QSAR by Faizan Deshmukh

Introduction of QSAR, Steps involved in QSAR, Hansch Analysis, Free Wilson Analysis, Mixed Approach method, Advantage,Disadvantage and Application of QSAR.

1. Presented By Deshmukh Md Faizan M. Pharm (2 nd Sem ) DEPARTMENT OF PHARMACEUTICAL CHEMISTRY, R. C. PATEL INSTITUTE OF PHARMACEUTICAL EDUCATION AND RESEARCH, SHIRPUR QSAR Compounds + biological activity New compounds with improved biological activity QSAR 1

2. CONTENTS Introduction Objectives Steps in QSAR Hansch Analysis Free Wilson Approach Mixed Approach Advantages of QSAR Disadvantages of QSAR Application of QSAR 2

3. INTRODUCTION QSAR is an attempt to remove the element of luck from drug design by establishing a mathematical relationship in the form of an equation between biological activity and measurable physicochemical parameters. QSAR is mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics. QSAR(Quantitative Structure Activity Relationship) are based on the assumption that the structure of molecule (i.e. its geometric, steric and electronic properties) must contain the features responsible for its physical, chemical and biological properties. QSAR in simplest terms, is a method for building computational or mathematical models which attempts to find a statistically significant correlation between structure and function. 3

4. OBJECTIVE QSAR makes it easy now to reach the conclusion for any of the congener that still not in process, in way that whether it will optimal and profitable or not. To predict the biological activities of untested and sometimes yet unavailable compounds. To optimize the existing leads so as to improve their biological activities. Quantitative relationship between the structure and physiochemical properties of substances and their biological activity are being used as the foundation stone in search of new medicines 4

5. STEPS IN QSAR 5

6. 1. STEPS IN QSAR (STRUCTURE ENTRY AND MODELLING ) Structure are sketched using standard drawing software's commercial or freeware Molecular modelling for the generation of low energy conformation 6

7. 2.STEPS IN QSAR (DESCRIPTOR GENERATION) Physico-chemical properties that describe some aspect of the chemical structure Empirical Descriptors:- determined experimentally NMR chemical shift, Melting point. Some of the experimentally determined physico-chemical parameters: log P = C or g/ C aqu π x =log P (R-X) – log P (R-H) Theoretical Descriptors:- Calculated (theoretical) topological ,BCUT. 7

8. 0D Descriptors : ( i.e. Constitutional Descriptor) molecular weight, no.of atoms, no of non-H atoms, no. of bonds, no. of heteroatom's, no. of multiple bonds (nMB), no. of rings, no. of circuits, no.of H-bond donors, no. of h-bond acceptors, no. of Nitrogen atoms (nN). Number of certain chemical groups and functionalities in the molecule. Total no of bonds in the molecule. Number of rings, no of rings divided by the total no of atoms. 1D Descriptors: include structural fragments, hydrophobicity and MR. Hydrophobic Parameters are π and log P. Molar Refractivity (MR) Determined from the refractive index, the molecular weight, and density of crystal. 8

9. 2D Descriptors: Topological Descriptors based on graph theory concepts. These descriptors have been widely used in QSAR studies. They help to differentiate the molecules according mostly to their size. Isopentane Graph representation Node Edges Graph Invariants(topological descriptors) Isopentane = Four edges, Five nodes and the adjacency Relationship implicitly in the structure 9

10. 3D Descriptor: Geometrical Descriptor- This descriptors using the atomic coordinates ( x,y,z ) of a molecules are therefore called 3D descriptors. Encode the 3D aspect of the structure, Vander Waals volume, Molecular Surface. Shadow areas, solvent accessible areas, etc. Quantum Mechanical Descriptor- Encode the aspect of the structures that are related to the electrons. Electronic descriptor include, HOMO or LUMO Energies, partial atomic charges, etc. 10

11. Comparative Molecular Field Analysis ( CoMFA ) The comparative molecular field analysis a grid based technique, most widely used tools for three dimensional structure-activity relationship study. In this method the molecule-receptor interaction is represented by the steric and electrostatic field exerted by each molecule. 11

