Quantitative structure activity relationships

Quantitative Structure- Activity Relationships
(QSAR)
BY:AMIYA KUMAR GHOSH
Department of pharmaceutical chemistry
University Department of Pharmaceutical Science
Contact:(+91)8910679352
Email: amiyaghosh94@gmail.com
University Department of Pharmaceutical Science, Utkal University
Bhubanweswer, Vani Vihar, Odisha- 751004

Rationale for QSAR Studies
•
In drug design, in vitro potency addresses only
part of the need; a successful drug must also
be able to reach its target in the body while
still in its active form.
•
The in vivo activity of a substance is a
composite of many factors, including the
intrinsic reactivity of the drug, its solubility in
water, its ability to pass the blood-brain
barrier, its non- reactivity with non-target
molecules that it encounters on its way to the

•
A quantitative structure-activity relationship
(QSAR) correlates measurable or calculable
physical or molecular properties to some
specific biological activity in terms of an
equation.
•
Once a valid QSAR has been determined, it
should be possible to predict the biological
activity of related drug candidates before they
are put through expensive and time-
consuming biological testing. In some cases,

History of QSAR
•
The first application of QSAR is attributed to
Hansch (1969), who developed an equation
that related biological activity to certain
electronic characteristics and the
hydrophobicity of a set of structures.
log (1/C) = k1log P - k2(log P)2 + k3s + k4
for: C = minimum effective dose
P = octanol - water partition coefficient

Hansch’s Approach
•
Log P is a measure of the drug’s
hydrophobicity, which was selected as a
measure of its ability to pass through cell
membranes.
•
The log P (or log Po/w) value reflects the
relative solubility of the drug in octanol
(representing the lipid bilayer of a cell
membrane) and water (the fluid within the
cell and in blood).
•
Log P values may be measured experimentally

Calculating Log P
Partition Coefficient P =
[Drug in octanol]
[Drug in water]
High P High hydrophobicity
Log P = Log K (o/w) = Log ([X]octanol/[X]water)
most programs use a group additivity approach:
1 Aromatic ring 0.780
7 H’s on Carbon 1.589
1 C-Br bond -0.120
1 alkyl C 0.195 Sum = 2.924 = calc. log P
some use more complicated algorithms, including factors such as the dipole
moment, molecular size and shape.
CH2 Br

Activity of drugs is often related to P
e.g. binding of drugs to serum albumin
(straight line - limited range of log P)
Log (1/C)
Log P
. .
.
.. .
. ..
0.78 3.82
Log 1
C
æ
è
ö
ø = 0.75 logP + 2.30
•
Binding increases as log P increases
•
Binding is greater for hydrophobic drugs

•
Example 2 General anaesthetic activity of ethers
•
(parabolic curve - larger range of log P values)
Log P
o
Log P
Log (1/C)
Log
1
C
æ
è
ö
ø = - 0.22(logP)2 + 1.04 logP + 2.16
Optimum value of log P for anaesthetic activity = log Po

Hydrophobicity of the
Molecule
•
QSAR equations are only applicable to
compounds in the same structural class
(e.g. ethers)
•
However, log Po is similar for anaesthetics
of different structural classes (ca. 2.3)
•
Structures with log P ca. 2.3 enter the CNS
easily

. Hydrophobicity of Substituents
- the substituent hydrophobicity constant (p)
Benzene
(LogP = 2.13)
Chlorobenzene
(LogP = 2.84)
Benzamide
(LogP = 0.64)
Cl CONH2Example :
•
Positive values imply substituents are more hydrophobic than H
•
Negative values imply substituents are less
hydrophobic than H

Hydrophobicity of Substituents
- the substituent hydrophobicity constant
(p)Example :
meta-Chlorobenzamide
Cl
CONH2
Log P(theory) = log P(benzene) + pCl +
pCONH
= 2.13 + 0.71 - 1.49
= 1.35
Log P (observed) = 1.51
2
•
A QSAR equation may include both P and p.
•
P measures the importance of a molecule’s overall hydrophobicity (relevant to
absorption, binding etc)
•
p identifies specific regions of the molecule which might interact with hydrophobic
regions in the binding site

Hammett substituent constant (s)
•
The Hammett substituent constant (s) reflects
the drug molecule’s intrinsic reactivity, related
to electronic factors caused by aryl
substituents.
•
In chemical reactions, aromatic ring
substituents can alter the rate of reaction by
up to 6 orders of magnitude!
•
For example, the rate of the reaction below is
~105 times slower when X = NO2 than when X
= CH3
CH3OH
C Cl
H
X

