Prerequisites For Carrying Out QSAR Studies Biology Essay

Published: Last Edited:

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Drug design is an iterative process which begins when a chemist identifies a compound that displays an interesting biological profile and ends with optimizing both the activity profile for the molecule and its chemical synthesis. A traditional approach to the drug discovery program relies on step wise synthesis and screening of a large number of compounds to optimize activity profiles. In 'rational' design, it is essential to identify a molecular target specific to a disease process or an infectious pathogen. The important prerequisite for drug design is the determination of the molecular structure of the target.

Quantitative Structure Activity Relationship is a methodology which is used to correlate biological property of molecule with molecular descriptors derived from chemical structures.

Importance of QSAR study

The number of compounds required for synthesis in order to place 10 substituents on the four open positions of an asymmetrically disubstituted benzene ring system is approximately 10,000. An alternative approach for compound optimization is to develop a theory that quantitatively relates variation in biological activity to changes in molecular descriptors which can be easily obtained for each compound. If a valid QSAR has been determined, it is possible to predict the biological activity of the related drug candidates before they are put through expensive and time consuming biological testing i.e. activity can be predicted without synthesizing the new molecule. Some time only the computed values need to be known to make an assessment. QSAR can be extensively used for the prediction of physicochemical properties in chemical, environmental, and pharmaceutical areas.

A QSAR attempts to find consistent relationship between the variations in the molecular properties and the biological activity for a series of compounds so that these equations can be used to evaluate new chemical entities.

A QSAR generally takes the form of a linear equation

Biological Activity = Const + (C1 + (C2 + (C3 +...

Here the parameters P1 through Pn were computed for each molecule in the series and the coefficients C1 through Cn were calculated by fitting variations in the parameters and the biological activity.

Applications of QSAR

At present, QSAR science founded on the systematic use of mathematical models and on the multivariate point of view is one of the basic tools of drug design.

QSAR has been applied successfully and extensively to find predictive models for activity of bioactive agents.

It has also been applied to the following areas related to discovery and subsequent development of bioactive agents;

drug like from non drug like molecules

drug resistance

toxicity prediction

physicochemical properties prediction

gastrointestinal absorption

activity of peptides

data mining

drug metabolism

prediction of pharmacokinetic and ADME properties.

Prerequisites for carrying out QSAR studies

Multiple readings for a given observation should be reproducible and have relatively small errors.

Compounds selected to describe the "chemical space" of experiments (the training set) should be diverse.

For a QSAR study the data must be expressed in terms of the free energy changes that occur during the biological response.

Set of parameters must be easily obtainable and should be related to receptor affinity.

There should be method for detecting a relationship between the parameters and binding data.

There must be validation method for the developed QSAR model.

Methodology of QSAR study

There are three groups osf chemoinformatic methods for building QSAR model. They are extracting descriptors from molecular structure, choosing those informative in the context of the analyzed activity, and finally, using the values of the descriptors as independent variables to define a mapping that correlates them with the activity in question.

Generation of Molecular Descriptors from Structure

Molecular descriptors have to be generated since the structure cannot be directly used for creating structure activity mapping for reasons stemming from chemistry and computer sciences. First, the chemical structures do not usually contain in an explicit form the information that relates to activity. Second, chemical structures of compounds are diverse in size and nature and as such do not fit into this model directly. To circumvent this obstacle, molecular descriptors convert the structure to the form of well-defined sets of numerical values.

Selection of Molecular Descriptors

Molecular descriptors should be correlated significantly with the activity. Some statistical methods require more number of compounds than the number of descriptors; large descriptor set require large data sets. To tackle this problem, a wide range of methods for automated narrowing of the set of descriptors to the most informative ones is used in QSAR analysis.

Mapping the Descriptors to Activity

Once the relevant molecular descriptors are computed and selected, the final task is to create a function between their values and the analyzed activity. The most accurate mapping function is usually fitted based on the information available in the training set.

Molecular descriptors

Molecular descriptors are numerical values that characterize the properties of molecules and encode structural features of molecules as numerical descriptors. Molecular descriptors map the structure of the compound into a set of numerical or binary values which represent various molecular properties that are deemed to be important for explaining the activity.

2D QSAR descriptors

The 2D QSAR descriptors are independent from the 3D orientation of the compound. These descriptors range from simple measures of entities constituting the molecule, through its topological and geometrical properties to computed electrostatic and quantum-chemical descriptors or advanced fragment-counting methods.

Constitutional Descriptors

Constitutional descriptors confine the properties of a molecule that is related to elements constituting its structure. These descriptors provide a fast and easy method of computation. Constitutional descriptors include molecular weight, total number of atoms in the molecule and numbers of atoms of different identity. They also includes the total numbers of single, double, triple or aromatic type bonds, as well as number of aromatic rings.

Electrostatic and Quantum-Chemical Descriptors

Electrostatic descriptors give information on electronic nature of the molecule. These include descriptors containing information on atomic net and partial charges. Solvent-accessible atomic surface areas are informative electrostatic descriptors for modeling intermolecular hydrogen bonding. Energies of highest occupied and lowest unoccupied molecular orbital forms the quantum- chemical descriptors.

