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Quantitative Structure-Activity Relationships QSAR

QSAR: Systematic approach to lead compound optimization. Assume drug action is related to the physical properties of the ligand. Historical Galileo Galilei (1564-1642) Richet (1893) Overton and Meyer (1890's) Ferguson. Applications of QSAR (Hansch Analysis). 1) Classification 2)

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Quantitative Structure-Activity Relationships QSAR

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    1. Quantitative Structure-Activity Relationships (QSAR)

    2. QSAR: Systematic approach to lead compound optimization

    3. Applications of QSAR (Hansch Analysis) 1) Classification 2) Diagnosis of Mechanism of Drug Action B.A. = 0.94 logP + 0.87, r = 0.97, n = 51 3) Prediction of Activity (congeneric series) 4) Lead Compound Optimization

    4. Sulmazole

    5. Hammett electronic parameter or substituent constant, s

    6. Positive versus negative s values for chemical substituents, x.

    7. Example of resonance forms that stabilize the negatively charged carboxylate in p-nitrobenzoic acid

    8. General utility of ? values: Saponification of substituted ethyl benzoates

    9. The Hammett constants, s,, can be related to the free energy of ionization via the vant Hoff relationship (In this case s would correspond to the equilibrium constant, K, allowing for Hammett relationships to also be referred to as a linear free energy relationship (LFER)).

    10. Ester hydrolysis reaction and equation used to define the Taft steric parameter, Es

    11. Equation for the molar refractivity

    12. Consideration of asymmetric shape of functional groups and molecules in QSAR

    13. Application of QSAR to biological systems (Biological Hammett Relationship): Hansch, 1962

    14. Equations for the determination of the partition coefficient, P, and the hydrophobicity parameter, px

    15. Determination of partition coefficient, P

    16. Example of calculation of log P

    17. Example of a linear equation where multiple variables are used to obtain a correlation with biological activity (1/C).

    18. Multiple Regression Analysis Hypothetical training set of biological activities, hydrophobicities and sigma values

    19. Individual plots of log(1/C) versus p or s, including least-squares analysis

    20. Example of multiple regression least squares fitting

    21. Application of multiple regression to the training set BA = -0.51 p + 0.23 s + 0.90, r2 = 0.90 Versus (from linear regression) BA = -0.63 p + 1.20, r2 = 0.73 and BA = 0.37 s + 0.30, r2 = 0.49

    22. Multiple regression alone still didn’t work!

    23. Log P versus biological activity (y = -x2) parabolic plot

    24. Hansch equation

    25. Example of an extended Hansch Equation where the Taft steric parameter, Es, has been included.

    26. Advantages of Hansch analysis A) Use of descriptors (p, s, Es etc.) from small organic molecules may be applied to biological systems. B) Predictions are quantitative and may be evaluated statistically. C) Quick and easy. D) Potential extrapolation: conclusions reached may be extended to chemical substituents not included in the original analysis.

    27. Disadvantages of Hansch analysis A) Descriptors required for substituents being studied. B) Large number of compounds required (training set for which physicochemical parameters and biological activity is available). C) Limitations associated with using small molecule descriptors, such as steric factors, on biological systems (i.e. descriptors from physical chemistry). D) Partial protontation of drugs at physiological conditions (can be included in mathematical model). E) Predictions limited to structural class (congeneric series). F) Extrapolations beyond the values of descriptors used in the study are limited. G) Correlation between physical descriptors. For example, the hydrophobicity will have some correlation with the size and, thus, the Taft steric term.

    28. QSAR interpolations versus extrapolations Spanned Substituent Space (SSS): range of physical properties covered by the compounds in the training set. Interpolative predictions: within SSS Extrapolative predictions: beyond SSS

    29. Statistical Significance in QSAR

    30. Free and Wilson Model

    31. Example of Free and Wilson Approach

    32. Combine QSAR and Free and Wilson

    33. Topliss Decision Tree for a Sulfa Drug

    34. Craig plot of hydrophobicity versus smeta

    35. Craig plot of hydrophobicity versus the Taft Steric Term, Es 

    36. Batchwise Approach

    37. Potency order for various Parameter Dependencies for the Batchwise Approach

    38. New substituent selections based on parameter dependencies from the Batchwise approach

    39. Example of Batchwise approach

    40. Physiochemical parameters used in QSAR Investigations.

    42. Additional descriptors for Hansch Analysis

    43. 3D QSAR or Compartive Molecular Field Analysis (CoMFA) QSAR approach to deal with interactions of molecules with their environment taking into account 3D shape. Electrostatic and Steric interactions at selected points around molecules replace physical parameters in normal QSAR

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