The Drug Discovery Process Involves Mainly Six Steps Biology Essay


In the past, drugs were discovered through a series of time consuming, complex processes against various invivo biological screens. The pharmacokinetic properties, including ADME and potential toxicity of compounds were then scrutinized. This pattern has been reformulated today in several ways.

Drug discovery process involves mainly six steps: target identification, target validation, lead identification, lead optimization and then pre-clinical trials and finally clinical trials. The first step is the identification of known pathological event in an organism and starts with the development of a therapeutic assumption to combat this process. This is known as target identification. This potential target needs a confirmation , hence a proper biological reaction is to be carried out known as target validation. Lead identification stage is the stage which screened to find 'lead' from collection of physically or virtually available compounds. After establishing lead compound the next phase is the lead optimization to get the desirable qualities of the lead. Finally each new chemical compound has to be tested in animals and then in human.


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Drug design is a process for developing a drug with high therapeutic index and specific action. It is also called as rational drug design. It is the creative process of finding novel medications based on the information of biological target. Drug design includes the design of small molecules ,which are having similarity in shape and charge to the biomolecular target to which the drugs will bind. Here drug is an organic small molecule which will either activate or inhibit the function of a protein. Thus it will result in a good therapeutic efficiency to the patient. This kind of modeling is computer aided drug design.

Aim of Drug design

• To improve potency

• To modify specificity of action

• To improve duration of action

• To reduce toxicity

• To effect ease of application or administration or handling

• To improve stability

• To reduce cost of production

Drug design help to explain the following:

The effects of biological compounds on the basis of chemical properties of the molecule involved.

Different processes by which the drugs shows their pharmacological effects.

How the drugs react with the sites to produce a particular pharmacological response.

How the drugs are detoxicated, metabolized or eliminated by organism.

The relationship between chemical structure and biological activity.

Different methods of drug design are:

Ligand based drug design

Struture based drug design

De novo design

Homology modeling

All these are used to identify lead compounds .Struture based drug design is based on 3D structure of the target and thus it is most reliable.


The ultimate goal of structure-based drug design is to develop a simple, robust process that starts with a high-resolution crystal structure of a validated biological macromolecular target and finally generates an easily synthesized, high-affinity small molecule with desirable pharmacological properties. It represents exactly how the molecule interacts with its target protein. This structural information can be obtained by using X-ray crystallography or Nuclear magnetic resonance spectroscopy (NMR).

Typically, the process involves

Obtaining the structure of the target protein

Identification of active site

Virtual screening of a small molecule database

Identification of potential ligands based on a chosen scoring function

Finding the Right Target

In SBDD ,a protein is the target which is closely related to a disease. This protein plays a major role in signaling pathway that is disturbed in a diseased condition. These proteins may be an ion channel, a membrane receptor or an enzyme .The preferred targets are enzymes as they have binding pockets for substrates and inhibitors.

Active site identification:

It evaluates the protein to select the binding pocket, develops interaction sites within the binding pocket and then produces the needed data for Ligand fragment linkage. The 3D structure of the protein and a pre-docked ligand in PDB format, and their atomic properties are needed. Both ligand and protein atoms need to be classified as hydrophobic atom, H-bond donor, H-bond acceptor , and polar atom. From this, which type of chemical parts can be placed into their corresponding spots in the ligand binding region of the receptor can be found out. The main targets currently selected for drug discovery efforts are proteins.


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Virtual screening is a process for searching ligands ie; small biologically active molecule by means of computer assisted technique on the basis of biological structure. The crucial aim of virtual screening is the reduction of the enormous small organic molecules, to synthesize and screen against a particular target protein, to a manageable number of the compound as drug candidate.

Virtual screening is divided mainly into two types.

Ligand based virtual screening

It involves screening using active compounds as templates. These screening techniques mainly based on comparing molecular similarity analyzes of compounds with known and unknown moiety, based on the methods of used algorithm.

Structural based virtual screening (docking)

It is based on molecular docking. Docking is a computational tool to calculate the binding affinities and protein- ligand interaction .