Topological Descriptors

The topological descriptors treat the structure of the compound as a graph, with atoms as vertices and covalent bonds as edges. On this aspect many indices quantifying molecular connectivity were defined, starting with Wiener index, which counts the total number of bonds in shortest paths between all pairs of non-hydrogen atoms. Other topological descriptors include Randic indices x, defined as sum of geometric averages of edge degrees of atoms within paths of given lengths, Balaban's J index and Shultz index. Kier and Hall indices xv or Gálvez topological charge indices capture the information about valence electrons. The first ones use geometric averages of valence connectivities along paths. The latter measure topological valences of atoms and net charges transfer between pair of atoms separated by a given number of bonds.

Geometrical Descriptors

Geometrical descriptors define the spatial arrangement of atoms constituting the molecule. These descriptors confine information on molecular surface which is obtained from atomic van der Waals areas and their overlap. Molecular volume may be obtained from atomic van der Waals volumes. Geometrical descriptors include principal moments of inertia and gravitational indices, which provides the information on spatial arrangement of the atoms in molecule. Shadow areas, obtained by projection of the molecule to its two principal axes are also used.

3D QSAR descriptors

The 3D-QSAR methodology is computationally more complex than 2D-QSAR approach. In 3D QSAR, several steps are needed to obtain numerical descriptors of the compound structure. First, the conformation of the compound has to be determined either from experimental data or molecular mechanics and then refined by minimizing the energy. Next, the conformers in dataset have to be uniformly aligned in space. Finally, the space with immersed conformer is probed computationally for various descriptors. Some methods which are independent of the compound alignment have also been developed.

Comparative Molecular Field Analysis (CoMFA)

CoMFA uses electrostatic (Coulombic) and steric (van der Waals) energy fields defined by the inspected compound. The aligned molecule is placed in a 3D grid and in each point of the grid lattice a probe atom with unit charge is placed and the potentials (Coulomb and Lennard-Jones) of the energy fields are computed. Then, they serve as descriptors in further analysis, typically using partial least squares regression analysis. This analysis allows for identifying structure regions positively and negatively related to the activity in question.

Comparative Molecular Similarity Indices Analysis (CoMSIA)

CoMSIA is similar to CoMFA in the aspect of atom probing throughout the regular grid lattice in which the molecules are immersed. The similarity between probe atom and the analyzed molecule are calculated. Compared to CoMFA, CoMSIA uses a different potential function, namely the Gaussian-type function. Steric, electrostatic, and hydrophobic properties are then calculated; hence the probe atom has unit hydrophobicity as additional property. The use of Gaussian-type potential function allows for accurate information in grid points located within the molecule. In CoMFA, unacceptably large values are obtained in these points due to the nature of the potential functions and arbitrary cut-offs that have to be applied.

Automatic selection of relevant molecular descriptors

There are certain automatic methods for selecting the best descriptors or features which can be used for the construction of QSAR model. They are wrapper approach method and filtering method

Filtering Methods

These are applied independent of the mapping method used. These are executed prior to the mapping to reduce the number of descriptors following some objective criteria like inter-descriptor correlation. Filtering methods include correlation - based methods, methods based on information theory and statistical criteria.

Wrapper methods

Wrapper technique operates in conjunction with a mapping algorithm. The error of the mapping algorithm for a given subset measured guides the choice of best subset of descriptors e.g. with cross validation. These include genetic algorithm, simulated annealing, sequential feature forward selection and sequential backward feature elimination.

Hybrid methods

In these methods fusion of the above two approaches is utilized. A rapid objective method can be used as a preliminary filter to narrow the feature set. Next, one or more accurate but slower subjective methods are employed.

Mapping the molecular structure to activity

After the selection of relevant descriptors, the final step in building a QSAR model is to derive the mapping between the activity and the values of the features. Mapping by linear models are simple but non-linear methods extend this approach to more complex relations.

Linear models

Linear models predict the activity as linear function of molecular descriptors. For small data sets of similar compounds, linear models are easily interpretable and sufficiently accurate.

Multiple Linear Regression (MLR)

In MLR models, the activity to be predicted should be linear function of all the descriptors. Coefficients of the function are estimated from the training set and these free parameters are selected to minimize the squares of the errors between the predicted and the actual activity. The main drawback of MLR analysis is the large descriptors-to-compounds ratio or multicollinear descriptors in general, which makes the results unstable. The advantage of MLR is that it exhibits lower cross validation error than partial least squares, both using 4D-QSAR fingerprints.

The new methodologies based on MLR developed recently are: Best multiple linear Regression (BMLR), Heuristic Method (HM), Genetic Algorithm based Multiple Linear Regression (GA-MLR), Stepwise MLR, Factor Analysis MLR and so on.

BMLR works well when the number of compounds does not exceed the number of molecular descriptors by at least a factor of five. As the number of descriptors increases, the modeling process will become time consuming. So to speed up the calculation, descriptors with insignificant variance within the data set should be rejected.