C OCH3 + HCl
H

X

Hammett Equation
•
Hammett observed a linear free energy
relationship between the log of the relative
rate constants for ester hydrolysis and the log
of the relative acid ionization (equilibrium)
constants for a series of substituted benzoic
esters & acids.
log (kx/kH) = log (Kx/KH) = rs
•
He arbitrarily assigned r, the reaction

Definition of Hammett r
C
O
OH
X
C
O
O
X
+ H
substituent p Eq. constant log K
-NH2 -0.66 0.00000554 -5.25649
-OCH3 -0.27 0.000015 -4.82391
-CH3 -0.17 0.000023 -4.63827
-H 0.00 0.000034 -4.46852
-Cl 0.23 0.000055 -4.25964
-COCH3 0.5 0.000088 -4.05552
-CN 0.66 0.000128 -3.89279
-NO2 0.78 0.000166 -3.77989
Hammett Plot
y = 0.9992x - 4.5305
R2
= 0.9907
-5.3
-5.1
-4.9
-4.7
-4.5
-4.3
-4.1
-3.9
-3.7
-1 -0.5 0 0.5 1
sigma p
LogK
These sp values are obtained from the best fit line having a slope = 1

Hammett Plot
•
Aryl substituent constants (s) were
determined by measuring the effect of a
substituent on a reaction rate (or Keq). These
are listed in tables, and are constant in widely
different reactions.
•
Reaction constants (r) for other reactions may
also be determined by comparison of the
relative rates (or Keq) of two differently
substituted reactants, using the substituent
constants described above.
•
Some of these values (s and r) are listed on

Hammett Rho & Sigma Values
•
Reaction (Rho) Values r
CH2COCH3
O
CH2CO + CH3OH
O
OH
 = + 2.4
X X
CH3OH
C Cl
H
X

C OCH3 + HCl
H

X  = - 5.0
Substituent (Sigma) Values s (the electronic effect of the substituent;
negative values are electron donating)
p-NH2 -0.66 p-Cl 0.23
p-OCH3 -0.27 p-COCH3 0.50
p-CH3 -0.17 p-CN 0.66
m-CH3 -0.07 p-NO2 0.78

Hammett Substituent
Constant (s)
•
X= electron withdrawing group (e.g. NO2)
+
X = e le c t r o n
w it h d r a w in g
g r o u p
X
C O 2C O 2 H
X
H
Charge is stabilised by X
Equilibrium shifts to right
KX > KHsX = log
KX
KH
= logKX - logKH
Positive value

•
X= electron donating group (e.g. CH3)
+
X = e le c tr o n
w ith d r a w in g
g r o u p
X
C O 2C O 2 H
X
H
Charge destabilised
Equilibrium shifts to left
KX < KH
s X = log
KX
KH
= logKX - logKH
Negative value

•
s value depends on inductive and resonance effects
•
s value depends on whether the substituent is meta or para
•
ortho values are invalid due to steric factors
sp (NO2) = 0.78 sm (NO2) = 0.71
D R U G
N
O
O
meta-Substitution
e-withdrawing (inductive effect only)
N
O O
D R U G D R U G
N
OO
N
O O
D R U G D R U G
N
OO
para-Substitution
e-withdrawing
(inductive +
resonance effects)
EXAMPLES:

•
EXAMPLES:
sm (OH) = 0.12 sp (OH) = -0.37
D R U G
O H
meta-Substitution
e-withdrawing (inductive effect only)
D R U G
O H
D R U G D R U G
O H O H
D R U G
O H
para-Substitution
e-donating by resonance
more important than
inductive effect

•
QSAR Equation:
X
O P
O
O E t
O E t
log 1
C
æ
è
ö
ø = 2.282s - 0.348
Diethylphenylphosphates
(Insecticides)
Conclusion : e-withdrawing substituents increase activity

Steric Factors
•
Molar Refractivity (MR) - a measure of a substituent’s volume
MR =
(n2
-1)
(n2
- 2)
x
mol. wt.
density
Correction factor
for polarisation
(n=index of
refraction)
Defines volume
This is a measure of the volume occupied by an atom or group of atoms. The
molar refractivity is obtained from the following equation:

•
Taft’s Steric Factor (Es)
•
Measured by comparing the rates of hydrolysis of substituted aliphatic esters
against a standard ester under acidic conditions
Es = log kx - log ko kx represents the rate of hydrolysis of a substituted
ester
ko represents the rate of hydrolysis of the parent ester
•
Limited to substituents which interact sterically with the tetrahedral transition
state for the reaction
•
Cannot be used for substituents which interact with the transition state by
resonance or hydrogen bonding
•
May undervalue the steric effect of groups in an intermolecular process (i.e. a drug
binding to a receptor)

Verloop steric parameter
•
Another approach to measuring the steric
factor involves a computer programme called
STERIMOL which calculates steric substituent
values(Verloop steric parameters)from
standard bond angles, van der Waals radii,
bond lengths, and possible conformations for
the substituent. Unlike £s, the Verloop steric
parameter can be measured for any
substituent.

Molecular Properties in QSAR
•
Many other molecular properties have been
incorporated into QSAR studies; some of these
are measurable physical properties, such as:
– density  pKa
– ionization energy  boiling point
– Hvaporization  refractive index
– molecular weight  dipole moment (m)
– Hhydration  reduction potential
– lipophilicity parameter

Molecular Properties in QSAR
•
Other molecular properties (descriptors) that
have been incorporated into QSAR studies
include calculated properties, such as:
– ovality  surface area, molec. volume
– HOMO energy  LUMO energy
– polarizability  charges on individual atoms
– molecular volume  solvent accessible surface
area
– vdW surface area  maximum + and - charge

QSAR Methodology
•
Often it is found that several descriptors are
correlated; that is, they describe observables
that are closely related, such as MW and
boiling point in a homologous series.
•
Statistical analysis is used to determine which
of the variables best describe (correlate with)
the observed biological activity, and which are
cross-correlated. The final QSAR involves only
the most important 3 to 5 descriptors,
eliminating those with high cross-correlation.

Limit to the # of Descriptors
•
The data set should contain at least 5 times as
many compounds as descriptors in the QSAR.
•
The reason for this is that too few compounds
relative to the number of descriptors will give
a falsely high correlation:
– 2 points exactly determine a line (2 comp’ds, 2
prop)
– 3 points exactly determine a plane (etc., etc.)
– A data set of drug candidates that is similar in
size to the number of descriptors will give a high
(and meaningless) correlation.

Statistical Analysis of Data
•
Multiple linear regression analysis can be
accomplished using standard statistical
software, typically incorporated into
sophisticated (and expensive) drug design
software packages, such as MSI’s Cerius2
(academic price, over $20K)
•
An inexpensive statistical analysis software
StatMost (academic price, $39) works just
fine.
•
To discover correlated variables and

Hansch Equation
•
A QSAR equation relating various physicochemical properties to the biological activity
of a series of compounds
•
Usually includes log P, electronic and steric factors
•
Start with simple equations and elaborate as more structures are synthesised
•
Typical equation for a wide range of log P is parabolic
Log 1
C
æ
è
ö
ø = -k (logP)2 + k2 logP + k3 s + k4 Es + k51

Example: Adrenergic blocking activity of b-halo-b-arylamines
C H C H 2 N R R '
XY
Log
1
C
æ
è
ö
ø = 1.22 p - 1.59 s + 7.89
Conclusions:
•
Activity increases if p is +ve (i.e. hydrophobic substituents)
•
Activity increases if s is negative (i.e. e-donating substituents)

•
Example: Antimalarial activity of phenanthrene aminocarbinols
X
Y
( H O ) H C
C H 2 N H R 'R "
Log
1
C
æ
è
ö
ø = -0.015 (logP)2 + 0.14 logP+ 0.27SpX + 0.40SpY + 0.65 SsX+ 0.88SsY + 2.34
Conclusions:
•
Activity increases slightly as log P (hydrophobicity) increases (note that the constant
is only 0.14)
•
Parabolic equation implies an optimum log Po value for activity
•
Activity increases for hydrophobic substituents (esp. ring Y)
•
Activity increases for e-withdrawing substituents (esp. ring Y)

•
Choosing suitable substituents
Substituents must be chosen to satisfy the following criteria:
•
A range of values for each physicochemical property studied
•
values must not be correlated for different properties (i.e. they must be orthogonal
in value)
•
at least 5 structures are required for each parameter studied
Substituent H Me Et n-Pr n-Bu
p 0.00 0.56 1.02 1.50 2.13
MR 0.10 0.56 1.03 1.55 1.96
Correlated values.
Are any differences
due to p or MR?
Substituent H Me OMe NHCONH2 I CN
p 0.00 0.56 -0.02 -1.30 1.12 -0.57
MR 0.10 0.56 0.79 1.37 1.39 0.63
No correlation in values
Valid for analysing effec
of p and MR.