Structure based virtual screening

High throughput screening, docking and scoring techniques can be applied to computational screening for a database of hundreds of thousand of compound against a target of proteins. Computational methods predict the 3D structure of a protein ligand complex which are often referred to as molecular docking approaches. High throughput screening is important ie: identification of a new lead compound in drug discovery through physical screening of large libraries of candidates against a biological target. Molecular docking requires analysis of large set of small ligand at acceptable time.

Virtual screening predicts a practical route to synthesis new reagents and are useful for research. Molecular docking based virtual screening can reduce the number of steps for the synthesis of possible chemical compound. .So it is important in drug discovery. It can utilize several computational techniques depending on the amount and type of confirmation available about the target and compound.


The properties of a compound should be calculated computationally.

Because VS is not a stand-alone process

Less amount of information available when building a compound library

By using various methods described above lead moiety is identified by various softwares like iGEMDOCK, AUTODOCK, DOCK. This lead molecule is being subjected to docking to know the interaction between the protein of interest and the lead. A graphical-automatic drug discovery system, called iGEMDOCK used for integrating docking, screening, post-analysis, and visualization. To our best knowledge, iGEMDOCK is the first system which combines structure-based virtual screening and post-screening analysis. The lead molecule which is identified by virtual screening are taken for lead optimization by knowing ADME datas and drug likeliness properties.


Drug discovery and development processes are generally expensive . The majority of the cost needed for the clinical phase of drug development. Poor pharmacokinetic profile is the main reason for the failure of clinical phase of drug development, other than the toxicity.

Nowadays, testing of drug metabolism, pharmacokinetics and toxicity are done before evaluating a compound in the clinic. The rate at which biological screening data obtained has significantly increased, and high-throughput screening (HTS) facilities are now widespread at large pharmaceutical companies. Combinatorial chemistry makes large sequence of closely related libraries of chemicals using the same chemical reaction and appropriate reagents. Such libraries are run through the HTS to make hits and which further, more focused, and then their series are designed in the next round.

As the capability for biological screening and chemical synthesis have dramatically increased, the demands for large quantities of early information on absorption, distribution, metabolism, excretion (ADME) (together called ADME data)have increased simultaneously.Various medium and high throughput in vitro ADME screens are therefore now in use. Before the pharmaceutical development of molecules for the treatment of disease it is recommended that the pharmacokinetic properties of each compound should be considered prior to its selection as a drug candidate.

In addition, there is an increasing need for good tools for predicting these properties Various softwares and servers are available, one such is Accelrys accord for excel.

ADMET datas to be predicted:

A deeper understanding of the relationships between important ADME parameters and molecular structures and properties have been used to develop in silico models that allow the early estimation of several ADME properties. Among other important issues, we want to predict properties that provide information about dose size and dose frequency such as oral absorption, bioavailability, brain penetration, clearance and volume of distribution .

Need of ADMET data:

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ADME/Tox properties are important parameters for the selection of drug candidates for development.

The need for ADMET information starts with the design of new compounds.

This information can influence the decision to proceed with synthesis either via traditional medicinal chemistry or combinatorial chemistry strategies. Obviously, at this stage, computational approaches are the only option for getting this information.

Drug candidate selection involving both pharmacological properties and ADME/Tox screening should lead to an enhanced probability of clinical success.

This will inevitably save time and costs in drug discovery and development. ADME properties can be influenced by various physico- chemical properties like

Molecular weight

Hydrogen donors

Hydrogen acceptors

Log P etc

Such type of informations can be obtained by knowing the drug likeliness properties.


The concepts of drug likeness play an essential role in the transformation of a clinical candidate to a marketed product. These likeliness properties can be found out from the molecular structure before synthesizing and testing a substance.

A drug should have both water and fat solubility because an orally administered drug has to go through the intestinal lining, carried in aqueous blood and penetrate the lipid cellular membrane to reach the inside of the cell. Partition coefficient known as log P is used to estimate solubility.

