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AMITY INSTITUTE OF BIOTECHNOLOGYAMITY UNIVERSITY

ASSIGNMENT NO. I

PHARMACEUTICAL CHEMISTRY AND DRUG DESIGN

SUBMITTED TO: SUBMITTED BY:DR. MONALISA HIMANSHU BHANSALI(57)FACULTY,AIB JAIDEEP GOYAL (58) KU LDEEP SINGH(69) MAYANK JAIN (77)

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TABLE OF CONTENTS

1 INTRODUCTION 3

2 REVIEW OF LITERATURE 4

3 STATISTICAL CONCEPTS 7

4QSAR APPROACH TO PHYSICOCHEMICAL

PROPERTIES8

5 3-D QSAR AND COMFA 17

6 APPLICATION OF QSAR 20

7 SUMMARY 23

8 REFERENCES 24

INTRODUCTION

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Quantitative structure-activity relationship (QSAR) is the process by which chemical

structure is quantitatively correlated with a well defined process, such as biological

activity or chemical reactivity. For example, biological activity can be expressed

quantitatively as in the concentration of a substance required to give a certain biological

response.

Additionally, when physicochemical properties or structures are expressed by numbers,

one can form a mathematical relationship, or quantitative structure-activity relationship,

between the two. The mathematical expression can then be used to predict the biological

response of other chemical structures.

QSAR's most general mathematical form is:

Activity = f (physiochemical properties and/or structural properties)

The different physical properties that influence biological activity and use of those

properties in the development of mathematical models that relate the physical properties

to biological activity, may be used to understand and predict drug action.

In summarized manner, QSAR is a mathematical relationship between a biological

activity of a molecular system and its geometric and chemical characteristics and thus it

attempts to find consistent relationship between biological activity and molecular

properties.

REVIEW OF LITERATURE

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Receptor-based 3D-QSAR studies of checkpoint Wee1 kinase inhibitors.

Wichapong K, Lindner M, Pianwanit S, Kokpol S, Sippl W.

Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330,

Thailand; Institute of Pharmaceutical Chemistry, Martin-Luther-University Halle-Wittenberg,

06120 Halle (Saale), Germany.

One hundred and seventy-four pyrrolo[3,4-c]carbazole-1,3(2H,6H)-dione derivatives reported as

inhibitors of the kinase Wee1 were used for a molecular docking and three-dimensional

quantitative structure-activity relationship (3D-QSAR) study. Due to the availability of the three-

dimensional structure of the Wee1 kinase a receptor-based alignment strategy was applied. Six

available Wee1-inhibitor crystal structures were analyzed using the docking program GOLD

resulting in a good reproduction of the experimentally derived position and interaction of the

cocrystallized inhibitors. Since only a low correlation between docking scores and inhibitory

activities was obtained for the series of 174 inhibitors a receptor-based 3D-QSAR study was

performed, dividing the data set into 144 training set molecules and an external test set of 30

compounds. Besides the ligand alignment derived from the docking study we tested several other

alignment procedures as basis for the 3D-QSAR analysis. The most predictive model was

obtained using the alignment from the GOLD docking study. The CoMFA model was found to

be robust (q(LOO)(2)=0.764 and r(2)=0.870). The predictive ability of the model was further

examined by carrying out leave-20%-out and leave-50%-out cross-validation (q(2)=0.747 for

leave-20%-out and 0.737 for leave-50%-out) and predicting the activities of 30 inhibitors used as

external test set (r(pred)(2)=0.790). The graphical analysis of the CoMFA contour plot together

with the key residues of the binding pocket provided important insight into the relevant

interactions of the inhibitors. The results not only provide information about the essential

features of potent Wee1 inhibitors but also show the advantage of using receptor-based

alignment for 3D-QSAR analysis.

PMID: 18976834 [PubMed - as supplied by publisher]

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Promises and Pitfalls of Quantitative Structure-Activity Relationship Approaches for

Predicting Metabolism and Toxicity.

