Qsar by hansch analysis

brief about qsar

1. QSAR
By: DIVYANSHI
M.PHARM

2. Contents
•Introduction
•Definition
•Graphs and equations
•Regression coefficient (r)
•Physicochemical properties : Hydrophobicity
• Steric effect
• Electronic effect
•Advantages
•Disadvantages
•Application

3. •Hansch analysis equation
•Merits
•Demerits
•Reference

4. •ÞÞ INTRODUCTIONINTRODUCTION
The identification of a new drug molecule requires a
lot of synthesis, time and money. It was identified
that out of billion molecules synthesized, around one
or two molecules reach the clinical trials. This
produces hurdle in the discovery new chemical
entities (NCEs) for the treatment of various diseases.

5. The quantitative structure activity relationship
approach has proved extremely useful in
tackling this problem.
QSAR approach attempts to identify and
quantify the physicochemical properties of a
drug and to see whether any of these
properties has an effect on the drug’s
biological activity.

6. Graphs and equations
A range of compounds is synthesized in order to vary
one physicochemical property and to test how this
affects the biological activity. A graph is then plot the
biological activity 0n the y-axis versus the
physicochemical feature on x-axis.

7. The best line will be the closet to the data points . To measure
how close the data points vertical lines are drawn from each
points. The best line through the points will be the line where
total is minimum.
•
Log P
Log (1/C)

8. Regression or correlation coefficient (r) :
It is measure of how well the equation explains the
variance in activity observed in terms of
physicochemical parameters present in the
equation.
To illustrate ‘r’ following numerical data will be
used.
There are 5 compounds in the study (n=5).
Yexp = log(observed activity)
X = physicochemical property

9. Compound
(n=5)
Physicoch
emical
parameter

(X)
Yexp
ymean
=-0.214
YcalcYexp-Ycalc(Yexp-
Ycal)2
Sscal=
0.19
Yexp-
Ymean
(Yexp-
Ymean)2
Ssmean=
0.52
1 0.23 0.049-0.120.179 0.0230.2630.0453
2 0.23 0.039-0.0370.166 0.0640.2530.0564
3 -0.17 0 0.06-0.057 0.0650.4350.0135
4 0 -0.155-0.022-0.133 0.087-0.6750.0876
5 1.27 -0.468-0.445-0.422 0.098-0.5460.0657

10. The QSAR equation derived from the data is:
log(activity)=Ycalc= k1 X + k2
= -0.47 – 0.002
The correlation coefficient r for the above equation
calculated using :
r2 = 1 – SScalc / SSmean
Where SScalc= measure how much the experimental
activity of compounds varies from calculated value.
SSmean= measure how much the experimental activity
varies from the mean of all experimental activities.

11. If there is a correlation between the activity (Y) and the
parameter (X), the line of the equation should pass closer to
the data pts than representing mean.
It means:
SScalc < SSmean
According to data:
r2= 1 – 0.1912/ 0.5279
= o.638
This shows 64% variability in
activity due to parameter
X . This should be less
than 80% so equation is
not good one.
For a perfect correlation
calculated values for
activity = experimental
ones.
r2= 1

12. ÞPHYSICOCHEMICAL PROPERTIES

a) Hydrophobicity Log P (partition coefficient)

LogP = [drug] in octanol / [drug] in water
· Vary log P & see how this affects the biological
activity.
·Biological activity normally expressed as 1/C, where C
= [drug] required to achieve a defined level of
biological activity. The more active drugs require
lower concentration.

13. ·Plot log 1/C vs. log P
·Typically over a small range of log P, e.g. 1-4, a straight
line is obtained
log 1/C = k1 log P + k2
If graph is extended to very high log P values then get
parabolic curve. Reasons:
· poorly soluble in aqueous phase
· trapped in fat depots
· more susceptible to metabolism

14. Straight line

15. For parabolic curve
Log 1/C = -k (logp)2 + k2 logp + k3
·The substituent hydrophobicity constant(p)
px = logpx – logph
Ph= partition coefficient of std compound
Px= partition coefficient for std with substituent
b) Steric Effect
The bulk, size and shape of drug will influence how easily
it can approach and interact with binding site. A bulky
substituent may help to orientate a drug properly for
maximum binding n increase activity.