HM is an advanced algorithm based on MLR. The selection of descriptors is done as follows: first of all, all descriptors are checked to ensure that values of each descriptor are available for each structure. If the values for the descriptors are not available for every structure, then the data are discarded. If the values of descriptors are constant in the data set, they are also discarded. Then all possible one-parameter regression models are tested and the insignificant descriptors are rejected. In the next step, the pair correlation matrixes of descriptors are calculated and this further reduces the descriptor pool by eliminating highly correlated descriptors. Finally the intercorrelation is validated and the goodness of the correlation is tested by the square of coefficient regression (R2), square of cross-validate coefficient regression (q2), the F-test (F), and the standard deviation (S).

Partial Least Squares (PLS)

PLS is a suitable method for overcoming the problems in MLR due to multicollinear or over-abundant descriptors. The PLS tries to indirectly obtain knowledge on the latent variables, the scores and the loadings. The scores are orthogonal and are able to capture the descriptor information, which allow good prediction of the activity. The score vectors are estimated iteratively. The first one can be derived using the first eigenvector of the activity descriptor combined variance-covariance matrix. Next, the descriptor matrix is deflated by subtracting the information explained by the first score vector. The matrix resulting from the above is used in the derivation of the second score vector, which followed by consecutive deflation, closes the iteration loop. In each iteration step, the coefficient relating the score vector to the activity is also determined.

Recently evolved PLS are Genetic Partial Least Squares (G/PLS), Factor Analysis Partial Least Squares (FA-PLS) and Orthogonal Signal Correction Partial Least Squares (OSC-PLS).

Linear Discriminant Analysis (LDA)

LDA is a classification method that creates a linear transformation of the original feature space into a space which maximizes the interclass separability and minimizes the within-class variance. The procedure involves solving a generalized eigenvalue problem based on the between-class and within class covariance matrices. Thus to avoid ill-conditioning of the eigenvalue problem, the number of features has to be significantly smaller than the number of observations. To avoid the above problem principal component analysis can be applied to reduce the dimension of the input data. LDA is used to create QSAR models e.g. for prediction of model validity for new compounds where it fared better than PLS, but worse than non-linear neural network.

Non-Linear Models

Non-linear models extended the structure-activity relationships to non-linear functions of input descriptors. These models are more accurate, especially for large and diverse datasets. However, they are usually harder to interpret. Complex non-linear models may also fall to over fitting i.e., low generalization to compounds unseen during training.

Artificial Neural Networks (ANN)

Artificial Neural Networks are biologically inspired prediction methods which are based on the architecture of a network of neurons. In this, during the prediction, the information flows only in the direction from the input descriptors, through a set of layers, to the output of the networks. Disadvantage of this is that it has a tendency to over fit the data, leading to a significant level of difficulty in ascertaining as to which descriptors are most significant in the resulting model. The most frequently used neural networks are Radial Basis Function Neural Network (RBFNN) and General Regression Neural Network (GRNN).

Support Vector Machines (SVM)

SVM stems from the structural risk minimization principle, with the linear support vector classifier as its most basic member. This aims at creating a decision hyper plane that maximizes the margin, i.e., the distance from the hyper plane to the nearest examples from each of the classes. The most important objective function is unimodal and thus can be optimized effectively to global optimum. Simply, compounds from different classes can be separated by linear hyperplane; such hyperplane is defined solely by its nearest compounds from the training set. Such compounds are referred to as support vectors, which give the name to the whole method.

These methods have been extended into Support Vector Regression (SVR) to handle regression problems. SVM methods have been shown to exhibit low prediction error in QSAR.

Gene Expression Programming (GEP)

GEP was invented by Ferreira in 1999 and was developed from genetic algorithms and genetic programming (GP). GEP is very simple compared to cellular gene progression. This mainly includes two sides: the chromosomes and the expression trees (ETs). The process of information translation in gene code is very simple, such as a one-to-one relationship between the symbols of the chromosome and the functions or terminals they represent. GEP determines the rules for spatial organization of the functions and terminals in the ETs and the type of interaction between sub-ETs. Hence the language of the genes and the ETs represent the language of GEP.


QSAR modeling involves three main steps each of which contains its own group of pitfalls

(1) Input data preparation and preprocessing

(2) Model generation and validation

(3) Analysis of results

Pitfalls Concerned with input Data Preparation and Preprocessing

Incompatible Concepts and Contraindications for QSAR

Multiconditionality: In silico studies have hardware and software limitations since the drug action is based on a sequence of complicated physiochemical events that are either still unknown or not fully understood on a molecular level. QSAR and QSPR describe quantitatively ADMET processes and can only fragmentally reproduce real observations.

Common Action Mechanism and Multiple Binding Modes: The occurrence of various binding modes (MBM) of the very same ligand to its target molecule makes the model complicated. So the QSAR is conducted under the silent assumption that no multiple binding modes are present when comparing molecular similarities.

Multiple Targets and Multipotency: Normal QSAR which works with cell-free data is not affected by drug binding to multiple targets which occur only when a molecule in lower doses binds to a biomolecule with higher affinity. But in higher doses the same ligand may bind to other targets with lower affinity.