X
Y
N
CH3
CH3Br
Anti-adrenergic Activity and Physicochemical Properties
of 3,4- disubstituted N,N-dimethyl-a-bromophenethylamines
p = Lipophilicity parameter
s+ = Hammett Sigma+ (for benzylic cations)
Es(meta) = Taft’s steric parameter
Example of a QSAR

m-X p-Y p s+ Es(meta) log (1/C)obs log (1/C)a log (1/C)b
H H 0.00 0.00 1.24 7.46 7.82 7.88
F H 0.13 0.35 0.78 7.52 7.45 7.43
H F 0.15 -0.07 1.24 8.16 8.09 8.17
Cl H 0.76 0.40 0.27 8.16 8.11 8.05
Cl F 0.91 0.33 0.27 8.19 8.38 8.34
Br H 0.94 0.41 0.08 8.30 8.30 8.22
I H 1.15 0.36 -0.16 8.40 8.61 8.51
Me H 0.51 -0.07 0.00 8.46 8.51 8.36
Br F 1.09 0.34 0.08 8.57 8.57 8.51
H Cl 0.70 0.11 1.24 8.68 8.46 8.60
Me F 0.66 -0.14 0.00 8.82 8.78 8.65
H Br 1.02 0.15 1.24 8.89 8.77 8.94
Cl Cl 1.46 0.51 0.27 8.89 8.75 8.77

QSAR Equation a: (using 2 variables)
log (1/C) = 1.151 p - 1.464 s+ + 7.817
(n = 22; r = 0.945)
QSAR Equation b: (using 3 variables)
log (1/C) = 1.259 p - 1.460 s+ + 0.208
Es(meta) + 7.619 (n = 22; r = 0.959)

QSAR of Antifungal Neolignans
•
The PM3 semi-empirical method was employed to calculate a
set of molecular properties (descriptors) of 18 neolignan
compounds with activities against Epidermophyton floccosum,
a most susceptible species of dermophytes. The correlation
between biological activity and structural properties was
obtained by using the multiple linear regression method. The
QSAR showed not only statistical significance but also
predictive ability. The significant molecular descriptors related
to the compounds with antifungal activity were: hydration
energy (HE) and the charge on C1' carbon atom (Q1'). The
model obtained was applied to a set of 10 new compounds
derived from neolignans; five of them presented promising
biological activities against E. floccosum.

Descriptors Used
•
Log P: the values of this property were obtained from the hydrophobic
parameters of the substituents;
•
superficial area (A) and molecular volume (V), log of the partition
coefficient (Log P), hydration energy (HE): properties evaluated with the
molecular modeling package HyperChem 5.0;
•
partial atomic charges (Qn) and bond orders (Ln) derived from the
electrostatic potential;
•
energy of the HOMO (H) and LUMO (L) frontier orbitals;
•
hardness (h): obtained from the equation h =(ELUMO-EHOMO)/2;
•
Mulliken electronegativity (c): calculated from the equation c = -
(EHOMO+ELUMO)/2;
•
other electronic properties were calculated: total energy (ET), heat of
formation (DHf); ionization potential (IP), dipole moment (m) and
polarizability (POL), whose values were obtained from the molecular
orbital pprogram Ampac 5.0.

Two Most Important Descriptors

Antifungal QSAR
Log 1/C = -2.85 - 0.38 HE - 1.45 Q1'
F=29.63, R2=0.86, Q2=0.80, SEP=0.
where:
F is the Fisher test for significance of the eq’n.
R2 is the general correlation coefficient,
Q2 is the predictive capability, and
SEP is the standard error of prediction.
A.A.C. Pinheiro, R.S. Borges, L.S. Santos, C.N. Alves,
Journal of Molecular Structure: THEOCHEM, Vol 672, pp 215-219 (2004).