Zvinavashe E, Murk AJ, Rietjens IM.

[email protected].

The description of quantitative structure-activity relationship (QSAR) models has been a topic

for scientific research for more than 40 years and a topic within the regulatory framework for

more than 20 years. At present, efforts on QSAR development are increasing because of their

promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments.

However, their acceptance in risk assessment seems to require a more standardized and scientific

underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for

QSAR model development recently proposed by the Organization for Economic Cooperation

and Development (OECD) [ Organization for Economic Cooperation and Development (OECD)

( 2007) Guidance document on the validation of (quantitative) structure-activity relationships

[(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and

Assessment No. 69, Paris ] are expected to help increase the acceptability of QSAR models for

regulatory purposes. The guidelines recommend that QSAR models should be associated with (i)

a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv)

appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic

interpretation, if possible [ Organization for Economic Cooperation and Development (OECD)

( 2007) Guidance document on the validation of (quantitative) structure-activity relationships

[(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR

model development and their rationale, as well as the promises and pitfalls of using QSAR

approaches and these guidelines for predicting metabolism and toxicity of new and existing

chemicals.

PMID: 18956846 [PubMed - as supplied by publisher]

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Quantitative Three Dimensional Structure Linear Interaction Energy Model of 5'-O-[N-

(Salicyl)sulfamoyl]adenosine and the Aryl Acid Adenylating Enzyme MbtA.

Labello NP, Bennett EM, Ferguson DM, Aldrich CC.

MbtA (a salicyl AMP ligase) is a key target for the design of new antitubercular agents. On the

basis of structure-activity relationship (SAR) data generated in our laboratory, a structure-based

model is developed to predict the binding affinities of aryl acid-AMP bisubstrate inhibitors of

MbtA. The approach described takes advantage of the linear interaction energy (LIE) technique

to derive linear equations relating ligand structure to function. With only two parameters derived

from molecular dynamics simulations, good correlation ( R (2) = 0.70) was achieved for a set of

31 inhibitors with binding affinities spanning 6 orders of magnitude. The results were applied to

understand the effect of steric and heteroatom substitutions on bisubstrate ligand binding and to

predict second generation inhibitors of MbtA. The resulting model was further validated by

chemical synthesis of a novel inhibitor with a predicted LIE binding affinity of 1.6 nM and a

subsequently determined experimental K i (app) of 0.7 nM.

PMID: 18959400 [PubMed - as supplied by publisher

Simple and highly predictive QSAR method: application to a series of (S)-N-[(1-ethyl-2-

pyrrolidinyl)methyl]-6-methoxybenzamides.

Freitas MP, Martins JA.

EMS Sigma Pharma - R&D, Rodovia SP 101 Km 08, Hortolândia SP 13186-401, Brazil.

A simple quantitative structure-activity relationship (QSAR) method of analysis used to predict

biological activity for congeneric series of compounds is reported. This method is based on the

application of bilinear or multilinear partial least squares regression to a data set, which is a

binary matrix representing the substituents of a framework. It is appraised here to a series of (S)-

N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides, compounds with affinity towards the

dopamine D(2) receptor subtype and showed high predictive ability, even when compared to a

refined three-dimensional (3D) approach.

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STATISTICAL CONCEPTS

In the simplest situation, a range of compounds are synthesized in order to vary one

physicochemical property (e.g. log p) and to test how this affects the biological activity (log

1/C). A graph is then drawn to plot the biological activity on the y axis versus the

physicochemical feature on the x axis. It is then necessary to draw the best possible line

through the data points on the graph. This is done by a procedure known as “Linear

regression analysis by the least square method”.

Fig 1 : linear regression method

As the data points are scattered on the either side of the line so to measure how close the data

points are, vertical lines are drawn from each point. These verticals are measured and then

squared. The squares are then added up to give a total. The best line through the points will

be the line where this total is a minimum.

