Qsar by hansch analysis

its good presentation on qsar through hansch analysis.

1. QSAR BY HANSCH ANALYSIS Presented by – Vishal Singh Solanki Guided by – Dr. A. Basu

2. CONTENT Introduction Graph and equation Regression or correlation coefficient Physicochemical properties Application of qsar Advantages Dis advantages Hansch equation References

3. INTRODUCTION 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. The quantitative structure activity relationship approach has proved extremely useful in overcome 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.

4. 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.

5. 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. Conti…

6. 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 6 compounds in the study (n=6). Yexp = log(observed activity) X = physicochemical property

7. ( Yexp – Ycalc )2 = Sscalc ( Yexp – Ymean )2 = SSmean Conti…

8. 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. Conti…

9. 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 Conti…

10. a) Hydrophobicity Log P (partition coefficient) Log P = [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. PHYSICOCHEMICAL PROPERTIES

11. 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 Conti…

12. Straight line For parabolic curve Log 1/C = -k ( logp )2 + k2 logp + k3  The substituent hydrophobicity constant( ) x = logpx – logph Where- Ph = partition coefficient of std compound Px = partition coefficient for std with substit uent   

13. 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 . 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 Kx - represents rate of hydrolysis of an aliphatic ester having substituent X K0 - represent rate of hydrolysis of reference ester.

14. 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.

15. c) Electronic Effect Hammet substituent constant ( ) This is a measure of the electron-withdrawing or electron-donating ability of a substituent. x = log(kx/kh)= logkx – logkh kh dissociation constant (H signifies that there is no substituents on aromatic ring).

16. Examples

17. 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  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  value) Conti…

18. Application of QSAR  Diagnosis of MOA of drug.  Prediction of activity.  Prediction of toxicity.  Lead compound optimization.  Environmental chemistry.

19. Advantages  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

20. Dis advantages  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

21. 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 . They relate biological activity to the most commonly used physicochemical properties ( logp or ,  and a steric factor).  If the range of hydrophobicity values is limited then the equation will be linear , as follows:

22. Log(1/C) = k1 logP + k2  + k3 Es + k4 If the logP values are spread over the large range, then the equation will be parabolic: LogP = -k1 ( logP )2 + K2 logP + k3  + k4 Es +k5 (the constants k1-k5 are determined by computer software in order to get best fitting equation) HANSCH EQUATION

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

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

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

26. conclusion QSAR relate the physiological properties of a series of drug to their biological activity by means of mathematically equation.

27. REFERENCES 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

28. T H E E N D !