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Physicochemical Properties in Drug Design: … · molecular properties in drug design. Navigation...
Transcript of Physicochemical Properties in Drug Design: … · molecular properties in drug design. Navigation...
Messina 22/3/12
Anna Tsantili-Kakoulidou
Department of Pharmaceutical Chemistry,
School of Pharmacy, University of Athens
The impact of physicochemical and
molecular properties in drug design.
Navigation in the 'druglike' chemical
space
Attrition reasons in drug development
miscellaneous
Side effects
Commercial
Efficacy
Pharmacokinetics
Toxicity
5% 10%
5%
30% 40
%
10%
ADME
Kola et al., Nat. Rev. Drug Discov., 2004, 3, 711-715.
Attrition reasons in drug development
Increase in cost for 1 success
Discovery Discovery
Preclinical
Phase I
Phase II
Phase II
PhaseIII/File
PhaseIII/File
Launch
Launch
1.1 Β$
1.7 Β$
1995-2000 2000-2002
Phase I
Preclinical
2
1.5
1.0
0.5
0
From Molecule to Medicine
The Medicinal Chemist creates the pillars of the
whole process which leads from molecule to
medicine
She / He has the responsibility to provide the right
molecules, which will have more chances to become
a medicine
From Molecule to Medicine
Focus on the parts ?
Focus on the whole ?
Towards a systems approach to
Drug Development
Multi-objective drug design
Pareto optimization evolutionary genetic algorithms
Pareto optimization-multiple best
solutions
Target(s) assessment Lead identification Lead Optimization
PhysChem Profiling and in silico/in vitro ADE /Met/Tox
Integrated Drug Discovery Research Process
Medicinal Chemistry activities
Computer-Aided Drug Design-Molecular Modeling
…..which properties are we looking for?
Physicochemical Properties
Molecular Properties
Lipophilicity
Ionization
Solubility
HA
HD
Rotatable bonds
Partial charges, Energies,
Chemical atoms
Bond types
Bond distancies
Connectivity,
electrotopological…..
Shape, volume, MW, dipole moments, polarizability….
PSA
vast number of descriptors for chemical
structures
Descriptors everywhere…Too
much information…..?
Noise/Information ?
Relevant information/irrelevant information?
Multivariate Data Analysis PCA/PLS – can treat multiple end points
Artificial Neural Networks ANN
Multiple Linear regression + genetic algorithms for variable
selection
Knowledge lost in information ?
Druglikeness lost in vast descriptors pool ?
The interplay between lipophilicity, ionization and solubility
Lipophilicity Ionization
Solubility
Binding
Oral Absorption/Formulation
Absorption/Permeability
QSAR QSPR
Physicochemical properties influencing
oral drug absorption
The impact of solubility- Definitions
Higly soluble
Very soluble
soluble
Adequately soluble
Low soluble
Very low soluble
Practically insoluble
parts per solvent (water)
1
1-10
10-30
30-100
100-1000
1000-10.000
10.000
Solubility range
mg/ml
>1000
100-1000
33-100
10-33
1-10
0.1-1
0.1
Solubility should be considered in pH range 1.5-8
The impact of solubility
Bio-Classification System, BCS
Highly desired class
Bioequivalence is
waived
The impact of solubility
The impact of solubility
Shugarts and Benet, Pharm Res. 2009. 26(9): 2039–2054
The impact of lipophilicity
The primary importance of lipophilicity was established by
the pioneer work of Hansch since early ’60s
Octanol water became since then the reference system
logP influences both pharmacokinetics and
pharmacodynamics with implications in toxicity as well.
Lipophilicity and Metabolism
Lipophilicity and CNS side effects
Lipophilicity and Toxicity
The impact of lipophilicity
Lipophilicity and permeability
Lipophilicity and receptor binding
Lipophilicity and affinity to transporters
Lipophilicity in Drug Design, general guidelines
Minimum Hydrophobicity concept
(Hansch et al, 1987)
Rule of 5 (Lipinski et al, 1997)
Minimum Hydrophobicity concept
• Drug Design should be oriented to molecules with no more
lipophilicity than that required for their biological action
(permeability and affinity).