Pitfalls Concerned with Model Generation and Validation

Selection of Predictor Variables

Meaningless Descriptor Selection: Not all descriptor are useful to describe the electronic and hydrophobic effects. So inclusion of a large number of descriptors in QSAR studies should be discouraged.


The number of independent variables in a final QSAR model should be as low as possible so that the reintroduction of collinear variables improves greatly the R2 or Q2 in LOO examination but deteriorates future model applicability.

Errors of Descriptor Calculations: Poor correlation results are eventually due to experimental or computational errors.

Robust Statistical Procedures and "Black Boxes"

QASR studies are done with user friendly softwares so that the models can be developed with out a detailed understanding of the underlying theories and statistics.

Pitfalls Concerned with Model Interpretation

Unrelatedness, also known as the "Correlation Problem"

A good correlation is often mistakenly interpreted as a proof of causality. In addition to this, achieving significance through MLR or PLS means only "significance on a statistical level" and nothing more. So a significant variable or model can be completely irrelevant on pharmacological grounds.

Chance Correlation

Having some independent variable correlating with activity does not necessarily mean that the corresponding feature is directly involved in explaining SAR.

Multiple Solutions

MLR leads to a model that does not describe all levels of explanatory complexity in nature. The computed model tries to simplify the problem and intends to approximate to reality and perform predictions of new data points along the regression line. So various solutions are there for a problem with different dimensionality. If there are large numbers of independent variables at hand, they may be dependent on the others (overlap, redundancy) despite their assuring name. Multiple solutions are always possible and are not necessarily indicators of wrong models.

Extrapolation and Interpolation

Theoretically, the activities can be predicted either by interpolation between the observed data points or by extrapolation to areas outside the variable levels. The activity of the new test molecules is predicted by the established equations but their structures behave differently and are poorly described by the chemical properties of the equations. So the inappropriate extrapolation or interpolation makes the prediction a risky operation.

Advantages of QSAR

QSAR quantifies the relationship between structure and activity and provides an understanding of the effect of structure on activity.

QSAR makes predictions leading to the synthesis of novel analogues.

The results can be used to help in understanding interactions between functional groups in the molecules of greatest activity with those of their target.

Disadvantages of QSAR

False correlations may arise because biological data are subject to considerable experimental error (noisy data).

If training data set is not large enough, the data collected may not reflect the complete property space. Consequently, many QSAR results cannot be used to confidently predict the most likely compounds with the best activity.

A feature of QSAR is that it not always reliable. This is particularly serious because 3D structures of ligands binding to receptor may not be available. Common approach is to minimize structure, but that may not represent the reality well.

In the present work QSAR models were developed using TSAR 3.3 software. The results from the antimicrobial studies on the activity of the synthesized compounds were converted to inverse log scale. These data of the synthesized compounds were divided in to two sets viz training set and test set for external validation. From the QSAR equation generated, activity of a new set of compounds could be predicted.


Antimicrobial drugs are the greatest contribution of the 20th century to therapeutics. The study of antimicrobial agents embraces not only antibacterial compounds, but also agents with antiviral, antifungal and anti protozoal actions. They may even exhibit anthelmintic action. Drugs in this class differ from all others in that they are designed to inhibit or kill the infecting organism and to have minimum or no effect on the recipient. This type of therapy is generally called chemotherapy which has come to mean treatment of systemic infections with specific drugs that selectively suppress the infecting micro organism without affecting the host.


Antibacterial agents are agents that are 'selectively' toxic for bacteria, either killing them (bactericidal) or inhibiting their growth (bacteriostatic) without causing harm to the patient. These compounds selectively act on structures found in bacteria and not in the host.

Antibacterial agents work most effectively in conjugation with an active immune system to kill infecting bacteria in the host. After isolation of pure colonies, the susceptibility of the bacterial isolates can be tested against a variety of antibacterial agents. The minimal inhibitory concentration (MIC) refers to the lowest concentration of an antimicrobial agent that stops visible growth. The diameter of the zone of inhibition around a disc impregnated with antimicrobial agent (Kirby-Bauer) method is another measure of its activity.

Antibacterial agents used in the treatment of infectious diseases

Antibacterial agents may be either antibiotics, which are natural substances produced by certain groups of micro organisms or chemotherapeutic agents which are chemically synthesized. A hybrid substance is a semi synthetic antibiotic produced by the chemist by chemically modifying a molecular version produced by the micro organism to impart desired properties.

Semi synthetic antibacterial agents

Sulphonamides and sulphones

Prontosil rubrum, a sulphonamide azo dye, was the first clinically useful systemic antibacterial agent to be discovered. The sulphanilamide formed in vivo was responsible for the antibacterial action of prontosil. Examples for sulphonamides are sulfamethizole, sulfathiazole, sulfisoxazole, sulfacetamide, sulfamethoxazole, sulfadiazine etc.

Table No.1








Silver sulfadiazine Ag+




Antimicrobial spectrum

Sulphonamides are primarily bacteriostatic. Many Gram positive and Gram negative bacteria, mycobacteria, some large viruses, protozoa and fungi are susceptible to the action of sulphonamides and sulphones.

