Quantitative Structure Activity Relationship

In this slide I Presented the introduction of QSAR, 2D-QSAR and 3D-QSAR including its Method, Classification,Limitation and Application.

1. QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP Presented By Rani T. Bhagat M. Pharmacy (Pharmaceutical Chemistry) School of Pharmacy, SRTM University, Nanded

2. INTRODUCTION Quantitative Structure Activity Relationship (QSAR) are mathematical relationship between chemical structure and pharmacological activity in a quantitative manner for series of compound. The fundamental principle involved is difference in structural properties is responsible for variations in biological activities of the compound. Biological activity=Functions(parameters) Physico -chemical parameters: Hydrophobicity of substituents Electronic properties of substituents Hydrophobicity of the molecule Steric Properties of substituents

3. 2D- QSAR It is powerful tool for explaining the relationship between chemical structure and experimental observation . The numerical descriptors used to translate a chemical structure into mathematical . 2D QSAR models are used routinely during the process of optimization of a chemical series towards a candidate for clinical trials. It can be classified based on parameters and description as: 1)2D vs 3D and Classical vs Non Classical 2)QSAR-QSPR-QSMR-QSTR 3)2D QSAR for drug design

4. METHODS

5. 3D-QSAR In 3D QSAR ,3D properties of a molecule are considered as whole rather than consideration individual substituents. 3D-QSAR involve the analysis of the quantitative relationship between the biological activity of a set of compound and their three – dimensional properties using statistical correlation methods. 3D QSAR revolve around the important features of a molecule , its overall size and shape ,and its electronic properties . 3D QSAR is an extension of classical QSAR which exploits the 3 dimensional properties of the the ligands to predict their biological activity using robust stastical analysis like PLS.

6. 3D-QSAR 3D-QSAR uses probe – based sampling within a molecular lattices to determine three- dimentional properties of molecules and can then correlate these 3D descriptors with biological activity. No QSAR model can replace the experimental technique are also free from errors. Some of the major factors like desolvation energetics, temperature, diffusion, transport ,pH ,salt concentration etc.which contribute to all overall free energy of binding are difficult to handle ,and thus usually ignored. Regardless of all such problems, QSAR become a useful alternative approach.

7. CLASSIFICATION

8. CoMFA CoMFA (Comparative Molecular Field Analysis) In 1987, Cramer developed the predecessor of 3D approaches called Dynamic Lattice – Oriented Molecular Modelling System (DYLOMMS) that involve the use of PCA to extract vectors from the molecular interaction fields ,which are then correlate with biological activity. Soon after be modified it by combining the two existing technique , partial least square method used to develop a powerful 3D-QSAR methodology, CoMFA . CoMFA is that difference in target property eg . Biological activity, are often closely related to equivalent change in shape and strenght of non-covalent interaction fields surrounding the molecule. The molecule placed in cubic grid and the interaction energies between the molecule and a defined probe are calculated for each grid point.

9. LIMITATION OF CoMFA Too many adjustable parameters like overall orientation ,Lattice placement, step size ,probe atom type. Uncertainty in selection of compounds and variable . Fragmented contour maps with variables selection procedure . Hydrophobicity not well quantified. Cut-off limit used. Imperfections in potential energy function. Various practicle problem with PLS . Applicable only to in vitro data.

10. CoMSIA Comparative Molecular Similarity Indices Analysis ( CoMSIA ) was developed to overcome certain limitation of CoMFA . In CoMFA ,molecular similarity indices calculated from modified Steric , electrostatic, hydrophobic and hydrogen bonding properties. These indices are estimated indirectly by compairing the similarity of each molecule in the dataset with a common probe atom positioned at the interaction of a surrounding grid\lattice. For computing similarity at all grid points,the mutual distance between the probe atom and the atoms of the molecule in the aligned dataset are also taken into account.

11. DIFFERENCE BETWEEN CoMFA CoMSIA Function type Lennard-Jomes potential, Coulomb potential Gaussian Description Interaction energy Similarity indices Cut-off Required Not required Field Steric , Electrostatic Steric , Electrostatic, Hydrophobic Contour map Not contiguous Contiguous Model reproducibility Poor Good

12. APPLICATION QSAR in Chromatography: Quantitative Structure- Retension Relationship(QSRR). Used for predict the ADME Properties. Used in drug designing, virtual screening. Used for prediction of Harmful Human Health Effects of chemicals from Structures. The role of QSAR methodology in the Regulatory Assessment of Chemicals. Used for optimize the properties of lead compounds