It has to be sufficiently water soluble because the drug is transported in aqueous media like blood and intercellular fluid. Solubility in water can be estimated from the number of hydrogen bond donors vs alkyl side chain in the molecule.

Low water solubility leads to slow absorption and action. Too many hydrogen bond donors leads to low fat solubility, so that the drug cannot penetrate the cell wall reach the inside of the cell.

Smaller molecular weight is better, because diffusion is directly affected. 80% of traded drugs have molecular weights under 450 Dalton.

Ideally, using a drug likeness index leads to rejecting nonviable lead compounds before they are even synthesized. One of the traditional rules of thumb is Lipinski's Rule of Five. For calculating this there are lots of softwares available. Molinspiration server is used here.

Lipinski's Rule of five:

Is a rule of thumb which evaluates drug likeness and it determine whether a chemical compound can make an orally active drug in human. It may have specific pharmaceutical or biological properties that would make it a likely orally active drug in humans. Most medications are relatively small lipophilic molecule. Based on this Christopher A Lipinski formulated this rule. The rule is important for drug development where a pharmacologically active lead structure is optimized stepwise for increased activity and selectivity as well as drug like properties as described by Lipinski's rule.

The rule:

Lipinski's rule of five states that, in general , an orally active drug has:

Not more than 5 hydrogen bond donors(OH&NH group)

Not more than 10 hydrogen bond acceptors(N&O group)

Molecular weight under 500g

Partition co efficient log P less than 5

Number of violations less than 5

It is to be noted that all the numbers are multiples of five, which is the basis for the rules name.


Partition co efficient LogP is -0.4 to 15.6 range

Molecular refractivity from 40 to 130

Molecular weight from 160 to 480

No. of. Heavy atoms from 20 to 70.

Once the lead is being optimized , it is taken for the docking studies.

1.6. DOCKING 10-11

Docking is a technique which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. The strength of association or binding affinity between two molecule can be predicted from this orientation. It is the insilico method of binding two molecules together in 3 dimensional space. It is an essential for structure-based drug design, because it is used to identify the potential drug antagonists. Antagonists are ligands that block the protein from an agonist by binding to a receptor.

A major advantage of docking of small molecules is that chemical diversity can be attained without much time and effort into physical screening. It is used to predict the affinity and activity of small molecule by calculating the binding orientation of small molecule of drug candidates to their protein target. Thus docking is important in the rational design of drugs. Molecular docking is defined as optimization problem which predict the best fit orientation of ligands that bind to protein .During this process to get best fit , ligand and protein adjust their orientation this is referred as induced fit. The final aim of docking is to find out the three dimensional structure of macromolecular complex present in the living organism. Scoring function help to rank the possible candidate structure and thus confirm the structure which most prone to occur in nature.

Docking approaches:

Mainly two approaches

Here one approach describes the protein and the ligand as complementary surfaces called matching technique. In the second approach the ligand -protein pair wise interaction energies are calculated. This is the actual docking process. Both have some advantage and limitations

Mechanics of docking:

The first requirement of docking is the structure of protein of interest. A main input is the protein structure and data bases of potential ligand. Search algorithm and scoring functions determines the success of docking.

Search algorithm:

Search space consists of all orientations and confirmations of the protein paired with the ligand. But it is difficult to explore the search space ie; calculating all possible distortions of every molecule and all translational and rotational orientation of the ligand relative to the protein. Different conformational of the ligand and receptor molecule. Stochatic torsional searches about rotational bonds molecular dynamic simulations genetic algorithm for low energy confirmations.

Scoring function:

Mathematical technique used to predict the binding affinity between two molecules after docking. The two molecules are drug and biological target strength of other types of intermolecular interactions can be predicted. Scoring function represent the energy of pose ie; represent favorable binding interactions. Stable systems have low (negative) energy and thus have good binding interactions. Another approach is to produce statistical potential from protein data bank and find out the fit of pose in accordance with the standard potential.