QSAR-Calculated Antifungal Activity

Example of a Pharmacophore 2D
Hypothesis and Alignment

Craig Plot
•
Craig plot shows values for 2 different physicochemical properties for various
substituents
.
+
-
- .2 5
. 7 5
. 5 0
1 .0
- 1 . 0
- .7 5
- .5 0
.2 5
- .4- .8- 1 .2- 1 .6- 2 .0 2 .01 .61 .2.8.4.
. . .
.
.
.
.
.. . .
.
.
.
.
.
.....
C F 3 S O 2
C F 3
M e
C l B r
I
O C F 3
F
N M e 2
O C H 3
O H
N H 2
C H 3 C O N H
C O 2 H
C H 3 C O
C N
N O 2
C H 3 S O 2
C O N H 2
S O 2 N H 2
E t
t -B u t y l
S F 5
-p +p
+s -p +s +p
-s -p
-s +p

•
Allows an easy identification of suitable substituents for a QSAR
analysis which includes both relevant properties
•
Choose a substituent from each quadrant to ensure orthogonality
•
Choose substituents with a range of values for each property

Topliss Scheme
•
Used to decide which substituents to use if optimising compounds
one by one (where synthesis is complex and slow)
Example: Aromatic substituents
L E M
ML EL E M
L E M
L E M
S e e C e n t r a l
B r a n c h
L E M
H
4-Cl
4-CH34-OMe 3,4-Cl2
4-But
3-CF3 -4-Cl
3-Cl 3-Cl 4-CF3
2,4-Cl2
4-NO2
3-NMe2
3-CF3 -4-NO2
3-CH3
2-Cl
4-NO2
3-CF3
3,5-Cl2
3-NO2
4-F
4-NMe2
3-Me-4-NMe2
4-NH2

Rationale

Replace H with
para-Cl (+p and +s)
+p and/or +s
advantageous
favourable p
unfavourable s
+p and/or +s
disadvantageous
Act. Little
change
Act.
add second Cl to
increase p and s
further
replace with OMe
(-p and -s)
replace with Me
(+p and -s)
Further changes suggested based on arguments of p, s and
steric strain
Topliss Scheme

Topliss Scheme
Example
M = M o r e A c t iv it y
L = L e s s A c t iv it y
E = E q u a l A c t iv it y
H ig h
P o t e n c y
*
-
M
L
E
M
H
4 - C l
3 ,4 - C l 2
4 - B r
4 - N O 2
1
2
3
4
5
B io lo g ic a l
A c t iv it y
RO r d e r o f
S y n t h e s is
R
SO2 NH2

Topliss Scheme
Example
*
*
O r d e r o f
S y n t h e s is
R B io lo g ic a l
A c t iv it y
1
2
3
4
5
6
7
8
H
4 - C l
4 - M e O
3 - C l
3 - C F 3
3 - B r
3 - I
3 ,5 - C l 2
-
L
L
M
L
M
L
M
*
H ig h
P o t e n c y
M = M o r e A c t iv it y
L = L e s s A c t iv it y
E = E q u a l A c t iv it y
R N N
N
C H 2 C H 2 C O 2 H
N

Bio-isosteres
•
Choose substituents with similar physicochemical properties (e.g. CN, NO2
and COMe could be bio-isosteres)
•
Choose bio-isosteres based on most important physicochemical property
(e.g. COMe & SOMe are similar in sp; SOMe and SO2Me are
similar in p)
p -0.55 0.40 -1.58 -1.63 -1.82 -1.51
sp 0.50 0.84 0.49 0.72 0.57 0.36
sm 0.38 0.66 0.52 0.60 0.46 0.35
MR 11.2 21.5 13.7 13.5 16.9 19.2
S u b s t it u e n t C
O
C H 3
C
C H 3
C
N C C N
S
C H 3
O
S
O
C H 3
O
S
O
N H C H 3
O
C
O
N M e 2

Free-Wilson Approach
•
The biological activity of the parent structure is measured and compared with
the activity of analogues bearing different substituents
•
An equation is derived relating biological activity to the presence or absence of
particular substituents
Activity = k1X1 + k2X2 +.…knXn + Z
•
Xn is an indicator variable which is given the value 0 or 1 depending on whether
the substituent (n) is present or not
•
The contribution of each substituent (n) to activity is determined by the value of
kn
•
Z is a constant representing the overall activity of the structures studied
Method

Free-Wilson Approach
•
No need for physicochemical constants or tables
•
Useful for structures with unusual substituents
•
Useful for quantifying the biological effects of molecular features that cannot
be quantified or tabulated by the Hansch method
Advantages
Disadvantages
•
A large number of analogues need to be synthesised to represent each different
substituent and each different position of a substituent
•
It is difficult to rationalise why specific substituents are good or bad for
activity
•
The effects of different substituents may not be additive
(e.g. intramolecular interactions)

