drug design, methodology of COMFA classification, Applications

1. 3D QSAR APPROACHES
STRUCTURE
ROHIT PAL
M.PHARMACY
1ST YEAR
DEPARTMENT OF PHARMACEUTICAL CHEMISTRY

2. INTRODUCTION TO QSAR
• To relate the biological activity of a series of compounds to their physicochemical parameters in a
quantitative fashion using a mathematical formula.
• The fundamental principle involved is difference in structural properties is responsible for
variations in biological activities of the compound.
• Physico-chemical parameters: Hydrophobicity of substituents
Electronic properties of substituents
Hydrophobicity of the molecule
Steric properties of substituents

3. • Hansch Analysis:
Corelates biological activity with physico-chemical parsmeters.
Log(1/c) = k1 logP + k2 σ + k3 Es + k4
• Free-Wilson Analysis:
Corelates biological activity with certain structural features of the compound.
Limitation:
Does not consider 3D structure.
No graphical output thereby making the interpretation of results in familiar chemical terms,
frequently difficult if not impossible

4. 3D QSAR
• 3D QSAR is an extension of classical QSAR which exploits the 3 dimensional properties of the
ligands to predict their biological activity using robust stastical analysis like PLS, G/PLS, ANN
etc.
• 3D-QSAR uses probe-based sampling within a molecular lattice to determine three-dimensional
properties of molecules and can then correlate these 3D descriptors with biological activity.
• No QSAR model can replace the experimental assays, though experimental techniques are also
not free from errors.
• Some of the major factors like desolvation energetics, temperature, diffusion, transport, pH, salt
concentration etc. which contribute to the overall free energy of binding are difficult to handle,
and thus usually ignored.
• Regardless of all such problems, QSAR becomes a useful alternative approach.

5. CLASSIFICATION OF 3D-QSAR

6. 3D QSAR -APPROACHES
• STEREOCHEMISTRY
• ACTIVE SITE INTERACTION
• COMPARITIVE MOLECULAR FIELD ANALYSIS (CoMFA)

7. STEREOCHEMISTRY AND DRUG ACTION
• stereochemistry of drugs plays an important role for the biological activity.
• According to Ariens and Lehman, chirality has an important influence on biological activity.
• This situation is even worse in diastereomeric mixtures for two reasons.
 2n species are involved.
 the relative amounts of the different racemate in the mixture vary largely.

8. • Eg:
Labetalol, a β anti adrenergic drug having two centers of optical
asymmetry shows different pharmacological actions for its
enantiomers.

9. • According to Pfeiffer’s, the activity ratio of the active Vs the less
active enantiomer increases with increasing activity of more
active one.
• Schaper derived quantitative as well as qualitative models for
the dependence of the biological activity of a racemate on the
activity of pure enantiomers.

10. ACTIVE SITE INTERACTION :
• Pharmacophore pattern searching and receptor mapping use information from the
QSAR’s in the different positions of the ligand and also from the ligand with restricted
internal rotations (rigid analogs).
• Thus derive the pharmacophore and to conclude on the properties at the different sites of
the receptor surface (the receptor map) .

11. • The interaction energies of ligand to hypothetical receptor
sites have been performed by Holtje. Simple organic
molecules are models of different amino acid side chains.
• E g: n-propane for aliphatic amino acids, acetamide for
amide side chains, methanol for serine, toluene for aromatic
amino acids etc.

12. • The interaction energies of each molecule are calculated using
several of these probes.
• All analogs of a series are placed in standard geometries and in
certain distances to the hypothetical amino acid side chains.
• The resulting energies are then correlated to receptor affinities or
to biological activities.

13. ACTIVE SITE INTERACTION MODELS
GRID
• The GRID program is a computational procedure similar to CoMFA .
• It is used to predict specific noncovalent interactions between a molecule of known 3D
structure and a small chemical group (the probe) whose properties are defined by the user.
• Probes are placed on the grid points, and interaction energies with a molecule are calculated.
• Therefore, GRID calculates not only steric and electrostatic potential, but also the hydrogen-
bonding potential using a hydrogen bond donor and acceptor, and the hydrophobic potential
using a “DRY probe.”
• other probes that are regularly used singly include
methyl group,
the amine (NH2) group,
the carboxylate group,
and the hydroxyl group.

14. • Contour surfaces are calculated at various energy levels for each probe for every point on the
grid and are displayed graphically along with the protein structure.
• While negative energy levels of the contours describe regions at which ligand binding should be
favored, positive energy levels normally characterize the shape of the target.
• Advantages:
• The use of a 6-4 potential function for calculating the interaction energies, which is smoother
than the 6-12 form of the Lennard-Jones type in CoMFA
• The availability of different types of probes to make the calculation more diverse and wide open.

15. Hint interaction field analysis
• Hint interaction field analysis (HIFA) is a newly developed, alignment-dependent 3DQSAR
method.
• This method is employed to calculate empirical hydrophobic interaction . It is an extension of
the CoMFA approach.
• As a result of the introduction of hydrophobicity calculation in CoMFA, the predicative
capability of this type of QSAR model has enhanced.
• It calculates key hydrophobic features that are atom-based analogs of the fragment constant.
•

16. • The methodology of HIFA includes two steps:
• 1. Calculating the hydrophobic field interaction by aligning the ligands (in the same
manner as with CoMFA).
• 2. Placing the aligned ligands into a grid, followed by interpreting the net sum of
hydrophobic interaction

17. • DISTANCE GEOMETRY
• This is an approach to calculate 3D Co-ordinates from a set of distances.
• It is used for the calculation of 3D structures of organic compounds, peptides and
small proteins from 2D measurements.
• Approximate 3D structures of the ligands are constructed and low energy
conformations are selected.