• This principle is applicable either for compounds designed to
act in the periphery, as well as for molecules designed to
stimulate receptors in the CNS. For the latter case there is
evidence that the optimum lipophilicity for BBB penetration
(logP~2) is related to undesirable sedative activity.
Hydrophobic binding by displacing water
molecules from the binding pocket is non-specific
interaction increasing the chance to off target
binding and non-specific toxicity
Lipinski’s rule of 5
Pairwise unfavorable molecular/physicochemical
characteristics
MW > 500
clogP > 5
HD > 5
HA > 10 Lipinski et al Adv.Drug Deliv.Rev. 23, 3-25, 1997
size
lipophilicity
hydrogen bonding
capability
For oral administration !
Druglike characteristics-Property based drug design
clogD7.4: -1 to 3
PSA 120 Ǻ
RB 10
size
lipophilicity
MW 500
clogP 5
HD 5
HA 10
hydrogen bonding
capability
polarity
flexibility
Ratio of logD/logP ??
CMR 4 -13 Å3
RNG 2-4
The Golden Triangle
Johnson et al, Biorg. Med. Chem Lett. 2009, 19, 5560-64
Lipophilicity measured by HPLC
Leadlike Characteristics
For fragment based lead discovery:
MW 300
clogP 3
HD 3
HA 6 Drug Discovery Today 2003,
8, 876-877
Ligand efficiency
LE= ΔG
ΝΗ
LE= = RT*pIC50
ΝΗ
1.4*pIC50
ΝΗ
ΝΗ Number of Heavy Atoms
Applicable to fragment based design
Hopkins et al Drug Discov. Today, 2004, 9, 430-1
Ligand Lipophilicity Efficiency
LLE=pIC50-logP
LLE 6 IC50 10nM with logP 2
10 nM with logP 3
LLE =5 In agreement with minimum
hydrophobicity concept
Leeson et al, Nat.RevDrug. Discov., 2007, 6, 881-890
Ligand Lipophilicity Efficiency- Implications in thermodynamics
Drug –receptor interaction should be optimized in regard rather to the enthalpic component through specific interactions
Increase in entropy through hydrophobic binding increases the
risk of undesired effects ( next to poor ADME properties)
-ΔG= -ΔH+TΔS
Other metrics
Percent Efficiency Index, PEI:
Percent Inh (as a fraction between 0 and 1 divided by MW (kDa)
PEI=f(Inh)/Mw
Binding Efficiency Index, BEI= pIC50/Mw (kDa)
Surface Efficiency Index, SEI= pIC50/PSA (in 100s Ǻ)
Ligand Efficiency Dependent Lipophilicity, LEDL= logP/LE
Abad-Zapatero C, Expert Opin.Drug Discov..2007,2, 469-488
Keseru et al, Nat.Rev. Drug Discov. 2009, 8, 203-12
Rule of Thumbs, Calculated Metrics…
Advantages:
They are simple
They are easy to interpret
Disadvantages:
They are simple…easy to be over-interpreted
They do not account for uncertainty
IC50 lipophilicity
They do not account for therapeutic categories,
are considered as universal but actually they are
not.
Rule of Thumbs, Calculated metrics…How reliable?? – Use and abuse
How reliable are logP predictions?
Which are the requirements for target receptor ?