Free-Wilson /Hansch Approach
•
It is possible to use indicator variables as part of a Hansch equation - see
following Case Study
Advantages

Case Study
QSAR analysis of pyranenamines (SK & F)
(Anti-allergy compounds)
O O O
N H
O O H O H X
Y
Z
3
4
5

Stage 1 19 structures were synthesised to study p and s
CLog
1æ
è
ö
ø = -0.14Sp - 1.35(Ss)2
- 0.72
Sp and Ss = total values for p and s for all substituents
Conclusions:
•
Activity drops as p increases
•
Hydrophobic substituents are bad for activity - unusual
•
Any value of s results in a drop in activity
•
Substituents should not be e-donating or e-withdrawing (activity falls if s is +ve or
-ve)
Case Study
O O O
N H
O O H O H X
Y
Z
3
4
5

Stage 2 61 structures were synthesised, concentrating on hydrophilic substituents to
test the first equation
Anomalies
a) 3-NHCOMe, 3-NHCOEt, 3-NHCOPr.
Activity should drop as alkyl group becomes bigger and more
hydrophobic, but the activity is similar for all three substituents
b) OH, SH, NH2 and NHCOR at position 5 : Activity is greater than expected
c) NHSO2R : Activity is worse than expected
d) 3,5-(CF3)2 and 3,5(NHMe)2 : Activity is greater than expected
e) 4-Acyloxy : Activity is 5 x greater than expected
Case Study
O O O
N H
O O H O H X
Y
Z
3
4
5

a) 3-NHCOMe, 3-NHCOEt, 3-NHCOPr.
Possible steric factor at work. Increasing the size of R may be good for activity and
balances out the detrimental effect of increasing hydrophobicity
b) OH, SH, NH2, and NHCOR at position 5
Possibly involved in H-bonding
c) NHSO2R
Exception to H-bonding theory - perhaps bad for steric or electronic reasons
d) 3,5-(CF3)2 and 3,5-(NHMe)2
The only disubstituted structures where a substituent at position 5 was electron
withdrawing
e) 4-Acyloxy
Presumably acts as a prodrug allowing easier crossing of cell membranes.
The group is hydrolysed once across the membrane.
Case Study
O O O
N H
O O H O H X
Y
Z
3
4
5
Theories

Stage 3 Alter the QSAR equation to take account of new results
Log
1
C
æ
è
ö
ø = -0.30Sp - 1.35(Ss)2 + 2.0(F-5) + 0.39(345-HBD) -0.63(NHSO2)
+0.78(M-V) + 0.72(4-OCO) - 0.75
Conclusions
(F-5) e-withdrawing group at position 5 increases activity
(based on only 2 compounds though)
(3,4,5-HBD) H-bond donor group at positions 3, 4,or 5 is good for activity
Term = 1 if a HBD group is at any of these positions
Term = 2 if HBD groups are at two of these positions
Term = 0 if no HBD group is present at these positions
Each HBD group increases activity by 0.39
(NHSO2) Equals 1 if NHSO2 is present (bad for activity by -0.63).
Equals zero if group is absent.
(M-V) Volume of any meta substituent. Large substituents at meta
position increase activity
4-O-CO Equals 1 if acyloxy group is present (activity increases by 0.72).
Equals 0 if group absent
Case Study
O O O
N H
O O H O H X
Y
Z
3
4
5

Stage 3 Alter the QSAR equation to take account of new results
Log
1
C
æ
è
ö
ø = -0.30Sp - 1.35(Ss)2 + 2.0(F-5) + 0.39(345-HBD) -0.63(NHSO2)
+0.78(M-V) + 0.72(4-OCO) - 0.75
The terms (3,4,5-HBD), (NHSO2), and 4-O-CO are examples of indicator
variables used in the free-Wilson approach and included in a Hansch equation
Case Study
O O O
N H
O O H O H X
Y
Z
3
4
5

Stage 4
37 Structures were synthesised to test steric and F-5 parameters, as well as the effects of
hydrophilic, H-bonding groups
Anomalies
Two H-bonding groups are bad if they are ortho to each other
Explanation
Possibly groups at the ortho position bond with each other rather than with the receptor
- an intramolecular interaction
Case Study
O O O
N H
O O H O H X
Y
Z
3
4
5