Various softwares used:

Over 60 docking software systems and more than 30 scoring functions are reported. Only some of the software were made available and a limited number of them are widely used (AutoDock, DOCK, FlexX, GOLD, Glide, ICM, QXP/Flo+, Surflex).Usually molecule docking is implemented as part of software packages for molecule design and simulation and more than one search method and scoring functions are provided in order to increase the accuracy of the simulations.


Autodock is a group of computerized docking tools. It is used to predict the molecules such as candidates or substrate, which bind to a of 3D structure of known receptor. Application of AutoDock requires several separate pre-docking steps, e.g., ligand preparation, receptor preparation, and grid map calculations, before the actual docking process can take place. Existing tools, such as AutoDock Tools (ADT) are used for this. AutoDock works in Linux platform. As a user friendly interphase Cygwin is also used.

AutoDock uses Monte Carlo method and simulated annealing in combination with genetic algorithm for building the possible conformations. The genetic algorithm is used for global optimization. The local search method is energy minimization and Amber "force field" model is used for evaluation of the binding positions together with several scoring functions based on the free energy.

It is mainly consists of 2 main parts:

Autodock it performs the docking of the ligand to a set of grids describing the target protein.

Auto grid pre calculate the grid.

Autodock has three improvements

The docking results are more accurate and reliable.

It can optionally model flexibility in the target macromolecule.

It enable auto docks use in evaluating protein -protein interactions.

Autodock 4.2 has many steps which includes preparation of ligand and receptor files and calculation of acidity maps around the receptor and ligand by a 3D grid. Then defining the docking parameters and running the docking simulations.

Autodock needs grid map for each atom present in a molecule to be docked. It is calculated by auto grid .A grid map means a three dimensional network of equally spaced points which are centered in the active site of macromolecule.Each points in the grid box is the potential energy of a probe atom or functional group of that position.

AutoDock has applications in:

X-ray crystallography

Structure-based drug design

Lead optimization

Virtual screening (HTS)

Combinatorial library design

Protein-protein docking

chemical mechanism studies


Tuberculosis (TB) is an olden days infectious disease of worldwide influence, which is re-emerged with multi-drug resistant strains (MDR-TB) and acquired immune deficiency syndrome (AIDS). In 1882, Robert Koch discovered a staining technique that enabled him to see Mycobacterium tuberculosis. World Health Organization (WHO), reported that, one third of the world's population are infected with Mycobacterium tuberculosis (MTB) and 8.2 million new TB cases will occur worldwide in 2020.Further, it takes more than 40 years for the discovery of a new TB drug .Therefore, it is crucial to synthesis novel anti-tubercular drugs which are equally effective against MTB and MDR-TB, and reduce the duration of therapy and potential leads for further studies of drug development.

Synergistic effect of simultaneous HIV and TB infection

The influence of simultaneous human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (Mtb) infection on disease succession of both diseases clearly recognized in the past years. Mtb infection is the most important cause of mortality in HIV-infected patients and also HIV is a predisposing factor for Mtb-infected subjects to reactivation and progression to active tuberculosis disease (TB).

Multi drug resistant tuberculosis

It is defined as a type of TB which is resistant to INH and RMP. Multi drug-resistant tuberculosis was not due to a single event but due to the result of the stepwise addition of independent mutations in the genes encoding drug targets. The main reasons for multidrug resistant tuberculosis are the insufficient delivery and prescription of chemotherapy, an insufficent number of active drugs in the regimen or poor compliance.

Extensively drug resistant tuberculosis

(XDR-TB) is defined as a type of MDR-TB which is resistant to quinolones and also to any of these including kanamycin, capreomycin, or amikacin. XDR-TB is a type of TB caused by bacteria which are resistant to the most effective anti-TB drugs. XDR-TB strains have emerged from the mismanagement of multidrug-resistant TB (MDR-TB) and once formed, it can spread from one person to another. XDR-TB is always associated with a higher mortality rate when compared to MDR-TB .


