2. Apr 05/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
3. Apr 05/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….
4. Apr 05/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
9. Apr 05/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…..
Analyze a pool of structures to find out how
attractive they are to us…..
12. Apr 05/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….
13. Apr 05/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
15. Apr 05/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
16. Apr 05/AMJ
CNS model
12 descriptors
3 components,
R2X=0.71
CNS ”World”
CNS drug space
Blue dots define::
PCA
17. Apr 05/AMJ
CNS ”world” sub classes
O
O
O
O
N
N
O O
O
N
O
N
O
N
N
N
N O
O
O
O
Br
H
H
Chiral
18. Apr 05/AMJ
Model used to predict CNS-likeness
N
N N
O O
O
I
I
I
O O
O
O
O
O
N
N
N
N
N
N
O
O
O
O
S
N
N
S
O
N
N
O
O
O
O O
O
O
O
N
N
O
O
N
N
N
O
O
N
N
O
O
O
O
H
H
H H
Chiral
O
N
N
F
19. Apr 05/AMJ
Structural clustering based on keys
0.349 1
1 38
3 6 13 19 26 31
clust_benzo (order)
N
N O
O
Cl
Cl
N
N O
O
Cl
N
N
Cl O
O
O
…01000100110001….
C=O C=C
C-N
Similarity by Tanimoto:
Tc= Bc/(B1 + B2 – Bc)
20. Apr 05/AMJ
Structural analysis
o Clustering
o Virtual screening – looking for structural similar
compounds in a large pool of structures…..
21. Apr 05/AMJ
I have talked about overall profiling of a large
number of compounds…… in terms of CNS-likeness
… now I will turn to talk about prediction
of more specific characteristics like biological activity
and ADME properties…..
Quantitative Structure Activity Relationship
or
Quantitative Structure Property Relationship
22. Apr 05/AMJ
In house QSAR study
-0,5
0
0,5
1
1,5
2
2,5
0 1000 2000 3000 4000
IC50
SigmaP/pi
sigmaP
pi
N
N
O
O
S
R
Correlation between Glyt-1 inhibitor activity and pi
(lipophilicity) and SigmaP (electronic characteristics)
for the R substituent
23. Apr 05/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…..
24. Apr 05/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
26. Apr 05/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
27. Apr 05/AMJ
Selective Serotonin Reuptake Inhibitors
(SSRIs)
N
N
CH3
CH3
Br
O
N
F
CH3
CH3
CN
O
F3C
NHCH3
NHCH3
Cl
Cl
N
H
NH
O
O
F NH
O
N
O
NH2
O
F3C
From
TCAs
to
SSRIs
and
Beyond
zimelidine
28.04.1971
citalopram
cipramil/celexa
14.1.1976
First synt. Aug 1972
fluoxetine
prozac/fontex
10.1.1974
First synt. May 1972
sertraline
zoloft
1.11.1979
indalpine
12.12.1975
paroxetine
paxil/seroxat
30.1.1973
fluvoxamine
fevarin
20.3.1975
29. Apr 05/AMJ
Pharmacophore modelling example
Fluoxetine
Citalopram
Paroxetine
Sertraline
Chapter 13. Pharmacophore Modeling by Automated Methods: Possibilities and Limitations M.Langgård, B.Bjørnholm, K.Gundertofte
In "Pharmacophore Perception, Development, and use in Drug Design". Edited by Osman F. Güne
International University Line (2000)
31. Apr 05/AMJ
Privileged structures
”A single ring system, the 5-phenyl-1,4-benzodiazepine
ring, provides ligands for a surprisingly diverse
collection of receptors…..”
Evans et al., J. Med.Chem 1988, 31, 2235-2246
32. Apr 05/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
characterised
ChemBioChem 2002, 3, 928-944
34. Apr 05/AMJ
Amino acid ”hot spots”
Didier Rognan at the 5ht international workshop in New Approaches
In drug design & discovery, Marburg 21-24 marts 2005
Priviledged
sub structure
for target
T1 and T2
Examine which
amino acids are
conserved in binding
pocket for T1 and T2
Amino acid ”HOT SPOTS”
Align
T1 and T2
Look for these
in other GPCR’s
Linking target and ligand side…..
35. Apr 05/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 Letters
2001 (11), 2007-2009
36. Apr 05/AMJ
Comparison between SERT and GLYT-1
SERT model From Na+/H+
antiporter, J. Pharmacol &
Exp Therapeutics, 307, 34-41
GLYT1 sequence; RED: conserved residues
GREY: conservative mutations
Y102
F288 Y310
37. Apr 05/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
38. Apr 05/AMJ
”Hit finding”
Drug discovery ~ Looking for
a needle in a haystack
Filtering of compounds ~
remove some of the hay