Stage 5 Revise Equation
a) Increasing the hydrophilicity of substituents allows the identification of an
optimum value for p (Sp = -5). The equation is now parabolic (-0.034 (Sp)2)
b) The optimum value of Sp is very low and implies a hydrophilic binding site
c) R-5 implies that resonance effects are important at position 5
d) HB-INTRA equals 1 for H-bonding groups ortho to each other (act. drops -086)
equals 0 if H-bonding groups are not ortho to each other
e) The steric parameter is no longer significant and is not present
Log
1
C
æ
è
ö
ø = -0.034(Sp)2
-0.33Sp + 4.3(F-5) + 1.3 (R-5) - 1.7(Ss)2
+ 0.73(345-HBD)
- 0.86 (HB-INTRA)- 0.69(NHSO2) + 0.72(4-OCO)- 0.59
Case Study
O O O
N H
O O H O H X
Y
Z
3
4
5

Stage 6 Optimum Structure and binding theory
Case Study
NH3
X
X
XXH
5
3
N H
N H
C
O
C H
O H
C H 2 O H
C H C H 2 O HC
O O H
R H N

NOTES on the optimum structure
•
It has unusual NHCOCH(OH)CH2OH groups at positions 3 and 5
•
It is 1000 times more active than the lead compound
•
The substituents at positions 3 and 5
•
are highly polar,
•
are capable of H-bonding,
•
are at the meta positions and are not ortho to each other
•
allow a favourable F-5 parameter for the substituent at position 5
•
The structure has a negligible (Ss)2 value
Case Study

3 Dimensional QSAR Methods
• Important regions of bioactive molecules
are “mapped” in 3D space, such that
regions of hydrophobicity, hydrophilicity,
H-bonding acceptor, H-bond donor, p-
donor, etc. are rendered so that they
overlap, and a general 3D pattern of the
functionally significant regions of a drug
are determined.
• CoMFA (Comparative
Molecular Field Analysis)
is one such approach:

CoMFA of Testosterone
Blue means electronegative
groups enhance, red means
Electn’g. gr’ps reduce binding
Green means bulky groups
enhance, yellow means they
reduce binding

3D-QSAR
•
Physical properties are measured for the molecule as a whole
•
Properties are calculated using computer software
•
No experimental constants or measurements are involved
•
Properties are known as ‘Fields’
•
Steric field - defines the size and shape of the molecule
•
Electrostatic field - defines electron rich/poor regions of
molecule
•
Hydrophobic properties are relatively unimportant
Advantages over QSAR
•
No reliance on experimental values
•
Can be applied to molecules with unusual substituents
•
Not restricted to molecules of the same structural class
•
Predictive capability

3D-QSAR
•
Comparative molecular field analysis (CoMFA) - Tripos
•
Build each molecule using modelling software
•
Identify the active conformation for each molecule
•
Identify the pharmacophore
Method
N H C H 3
O H
H O
H O
Active conformation
Build 3D
model
Define pharmacophore

3D-QSAR
•
Place the pharmacophore into a lattice of grid points
Method
•
Each grid point defines a point in space
Grid points
.
.
.
.
.

3D-QSAR
Method
•
Each grid point defines a point in space
Grid points
.
.
.
.
.
•
Position molecule to match the pharmacophore

3D-QSAR
•
A probe atom is placed at each grid point in turn
Method
•
Probe atom = a proton or sp3 hybridised carbocation
.
.
.
.
.
Probe atom

3D-QSAR
•
A probe atom is placed at each grid point in turn
Method
•
Measure the steric or electrostatic interaction of the probe
atom with the molecule at each grid point
.
.
.
.
.
Probe atom

3D-QSAR
•
The closer the probe atom to the molecule, the higher the steric energy
•
Can define the shape of the molecule by identifying grid points of equal steric
energy (contour line)
•
Favourable electrostatic interactions with the positively charged probe indicate
molecular regions which are negative in nature
•
Unfavourable electrostatic interactions with the positively charged probe indicate
molecular regions which are positive in nature
•
Can define electrostatic fields by identifying grid points of equal energy (contour
line)
•
Repeat the procedure for each molecule in turn
•
Compare the fields of each molecule with their biological activity
•
Can then identify steric and electrostatic fields which are favourable or
unfavourable for activity
Method

3D-QSAR
Method
Compound Biological Steric fields (S) Electrostatic fields (E)
activity at grid points (001-998) at grid points (001-098)
S001 S002 S003 S004 S005 etc E001 E002 E003 E004 E005 etc
1 5.1
2 6.8
3 5.3
4 6.4
5 6.1
Tabulate fields for each
compound at each grid point
Partial least squares
analysis (PLS)
QSAR equation Activity = aS001 + bS002 +……..mS998 + nE001 +…….+yE998 + z
. .
.
.
.