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The equation of the straight line will be y = kx + k’ where k and k’ are constants. By varying

k and k’, different equations are obtained until the best line is obtained.

Now to check the significance of the equation, it can be checked by regression coefficient(r).

For a perfect fit r2 = 1. Good fits have values of 0.95 or above.

The problem of QSAR is to find coefficients C0,C1,...Cn such that:

Biological activity = C0+(C1*P1)+...+(Cn*Pn)

and the prediction error is minimized for a list of given m compounds. Partial least squares

(PLS) is a technique used for computation of the coefficients of structural descriptors.

QSAR APPROACH TO PHYSICOCHEMICAL PROPERTIES

There are many physical, chemical and structural properties which have been studied by the

QSAR approach, but the most commonly studied are hydrophobic, electronic, and steric.

This is because it is possible to quantify these effects relatively easily.

In particular, hydrophobic interactions are more easily quantified for complete molecules or

for individual substances rather electronic, and steric properties are difficult and

quantification is really feasible for individual substituent.

The three most studied physicochemical properties will now be considered in more detail-

a) Hydrophobicity

The hydrophobic character of a drug is crucial to determine how easily it crosses cell

membrane and may also be important in receptor interactions. Changing substituents on a

drug may have significant effects on its hydrophobic character and hence its biological

activity.

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Affinity of a compound for a lipid (hydrophobic environment) that can be quantified using

octanol-water partition coefficients yielding hydrophobic substituent constants (π).

Equations for the determination of the partition coefficient, P, and the hydrophobicity

parameters, πx :-

Where Px is the partition coefficient of the substituted compound x and PH is the partition

coefficient of the parent (unsubstituted) compound.

Larger P, more hydrophobic (nonpolar compound or lipophilic): therefore, the larger and

positive the value of π, the more hydrophobic. For a hydrophilic (polar compound or

lipophobic) the value of Px will be less than that of PH, such that the ratio Px/PH will be a

fraction leading to π being negative.

The P value of substituted compounds is then determined by adding the πx values for the

individual substitutents to that of the parent compound.

Determination of Partition Coefficients

Experimental log P or π from octanol-water systems limited to log P betweeen -2 to 3 due to

difficulties in `shaker-flask' measurements. Alternative is to use different solvents: hexane/water

or ether/water. Retention, RM, from reversed phase TLC or HPLC. More hydrophobic

compounds will interact more favorably with the nonpolar solid phase of the column or TLC

plate, thereby moving more slowly and leading to a longer retention values.

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Calculation of log P (substitutent contributions to P are not directly additive due to interactions

between the substitutents as they are added to the parent compound). Equation for the calculation

of log P using fragmental constants and interaction factors.:-

In the equation fi are “fragmental constants” associated with chemical substituents (i.e. these are

the π values), Fi are “interaction factors” to account for intramolecular electronic, steric or

hydrogen-bonding interactions between the substitutents that lead to the contribution of those

substituents not being additive. This or similar approaches are now widely used and the values of

fi and Fi are known for a huge number of substituents and pairs of substituents, respectively.

This approach avoids experiments being performed to determine P values for all compounds in a

QSAR study.

Hypothetical example for o-fluorotoluene

log P of benzene = 2.5 (parent compound)

fi of methyl = 0.6

fi or aromatic fluorine = -0.4

Fi for fluorine atom ortho to a methyl group is -0.3

Therefore, log Po-fluorotoluene = 2.5 + 0.6 + (-0.4) + (-0.3) = 2.4

Multiple linear regression analysis allows for solving equations with multiple variables

(parameters) as required to include π with σ terms as in the following relationship-

log (1/C) = k1π + k2σ + k3

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.C is the concentration of a compound producing a biological response (eg. LD50, IC50). k1 or

k2 is analogous to ρ in the Hammett equation (similar to the slope, indicates the contribution of

the physical property associated with it to the biological activity), k3 is the biological activity of

the hypothetical parent compound (the value when σ and π = 0.0, which occurs when the

substituents are all hydrogens).

b) Electronic effects

These effects of various substituents will clearly have an effect on a drug ionization or polarity.