Classification of drugs:

First line anti-tuberculous drugs:

Isoniazid ( INH ) or H,

Pyrazinamide ( PZA) or Z,

Rifampicin (RMP) or R,

Streptomycin (STM ) or S.

Ethambutol ( EMB) or E,

Current TB therapy, also known as DOTS (directly observed treatment, short-course) consists of an initial phase of treatment with 4 drugs, INH, RMP, PZA and EMB, for 2 months daily, followed by treatment with INH and RIF for another 4 months, three times a week.

Second line anti tubercular agents:

Aminoglycosides e.g., amikacin(AMK), kanamycin(KM)

polypeptidese.g., capreomycin, viomycin, enviomycin

fluoroquinolonese.g., ciprofloxacin(CIP), levofloxacin, moxifloxacin(MXF);

Thioamides e.g. Ethionamide, Prothionamide


p-amino salicylic acid (PAS or P)


This second line agents are less effective than the first-line drugs (e.g., p-aminosalicylic acid)

It is having toxic side-effects (e.g., cycloserine)

it is unavailable in many countries (e.g., fluoroquinolones)

Third line anti tuberculous agents





Vitamin D



Macrolide antibiotics e.g, clarithromycin

This may either not be very effective (e.g., clarithromycin) or their efficacy has not been proven (e.g., linezolid, R207910). Rifabutin is impractically expensive.

Identification of new drug targets

The complete genome sequence of M.tuberculosis provides an opportunity for a more focussed approach towards the identification of new drug targets. An important advantage is the possibility of identifying a novel target that is present in various bacteria and consequently designing an antibiotic that could be active against a wide range of bacteria.

Some genes and their products reported in literature that can serve as drug target are discussed below.

Genes of signal transduction

Genes involved in dormancy or persistence

Genes involved in cell wall synthesis

Virulence genes

Genes of signal transduction

Genes of other metabolic pathways

Fatty acid biosynthesis

Fatty acid biosynthesis has been shown to be an essential pathway to the causative organisms of tuberculosis. One integral component of the fatty acid biosynthesis pathway, enoyl acyl-carrier-protein (ACP) reductase, has repeatedly been validated as an appropriate drug target in other organisms.

Mycolic acids are the major constituent of M.tuberculosis cell wall structure. They form a highly impermeable protective layer .As mycolic acids possess such critical roles in tuberculosis for host cell evasion, enzymes involved in their biosynthesis offer attractive targets in the battle against tuberculosis. Mycolic acids, defined as alkyl hydroxy long chain fatty acids, The fine structure of mycolic acids is associated with virulence of M. tuberculosis.The process of fatty acid biosynthesis holds the central role in the production of mycolic acids. Two distinct types of fatty acid biosynthesis, FAS-I and FAS-II, have been characterized within M. tuberculosis.

Fatty acid biosynthesis (FASII) pathway in M. tuberculosis:

The FASII pathway in MTB is involved in elongating fatty acids from the FASI cycle. The acyl carrier protein, AcpM, is used to carry the elongating fatty acid through the cycle. AcpM is first condensed with malonyl CoA via FabD, the malonyl CoA:AcpM acyltransferase. The pathway is then initiated by another condensation reaction involving the long chain acyl-CoA provided from the FASI pathway with the malonyl-AcpM via FabH, a β-ketoacyl-AcpM synthase III, thereby liberating CO2. Next, the β-ketoacyl-AcpM is reduced by MabA (FabG), a NADPH-dependent 3-ketoacyl-AcpM reductase to the corresponding 3-hydroxy AcpM. This is dehydrated to give the 2-enoyl-AcpM product. The final step involved InhA (FabI), the enoyl-AcpM reductase which reduces the alkene to yield the saturated fatty acid. Successive cycles of elongation are initiated by the condensation of another malonyl-AcpM by either of the β-ketoacyl-AcpM synthases, KasA or KasB with the growing acyl-AcpM product formed from InhA. For each round of this cycle, two carbons are added resulting in a C50-60 fatty acid chain.