3D-QSAR
•
Define fields using contour maps round a representative molecule
Method

3D-QSAR- CASESTUDY
Tacrine
Anticholinesterase used in the treatment of Alzheimer’s disease
N
N H 2

3D-QSAR- CASESTUDY
Conclusions
•
Large groups at position 7 are detrimental
•
Groups at positions 6 & 7 should be electron withdrawing
•
No hydrophobic effect
Conventional QSAR Study
12 analogues were synthesised to relate their activity with the hydrophobic, steric and
electronic properties of substituents at positions 6 and 7
N
N H 2
R 1
R 2 6
7
9
Cè øLog
1æ ö = pIC50 = -3.09 MR(R
1
) + 1.43F(R
1
,R
2
) + 7.00
Substituents: CH3, Cl, NO2, OCH3, NH2, F
(Spread of values with no correlation)

3D-QSAR- CASESTUDY
CoMFA Study
Analysis includes tetracyclic anticholinesterase inhibitors (II)
N
N H 2
R 1
R 2
R 3
R 4
R 5I I
1
2
3
8
7
•
Not possible to include above structures in a conventional QSAR analysis since they
are a different structural class
•
Molecules belonging to different structural classes must be aligned properly
according to a shared pharmacophore

3D-QSAR- CASESTUDY
Possible Alignment
Overlay
Good overlay but assumes similar binding modes

3D-QSAR- CASESTUDY
•
A tacrine / enzyme complex was crystallised and analysed
•
Results revealed the mode of binding for tacrine
•
Molecular modelling was used to modify tacrine to structure (II) whilst still bound
to the binding site (in silico)
•
The complex was minimised to find the most stable binding mode for structure II
•
The binding mode for (II) proved to be different from tacrine
X-Ray Crystallography

3D-QSAR- CASESTUDY
•
Analogues of each type of structure were aligned according to the parent structure
•
Analysis shows the steric factor is solely responsible for activity
7
6
•
Blue areas - addition of steric bulk increases activity
•
Red areas - addition of steric bulk decreases activity
Alignment

3D-QSAR- CASESTUDY
Prediction
6-Bromo analogue of tacrine predicted to be active (pIC50 = 7.40)
Actual pIC50 = 7.18
NB r
N H 2

REFERENCES
•
1. C. Hansch and A. Leo, Substituent Constants for Correlation Analysis in Chemistry and Biology,John
Wiley & Sons, New York, 1979.
•
2. D. J. Livingstone, J. Chem. Znf. Comput. Sci.,40,195 (2000).
•
3. C. Hansch, A. Kurup, R. Garg, and H. Gao,Chem. Rev., 101,619 (2001).
•
4. H. Kubinyi in M. Wolff, Ed., Burger's MedicinalChemistry and Drug Discovery, Volume 1:Principles and
Practice, John Wiley & Sons,New York, 1995, p. 497.
•
5. A. Crum-Brown and T. R. Fraser, Trans. R.Soc. Edinburgh, 25, 151 (1868).
•
6. C. Richet and C. R. Seancs, Soc. Biol. Ses. Fil.,9,775 (1893).
•
7. H. Meyer, Arch. Exp. Pathol. Pharmakol., 42,109 (1899).
•
8. E. Overton, Studien Uber die Narkose, Fischer,Jena, Germany, 1901.
•
9. J. Ferguson, Proc. R. Soc. London Ser. B, 127,387 (1939).
•
10. A. Albert, S. Rubbo, R. Goldacre, M. Darcy, and J. Stove, Br. J. Exp. Pathol., 26, 160 (1945).
•
11. A. Albert, Selective Toxicity: The Physicochemical Bases of Therapy, 7th ed., Chapman and Hall,
London, 1985, p. 33.
•
12. P. H. Bell and R. 0. Roblin, Jr.J. Am. Chem.SOC.6, 4,2905 (1942).
•
13. L. P. Hammett, Chem. Rev., 17,125 (1935).

Quantitative structure activity relationships

Quantitative structure activity relationships

More Related Content

What's hot (20)

Similar to Quantitative structure activity relationships (20)

Recently uploaded (20)

Quantitative structure activity relationships