Qsar ppt

Quantitative Structure Activity Relationship

1. Role of QSAR and De novo Drug design in Drug Discovery Tanushree Karmakar M.Pharm (Pharmacology) 1 st Year 1 Dr. B.C Roy College of Pharmacy and A.H.S

2. Contents: Process of Drug Discovery Drug Designing Strategies of structure based drug design Concept of Docking QSAR and Drug Designing QSAR Steps Descriptors used in QSAR De novo drug design QSAR model Validation and statistical analysis 2D-QSAR and 3D-QSAR methods Applications of QSAR 2

3. Process of Drug Discovery The process of modern drug discovery starts with the identification of disease and therapeutic target of interest , include phases , methodologies ,lead identification , characterization , formulation , pharmacological studies , PK profile , safety and clinical studies. General steps: Target Selection or discovery Lead discovery : Lead generation and Optimization In vitro Studies Pre-clinical and clinical studies. A drug can be discovered from following approaches: From natural sources Screening Chemical modification of known drugs Observation of side effects Rational  Serendipity 3

4. Drug Designing Also referred as Rational drug design. Inventive process of finding new medications or interventions based on the knowledge of biological target. More focussed approach that uses structural information about the drug receptor or targets on one of its ligands as a basis to design , identify or create leads. Types of Structure based drug design: Receptor based drug design Ligand based drug design Factors Governing Drug design: Relationships between physico -chemical features and biological properties that need to be established retrospectively. Quantitative structure-activity relationships ( QSARs). Disease etiologies and various biochemical processes involved. 4

5. Strategies Of Structure Based Drug Design 5 Pharmacophore Identification Pharmacophore Modification Fit for the receptor Potential Drug Yes No Active site Identification Ligand fragments growing Fit for the receptor Complete Growing Potential Drug Change Fragment No Yes Yes No

6. Concept of Docking Docking refers to the ability to position a ligand in the active or a designated site of a protein and calculate the specific binding affinities and conformations at a receptor site . Attempts to find the “best” matching between two molecules. It includes finding the Right Key for the Lock . Software for Docking: DOCK, AUTODOCK,AUTODOCK Vina . 6 https://en.wikipedia.org/wiki/Docking_(molecular)

7. Main tasks of docking tools: Sampling of conformational (ligand) space. Scoring protein-ligand complexes Molecular Docking involves: Identification of the ligand’s correct binding geometry (pose) in the binding site (Binding Mode) Molecular Docking Prediction of the binding affinity (Scoring Function) 7 https:// www.intechopen.com/books/protein-engineering-technology-and-application/protein-protein-and-protein-ligand-docking

8. QSAR and Drug designing Attempts to correlate structural, chemical, and physical properties with biological activity by providing scientific credible tools for predicting and classifying biological activities of untested chemicals. Involves the derivation of mathematical formula which relates the biological activities of a group of compounds to their measurable physicochemical parameters. Depends on the theory of Lipinski Rule of Five: Drug Likeliness Screening: Method for evaluating the drug-like properties of a compound. Rule of five (RO5) is a rule of thumb to evaluate drug likeness or determine if a chemical compound with a certain pharmacological or biological activity has properties that would make it a active drug . QSAR’s general mathematical form is represented by the following equation: Biological Activity = f (Physicochemical Property) -Activity is expressed as log(1/c). C is the minimum concentration required to cause a defined biological response. 8

9. For a compound i , the linear equation that relates molecular properties, x 1 , x 2 .., x n to the desired activity, y is : y i = x i1 b 1 +x i2 b 2 +………….+x in b n +e i Expressing the previous equation in a compact form for the general case of n selected descriptors, the QSAR equation results into: y i =∑nx i b i +e i Where , b’s are linear slope that express the correlation of particular molecular property x i with the activity y i of the compound i ; and e i is a constant. 9

10. QSAR steps: General stages of QSAR model Development: Preparing molecules for QSAR study. Collection, design and calculation of values for all descriptors for all ligands in training sets. Selecting descriptors that can properly relate chemical structure to biological activities. Creating model using training set : Quantitative description of structural variation and choice of the QSAR model . Applying statistical methods that correlate changes in structure with changes in biological activity. Synthesis and Biological testing . Data analysis and Validation of the QSAR models (Internal and External). Interpretation of results for the proposal of new compounds : Based on statistical experimental design and multivariate data analysis. Obtaining a good quality QSAR model with the ability to predict activity of a chemical outside the training set depends upon many factors in the approach and execution of each individual steps. 10

11. Descriptors/Parameters used in QSAR Measure of the potential contribution of its group to a particular property of the parent drug. Numerical representation of chemical information encoded within a molecular structure via mathematical procedure. The information content of structure descriptors depends on two major factors: (1) The molecular representation of compounds. (2) The algorithm which is used for the calculation of the descriptor. The three major types of parameters initially suggested are : (1 ) Hydrophobic : Partition coefficient (log P) ; Hansch’s substitution constant (π ) ( 2) Electronic : Hammett constant ( σ, σ +, σ ) ; Taft’s inductive (polar) constant ( σ *) ( 3) Steric : Taft’s steric parameter (Es) ; Molar volume (MV) 11

12. Various types of Descriptors: Constitutional descriptors Geometrical descriptors Charge descriptors Topological descriptors Polarizable parameters Molecular descriptors Connectivity indices Functional group counts Information indices 12