• rules are not universal and are typically based on identification of
orally administered drugs
• Respecting druglike properties decreases the chance for poor
pharmacokinetics but correlation with ADME/Tox is just indicative
Uncertainties in Lipophilicity prediction…
For complex structures different predictions are provided by
different systems
Most calculative approaches concern the lipophilicity of the
neutral species- Uncertainties are larger for logD of ionized
molecules
Lipophilicity predictions are based on experimental data
Lipophilicity (logP) prediction
Fragmental Systems
logP = anfn+ Q
Rekker (PrologPp, Sybyl)
Leo-Hansch (ClogP) (s=0.398)
Meylan-Howard
(s=0.216)
Atomic contribution systems
logP = anαn
Ghose-Crippen (PrologP, ChemPlus)
(s=0.47)
Broto (PrologP)
(s=0.4)
Artificial Neural Networks based on:
Fragments or atomic contributions
(AlogPS)
Electrotopological indices
Algorithms incorporating
similarity rules to refine
predictions ( PharmaAlgorithms)
Reliability of logP predictions
Tetko et al Chemistry & Biodiversity, 2009 ,1837-1845
Tetko et al Chemistry & Biodiversity 2009, , 1837-1845
Uncertainty in logP predictions
logD predicted versus logD experimental
clogD=clogP-Q pKa
Partitioning of ion pairs
pKa predicted- global versus local
models
Lipophilicity- Predicted or measured?
Global Models
Local Models
Small sets of congeneric compounds
Large compound libraries
When compounds are not there
When compounds are there
Local models for
further improvement
Alternative Lipophilicity Parameters
Reversed- Phase Chromatographic lipophilicity indices
logk, logkw HPLC
logP=alogk(w)+b
If logkw is used a ~ 1, b ~0
Standardization of the chromatographic
conditions: Basic and neutral drugs
logD7.4 = 1.03 (± 0.03)logkw + 0.14(± 0.07)
N = 64 r = 0.967 s = 0.288
logD7.4 = 1.13(± 0.02)logkw + 0.21(± 0.04)
N = 90 r = 0.982 s = 0.309
F. Lombardo, M.Y. Shalaeva, K.A. Tupper, F. Gao, J.Med.Chem. 44 (2001) 2490.
C. Giaginis, S. Theocharis, A.Tsantili-Kakoulidou, Anal.Chim.Acta, 573: 311-318, (2006)
Column ABZ
Column BDS
ElogD approach
Predicted or measured?
Example 1.
Pyrrolylacetic acid derivatives,
inhibitors of aldose reductase
Data used : % Inh at concentration 10-6 M,
converted to logit
Logit =log [%Inh/(100-%Inh)]
Pyrrolylacetic acid derivatives
Pyrrolylacetic acid derivatives: logP and logD predictions uncertainties
Δ CDR AT AT6 ANN ANN05 ClogP AΒ AlogPs
Δ ≤ 0.49 15 3 3 11 9 5 6 2
0.5 ≤ Δ ≤1 3 8 7 7 8 9 11 5
Δ > 1 0 7 8 0 1 4 1 11
MAE* 0.31 0.84 0.86 0.37 0.51 0.67 0.61 1.03
Δ CDR AT AT6 ANN ANN05 AB
Δ ≤ 0.49 8 9 10 12 15 2
0.5 ≤ Δ ≤1 10 7 7 4 3 7
Δ > 1 0 2 1 2 0 9
MAE* 0.47 0.50 0.44 0.43 0.29 1.09
logP
logD7.4
*MAE: Mean Average Error
Pyrrolylacetic acid derivatives: logP-Predicted or measured?
Pyrrolylacetic acid derivatives: PCA on
experimental and calculated logP and logD values
Loading plot
M. Chrysanthakopoulos ……A. Tsantili-Kakoulidou., Quant. Struct.-Act.
Relat., 28, 2009, 551-560.
Pyrrolylacetic acid derivatives: Relationship between logP and Aldose Reductase inhibitory activity
43210
logP
2
1
0
-1
logit
4321
clogP
2
1
0
-1
logit
Predicted logP Experimental logP
logit = -1.29(0.27) + 0.80(0.13)logPexp
n=14 r=0.872 s=0.293
logP- Predicted or measured?
Example 2.
Thiazolidinediones: PPAR- γ ligands
Hypoglycemic action
Data used: gene EC50 transactivation data
Experimental and calculated LogD7.4 values of Thiazolidinediones
O
C3H
CH3C3H
OH
CH3
O
S
NH
O
O
F
O
NH
S
O
O
NN
C3H C3H
OO
SS
NHNH
OO
OO
TZDs logD7.4
exp.