The pathway is shown in fig 3:

Fig 3: Pathway for fatty acid synthesis

1.8.enoyl acyl carrier protein reductase:

As mycolic acids possess such a critical role in the viability of tuberculosis against host cell defenses, the underlying pathways responsible for their synthesis were targeted. The focus of this study, M. tuberculosis enoyl-ACP reductase (InhA), is a NADH-dependent enzyme that participates in fatty acid biosynthesis. InhA catalyzes the final step of reduction in the FAS-II elongation cycle through the reduction of 2- trans-enoyl chains (> C12) to yield saturated chains InhA has been identified as an excellent anti-tubercular target for several reasons. EACP reductase is a part of the short chain dehydrogenase/reductase family of enzymes. The main characteristic of this family is a polypeptide backbone topology in which each subunit consists of a single domain with a central core that contains a Rossmann fold supporting an NADH binding site. Within EACP reductase, several B-helices and C-strands of the Rossmann fold extend beyond the NADH binding site, creating a deep crevice for the fatty acyl substrate. Part of this extension, referred to here as the "substrate binding loop" (residues 196-219), and consists of two perpendicular B-helices that form one side of the fatty acyl substrate binding crevice.

Recent crystallographic and molecular modeling studies of inhibitors into the EACP reductase active site which is made up of key residues Gly96, Phe97, Ile102, Met103, Phe149, Met155, Pro156, Ala157, Tyr158, Met161, Pro193, Met199, Val203, Ile215, Leu218 and Trp222 suggested that the formation of hydrogen bonding network among active site residues and NADH cofactor probably serves as the key feature that governs the orientation of a compound within the active site. The dual hydrogen bonding network involved the ligands oxygen atom, EACP reductase catalytic residue Tyr 158 and NADH. This hydrogen bonding network seems to be a conserved feature among all the EACP reductase inhibitor complexes identified so far. In addition with hydrogen bonding interactions, π-π interactions between nicotinamide ring of cofactor NADH and at least one of the rings in the ligand contribute significantly towards inhibitor binding to InhA active site. This π-π interaction also seems to be conserved among the EACP reductases of various species. Some hydrophobic interactions with Phe149, Pro193, Leu218 and Val203 are also important with respect to inhibitor binding.

By inhibiting Enoyl ACP reductase the Fatty acid synthesis II will be blocked. This will lead to shortage of lipids like lipoarabinomannan (LAM), trehalose dimycolate, and phthiocerol dimycocerate and mycolic acids. There by integrity of the cell structure is lost which will lead to death of the bacteria. Therefore, there is considerable potential for selective inhibition of bacterial fatty acid biosynthesis. Thus, there exists a potential for selective inhibition of Gram-positive and Gram-negative bacterial cell growth by the inhibition of the FabI of the enoyl ACP reductase enzyme.

1.9.Mycobacterium Enoyl ACP reductase inhibitors

Based on the mechanism of inhibition there are two types of inha inhibitors.

Indirect inhibitors:

The current front-line drug isoniazid (INH), targets the synthesis of mycolic acids which are necessary components required in maintaining the integrity of the complex Mycobacterial cell wall. There was a good evidence that INH targets InhA, the enoyl reductase (ENR) in the fatty acid biosynthesis (FASII) pathway. However, INH does not directly target InhA. As a prodrug ,INH must be activated by KatG, catalase-peroxidase enzyme with dual activities of catalase and peroxidase oxidizing INH to an acyl radical binding to the position 4 of nicotinamide adenine dinucleotide (NAD) to form an adduct.(Fig.4)This activated form of isoniazid then reacts with NAD+ to form the INH-NAD+ adduct which is a slow, tight-binding competitive inhibitor of InhA .

Fig.4.Formation of the INH-NAD (H) adduct

Mutations in KatG account for much of the resistance to INH. Therefore, it would be desirable to design an inhibitor that can bypass this initial activation step and target InhA directly. Such a compound may display promising activity against many of the drug-resistant strains of Mycobacterium tuberculosis. Thus leads to the arise of direct Enoyl ACP reducatase inhibitors.