This in turn may have an effect on how easily a drug can pass through cell membrane or how

strongly it can bind to a receptor. As far as substituents are concerned on an aromatic ring, the

measure used is known as Hammett substitution constant which is given by symbol σ.

Hammett electronic parameter or substituent constant, σx, is the electronic effect of

substituent x relative to hydrogen (i.e. the unsubstituted compound). σx is determined based on

the influence of a substitutent on the ionization of benzoic acid. Those values may then be

applied to many types of systems (e.g. inhibitor binding) as characterized by different values of

the reaction constant, ρ.

log (Kx/KH) = σ

Equation used in the determination of the Hammett constant where KH is the equilibrium or rate

constant for the parent (unsubstituted) compound and Kx is the equilibrium or rate constant for

the derivative: these values are measured experimentally.

Positive σx: chemical groups that withdraw electrons from the ring (e.g. -NO2) favor the

anion, thereby increasing K.

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Figure 2 :- Equilibrium between the unionized and ionized forms of benzoic acid and the definition of Ka

In the given example , Electron withdrawing groups will favor the equilibrium to the right, due to

the substituent pulling electron density out of the ring thereby decreasing the electron density on

the carboxylate and making formation of the negative species less unfavorable.

Shifting the equilibruim to the right increase Kx, such the Kx/KH will be greater than 1 and the

log of Kx/KH is positive, yielding a positive σ .In the case of para substituents that have

resonance structures, the resonance structures lead to the negative charge being distributed over

the entire molecule, such that thenegative species is much less unfavorable. Accordingly, this

leads to an increased Kx and, ultimately, a positive σ.

Negative σx: chemical groups that donate electrons into the ring (e.g. -OCH3), favor the neutral

species, thereby decreasing K.

Electron donating groups will increase the electron density of the ring and the carboxylate group,

thereby favoring the equilibrium in above example to the left. This yields a decrease in Kx, such

Kx/KH will be less than 1, and the log of Kx/KH will be negative, yielding a negative σ.

Substituent location on the ring affects the value of σ for the substituent. Meta and para σ values

for substituents are commonly used; however, ortho σ are often unreliable due to direct

interactions of the o substitutent with the functional group (e.g. the acid in benzoic acid).

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General use of sigma values-

The σx values are then applied directly to other equilibria or reactions. For example, for a

congeneric series of compounds determine a binding constant as a function of substituent for a

training set of compounds (different substitutents have different σx values), plot σx versus the

binding constant. The slope of that plot yields the reaction coefficient, ρ.

The value of ρ is an indication of the influence of the electronic effect on the binding constant. If

ρ > 1 then the electronic contribution of substituents is greater than it is for the ionization of

benzoic acid. If ρ < 1 then the electronic contribution of substituents is less than it is for the

ionization of benzoic acid.

Note that ρ can be less than 0 (e.g. a negative value), indicating that the effect is opposite that

occurring with respect to the ionization of benzoic acid.

It should be noted that when the data from a training set yields a positive slope, than increasing

σx (adding electron withdrawing substituents) will increase activity. Alternatively, when the data

from a training set yields a negative slope, than decreasing σx (adding electron donating

substituents) will increase activity.

Multiple substituents on a compound

When a compound has multiple substituents, the σ values of the individual substituents are

summed to yield the total σ for the compound (i.e. additive treatment).

Example:

Fig 3:- Use of σx to quantify the saponication of ethyl benzoates

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3. Steric factors

In order for a drug to interact with an enzyme or receptor, it has to approach, then bind to a

binding site. The bulk, size and shape of the drug may have an influence on this process.

Taft hypothesized that the size of a chemical substituent would affect reaction rates and

equilibria (i.e. steric effects (Es)). Investigated this via QSAR analysis of the rates of base-

catalyzed hydrolysis of aliphatic esters. This idea was an important step towards the effective

application to biological systems.