AB/
logD7.4
Pro
logD7.4
CGZ 4.63 3.14 4.06
TGZ 4.20 2.99 4.67
NGZ 4.09 3.50 3.97
PGZ 3.14 1.98 3.45
RGZ 2.63 1.35 3.25
4.12
4.22
4.04
2.57
-
ClogD7.4
S
O
NH
OOCH3
NN
NN
CH3CH3
OO
SS
NHNH
OO
OO
logkw 7.4
4.89
4.34
4.23
3.33
2.81
Experimental and calculated LogD7.4 values of Thiazolidinediones- ranking
Πειραματικές τιμές: CGZ >TGZ > NGZ > PGZ > RGZ
ClogD: TGZ > CGZ > NGZ > RGZ > PGZ
AB/logD: NGZ > CGZ > TGZ > PGZ > RGZ
PrologD: TGZ > CGZ > NGZ > PGZ > RGZ
???
Rosiglitazone
-2
-1
0
1
2
3
4
0 2 4 6 8 10 12
pH
log
D Exp
ADME
Pallas
Pioglitazone
-1.5
-0.5
0.5
1.5
2.5
3.5
0 2 4 6 8 10 12
pH
logD
Exp
ADME
Pallas
Thiazolidinediones: Relationship between calculated logD7.4 values and EC50 transactivation data
543
PrologD
8
7
6
5
log1/E
C50
logDo=4.17
4321
AB/logD
8
7
6
5
log1/E
C50
logDo=2.86
Thiazolidinediones: Relationship between experimental
lipophilicity indices and EC50 transactivation data
log(1/EC50) = -0.64logD7.4 + 8.60
R=0.913, s=0.27
5432
logD7.4
8
7
6
5
log1/E
C50
Giaginis C, Theocharis S.Tsantili-Kakoulidou A,
J.Chromatogr. B, 2007, 857,181-187
Lipophilicity may not be the property to
look for.
logP/logD as descriptor in QSAR. Predicted or Measured?
Within small congeneric series of complex
compounds calculated logP/logD may deviate from
experimental data and may lead to erroneous results
in correlation with biological activity.
For large sets of compounds (compound libraries)
calculated values are the only solution. Their use
provides the trend that biological activity follows in
respect to lipophilicity. In a later research stage they
should be replaced by experimental values.
Lipophilicity of PPAR-γ Agonists, thiazolidinediones+tyrosine analogues (n=177)
…..tracking the property space
4
5
6
7
8
9
10
-2 -1 0 1 2 3 4 5 6 7
logD7.4
pKi
Higher activity is associated with rather moderate lipophilicity
C. Giaginis, S. Theocharis, A. Tsantili-Kakoulidou,.
Expert Opin. Investig. Drugs, 2007, 16: 413-417
For most comps logP(ΑDME) range: 3.5-6
Lipophilicity of 177 PPAR-γ agonists, thiazolidinediones + tyrosine analogues …..tracking the property space
Comprehensive Medicinal Chemistry (n=984)
logD(ΑDME) range: 0.5-3
logP distribution for drugs
Peak of Gauss distribution at logP ~ 2
..tracking the property space of PPAR-γ agonists (Ν = 1152)
0% 1%2%
8%
15%
26%24%
16%
6%
2%0%
100 150 200 250 300 350 400 450 500 550 600 650 700 7500
50
100
150
200
250
300
350
No
of
co
mp
ou
nd
s
MW
Mean 446.63
Min 190.24
Max 675.63
0% 1%2%
8%
15%
26%24%
16%
6%
2%0%
100 150 200 250 300 350 400 450 500 550 600 650 700 7500
50
100
150
200
250
300
350
No
of
co
mp
ou
nd
s
MW
Mean 446.63
Min 190.24
Max 675.63
Violations of Lipinski’s rule of 5 concerning excess MW
and ClogP for 24% and 59% of compounds.