Mechanism of action

Sulphonamides inhibit the enzyme dihydropterate synthase, an important enzyme needed for the biosynthesis of folic acid derivatives and, ultimately, the thymidine required for DNA. They do this by competing at the active sites with p-aminobenoic acid, a normal structural component of folic acid derivatives.

Structure activity relationship

The amino and sulfonyl radicals on the benzene ring should be in the 1,4-positions for the activity; the amino group should be unsubstituted or should have a substituent that is removed readily in vivo.

Replacement of benzene ring by other ring systems or the introduction of additional substituent on it decreases or abolishes the activity.

Exchange of the SO2NH2 by SO2C6H4-p-NH2 retains activity whereas exchange of CONH2 by COC6H4-p-NH2 markedly reduces the activity.

N1- monosubstitution results in more active compounds where as N1- disubstitution in general leads to inactive compounds.

The N1- substitution should be such that its value would approximate the physiological pH.

Therapeutic uses

Systemic use of sulphonamides alone is rare now. Though they can be used for suppressive therapy of chronic urinary tract infection, for streptococcal pharyngitis and gum infection. In combination with trimethoprim, sulfamethoxazole is used for many bacterial infections due to P.carni and also in nocardiasis. Along with pyrimethamine, it is used for the treatment of malaria and toxoplasmosis. Topical silver sulfadiazine or mafenide is used for preventing infection of burn surfaces. Ocular sulfacetamide sodium is used for the treatment of trachoma.


The quinolone antimicrobials comprise a group of synthetic substances possessing in common an N-1-alkylated-3-carboxypyrid-4-one ring fused to another aromatic ring, which itself carries other substituents. The first quinolone to be marketed was nalidixic acid. Recent quinolones are referred to as the fluoroquinolones and these agents are now an important class of antimicrobial agents. Examples for quinolones are nalidixic acid, cinoxacin, norfloxacin, enoxacin, ciprofloxacin, ofloxcin, levofloxacin, lomefloxacin etc.

Table No.2













Antimicrobial spectrum

Fluoroquinolones are important broad spectrum antibacterial agents. All of them are active against species such as Enterobacter cloacae, Proteus mirabilis and Staphylococcus epidermis.

Mechanism of action

The quinolones inhibit the enzyme bacterial DNA gyrase, which nicks double stranded DNA, introduces negative supercoils and then reseals the nicked ends. The above process is essential to prevent excessive positive supercoiling of the strands when they separate to permit replication or transcription. The DNA gyrase consists of two A and two B subunits: A subunit carries out nicking of DNA, B subunit introduces negative supercoils and then A subunit reseals the strands. Fluoroquinolones bind to A subunit with high affinity and interfere with strand cutting and resealing function. In Gram positive bacteria the major target of action of fluoroquinolones is enzyme topoisomerase IV which nicks and separates daughter DNA strands after DNA replication.

Structure activity relationship

The minimum pharmacophore required for significant anti-bacterial activity consists of the 4-pyridone ring with a 3-carboxylic acid group.

Reduction of the 2, 3-double bond eliminates activity.

Most of the highly active quinolones have a fluorine atom at C6 because it increases lipophilicity, which facilitates penetration into the cells.

Analogs having piperazine group on C7 broadens the spectrum, especially to include Gram negative organisms.

Replacement of C8 by nitrogen to give a naphthyridine (example: enoxacin and trovafloxacin) increases bioavailability.

Fluorine at C6 enhances inhibition of DNA gyrase and provides activity against Staphylococci.

Addition of a second fluorine at C8 increases absorption and half-life.

Therapeutic uses

Nalidixic acid is primarily used as a urinary antiseptic. It has also been employed in diarrhoea caused by proteus spp, Shigella spp, Salmonella spp or E.coli. Ciprofloxacin is used in urinary tract infections, gonorrhoea, chancroid and typhoid.


The oxazolidinones are a new class of synthetic antibacterial agents with activity against a broad spectrum of Gram positive pathogens, including those pathogens resistant to currently used antibacterials. Optimization led to emergence of two highly active antibacterial drug candidates DUP 105 and DUP 721. Example is linezolid.

DUP 105, R=CH3SO,

DUP 721, R=CH3CO

Antimicrobial spectrum

Active against a number of therapeutically important multidrug resistant Gram positive organisms. Linezolid, the first oxazolidinone antibacterial is active against Gram negative anaerobes.

Mechanism of action

Linezolid and related oxazolidinones act through inhibition of the initiation phase bacterial protein synthesis. They can bind directly to a site on 23s ribosomal RNA of the bacterial 50s ribosomal subunit thereby preventing the formation of functional 70s initiation complex formed with 30s ribosomal subunit.

Therapeutic uses

Oral and intravenous linezolid have been reported to be equally effective in the treatment of SSTIs, urinary tract infections caused by methicillin sensitive or resistant staphylococcus spp. It is also used in bacteremia, intra abdominal abscesses and osteomyelitis.