12. 3.STEPS IN QSAR (FEATURE SELECTION AND MODELLING ) Traditional techniques in feature selection Multiple Linear Regression Analysis (MLR ) : This is computerised method which is correlate the biological activity with physico -chemical properties. This method check the impact of each variable ( physico -chemical properties) on biological activity. Principal Component Analysis (PCA): This is also computerised method, this method is superior than MLR since, MLR can correlate 5 times of physico -chemical properties than compounds, but this method PLA correlate all the physico -chemical properties with biological activity even these physico -chemical properties are more than the number of compounds. 12

13. Partial Least Square (PLS): Modification of the PCA technique, where the dependent variables are also extracted into a new component as to maximize the correlation with the extracted component, Has an additional advantages of modelling multiple dependent parameters. 13

14. 4.STEPS IN QSAR (MODEL VALIDATION) Selection and Validation of QSAR models The selection and validation of the QSAR model for virtual screening is of almost importance and should confer to the following recommendations- ƒCareful selection of independent variables ƒSignificance of the variables (Statistical parameters) Minimum number of compounds per variable ƒImportance of the model that corroborates with known biophysical data. 14

15. HANSCH ANALYSIS Hansch proposal that drug action could be divided into two stages: 1. the transport of the drug to its site of action; 2. the binding of the drug to the target site. Each of these stages is dependent on the chemical and physical properties of the drug and its target site. log 1/C = k1(partition parameter) + k2(electronic parameter) . + k3(steric parameter)+ k4 where C is the minimum concentration required to cause a specific biological response. Hansch equations often takes the general form: log 1/C = k1P - k2P 2 + k3s + k4ES + k5 15

16. LIMITATION OF HANSCH ANALYSIS A large number of compounds is required. Lead optimization technique, not a lead discovery technique. Drug Receptor interactions cant be studied. FREE WILSON APPROACH Related biological activity to the presence/absence of a specific functional group at a specific location on the parent molecule. It is the only numerical method which directly relates structural features with biological properties, in contrast to Hansch analysis where the physicochemical properties are corrrelated with biological activity values. 16

17. Log BA= contribution of unsubstituted parent compound + contribution of corresponding substituent B.A = µ + ∑ aij Where, i = number of position at which substitution occurs. j = number of the substituent at that position. μ = overall average. ADVANTAGES OF FREE WILSON APPROACH Simple, fast and cheap method where no substitutions constants like pi, sigma, Es etc. required. Greater the complexity of structure, large is the number of possible substituents at desired positions. Hence efficiency is high. Contribution of each substituent can clearly be identified. 17

18. DISADVANTAGE 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. MIXED APPROACH “ Kubinyi has presents the combination of Hansch and Free Wilson models as mixed approach”. log 1\C = k1π + k2σ + k3Fs + k Hansch approach log 1\C = π + ∑ aij Free-Wilson approach The mixed approach can be written as; log 1\C =∑ aij + ∑ kjΦj + k 18

19. ADVANTAGES OF QSAR 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 19

20. DISADVANTAGE OF QSAR False correlation may arise because biological data that are subject to considerable experimental error. If training dataset is not large enough, the data collected may not reflect the complete property space. consequently, many QSAR results cannot be used to confidently predict the ,most likely compounds of best activity. There are many successful applications but do not expect QSAR works all time. Also be aware of overfiting problem. 20

21. APPLICATION OF QSAR Prediction of activity. Prediction of toxicity. Lead compound optimization. 21

22. REFERENCE Graham L. Patrick, 2001. An Introduction to Medicinal Chemistry, Oxford University Press, Page no.258-270. Ashutosh Kar . 2005. Medicinal Chemistry, New Age International Publishers, Page no.19-33 Dr. V. M. Kulkarni , Dr. K.G. Bothara , 2006. Drug Design, Nirali Prakashan , Page no.8.25-8.27 . 22

23. THANK YOU 23