0% 0%1%
8%
15%
16%
18%19%
14%
6%
2%0% 0%
-1 0 1 2 3 4 5 6 7 8 9 10 11 12
ClogP
020
40
60
80
100
120
140
160
180
200
220
240
No
of
co
mp
ou
nd
s
Mean 5.43
Min 0.13
Max 10.63
0% 0%1%
8%
15%
16%
18%19%
14%
6%
2%0% 0%
-1 0 1 2 3 4 5 6 7 8 9 10 11 12
ClogP
020
40
60
80
100
120
140
160
180
200
220
240
No
of
co
mp
ou
nd
s
Mean 5.43
Min 0.13
Max 10.63
21% of compounds exceed both ClogP and MW upper limits
(Giaginis ….Tsantili-Kakoulidou, EuroQSAR 2010)
..tracking the property space of PPAR-γ agonists (Ν = 1152)
pEC50
No
of
co
mp
ou
nd
s
ClogP : <= 5, MW: <= 500
ClogP : <= 5,
MW: > 500
ClogP : > 5, MW : <= 500
ClogP: > 5, MW: > 500
16%
13%
27%
14%
2% 3%
7%
19%
70
20
40
60
80
100
120
140
160
180
pEC50
No
of
co
mp
ou
nd
s
ClogP : <= 5, MW: <= 500
ClogP : <= 5,
MW: > 500
ClogP : > 5, MW : <= 500
ClogP: > 5, MW: > 500
ClogP : <= 5, MW: <= 500
ClogP : <= 5,
MW: > 500
ClogP : > 5, MW : <= 500
ClogP: > 5, MW: > 500
16%
13%
27%
14%
2% 3%
7%
19%
70
20
40
60
80
100
120
140
160
180
40% of compounds with high PPAR-
affinity exceed (pEC50>7) both ClogP
and MW upper limits- twofold violation
of the rule of 5
0%2%
12%
19%
25%
30%
11%
2%0% 0%
0 2 4 6 8 10 12 14 16 18 20 220
50
100
150
200
250
300
350
400
No
of
co
mp
ou
nd
s
RB
Mean 9.77
Min 3
Max 23
0%2%
12%
19%
25%
30%
11%
2%0% 0%
0 2 4 6 8 10 12 14 16 18 20 220
50
100
150
200
250
300
350
400
No
of
co
mp
ou
nd
s
RB
Mean 9.77
Min 3
Max 23
43% RB > 10 68% RB > 8
…..tracking the property space of PPAR-γ agonists (Ν = 1152)
RNG
2% 2%
9%
27%
45%
14%
1%
0 1 2 3 4 5 60
100
200
300
400
500
600
No
of
co
mp
ou
nd
s
Mean 3.57
Min 0
Max 6
RNG
2% 2%
9%
27%
45%
14%
1%
0 1 2 3 4 5 60
100
200
300
400
500
600
No
of
co
mp
ou
nd
s
Mean 3.57
Min 0
Max 6
60% RING ≥ 4
Distribution of number of rotatable bonds Distribution of number of rings
..tracking the property space of PPAR-γ agonists (Ν = 1152)
53% CMR ≥ 12 Å3
0%
2%
7%
29%31%
22%
7%
1% 0% 0%
0 20 40 60 80 100 120 140 160 180 200
TPSA
0
50
100
150
200
250
300
350
400
No
of
co
mp
ou
nd
s
Mean 89.60
Min 26.30
Max 260.35
0%
2%
7%
29%31%
22%
7%
1% 0% 0%
0 20 40 60 80 100 120 140 160 180 200
TPSA
0
50
100
150
200
250
300
350
400
No
of
co
mp
ou
nd
s
Mean 89.60
Min 26.30
Max 260.35
0%
2%
7%
29%31%
22%
7%
1% 0% 0%
0 20 40 60 80 100 120 140 160 180 200
TPSA
0
50
100
150
200
250
300
350
400
No
of
co
mp
ou
nd
s
Mean 89.60
Min 26.30
Max 260.35
82% TPSA > 60 Å2
0%2%
11%
33% 34%
16%
3%
0% 0%
4 6 8 10 12 14 16 18 20 22
CMR
0
50
100
150
200
250
300
350
400
450
No
of
co
mp
ou
nd
s
Mean 12.25
Min 5.04
Max 18.89
0%2%
11%
33% 34%
16%
3%
0% 0%
4 6 8 10 12 14 16 18 20 22
CMR
0
50
100
150
200
250
300
350
400
450
No
of
co
mp
ou
nd
s
Mean 12.25
Min 5.04
Max 18.89
Distribution of Molar Refractivity Distribution of Polar Surface Area
