apr 05/amj computational decision support for drug design profiling of small molecule compound...
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Apr 05/AMJ
Computational decision support for drug design
Profiling of small molecule compound libraries
Anne Marie Munk Jørgensen
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
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….
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
Apr 05/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 05/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 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…..
Apr 05/AMJ
Choosing the right descriptors is difficult
Wolfgang Sauer, SMI 2004
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….
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
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
Apr 05/AMJ
CNS model
12 descriptors 3 components,R2X=0.71
CNS ”World”
CNS drug spaceBlue dots define::
PCA
Apr 05/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 05/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 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
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 05/AMJ
Structural analysis
o Clustering
o Virtual screening – looking for structural similar compounds in a large pool of structures…..
Apr 05/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 05/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 pi(lipophilicity) and SigmaP (electronic characteristics) for the R substituent
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…..
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
Apr 05/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 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
Apr 05/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 05/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 05/AMJ
Privileged structures
……. are ligand substructures that can provide high-affinity ligands for more than one target…..
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
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 characterisedChemBioChem 2002, 3, 928-944
Apr 05/AMJ
GPCR Privileged structures type of receptor
J. Med. Chem., 47 (4), 888 -899, 2004
Apr 05/AMJ
Amino acid ”hot spots”
Didier Rognan at the 5ht international workshop in New ApproachesIn drug design & discovery, Marburg 21-24 marts 2005
Priviledgedsub structurefor target T1 and T2
Examine whichamino acids areconserved in binding pocket for T1 and T2
Amino acid ”HOT SPOTS”
AlignT1 and T2
Look for thesein other GPCR’s
Linking target and ligand side…..
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 Letters2001 (11), 2007-2009
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 residuesGREY: conservative mutations
Y102F288 Y310
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
Apr 05/AMJ
”Hit finding”
Drug discovery ~ Looking for a needle in a haystack
Filtering of compounds ~ remove some of the hay
Apr 05/AMJ
Serendipity
“To look for the needle in the haystack -
and coming out with the farmer’s daughter”
Arvid Carlsson