13. Lipophilicity or Hydrophobicity It determines the ability of the drug molecule to cross the biological membrane . More the lipophilicity, more will be the biological activity. Also important in determining the receptor interactions . Partition Coefficient The hydrophobic character of a drug can be measured experimentally by testing the drug’s relative distribution in n- octanol /water system. This relative distribution is termed as partition coefficient. P = [drug]in n -octanol [drug]in aqueous system Hydrophobic compounds have high P value whereas hydrophilic compounds have a low P value. 13

14. Typically over a small range of log P , a straight line is obtained : log1/C= k1(log P)+k2 If graph is extended to very high log P values, then we get a parabolic curve: log1/C =- k1(log P)^2+k2logP+k3 14

15. Substituent hydrophobicity constant It is a measure of how hydrophobic a substituent is in relative to hydrogen which is calculated experimentally for a standard compound such as benzene with or without substituent X. π x= log Px -log PH Where π x is the hydrophobicity constant, Px is the partition coefficient for the standard compound with the substituent , PH is the partition coefficient of the standard compound . Steric Factors Steric substitution constant : It is a measure of the bulkiness of the group it represents and it effects on the closeness of contact between the drug and receptor site. It is much difficult to quantify. Namely : Taft’s steric factor (Es ) Molar refractivity (MR ) Verloop sterimol parameter 15

16. Electronic Effects Useful to measure the electronic effect of a substituent Given by Hammett substitution constant: Measure of electron withdrawing or electron donating ability of a substituent and is determined by measuring the dissociation of a series of substituted benzoic acid compared to the undissociated benzoic acid itself. Hammett constant takes into account both resonance and inductive effects; thus, the value depends on whether the substituent is para or meta substituted. - ortho position not measured due to steric effects. σ x = log ( Kx/K-benzoic acid) Where σ x is the H ammett constant , Kx is the dissociation constant of substituted benzoic acid. 16

17. Hansch Analysis Proposed that drug action could be divided into 2 stages: 1) Transport of drug to site & 2) Binding of drug to site Each of these stages depend upon the physical and chemical properties of the drug. It attempts to mathematically relate drug activity to measurable chemical property. Log 1/C = k1 (partition parameter) + k2 (electronic parameter) + k3 (steric parameter) + k4 Free Wilson Approach This method is based on the assumption that the introduction of a particular substituent at a particular molecular position , always leads to a quantitatively similar effect on biological potency of the whole molecules and expressed by the equation as BA= μ+Σaj For a series of chemical analogs , the biological activity is assumed to be the sum of intrinsic activity of the skeleton (μ) and the additive contribution of the substituents ( aj ). 17

18. De novo drug design De novo means starting from the beginning . Offers a broader exploration of chemical space and therefore makes it possible to identify novel ligand scaffolds . Design of novel chemical structures capable of interacting receptors with known structures. Approach to build a customized Ligand for a given receptor, involving ligand optimization. Ligand optimization can be done by analyzing protein active site properties that could be probable area of contact by the ligand using molecular modeling tools. Types of de novo drug design : Manual design Automated design : Revolves around the scoring functions used to estimate binding affinities .It is prone to generating structures which are either difficult or impossible to synthesize. 18

19. 19 De novo design Classes of design methods: Methods that analyze active site Methods that dock whole molecule Methods that connect molecular fragments or atoms together to produce a ligand: Site- point connection methods Fragment connection methods Sequential build up methods Random connection methods Some de novo design methods are : DOCK,AUTODOCK,CAVEAT,GRID,LUDI,SPROUT http://www.medicilon.com/de-novo-drug-design /

20. Methods for validating QSAR models : Internal validation : Least Squares Fit Fit of the Model Cross-validation Bootstrapping Randomization test (Y-Scrambling model) External validation Statistical analysis methods for predicting QSAR model : Regression Analysis Principle Component Analysis Partial Least square Analysis Clustered Analysis: Hierchial Clustering K-nearest neighboring method of clustering Artificial neuronal network 20

21. 2D-QSAR Methods : Free Energy Models : Hansch Analysis Mathematical Models :Free Wilson Analysis, Fujita Ban Modification Other Statistical methods : Discriminant Analysis , Principle component Analysis , Cluster Analysis , Combine Multivariate Analysis , Factor Analysis Pattern Recognition Topological Methods Quantum Mechanical Method 3D - QSAR Methods: Molecular shape analysis (MSA) Molecular topological difference (MTD) Comparative molecular movement analysis (COMMA) Hypothetical Active Site Lattice (HASL) Self Organizing Molecular Field Analysis (SOMFA) Comparative Molecular Field Analysis (COMFA) Comparative Molecular Similarity Indices (COMSIA) 21

22. Applications of QSAR Rational identification of new leads with pharmacological or biocidal activity. Identification of hazardous compounds at early stages of product development. Designing out of toxicity and side effects in new compounds. Prediction of variety of physio-chemical properties of molecules. 22

23. References: Medicinal Chemistry by Ashutosh K ar,Fourth Edition. QSAR: Hansch Analysis and Related Approaches by Hugo kubiany,VCH 1993. A Review on Computational Methods in Developing Quantitative Structure-Activity Relationship (QSAR );International Journal of Drug Design and Discovery : Volume 3 • Issue 3 • July – September 2012. 815-836. Validation of QSAR Models - Strategies and Importance ; International Journal of Drug Design and Discovery: Volume 2, Issue 3 ,July – September 2011. 511-519  23

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