmaP(electronic characteristics) for the R substituent

Apr 04/AMJ

ADME property predictions

Oral absorption …depends heavily on permeability and Solubility… high interest in predicting these things in silico…

Other things: Blood-brain Barrier penetration,clearance, Metabolism, tox…..

Apr 04/AMJ

Aqueous Solubility

QSRP model

n=775,R2=0.84, Q2=0.83

8 2D descriptors, Cerius2

Most important descriptors: logP, hba*hbd, hba, hbd

Drugs: –6 < logS < 0;

If error of 1 log unit is OK model predicts 60-80% of the compounds correctly

Journal of Medicinal Chemistry, 2003, Vol. 46, No. 17

Apr 04/AMJ

Permeability

QSRP

N= 13

R2=0.93 Q2= 0.83

Key descriptors:

PSA> Odbl >N-H > ..NPSA >SA

Polar descriptors important and …. size matters….

Simple Rule: PSA < 120 Å2

Journal of Medicinal Chemistry, 2003, Vol. 46, No. 4

Apr 04/AMJ

Pharmacophore modelling

….. Another method of biological activity prediction… Observations that modification of some parts of a ligand results in minor changes of activity, whereas modifications of other parts of the ligand result in large change of activity.

Pharmacophore element: Atom or functional group essential for biological activity

3D Pharmacophore mode: Collection of pharmacophore elements including their relative position in space

Apr 04/AMJ

Selective Serotonin Reuptake Inhibitors (SSRIs)

NN

CH3

CH3

Br

ON

F

CH3

CH3

CN

O

F3C

NHCH3

NHCH3

Cl

Cl

NH

NH

OO

F NH

O

NO

NH2

O

F3C

Fro

m T

CA

s to

SS

RIs

an

d B

eyo

nd

zimelidine28.04.1971

citalopramcipramil/celexa

14.1.1976First synt. Aug 1972

fluoxetineprozac/fontex

10.1.1974First synt. May 1972

sertralinezoloft

1.11.1979

indalpine12.12.1975

paroxetinepaxil/seroxat

30.1.1973

fluvoxaminefevarin

20.3.1975

Apr 04/AMJ

The mechanism of SSRI’s

Apr 04/AMJ

Pharmacophore modelling example

FluoxetineFluoxetine

CitalopramCitalopram

ParoxetineParoxetine

SertralineSertraline Chapter 13. Pharmacophore Modeling by Automated Methods: Possibilities and Limitations M.Langgård, B.Bjørnholm, K.GundertofteIn "Pharmacophore Perception, Development, and use in Drug Design". Edited by Osman F. GüneInternational University Line (2000)

Apr 04/AMJ

Privileged structures

……. are ligand substructures that are widely used to generate high-affinity ligands for more than one target

Apr 04/AMJ

G-protein coupled receptors

•7 TM

•Example:dopamine, serotonine, muscarinic, histamine, neurokinin

•Family A, B, C, A = Rhodopsin like

•In general low sequence homology even within each family, but highly conserved residues in the TM regions

•Small molecule ligands bind wholly or partly within the transmembrane region mainly in the region flanked by helix 3,5,6 and 7

•From site-directed mutagenesis studies, side chains involved in binding has been characterisedChemBioChem 2002, 3, 928-944

Apr 04/AMJ

GPCR Privileged structures type of receptor

J. Med. Chem., 47 (4), 888 -899, 2004

Apr 04/AMJ

Fluoxetine scaffold common for SERT and GLYT-1

CF3

O N COOH

O N

F

COOH

Atkinson et al, Mol. Pharm. 2001 (60),1414-1420

Gibson et al, Biorg. Med. Chem Letters2001 (11), 2007-2009

Apr 04/AMJ

Comparison between SERT and GLYT-1

SERT model From Na+/H+ antiporter, J. Pharmacol & Exp Therapeutics, 307, 34-41

GLYT1 sequence; RED: conserved residuesGREY: conservative mutations

Y102F288 Y310

Apr 04/AMJ

Resume

Computational methods for

o Compound library profiling, Chem GPS

o activity QSAR prediction and pharmacophore modelling

o Solubility and permeability QSPR prediction

o Privileged structures of GPCR’s

Apr 04/AMJ

”Hit finding”

Drug discovery ~ Looking for a needle in a haystack

Filtering of compounds ~ remove some of the hay

hit-finding

or

shit-finding

Apr 04/AMJ

Serendipity

“To look for the needle in the haystack -

and coming out with the farmer’s daughter”

Arvid Carlsson