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The process of drug discovery and its development is always an intense, lengthy and interdisciplinary endeavor. One of the fastest developing directions of the high tech investigation are drug discovery which in turn relies on the recent achievements of the quantum physics, chemistry and molecular biology, information technology and bioinformatics.
Identification of candidates, synthesis, characterization, screening and assays for therapeutic efficacy are the various process involved in the drug discovery. Once the compounds shows the value of results, then the drug development process begins in the prior to the clinical trials.
1.1 DRUG DESIGN
Drug design also called rational drug design is an inventive process for the finding of newer medications depending upon the knowledge of the biological target. Drug is a small organic molecule that activates or inhibits the function of biomolecules like proteins, which in turn results in a therapeutic benefit to the patient. Drug design involves design of the smaller molecules that are complimentary in shape and the charge to the bimolecular target to which they interact and hence will bind to it.
Rational drug design
Various computational methods to identify novel compounds or design compounds with optimal selectivity, efficacy and safety which would bind to the target is been utilized for understanding the concept of rational drug designing. Rational drug design is a focused approach when compared with the traditional trial and error method.
Structure based drug design, ligand based drug design, de novo design, and homology modelings are the various possible methods of drug design.
Structure based design is the most reliable and the advantageous one among all the other approaches because it depends on the 3D structure of the target.
1.2 STRUCTURE BASSED DRUG DESIGN (SBDD)
Structure based design also called direct drug design depends upon the knowledge of 3D of the biological target obtained from various methods like NMR spectroscopy or X-ray crystallography. Creation of the homology model of target is possible based upon experimental structure of the related protein if the experimental structure of the target is not available. The candidate of drug to bind with the target can be predicted based upon the biological target. The drug candidate selected should have high affinity and selectivity to the target and are designed using interactive graphics and by the intuition of a medicinal chemist. New drug candidate can also be suggested using various computational procedures. Use of SBDD has
become a standard procedure as a part of drug discovery and development in both academia and industry.
The process involves various steps like:
Obtaining the structure of the target protein.
Identification of the active sites.
Virtual screening of a small molecule database( which contains chemically diverse structures of small molecules in order of a million).
Identification of potential ligands based on a chosen scoring function.
"Target" is a naturally existing cellular or molecular structure which is involved in the pathology of interest that the drug-in-development is meant to act on. "Established targets" are the one for which a good scientific understanding is there which in turn is supported by a lengthy publication history of how the target functions in normal physiology and how does it involved in human pathology. "Established" gives direct relationship to the amount of background information available on a target, particularly the functional information. Based upon the information whether more or less, the more or less investment is required to develop a therapeutic directed against the target. Process used for gathering such functional information is called "target validation".
All the targets which are not the "established targets" but have been or are the subject of the drug discovery campaigns are called the "new targets". The "new targets" includes the proteins which are newly discovered or whose function now has become clear as result of basic scientific research.
Finding the correct target and identification of active sites
Protein is the main and usual target in a SBDD. These proteins are closely linked to a disease and plays major role in signaling pathway which is often disrupted under diseased condition. Enzymes often have binding pockets for substrates as well as inhibitors and hence are one of the favorite targets.
Proteins are the majority of the target selected for the drug discovery. There are two classes that predominate and they are:
G-protein-coupled receptors(or GPCRs)
G-protein coupled receptors
G-protein-coupled receptors involves in two principal signal transduction pathways and they are phosphatidylinositol signal pathway and cAMP signal pathway. A conformational change occurs in GPCR when a ligand binds to it and allows it to act as a guanine nucleotide exchange factor (GEF). By exchanging the bound GDP for a GTP, GPCR can activate G-protein. The Î± subunit along with bound GTP, gets dissociate from Î² and Î³ subunits and hence affects further signaling of intracellular proteins or target functional proteins directly depending upon the Î± subunit type.
A kinase enzyme modifying the proteins chemically by adding phosphate groups to them are called as protein kinase. The functional change in the target proteins by a change in the cellular location, enzymatic activity or association with other protein occurs as a result of phosphorylation.
Process of SBDD
With cloning, purification and structure determination of protein target, the process of SBDD begins. X-ray crystallography or homology modeling with crystal data, NMR are the most common and reliable source for the structural determination. Mostly acceptable crystal structure are those with a resolution of around 2.5á¾¸. R factor and R free factor drawn between model and the experimental data are the other parameters for good structure. R free value and R value must be around and below 25% respectively for good structure. Evaluation of other factors in the region of interest of the target like hydrogen bonding, temperature factors, Vander Waals interaction, etc should be done. The programs used to evaluate protein structures of the drug design are PROCHECK and WHATIF. Homology models of protein targets are evaluated by SWISS MODEL. After the evaluation the targets are examined for the correct binding site for the candidate drug. The target sites are often protuberance or pocket with hydrogen bond donors or acceptors or hydrophobic pockets and surfaces and is shown below in the figure:
Fig 1: FLOW CHART FOR STRUCTURE
BASED DRUG DESIGN
1.3 VIRTUAL SCREENING TECHNIQUES (4-7)
VS is a process in which computational methods(that exploit existing knowledge) are used to filter a library containing unmanageable compounds to a limited number of potentially promising compounds for the target enzymes or receptors of interest. The complex phenomenon of molecular recognition is modeled either by principle of similarity in which similar compounds are assumed to produce similar effect; or by the principle of complementarity in which the receptor of a biologically active compound is complimentary to the compound itself.(i.e. a lock and key model). The choice of the appropriate approach depends on the knowledge of the molecular structure of the active molecules and its receptor. Two methods are involved in this and they are:
Ligand based virtual screening method(LBVS)
Structure based virtual screening technique(SBVS)
Ligand based virtual screening method
They are further divided in to three classes and are:
Machine learning algorithms
A single 3D structure of a biologically active ligand is used as a template by ligand alignment for the super positioning and scoring of other 3D molecular structures from chemical libraries with respect to similarity of their characteristics like shape, interaction possibilities or physicochemical properties.