Fig 4:- Reaction (ester hydrolysis) and equation used to define the Taft steric parameter, Es

Since the reaction involves nucleophilic attack of the carbonyl carbon by a hydroxide ion, the

presence of a substitutent on the methyl group adjacent to the carbonyl C will hinder the

nucleophilic attack. Thus, bulky groups will block access to the carbonyl carbon, thereby

slowing the reaction, making kx < kH in all cases, such that log (kx/kH) will be negative.

Therefore, all Es values are negative (or zero for hydrogen). Using this approach Taft, and

others, generated an extensive list of Es values that may be applied to other reactions and

equilibria in the same way σx values are used.

Molar refractivity, MR

Originally proposed by Pauling and Pressman as a parameter for the correlation of dispersion

forces involved in the binding of haptens to antibodies. Determined from the refractive index, n,

the molecular weight, MW and the density of a crystal, d.

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Equation for the molar refractivity can be given by:-

MR = {(n2 −1)/(n2 +1)}{MW/d}

Since refractive index doesn't change significantly for organic molecules, the term is dominated

by the MW and density. Larger MW, larger the steric effect, while greater the density, the

smaller the steric effect (the molecules tend to pack better). A smaller MR for the same MW

indicates stronger interactions in the crystal (larger density indicates that the packing is better

due to stronger interactions) indicating that molecule may have stronger dispersion interactions

with the environment (e.g. a receptor).

Verloop steric parameters

Terms related to van der Waals shape of functional groups that may be determined for any

substituent. May be used for both symmetric and asymmetric functional groups at it takes into

account the shape of the substituent as well as the size.

Fig. 5 : - Asymmetric molecules: 5 terms to represent length and width in 4 perpendicular

directions

The Craig plot

Although tables of pi and sigma factors are available for a large range of substituents, it is often

easier to visualize the relative properties of different substituents by considering a plot where the

y axis is the value of the sigma factor and the x axis is the value of pi factor. Such a plot is

known as a Craig plot.

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Craig plot facilitates selection of substituents during a QSAR study, which vary widely in one

parameter but not the other.

The example is shown below-

Fig. 6 : - The Craig plot

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3-D QSAR AND CoMFA

3D-QSAR refers to the application of force field calculations requiring three-dimensional

structures, e.g. based on protein crystallography or molecule superposition. It uses computed

potentials, e.g. the Lennard-Jones potential, rather than experimental constants and is concerned

with the overall molecule rather than a single substituent. It examines the steric fields (shape of

the molecule) and the electrostatic fields based on the applied energy function.The created data

space is then usually reduced by a following feature extraction. In the literature it can be often

found that chemists have a preference for partial least squares (PLS) methods, since it applies the

feature extraction and induction in one step.

Thus 3-D QSAR’s concludes-

o Structural descriptors are of immense importance in every QSAR model.

o Common structural descriptors are pharmacophores and molecular fields.

o Superimposition of the molecules is necessary.

o 3D data has to be converted to 1D in order to use PLS.

3-D QSAR Assumptions

o The effect is produced by modeled compound and not it’s metabolites.

o The proposed conformation is the bioactive one.

o The binding site is the same for all modeled compounds.

o The biological activity is largely explained by enthalpic processes.

o Entropic terms are similar for all the compounds.

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o The system is considered to be at equilibrium, and kinetics aspects are usually not

considered.

o Pharmacokinetics: solvent effects, diffusion, transport are not included.

3-D Molecular fields : Example- CoMFA

o A molecular field may be represented by 3D grid.

o Each voxel represents attractive and repulsive forces between an interacting partner and a

target molecule.

o An interacting partner can be water, octanol or other solvents.

CoMFA

o Comparative Molecular Field Analysis (1988)

o Compute molecular fields grid

o Extract 3D descriptors

o Compute coefficients of QSAR equation

o standard: steric and electrostatic

o additional: H-bonding, indicator, parabolic and others.