…..tracking the property space of PPAR-γ
agonists (Ν = 1152)
Rule of 5 violations in respect to MW and logP for tyrosine analogs
Tyrosine analogs
SIMCA-P 10.5 - 11/9/2008 4:13:35 PM
Violation
score 2
Rule of 5 violations in respect to MW and logP for PPAR-γ agonists-indole derivatives
(N=120)
SIMCA-P 10.5 - 9/5/2008 7:11:32 PM
2-D QSAR for TZDs, indole, tyrosine based
PPAR-γ agonists using PLS analysis, n=135
5
6
7
8
9
5 6 7 8 9
Τυροσίνες
Θειαζολιδινεδιόνες
Ινδόλια
Φυσικοί αγωνιστές
RMSEE = 0.56
pK
iex
p
pKi pred
5
6
7
8
9
5 6 7 8 9
Τυροσίνες
Θειαζολιδινεδιόνες
Ινδόλια
Φυσικοί αγωνιστές
RMSEE = 0.56
pK
iex
p
pKi pred
Tyrosines
TZDs
Indoles
Natural ligands
n = 135, Α=2, R2 = 0.81, Q2 = 0.78, RMSEE = 0.56
0.00
0.20
0.40
0.60
0.80
1.00
1.20
MW Po
lW
AV
ol
Re
fW
AS
A
SA
Vo
lE
HB
An
NO
SA
SA
na
rC6
(ah
)n
Rin
gs
nR
ing
s(a
h)
nN
On
rn
O
ab
Ion
SA
SA
-np
nN
Or(
lf)
TP
SA
nO
nr
nC
H2
(2,3
)a
bIo
n(l
f)
nO
(ah
)n
arC
6
nN
r(lf
)n
NO
(lf)
nC
db
O(a
h)
WA
SA
-np
log
Pc
he
mN
eg
Fra
cti
nN
(lf)
HO
MO
-LU
MO
Dip
ol-
XZ
wit
tF
rac
RB
Variable
VIP
0.00
0.20
0.40
0.60
0.80
1.00
1.20
MW Po
lW
AV
ol
Re
fW
AS
A
SA
Vo
lE
HB
An
NO
SA
SA
na
rC6
(ah
)n
Rin
gs
nR
ing
s(a
h)
nN
On
rn
O
ab
Ion
SA
SA
-np
nN
Or(
lf)
TP
SA
nO
nr
nC
H2
(2,3
)a
bIo
n(l
f)
nO
(ah
)n
arC
6
nN
r(lf
)n
NO
(lf)
nC
db
O(a
h)
WA
SA
-np
log
Pc
he
mN
eg
Fra
cti
nN
(lf)
HO
MO
-LU
MO
Dip
ol-
XZ
wit
tF
rac
RB
Variable
VIP
2-D QSAR for tyrosine based PPAR-γ agonists
using PLS analysis
Bulk parameters lipophilicity
n = 93, Α=2, R2 = 0.82, Q2 = 0.78, RMSEE = 0.436
C. Giaginis, S. Theocharis, A. Tsantili-Kakoulidou, Chem. Biol. Drug Des. 2008, 72:257-64
Influence of MW and lipophilicity on affinity for tyrosine based PPAR- γ Agonists
5
6
7
8
9
5 6 7 8 9
pK
i exp
pKi pred
RMSEE = 0.685
Series (Variable MW)
Missing
Outside Below Range
Outside Above Range
228.37 - 350
350.01 - 500
500.01 - 680
SIMCA-P 10.5 - 3/26/2007 4:17:11 PM
5
6
7
8
9
5 6 7 8 9
pK
i exp
pKi pred
RMSEE = 0.685
Series (Variable logPchem)
Missing
Outside Below Range
Outside Above Range
2.16 - 3
3.01 - 4
4.01 - 5
5.01 - 6
6.01 - 7.8
SIMCA-P 10.5 - 3/26/2007 4:24:47 PM
Trend of binding to
increase with increase
of MW
No regular pattern of
binding with lipophilicity
Observed versus predicted pKi values according to relevant PLS model
2-D QSAR for indole based PPAR-γ agonists using PLS analysis
N=101 A=3, R2=0.82, Q2=0.80, RMSE=0.60
C. Giaginis, S. Theocharis, A. Tsantili-Kakoulidou QSAR and Comb. Sci., 2009, 28, 802-805
2-D QSAR for indole based PPAR-γ agonists using PLS analysis
5
6
7
8
9
10
5 6 7 8 9 10
pK
i (exp)
pKi (pred)
SIMCA-P 10.5 - 11/9/2008 5:21:53 PM
5
6
7
8
9
10
5 6 7 8 9 10
pK
i exp)
(pKi (pred)
SIMCA-P 10.5 - 11/9/2008 5:25:35 PM
Observed versus predicted pKi values:
Data points colored according to logP and MW upper limit discrimination
In red: logP5
In green: logP<5
In red: MW500
In green: MW<500
Comparison of tyrosine and indole PLS models
Coefficients plot
- 0 . 4 0
- 0 . 2 0
0 . 0 0
0 . 2 0
0 . 4 0
0 . 6 0
logP
chem
HB
A
RB
MW
aro
m R
ing
O
Halo
gen
SA
SA
gr
E L
UM
O
npS
AS
Agrid
Coeff
CS
[3](
pK
i )
Var ID
tyrosines
indoles
Comparison of local models
Tyrosines : lower impact of lipophilicity, compounds need not be very lipophilic
size is more important
rotatable bonds contribute positively
Indoles: negative contribution of rotatable bonds- compounds need to be
less flexible and more compact- negative effect of Surface Area
High impact of size and lipophilicity- compounds need to be large and
highly lipophilic
….but indole derivatives possess also PPAR-α activity
Multitarget approach: The case of dual
PPAR-α/γ agonists.
•Reduction of undesirable side
effects related with PPAR-γ
activation.
•Synergistic effect provoked by the
simultaneous activation of PPAR-α
and -γ.
Dual PPAR-
α/γ agonists.
Side effects:
edema,
obesity
2-D QSAR for PPAR-α-γ dual agonists using
PLS analysis (N=70)
A, B : Fracchiolla G, et al Chem.Med Chem.
(2007) 5 : 651-74
C:PIingali H (2008) 16:7117-7127, et al, Bioorg.Med.Chem.
(2008) 16:7117-7127
D,E:Suh Y.G, J.Med.Chem. (2008) 51:6318-6333
F,G : Pingali H, et al Bioorg.Med.Chem.Let.
(2008) :18 6471-6475
H,I,J : Benardeau A, et al Bioorg.Med.Chem.Let.
(2009) 19 : 2468-2473
Increased diversity of the data set
2-D QSAR for PPAR-α-γ dual agonists using
PLS analysis, N=70
Physicochemical descriptors, molecular 2D, 3D descriptors,
constitutional descriptors
Connectivity/Electrotopological descriptors
In total 140 Descriptors
2-D QSAR for PPAR-α-γ dual agonists using
PLS analysis (N=70)
Separate analysis of each type of activity
Simultaneous analysis of dual activity to derive a
consensus model
Use the whole pool of descriptors
Use only physicochemical/molecular descriptors
Influence of the set of descriptors in PLS
model quality
Physicochemical/Molecular
/constitutional descriptors
Entire set of descriptors
(MOLCONN-Z included)
N A R2 Q2 RMSEE R2 Q2 RMSEE
PPAR-α 65 2 0.784 0.723 0.596 0.855 0.768 0.474
PPAR-γ 65 2 0.756 0.702 0.583 0.849 0.775 0.463
PPAR-α/γ
Total
PPAR-α
PPAR-γ
64
2
0.717
0.765
0.668
0.674
0.729
0.619
0.629
0.674
0.739
0.803
0.676
0.693
0.766
0.618
0.576
0.666
Comparison of PPAR-α and PPAR-γ
PLS models Coefficients plot
Variables with
VIP>1 are
colored red
PPAR-α
PPAR-γ
R2=0.784, Q2= 0.723
R2=0.756, Q2: = 0.702
Comparison of PPAR-α / PPAR-γ PLS models
Lipophilicity, number of double bonds (C=C), E HOMO, number
of N atoms in rings seem to be more important for PPAR-α
activity, exhibiting positive contribution.