Direct inhibitors:

Based on the virtual screening several direct InhA inhibitors have been recognized.

Triclosan analogs


Pyrazole Derivatives

Pyrrolidine Carboxamides



More recently it has been reported that broad spectrum anti bacterial inhibitor triclosan (TCN) also actually targets InhA without KatG activation.

Interestingly, it is found that all of these InhA direct inhibitors have conserved interactions with the active site of InhA, a hydrogen bond network with the active amino acid Tyr158 and the 20-hydroxyl group of the nicotinamide ribose of the nucleotide and stacking interactions with the NAD+ nicotinamide ring. The interaction of triclosan with Inha is stabilized by the pep stacking interaction between the hydroxyl chloro phenyl ring and the hydroxyl group of a tyrosine from hydrogen bonding interactions with the hydroxyl group of triclosan. Ring B of triclosan makes several hydrophobic contacts with ENR. The ether oxygen of triclosan may also be critical in the formation of the stable ENR-triclosan-NAD+ complex, since the replacement of the group by a sulfur atom abolishes the inhibitory activity. These interactions provide a critical role for further development of structurally novel InhA direct inhibitors.

1.10.Antibacterial agents 25-27

Bacterial Infections

Bacteria are living unicellular organism.They look like balls, rods or spirals, under a microscope. Most bacteria are harmless but less than 1 percent makes people sick. Many are useful. Some bacteria help in making healthy foods like yogurt and cheese. Bacterias are used to digest food,and they destoy disease-causing cells and provide vitamins which are needed to body.

But some infectious bacteria may make you sick.They quickly reproduce in human's body and produce chemicals called toxins, which will damage our cellvand make you sick. Examples of infectious bacteria are S.pneumoniae S.aureus.and E. coli


Most E. coli strains are harmless, but some, such as serotype O157:H7 can cause serious food poisoning in humans. Virulent strains of E. coli can cause gastroenteritis , urinary tract infection, Neonatal Meningitis Haemolytic-Uremic Syndrome (HUS), Peritonitis, Mastitis, Septicemia etc.

Antibacterial agents specific to E.coli

Amoxicillin as well as other semi-synthetic penicillins








E. coli bacteria, the most prevalent gram-negative flora in the intestine. In a 2003 report, 42% of E. coli were resistant to one or more of the 12 antibiotics investigated. Resistance was highest to ampicillin (29.8%).

The increasing resistances of clinically important pathogens to antibiotic treatments are of world-wide concern. One approach to combat antibiotic resistance is to identify new antibacterial agents that operate with distinctly different mechanisms from the actions of currently available drugs. Another recent trend is to identify and exploit new molecular targets of pathogenic strains. Many novel validated targets have been identified from bacterial genome information.

Enoyl ACP reductase of Escherichia coli

The development of new antibacterial agents which are nonresistant and site active, the enzyme inhibitory drug targets have been the main target for the researchers. Enzymes that compromise the fatty acid synthetase (FAS) complex responsible for fatty acid biosynthesis are considered ideal targets for designing novel antibacterial agents. enoyl acp reductase is a key regulatory in fatty acid elongation, and it catalyses the NADH dependent stereo specific reduction of α,β unsaturated fatty acids bound to the acyl carrier protein. virtual screening and the databases revealed the importance of triclosan based e.coli- inha inhibitors which act as the antibacterial agents by inhibiting the enoyl ACP reductase (Inha) enzymes.

Reviews reveal that the main chains of Gly93-Ala95 involves van der Waals contacts with the bound NAD1. Hydrophobic interaction with Leu100, Tyr156, Met159, Ala196 and Ile200 and a couple of water molecules are been observed in the binding domain Ile192, Ala 196,Tyr 194,Tyr 146,Phe 203,Pro 191,Met 206, Ala 197, Gly 93 and Ala 95 forms the binding pocket.