CoMFA Molecular fields

o A grid wit energy fields is calculated by placing a probe atom at each voxel.

o The molecular fields are:

Steric (Lennard-Jones) interactions

Electrostatic (Coulombic) interactions

o A probe is sp3

carbon atom

with charge

of +1.0

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Fig. 7 :- In CoMFA 3-D QSAR each grid voxel corresponds to two variables in QSAR

equation: steric and electrostatic. The PLS technique is applied to compute the coefficients.

PROBLEMS

o Superposition: the molecules must be optimally aligned.

o Flexibility of the molecules.

3-D QSAR of CYP450 CAM with CoMFA

Maps of electrostatic fields :

BLUE - positive charges

RED - negative charges

Maps of steric fields:

GREEN - space filling areas for best

Kd

YELLOW - space conflicting areas

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Fig. 8 :- 3-D QSAR of CYP450CAM with CoMFA

Application of QSAR

1. Classification

At the initial stages of a study to obtain a relationship of gross molecular features or

physicochemical properties to qualitative experimental data.

i) Compounds are highly active, active, or inactive

ii) Compounds are an agonist, partial agonist, or antagonist

2. Drug Design via QSAR

Researchers have attempted for many years to develop drugs based on QSAR. Easy access to

computational resources was not available when these efforts began, so attempts consisted

primarily of statistical correlations of structural descriptors with biological activities. However,

as access to high-speed computers and graphics workstations became commonplace, this field

has evolved into what is often termed rational drug design or computer-assisted drug design.

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Examples will discuss the application of QSAR to drug design, which were relied primarily on

statistical correlation and some, on computer-based

visualization and modeling. An early example of QSAR in

drug design involves a series of 1-(X-phenyl)-3,3-dialkyl

triazenes.

These compounds were of interest for their anti-tumor activity, but they also were mutagenic.

QSAR was applied to understand how the structure might be modified to reduce the

mutagenicity without significantly decreasing the anti-tumor activity. Mutagenic activity was

evaluated in the Ames test, and from those data, the following QSAR was developed:

where C is the molar concentration required to give 30 revertants per 10*8 bacteria and is a

"through resonance" electronic parameter. From the equation, it is seen that factors that favor

mutagenicity are increased lipophilicity and electron-donating substituents.

Studies of the anti-tumor activity were done against L1210 leukemia in mice. From the data, the

following QSAR was developed:

where C is the molar concentration of compound producing a 40% increase in life span of mice,

MR is molar refractivity, which is a measure of molecular volume, and EsR is a steric parameter

for the R group. Based on these equations, mutagenicity is more sensitive than anti-tumor

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activity to the electronic effects of the substituents. Thus, electron-withdrawing substituents were

examined, as illustrated in the example below:

By substituting a sulfonamide group at the para position, the anti-tumor activity was reduced 1.2-

fold, whereas the mutagenicity was reduced by about 400-fold.

3. Lead Compound Optimization

Using QSAR to predict combination of steric, electronic and hydrophobic properties required to

achieve the desired properties (generally interpolative in nature).Different properties must be

optimized for different types of drugs.

Absorption through skin: topical applications

Transport properties: oral administration

Type of activity: compounds with multiple activities (requires use of multiple bioassays)

Maximize desirable activities while minimizing side and toxic effects (i.e. improve therapeutic

index). May also want to maximize the spectrum of selectivity of a potential antibiotic (i.e. range

of bacteria that the antibiotic is effective against). Develop individual QSARs with respect to

different types of activity. Do this for a variety of activities for the same set of test compounds.

Analyze the multiple QSARs simultaneously to find the ranges of physical properties that

maximize desirable activities and minimize undesirable activities.

Example: To get a neutral compound into the CNS the partition coefficient must be close to 2.0

and to keep it out of the CNS, stay away from 2.

