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!