By use of structurally diverse set of ligands that bind to a receptor, a coarse-grained 3D surrogate of receptor is generated in the pharmacophoric approach. These are usually done by calculating all of the possible superposition of predefined chemical groups which are recognized at the target binding sites and are responsible for the biological activity. This pharmacophore serves as template for the selection of the molecules which fulfill the specified geometrical constraints in the VS queries. At least one conformer from each active ligand must be applied with the pharmacophoric features while negative features are applied to inactive molecules.
Machine learning algorithms
They rely on QSARs which correlate biological data with molecular descriptors hence derives statistical models used to predict activity of novel compounds. Some of the examples of the machine learning techniques becoming popular tools of model building and virtual screening are: Self organizing maps(SOM), binary QSAR, Bayesian classifier algorithm, decision trees, k-nearest neighbor approach(kNN), artificial neural network(ANN), and support vector machine(SVM).
Structure based virtual screening technique
These include high throughput docking and receptor based pharmacophore design. Docking a large data base of ligands in to active sites of the receptor followed by applying scoring function that estimates the possibility of the ligand binding to the protein with high affinity is the process involved in high throughput docking. The most challenging step in this strategy is considered to be accurate scoring and ranking of the solutions. Knowledge based methods (PMF, Drug score, SMoG), master equation, regression based methods and methods solving the Poisson-Boltzmann equation(ZAP) are the commonly used scoring functions. Consensus scoring which combines information from different scoring functions are advisable to compensate the errors in single scores and to find out the probability of finding true ligands.
It is a time consuming process, the main disadvantage in virtual screening process. Building a small molecular library takes more time hence lead moieties from the reported papers are selected. Such type of approach is been used.
Various software's like iGEMDOCK, AUTODOCK, DOCK are used to identify the lead moiety. The docked molecule will be superimposed on one another and the ligand not docked will be in different places. Docking is done to know the interaction between the lead molecule and the protein of interest. iGEMDOCK , the graphical automatic drug discovery system is utilized for integrating docking, screening, post analysis and visualization. It is the 1st system combining post screening analysis and structure based virtual screening.
By knowing the ADME data's and the drug likeliness properties, the lead molecule taken by virtual screening is taken for optimization.
1.4 ROLE OF ADME DATAS (8-10)
By testing the compound s synthesized in time consuming multistep processes against a battery of in vivo biological screens, traditionally the drug were discovered. further the promising compounds were investigated for their pharmacokinetic properties ,metabolism and potential toxicity. Adverse finding would be halted or restarted to find another clinical candidate.
Today this whole process is reworked and the drug metabolism, pharmacokinetics and toxicity studies are done much earlier. The rate at which the biological data obtained is increased and high throughput screening is now largely used in pharmaceutical companies and biotech's. Combinatorial chemistry a newer approach to chemistry is been adopted with response to these developments. This newer approach helps to synthesize large series of closely related libraries of chemicals using appropriate reagents and with same chemical reaction. Then these libraries are run through HTS and are more focused series are designed and synthesized in next round. Various medium and high throughput in vitro ADME screens are now used because of the increased demands of information of ADME due to the increase in biological screening and chemical synthesis. Further good tools are required for predicting the said properties to serve two aims.
At the design stage of newer compounds and compound libraries to reduce the risk of attrition at later stage.
Optimize the screening and testing by looking at most promising compounds.
Various software's are available for this purpose. E.g. --------------------------
ADMET data's to be predicted
Early estimation of several ADME properties is done with the help of in silico models. For the development of the in silico models a deeper understanding is required for the relationship between important ADME parameters and molecular structure and their properties. Also properties like oral absorption, bioavailability, brain penetration, clearance and volume of distribution, hail life, etc must be predicted to give vital information about dose size and frequency.
BOX 1- ADME data's predicted.
Need of ADMET data
For the drug candidate selection and development, ADME/Tox properties play an important role.
For the design of the newer compounds ADMET information is required.
Based on this information we can decide by using which way( traditional medicinal chemistry or combinatorial chemistry strategies) the synthesis can be proceeded.
Probability of clinical success is more with the selection of drug candidate using pharmacological properties and ADME/Tox screening.