16. Kx represents rate of hydrolysis of an aliphatic ester
having substituent X
K0 represent rate of hydrolysis of reference ester.
Examples are:
Taft’s steric factor (Es) (~1956), the value for Es
can be obtained by comparing the rates of
hydrolysis of substituted aliphatic esters against a
std ester under acidic conditions.
Es = logkx – logk0

17. Substituent H F Me Et n-Pr n-Bu i-Pr i-Bu
Es 1.240.780 -0.07-0.36-0.39-0.47-0.93
•For example: reference ester is X=Me
•Substituent H n F smaller than Me result in faster
hydrolysis , Es value = +ve
•Substituent larger than Me reduce rate of
hydrolysis, Es value = -ve

18. Verloop steric parameter
It involves a computer program called sterimol which
calculates steric substituent values from std bond
angles, van der waals radii, bond lengths n
conformations
Example : L= length of substituent
B1-B4= radii of grp in different dimensions.

19. c) Electronic Effect
•Hammet substituent constant (s)
This is a measure of the electron-withdrawing or

electron-donating ability of a substituent.
sx = log(kx/kh)= logkx – logkh
kh dissociation constant (H signifies that there is
no substituents on aromatic ring).

20. Examples
COOH COO+ H K
0
COOH COO+ H KpX X
COOH COO+ H Km
X X
s
para = log
10
s
meta = log
10
Kp
Km
K
0
K
0

21. Electron withdrawing group, result in the aromatic ring
having a stronger and stabilizing influence on
carboxylate anion.
The equilibrium shift more to ionized form such n larger
kx value. (+ve s value)
If substituent X is an electron donating group such as
alkyl, then aromatic ring less able to stabilize the
carboxylate ion. Equilibrium shifts to left n smaller kx
value. ( -ve s value)

22. Application of QSAR
·Diagnosis of MOA of drug.
·Prediction of activity.
·Prediction of toxicity.
·Lead compound optimization.
·Environmental chemistry.

23. AdvantagesAdvantages
·Quantifying the relationship between structure
and activity provides an understanding of the
effect of structure on activity.
•It is also possible to make predictions leading to
synthesis of novel analogues.
··The results can be used to help understand
interaction between functional groups in the
molecules of greatest activity with those of their
target

24. Disadvantages
··False correlations because biological data that are
considerable experimental error.
·If training dataset is not large enough , the data
collected may not reflect the complete property
·Features may not be reliable. This is particularly
serious for 3D features because 3D structures of
ligands binding to receptor may not available

25. HANSCH EQUATION
In a simple situation where biological activity is related
to only one such property, a simple equation can be
drawn up. The biological activity of most drugs, however,
is related to a combination physicochemical properties.
In such cases, simple equations involving only one
parameter are relevant only if the other parameters are
kept constant. In reality, this is not easy to achieve and
equations which relate biological activity to a number of
different parameters are known as HANSCH
EQUATION.

26. •Not all parameters will necessarily be
significant .
•For example, the adrenergic blocking activity
of beta-halo-arylamines was related to p and
s n did not include a steric factor .

log 1/C = 1.22 p – 1.59 s + 7.89
(n=22; s=0.238; r= 0.918

27. Hansch equations
log 1/C = 0.398 p + 1.089 s + 1.03 Es + 4.541
(n=9; r= 0.955)
log C
b
= 0.765 p = 0.540 p
2
+ 1.505
log 1/c = 1.78 p – 0.12 s + 1.674

28. Merits of Hansch Analysis
1. Correlates activities with physicochemical
parameters
2. “Outside” predictions are possible

29. Limitations of Hansch analysis
•1. There must be parameter values available for the
substituent’s in the data set
•2. A large number of compounds is required.
•3. Depends on biological results (Chance of error)
•4. Interrelationship of parameters
•5. Groups should be selected in such a way that it
should contain at least one representative from each
cluster.

30. 6. Lead optimization technique, not a lead discovery
technique.
7. Risk of failure in “too far outside” predictions

31. Reference
•Patrick L. Graham “An introduction to
medicinal chemistry’’ 4
th
edition by Oxford
University , New York
•http://www.ccl.net/qsar/archives/0207/0029.html
•http://www.srmuniv.ac.in/downloads/qsar.pdf&sa=u&ved

32. THANK YOU