3. 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 principe 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
4. • 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
5. 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.
6.
7. CoMFA(Comparative Molecular Field Analysis)
• In 1987, Cramer developed the predecessor of 3D approaches called Dynamic
Lattice-Oriented Molecular Modeling System (DYLOMMS) that involves the use
of PCA to extract vectors from the molecular interaction fields, which are then
correlated with biological activities.
• Soon after he modified it by combining the two existing techniques, GRID and
PLS, to develop a powerful 3D QSAR methodology, Comparative Molecular
Field Analysis (CoMFA).
• The underlying idea of CoMFA is that differences in a target property, e.g.,
biological activity, are often closely related to equivalent changes in shapes and
strengths of non-covalent interaction fields surrounding the molecules.
• Hence, the molecules are placed in a cubic grid and the interaction energies
between the molecule and a defined probe are calculated for each grid point.
8. Protocol for CoMFA:
A standard CoMFA procedure, as implemented in the Sybyl Software,
follows the following sequential steps:
• Bioactive conformations of each molecule are determined.
• All the molecules are superimposed or aligned using either manual or automated
methods, in a manner defined by the supposed mode of interaction with the
receptor.
• The overlaid molecules are placed in the center of a lattice grid with a spacing of
2 Å.
• The algorithm compares, in three-dimensions, the steric and electrostatic fields
calculated around the molecules with different probe groups positioned at all
intersections of the lattice.
9. • The interaction energy or field values are correlated with the biological activity
data using PLS technique, which identifies and extracts the quantitative influence
of specific chemical features of molecules on their biological activity.
• The results are articulated as correlation equations with the number of latent
variable terms, each of which is a linear combination of original independent
lattice descriptors.
• For visual understanding, the PLS output is presented in the form of an
interactive graphics consisting of colored contour plots of coefficients of the
corresponding field variables at each lattice intersection, and showing the
imperative favorable and unfavorable regions in three dimensional space which
are considerably associated with the biological activity.
10. DRAWBACKS AND LIMITATIONS OF CoMFA
CoMFA has several pitfalls and imperfections:
• Too many adjustable parameters like overall orientation, lattice placement, step
size, probe atom type etc.
• Uncertainty in selection of compounds and variables
• Fragmented contour maps with variable selection procedures
• Hydrophobicity not well-quantified
• Cut-off limits used
• Low signal to noise ratio due to many useless field variables
• Imperfections in potential energy functions
• Various practical problems with PLS
• Applicable only to in vitro data
11. CoMSIA
• Comparative Molecular Similarity Indices Analysis (CoMSIA) was developed to
overcome certain limitations of CoMFA.
• In CoMSIA, molecular similarity indices calculated from modified SEAL
similarity fields are employed as descriptors to simultaneously consider steric,
electrostatic, hydrophobic and hydrogen bonding properties.
• These indices are estimated indirectly by comparing the similarity of each
molecule in the dataset with a common probe atom (having a radius of 1 Å,
charge of +1 and hydrophobicity of +1) positioned at the intersections of a
surrounding grid/lattice.
• For computing similarity at all grid points, the mutual distances between the
probe atom and the atoms of the molecules in the aligned dataset are also taken
into account.
12. • To describe this distance-dependence and calculate the molecular properties,
Gaussian-type functions are employed. Since the underlying Gaussian-type
functional forms are ‘smooth’ with no singularities, their slopes are not as steep as
the Coulombic and Lennard-Jones potentials in CoMFA; therefore, no arbitrary cut-
off limits are required to be defined.
• CoMSIA is provided by Tripos Inc. in the Sybyl software [33], along with CoMFA.