Sulmazole: original compound in clinical trials as antibacterial had a 4-OMe with a partition

coefficient of 2.59 (close to CNS magic number). During clinical trials some patients reported

seeing "bright visions." Therefore the 4-OMe was changed to a more polar 4-S(O)Me group,

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yielding a partition coefficient of 1.17, CNS absorption was decreased and the unwanted side

effect was eliminated. This is an example of bioisosteric replacement where the interaction of

the function group with the environment is maintained (i.e. hydrogen bond acceptor) but the

physical property (partition coefficient) changes.

Fig. 9 :- Structure of Sulmazole

Important aspect of the use of QSAR is the reduction of the use of animals in drug discovery:

QSAR allows for the elimination of compounds from further consideration prior to biological

testing, thereby minimizing the number of animals required.

4. Prediction of Activity

Predict activity of an unknown molecule via QSAR Develop QSAR for a "training set" of

compounds, then use the obtained mathematical relationship to predict the biological activity of

new compounds prior to their synthesis. Most accurate for congeneric series of compounds:

Congeneric Series: Collection of structurally related compounds that vary primarily only by

their substituents. For example, benzene, amino-benzene and chloro-benzene would represent a

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congeneric series, however, indole would not be part of the congeneric series due to it contain a

different ring system.

Fig. 10 :- Example of Congeneric series

Summary:

The physical properties of drugs, in part, dictate their biological activity. In addition, use of

descriptors of physical properties allow for the application of mathematical models to analyze

and predict drug activity. Thus QSAR is the study of different physical properties that influence

biological activity, use of those properties in the development of mathematical models that relate

the physical properties to biological activity, and how those mathematical models may be used to

understand and predict drug action. In determining qualitative structure relationship three

essential physicochemical properties such as hydrophobicity, electronic effect & steric effect are

studied. It is helpful in determining classification, diagnosis of mechanism of drug action,

prediction of drug activity, lead compound optimization.

References:

Hansch, C., Leo, A., and Taft, R.W. (1991) A Survey of Hammett Substituent Constants

and Resonance and Field Parameters. Chem. Rev., 91: 165-195.

Hansch, C., Leo, A., and Hoekman, D. (1995) Exploring QSAR - Hydrophobic,

Electronic, and Steric Constants. American Chemical Society, Washington, D.C.

Seydel, J.K. (1966) Prediction of in Vitro Activity of Sulfonamides, Using Hammett

Constants or Spectrophotometric Data of the Basic Amines for Calculation. Mol.

Pharmacol., 2: 259-265.

Hansch, C. (1969) A Quantitative Approach to Biochemical Structure-Activity Relationships.

Acct. Chem. Res. 2: 232-239.

Venger, B.H., Hansch, C., Hatheway, G.J., and Amrein, Y.U. (1979) Ames Test of 1-(X-

Phenyl)-3,3-dialkyltriazenes. A Quantitative Structure-Activity Study. J. Med. Chem., 22:

473-476.

Kumar, K., King, R.W., and Carey, P.R. (1974) Carbonic Anhydrase - Aromatic

Sulfonamide Complexes, A Resonance Raman Study. FEBS Lett. 48: 283-287.

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DesJarlais, R.L., Sheridan, R.P., Seibel, G.L., Dixon, J.S., Kuntz, I.D., and

Venkataraghavan, R. (1988) Using Shape Complementarity as an Initial Screen in

Designing Ligands for a Receptor Binding Site of Known Three-Dimensional Structure. J.

Med. Chem., 31: 722-729.

Web Resources:

http/www.netsci.org/Science/Compchem/feature11.html

http://www.statsoft.com/textbook/stmulreg.html

http://www.netsci.org/Science/Compchem/feature12.html

http://www.tdx.cesca.es/TESIS_UdG/AVAILABLE/TDX-1210104-133736//tags2de4.pdf

http://media.wiley.com/product_data/excerpt/03/04712709/0471270903.pdf