18. • APEX – 3 D
• It recognizes pharmacophores in biologically active molecules.
• This program compares the descriptors and their distances for active and
inactive analogues and stores the results as rules in a knowledge base,
which can be used to predict the activity of new compounds.

19. COMPARATIVE MOLECULAR FIELD ANALYSIS
• CoMFA is the 3D-QSAR approaches that furnish better information about the drug-receptor
intractons.
• It is developed by dick crammer in 1988.
• It involves placing of molecules in a grid and to correlate field properties of the molecules with
biological activities.
• CoMFA is an alignment-dependent, descriptor-based method, all aligned ligands are placed in
an energy grid, and by placing an appropriate probe at each lattice point, energy is calculated.
• The resultant energy calculated at each unit fraction corresponds to electrostatic (Coulombic)
and steric (van der Waals) properties.
• These computed values serve as descriptors for model development.

20. • These descriptor values are then correlated with biological responses employing a robust linear
regression method like partial least squares (PLS).
• The PLS results serve as an important signal to identify the favorable and unfavorable
electrostatic and steric potential and also correlate it with biological responses.

21. METHODOLOGY OF COMFA
• Group of compounds having a common pharmacophore is selected .
• The 3-dimensional structures of reasonable conformation must be generated from 2-dimensional
structures.
• The energy minimized structures are fitted to each other using pharmacophore hypothesis.
• Molecules are then aligned using active analog approach, distance geometry method

25. • CoMFA Once molecules are aligned, a grid or lattice is established which
surrounds the sets of analogues in potential receptor space.

27. • A Probe atom is placed at each grid point. Steric and electrostatic fields are calculated for
each molecule in every grid point.
• Next step in a CoMFA is a partial least square analysis to determine a minimal set of grid
points necessary to explain measured biological activities of the compounds.
• CoMFA results are often presented in a graphical form ;with contours :favorable and
unfavorable regions of different fields.

29. COUNTOUR PLOTS
• The results of CoMFA may also be displayed as counter plots showing the regions in
space where specific molecular properties increases or decreases the potency.
• The counter are coloured in green and yellow for positive and negative steric effects
respectively while blue and red for positive and negative electrostatic effects
respectively.
• Positive steric countour define the region where substituent size is proportional to
biological activity and negative steric countour highlights the area where
substituents decreases the potency.
• The positive electrostatic countour shows the region where +ve charges increases
the potency where as in negative electrostatic countour regions, -ve charges
increases the activity.

33. APPLICATIONS
• Predict the properties and activities of untested molecules• Compare different QSAR models
statistically and visually•
• Optimize the properties of a lead compound•
• Validate models of receptor binding sites•
• Generate hypotheses about the characteristics of a receptor binding site• Prioritize
compounds for synthesis or screening•
• There are now a few hundred practical applications of CoMFA in drug design.
• Most applications are in the field of
• – Ligand protein interactions
• – Describing affinity or inhibition constants
• – Correlate steric and electronic parameters

34. PROBLEMS IN COMFA
• The force field functions do not model all interaction types•
• Show singularities at the atomic positions•
• Deliberately defined cut-off values needed• Contour plots often not contiguously connected
• New approach: CoMSIA (Comparative Molecular Similarity Indices)• Does not calculate
interaction energies but distance-dependent similarity indices (similarity of probe to
molecule atoms) resulting in smooth contour plot.

35. DETERMINATION OF BIOACTIVE
CONFORMATIONS
• The bioactive conformation defines a particular conformation of the molecule in
which it is bound to the receptor.
• The intrinsic forces between the atoms in the molecule, as well as extrinsic
forces between the molecule and its surrounding environment, considerably
influence the bioactive conformation of the molecule.
• Experimental methods for creating bioactive conformations comprise the
techniques:-
• X-ray crystallography
• NMR spectroscopy

36. X-RAY CRYSTALLOGRAPHY
• The precise 3D structure of the macromolecules can be obtained by this method. Drug-
receptor complexes generated by X-ray crystallography logically offer the exact information,
but this method has several disadvantages:
• • The protein needs to be crystallized, and the formation of crystallizing media is not
typically like the physiological conditions.
• • There is a chance of structural distortion due to crystal packing.
• • Due to crystal instability and active-site occlusion, it is often not promising to disperse
substrates or other biologically applicable molecules into the existing crystals.
• • The positions of hydrogen atoms are tricky to be determined.
• • There is a possibility of errors in determining the structure of the ligand.

37. NMR SPECTROSCOPY
• The 3D structural data is obtained in the solution and is a method of selection when the molecule cannot be
crystallized through experimental ways, as in the case of the membrane-bound receptors or receptors, which
have not yet been isolated due to stability, resolution, or other issues.
• The imperative features of this method are:
• • As no protein crystallization is required, the conformation of the protein is not influenced by packing
forces of the crystal environment.
• • The solution conditions (pH, ionic strength, substrate, temperature, etc.) can be accustomed to match the
physiological conditions.
• • Significant information regarding dynamic aspects of molecular motion can be obtained.
• • It requires much less time but applicable to small molecules only.
• • The positions of hydrogen atoms can be resolved.
• • Apolar solvents may lead to an overprediction of hydrogen-bonding phenomena.
• • Structures generated from NMR may not be comparable to the ones obtained from the experiment and
frequently it may not signify the receptor-bound conformation.