13. APPLICATIONS:
1. QSAR in Chromatography: Quantitative Structure–Retention Relationships
(QSRRs)
2. The Use of QSAR and Computational Methods in Drug Design.
3. In Silico Approaches for Predicting ADME Properties.
4. Prediction of Harmful Human Health Effects of Chemicals from Structure.
5. Chemometric Methods and Theoretical Molecular Descriptors in Predictive
QSAR Modeling of the Environmental Behavior of Organic Pollutants
6. The Role of QSAR Methodology in the Regulatory Assessment of Chemicals
7. Nanomaterials – the Great Challenge for QSAR Modelers
14. Case study:Human Eosinophil Phosphodiesterase
• The phosphodiesterase type IV (PDE4) plays an important role in regulating
intracellular levels of cAMP and cGMP.
• PDE4 has highly expressed in inflammatory and immune cells and airway smooth
muscle and degrade the cAMP’s concentration.
• The inhibition of PDE4 increase the intracellular cAMP concentrations to kill
inflammatory cells and relax airway smooth muscle.
• To develop the selective PDE4 inhibitors as anti-inflammatory and asthmatic
drugs has attracted extensive research has been conducted. The QSAR studies of
PDE4 inhibitors have also been done by using CoMFA and CoMSIA methods.
15. • More potent and selective PDE4 inhibitors, a series of 5,6-dihydro-(9H) -
pyrazolo[3,4-c] -1,2,4-triazolo [4,3R]pyridine, were improved and synthesized
based on the structures of 7-oxo-4,5,6,7-tetrahydro-1H-pyrazolo[3,4-c] pyridine.
• In order to study the interaction mechanism of PDE4 with 31 new compounds,
the QSAR model was built by using the CoMFA.
5,6-dihydro-(9H) -pyrazolo[3,4-
c] -1,2,4-triazolo [4,3R]pyridine
16. Four compounds were randomly selected as test set, other twenty-seven compounds as
training set.
17. Method:
The structures of 31 compounds were built with molecular sketch program. Then
Gasteiger-Hückel charges were assigned to each atom and the energy minimization
of each molecule.
All the compounds studied had common rigid substructure. Therefore, the common
rigid substructure alignment was carried out by using database alignment tool. The
most active compound cyclobutyl substituent is used as template molecule.
18. • All aligned molecules were put into a 3D cubic lattice that extending at least 4 Å beyond
the volumes of all investigated molecules on all axes. In the 3D lattice, the grid spacing
was set to 2.0 Å in the x, y, and z directions.
• A sp3hybrized carbon atom with a charge of +1 was used as the probe atom, CoMFA
steric and electrostatic interaction fields were calculated.
• Partial least squares method was carried out to build the 3D-QSAR models. Leave-one-
out (LOO) cross-validated PLS analysis was used to check the predictive ability of the
models and to determine the optimal number of components to be used in the final
QSAR models.
• The PLS analysis gave a CoMFA model with cross-validated q2 value of 0.565 for 3
optimal components. The non-cross-validated PLS analysis of these compounds was
repeated with the optimal number of components and the R2 value was 0.867.
19.
20. CONCLUSION
CoMFA and CoMSIA are useful techniques in understanding pharmacological
properties of studied compounds, and they have been successfully used in modern
drug design. Despite of all the pitfalls it has now been globally used for drug
discovery based on well-established principles of statistics, is intrinsically a valuable
and viable medicinal chemistry tool whose application domain range from explaining
the structure-activity relationships quantitatively and retrospectively, to endowing
synthetic guidance leading to logical and experimentally testable hypotheses. Apart
from synthetic applications it has also been used in various other fields too.
21. Reference
1. An Introduction to Medicinal Chemistry FIFTH EDITION by Graham L. Patrick
2. Yuhong Xiang, Zhuoyong Zhang*, Aijing Xiao, and Jinxu Huo “Recent Studies of
QSAR on Inhibitors of Estrogen Receptor and Human Eosinophil
Phosphodiesterase” Department of Chemistry, Capital Normal University, Beijing
100048, P.R. China
3. Jitender Verma, Vijay M. Khedkar and Evans C. Coutinho “3D-QSAR in Drug
Design - A Review” Department of Pharmaceutical Chemistry, Bombay College of
Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India