Beta lactam antibiotics

These are antibiotics having a beta lactam ring. The two major groups are penicillins and cephalosporins. Monobactams and carbapenems are the newer additions. The penicillin subclass of beta lactam antibiotics is characterized by the presence of a substituted five membered thiazolidine ring fused to the beta lactam but in cephalosporin the beta lactam ring is annealed to a six membered dihydro thiazine ring. Example: Benzyl penicillin, methicilin, nafcilin, ampicilin, amoxicillin, cephalosporin C, cephapirin, cefazolin, cephalexin, cefadroxil, meropenem, ertapenem and aztreonam.

Table No.3



Penicillin G


Ampicillin (X=H)

Amoxicillin (X=OH)

Clavulanic acid Meropenem

Table No.4









Antimicrobial spectrum

Beta lactams (penicillin and cephalosporin) are active against Gram positive bacteria. Semisynthetic penicillins are active against both Gram positive and Gram negative bacteria. Clavulanic acid, monobactams and carboxypenems are also active against Gram positive and Gram negative bacteria.

Mechanism of action

Beta lactam antibiotics inhibit the transpeptidase so that cross linking does not takes place. Thus they inhibit the synthesis of peptidoglycan, which is the major polymer of the bacterial cell wall. Peptidoglycan maintains the cell shape and protects the microbe against osmotic forces. This capability depends on its net like structure which is composed of longer sugar chains cross linked by peptides.

Structure activity relationship

The substitution of a side-chain R group on the primary amine with an electron-withdrawing group decreases the electron density on the side chain carbonyl and protects the penicillins, in part, from acid degradation.

The stability of benzyl penicillin can be further increased by substitution of an electron withdrawing group at α-position of benzyl penicillin. Example: α-amino benzyl and α-halo benzyl penicillin.

Incorporation of an ionized or polar group or an acidic substituent at the α-position of the side chain benzyl carbon atom of benzyl penicillin imparts clinical activity against Gram negative bacilli.

All the natural penicillins are strongly dextrorotatory.

Therapeutic uses

Penicillin G is the drug of choice in streptococcal infections, pneumococcal infections, meningococcal infections, gonorrhoea, syphilis and diphtheria. Semi-synthetic penicillins are used in urinary tract infections, respiratory tract infections, meningitis, gonorrhoes, bacillary dysentery and cholesystitis. Cephalosporins are now extensively used for the treatment of respiratory, urinary and soft tissue infections caused by Gram negative organisms as an alternative to penicillin G in patients who develop rashes or other allergic reaction to the later. They are also used in septicemias and surgical prophylaxis.


These are class of antibiotics having four rings that form the minimal naphthacene teracycline. Position at the "bottom" of the molecule and most of ring A (position 2,3 and 4) represent the invariant pharmacophore region of the molecule, where modification are not tolerated without loss of antibiotic activity. Example: chlortetracyclin, oxytetracyclin, doxycyclin, minocyclin etc.

Table No.5

















Antimicrobial spectrum

Tetracycline is a broad spectrum antibiotic active against Gram positive and Gram negative bacteria, spirocheates, rickettsiae, mycoplasma and actinomycetes.

Mechanism of action

The tetracyclines are primarily bacteriostatic which inhibit protein synthesis by binding to 30s ribosome in susceptible organism. Subsequent to such binding, attachment of amino acyl-t-RNA to the mRNA ribosome complex is interfered with.

Structure activity relationship

A tetracycline backbone skeleton is essential for activity

The enolised system present at carbon 1 to 3 must be intact for good activity.

The amide function at C-2 is essential for activity.

Substitution at C-6 decreases chemical stability. Example: oxytetracyclin is chemically less stable than doxycyclin

C-7 substitution results in increased potency and the drug may sometimes be active against resistant microbial strains.

A cis type fusion between A/B with a α-hydroxyl group at 12a is necessary for retention of activity.

Therapeutic uses

Tetracyclines are the drugs of choice in veneral diseases, brucellosis, plague and rickettsial infections.


Chloramphenicol was initially obtained from Streptomyces venezuelae.


Antimicrobial spectrum

It is a broad spectrum antibiotic active against Gram positive and gram negative bacteria, ricketsiae, chlamedia, mycoplasma etc.

Mechanism of action

Chloramphenicol inhibits the protein synthesis by interfering with 'transfer' of the elongating peptide chain to the newly attached aminoacyl tRNA at the ribosome-mRNA complex. It specifically attaches to the 50s ribosome and thus may hinder the amino acid incorporation. It prevents formation of peptide bonds.

Structure activity relationship

P-nitro phenyl group:-

Replacement of the nitro group by other substituents leads to reduction in activity.

Shifting of the nitro group from the para position to the other positions also reduces the antibacterial activity.

Replacement of phenyl group by alicyclic moieties results in less potent compounds.

Dichloro acetamido side chain:-

Other dihalo derivatives of the side chain are less potent though the major activities are retained.

1,3-Propane diol:-

The primary alcoholic group on C-1 atom is essential for activity.

Therapeutic use

Chloramphenicol is used for typhoid fever, meningitis, anaerobic infections and intraocular infections and infection due to H.influenzae.

Amino glycoside antibiotics

They are group of natural and semi synthetic antibiotics having polybasic amino groups linked glycosidically to two or more amino sugars. They acquired great importance because they are active against tubercle bacilli. Example: Streptomycin, Kanamycin, neomycin, gentamycin, tobramycin, amikacin etc.