Bulk, in particular non polar, descriptors, number of sulfur
atoms in rings, Total Dipole Moment are more important for
PPAR-γ activity, the latter with negative contribution.
Rotatable bonds with positive contribution are equally
important for both types of activity.
PPAR-α and PPAR-γ PLS consensus model
a.PPAR-α
b. PPAR-γ
PLS Analysis for dual PPAR-α/γ-activity Consensus model
PPAR-α PPAR-γ
Coefficient Overview Plot
R2: 0.717, Q2=0.674
R2=0.765, Q2: 0.729 R2=0.668, Q2: 0.619
In the consensus α/γ model the differences
in the contribution of the various descriptors
are balanced .
Consensus PPAR-α and PPAR-γ PLS models
a.PPAR-α
b. PPAR-
Comparison of PLS results with Molecular
Simulation studies
Examination of the crystal structure, using appropriate downloaded
PDB entries- requirements of receptor subtypes
The backbone of LBD is very similar on both PPAR-α and γ
receptors (about 1.1Å RMSD), common feature -The very large
and rather flexible entrance of the binding pocket (1300-1400 Å3)
however:
A small displacement of helix 3 in PPAR-γ, compared to PPAR-
α, augments the volume of the binding pocket
There are at least 12 residues that differ within 6Å of the
ligand. - The binding site of PPAR-α is occupied by less polar
residues of larger size
PPAR-γ- tesaglitazar complex PPAR-α- tesaglitazar complex
The entrance of the binding pocket is very large (1300-1400 Å3) and
rather flexible allowing the accommodation of structurally diverse
compounds and rendering the bioactive conformation rather ambiguous
PPAR receptor topology
U-shaped conformation
Requirements for PPAR-α/γ selectivity
Crystal structure of (a) PPAR-α and
(b) PPAR-γ in complex with
aleglitazar (not shown)
(c) Superposition of both subtypes
including aleglitazar
Comparison of PLS results with Molecular
Simulation studies
Superposition of crystal structure of PPAR-γ in
complex with AZ 242 and Aleglitazar, revealing the
different orientation of the side chain of Phe363
Rigid Docking using Glide, version 5.7,
Schrödinger
Flexible Docking using MacroModel,
version 9.9, Schrödinger
Further supports the differentiation
in the effect of effect of lipophilicity
and size , as well as specific
structural features like S within rings
in PPAR-α or the oxazole nitrogen in
PPAR-γ
Conclusions
Molecular simulation can be complimentary to 2-D QSAR modeling.
Receptor subtype requirements imply high lipophilicity for
PPAR-α and large molecular size for PPAR-γ
Two fold violations of rule of 5 are justified in the case of dual
PPAR agonists
Overall comparison of local models for PPAR-γ
suggests specific differentiations in the case of
indole derivatives
Global models may work, but they do not
provide concrete universal guidance in all
cases- local models may also be useful.
Acknowledgements
Dpt. of Pharm.Chem.,University of Athens
Prof. Emmanuel Mikros
Costas Giaginis, PhD - Agean University
Marios Chrysanthakopoulos, PhD
George Lambrinidis, PhD
Antigoni Koletsou, MSC
Theodosia Vallianatou, MSc student
Dpt. of Pharm.Chem.,University of Thessaloniki
Prof. Vassilis Demopoulos
Thank you!