Antimicrobial spectrum

Amino glycosides have broad antibiotic spectra against aerobic Gram positive and Gram negative bacteria but are reserved for use in serious infections caused by Gram negative organism. They are active against Gram negative aerobes such as Mycobacterium tuberculosis, Acinetobacter spp, Citrobacter spp, E.coli and Klebsiella spp.

Mechanism of action

The amino glycosides bind to the 16s RNA portion of the 30s ribosomal subunit, impairing the proof reading function of the ribosome. A conformational change occurs in the Peptidyl A site of the ribosome on amino glycoside binding. This leads to mistranslation of RNA templates and the consequent selection of wrong amino acids and formation of so called non sense proteins. Their presence destroys the semipermiability of the microbial membrane and this damage cannot be repaired without de novo programmed protein biosynthesis.

Structure activity relationship

Amino sugar portion

The amino function at C-6 and C-2 serves as major target sites for bacterial inactivating enzymes.

Methylation at C-6 position does not decrease the activity but increases enzyme resistance.

Cleavage of 3-hydroxyl or the 4-hydroxyl or both the groups does not affect the activity.

Aminocyclitol ring

The acylation and ethylation at C-1 amino group though do not increase the activity, help to retain the antibacterial potency.

Therapeutic uses

Streptomycin is most commonly used for the treatment of tuberculosis and for treatment of gonorrhoea.

Macrolide antibiotics

These are antibiotics having a macrocyclic lactone ring with attached sugars. The clinically important members of this antibiotic family have two or more characteristic sugars (usually cladinose and desosamine) attached to the 14 membered ring. Example: erythromycin and roxithromycin; clarithromycin and azithromycin are the recent additions.

Erythromycin A

Antimicrobial spectrum

They exhibit a narrow spectrum of activity which includes activity against Gram positive and a few Gram negative organisms and the activity overlaps considerably with that of penicillin G. Erythromycin is highly active against S.pyogenes and S.pneumoniae, N.gonorrhoea and C.diphtheriae.

Mechanism of action

Erythromycin acts by inhibiting bacterial protein synthesis. It combines with 50s ribosome subunits and interferes with the 'translation'. After peptide bond formation between the newly attached amino acid and the nacent peptide chain at the acceptor (A) site, the elongated peptide is translocated back to the peptidyl (P) site, making the A site available for the next aminoacyl t-RNA attachment. This is prevented by erythromycin and the ribosome fails to move along the mRNA to expose the next codon and synthesis of larger proteins is specifically suppressed.

Therapeutic uses

Macrolide antibiotics are used as an alternative to penicillins in the treatment of Streptococcal pharyngitis, tonsillitis and mastoiditis. They are also used in diphtheria, syphilis and gonorrhoea.

Polypeptide antibiotics

These are low molecular weight cationic polypeptide antibiotics. All are powerful bactericidal agents, but are not used systemically due to toxicity. All these antibiotics are produced by bacteria. Clinically used ones are: polymixin B, bacitracin, colistin, thyrothricin.

Antimicrobial spectrum

Polymixin and colistin are active against Gram negative bacteria only.

Mechanism of action

They have a detergent like action on the cell membrane. They have high affinity for phospholipids. The peptide molecule gets oriented between the phospholipid and protein films in the Gram negative bacterial cell membrane causing membrane distortion and pseudopore formation. As a result ions, amino acids etc. leak out of the bacterial cell.

Therapeutic uses

Polypeptide antibiotics are topically used in combination with other antimicrobials for skin infections, burns, otitis externa, conjunctivitis, corneal ulcer etc. They are used orally in the treatment of Gram negative bacillary diarrhoeas, especially in infants and children.


Antifungal agents are drugs which are used against fungal infections. Fungal infections can be divided in to two types. They are

Superficial infections: affecting skin, nails, hair or mucous membrane.

Systemic infections: affecting deeper tissues and organs.

Targets for antifungal agents:

Cell wall biosynthesis

DNA biosynthesis

Miotic spindle

Fungal metabolism


Antifungal agents are classified as follows


Polyene antibiotics- amphotericin B, nystatin, natamycin.

Heterocyclic benzofurans- griseofulvin

Antimetaboites- flucytosine


Imidazoles- Topical agents:-clotromazole, miconazole, econazole.

Systemic agents: ketoconazole

Triazoles (systemic) agents : fluconazole, itraconazole

Allylamines- terbinafine, naftifine, butenafine

Other topical agents- benzoic acid, tolnaftate, cyclopiroxilamine.

New antifungal agents- Echinocandins, sordarin derivatives.

1. Antibiotic antifungals

a. Polyene antibiotics

The name polyene is derived from the highly double bonded structure. They are macrocyclic lactones with distinct hydrophilic and lipophilic regions in the molecules.

Amphotericin B and Nystatin

Amphotericin B is obtained from Streptomyces nodosus. It is the archetypal polyene antifungal agent and was the first systemic agent available to treat invasive fungal infections. The primary indications for its use in current therapy include invasive candidiasis, cryptococcosis, aspergillosis and histoplasmosis.

Nystatin is isolated from Streptomyces nourseii. It is used in the treatment of both gastrointestinal and local infections of candida albicans. It is also used for corneal, conjunctival and cutaneous candidiasis.

Mechanism of action:-

The polyenes act by binding to ergosterol, a sterol present in the membrane of sensitive fungi. The interaction with ergosterol leads to the formation of pores within the fungal cell membrane resulting in leakage of cellular contents such as sodium, potassium and hydrogen ions.

Structure activity relationship

Polyenes contain a large ring with 4-7 unsaturated conjugated double bonds. With increased conjugation, the activity and toxicity will increase.

Amphotericin B has 7 conjugated double bonds while nystatin has 6 conjugated double bonds. So amphoterin B is more active and more toxic.

b. Heterocyclic benzofurans


Griseofulvin was extracted from the fermentation broth of Penicillin griseofulvin and is active against most dermatophytes including Epidermaphyton spp, Trichophyton spp, and microsporium spp.


Mechanism of action:-

Griseofulvin acts as a fungistatic. It binds to the protein tubulin and interferes with the formation of the miotic spindle and thereby inhibits the cell division. It also affects the DNA replication.

Structure activity relationship:-

Out of 4 possible stereoisomers only (+) enantiomer is active

Chlorine replaced by fluorine - same activity

Chlorine replaced by bromine or hydrogen - decrease in activity

Placement o hydrogen on C5 - decrease in activity

Replacement of methoxy on ring C with propoxy or butoxy function- increase in activity.

2. Antimetabolites


Flucytosine is a fluorinated pyrimydine related to the anticancer agent fluorouracil. Therapeutically, flucytosine is used predominantly in combination with amphotericin B to treat cryptococcal meningitis and infections caused by Candida spp.


Mechanism of action:-

Flucytosine is a prodrug which gets converted in the fungal cell into 5-fluorouracil and then to 5- fluorodeoxouridylic acid which is an inhibitor of thymidylate synthesis. 5-fluorouracil is incorporated into fungal RNA, thereby disrupting transcription and translation. Selectivity is achieved because mammalian cells are unable to convert flucytosine to fluorouracil.

3. Azoles

Imidazoles and triazoles

These are presently the most extensively used antifungal drugs. The imidazoles and triazoles have broad spectrum of antifungal activity covering Dermatophytes, Candida, other fungi involved in deep mycosis, nocardia and some Gram positive and anaerobic bacteria.


Clotrimazole (X=H, Y=Cl) Ketoconazole

Flutrimazole (X=Y=F)


Fluconazole Voriconazole

Mechanism of action

All the azoles act by inhibiting ergosterol synthesis through inhibition of 14α-demethylase. The basic N3 atom of the azole forms a bond with the heme iron of the CYP 450 prosthetic group in the position normally occupied by the activated oxygen. Inhibition of the 14α-demethylase results in permeability changes, leaky membranes and malfunction of membranes embedded proteins. All these lead to fungal cell death.

Structure activity relationship

An imidazole or triazole heme-coordinating group, a halo substituted aromatic moiety separated from the azole moiety by two atoms and a side chain are needed for activity. The latter represents the feature of greatest diversity across the family.

A basic imidazole or 1,2,4-triazole with a pKa of 6.5-6.8 is essential for activity.

N3 of imidazole and N4 of triazole bind to p450 ion.

The most active ones have two or three aromatic rings, at least one of them is substituted with halogen or other non polar groups (2,4-dichlorophenyl, 1,4-dichlorophenyl or 2,4-difluorophenyl)

The most active azoles have fluoro group in the structure

Ring substitution at other positions makes the azole inactive

4. Allylamine


This orally and topically active drug which is effective against dermatophytes and candida belongs to a new allylamine class of antifungal agents. In contrast to the azoles which are primarily fungistatic, terbinafine is fungicidal.


Mechanism of action

All the allylamines act through inhibition of the enzyme squalene epoxidase. Inhibition of this enzyme causes two effects; first it results in a decrease in total sterol content of the fungal cell membrane. This alters the physico-chemical properties of the cell. Secondly, it results in a build up of the hydrocarbon squalene within the fungal cell which itself is toxic when present in abnormally high concentrations.

Structure activity relationship

Tertiary allylamine structural element was perceived to be essential for antifungal activity

Replacement of allylamine with suitably substituted benzylamine or homo propargylamine reduces the activity.

Two lipophilic domains linked to a central polar moiety by spacers of appropriate length is the minimum requirement for potent activity

For good activity, the polar moiety must be a tertiary amine and one of the lipophilic domains must consist of a bicyclic aromatic ring system such as naphthalene or benzothiophene.

5. New antifungal agents


Echinocandins are a new class of antifungal agents with a distinct mechanism of action. They act by inhibiting 1, 3-β-D-glucan synthase complex. This enzyme complex is responsible for the incorporation of glucose into glucan fibrils that the walls of most fungi are composed of but which are absent in human cells making echinocandins excellent selective antifungal agents. Depletion of glucan in the fungal cell wall leads to osmotic instability and cell death. Echinocandins are generally fungicidal, although they do not act as rapidly as amphotericin-B does.