Both time and cost related to the drug discovery and development can be saved.
Various physico-chemical properties like molecular weight, hydrogen donors, hydrogen acceptors, log P, etc can also influence the ADME properties. By knowing the drug likeliness properties, such information can be obtained.
1.5 Drug likeliness
Drug has to pass through intestinal lining, carried in blood which has got aqueous nature and also have to penetrate through lipid cellular membrane to reach to cells; hence the drug must have an optimum lipid and water solubility.
By knowing the number of hydrogen bonds vs. alkyl side chain in a molecule the water solubility can be estimated. Slower absorption and action of the drug takes place if the drug has got low water solubility while with the increase of number of hydrogen bonds the lipid solubility of the drug decreases hence unable to penetrate inside the cell.
Diffusion of the drug in to the cell is inversely proportional to the molecular weight. I.e. smaller the molecular weight better will be the drug diffusion. Most of the marketed drugs have got a molecular weight below 450 Da.
Drug likeliness index can be constructed based on this information. By using this index the procedure to reject the nonviable compounds before their synthesis can be done. Lipinski's rule of five is one
of the traditional rule; for calculating this many online server like mol inspiration server are available.
LIPINSKI'S RULE OF FIVE
Lipinski's rule of five is a thumb rule used for the evaluation of drug likeliness or tom determine the various properties of a chemical compound making it an orally active drug in humans. Based on the observation that most modified drugs are having relatively smaller lipophilic molecules Christopher A. Lipinski in 1997 formulated this rule. The molecular properties important for the drugs pharmacokinetics including ADME can be described by using this rule. This rule is also important for the drug selection and development. Drugs with higher molecular weight, more rings, more rotatable bonds and higher lipophilicity is obtained with the modification of the drug molecular structure.
Lipinski's rule of five states that, an orally active drug has:
Not more than 10 hydrogen bond acceptors( N and O group)
Not more than 5 hydrogen bond donors( NH and OH group)
Molecular weight must be under 500g
Partition coefficient log P should be less than 5
Number of violation must be less than 5.
All the numbers are the multiples of five which is the basis of this rule name.
Partition coefficient log P is -0.4 to 15.6 range.
Molecular refractivity from 40 to 130.
Molecular weight from 160 to 480.
Number of heavy atoms from 20 to 70.
once the lead is optimized then it is taken for the docking studies.
In the field of molecular modeling docking is a method which predicts the preferred orientation of one molecule to second when bound to each other to form a stable complex. The strength of association or binding affinity between two molecules can be predicted by the use of knowledge of preferred orientation. Binding orientation of the small molecule drug candidate to their protein targets can be predicted frequently by use of docking. In turn it helps in the prediction of affinity and action of small molecule, hence plays an important role in rational design of the drugs. Prediction of the 3D structure of the macromolecular complex of interest is the ultimate goal of docking. Docking produces plausible candidate which are ranked using methods like scoring functions to identify mostly occurring structure in nature.
Rigid body docking vs. flexible docking
If the bond angles, bond lengths and torsion angles of the components are not modified at any stage of complex generation, it is known as rigid body docking. Rigid body docking is inadequate when substantial conformational change occurs within the components at the time of complex formation. Docking procedures which permit conformational changes or flexible docking procedures, must intelligently select small subset of possible conformational changes for consideration.
Mechanics of docking
To perform docking the structure of the protein of interest is required. Biophysical techniques like NMR spectroscopy or X-ray crystallography are usually used for the structure determination. The protein structure and the database of potential ligand serves as input for docking program. The success of the docking depends upon components like scoring function and search algorithm.
This consists of all possible orientation and confirmation of the protein paired with the ligand. It is impossible to exhaustively explore the search space since it involve enumerating all possible desorption of each molecules and all possible rotational and translational orientations of the ligand relative to the protein at a given level of granularity. Most docking programs account for flexible ligand and several are attempting to model a flexible protein receptor. Each snapshot of the pair is referred as pose. Many strategies are there for sampling the search space and the examples are:
Use a coarse grained molecular dynamics simulation to propose energetically reasonable poses simulation.( direct search- simplex method; gradient based search- steepest descent, Fletcher-Reeves method, Newton-Raphson method; least squares methods- Marquardt method)
Simulated annealing( Monte Carlo based search of the parameter space)
Use a linear combination multiple structure determined for the same protein to emulate receptor flexibility.
Use a genetic algorithm to evolve new poses that are successfully more and more likely to represent favorable binding interactions greedy fragment based construction.
The scoring function takes a pose as input and returns a number indicating the likelihood that the pose represents a favorable binding interaction
Most scoring functions are physics based molecular mechanics force fields which estimates the energy of the pose. Lower the energy stable the system will be and thus likely to have binding interaction. An alternative approach is to derive a statistical potential for interaction from a large data base of protein ligand complexes like protein data bank and to evaluate the fitness of the pose according to the inferred potential.
There are a large number of structures from X-ray crystallography for complexes between proteins and high affinity ligands and fewer for low affinity ligands, later complexes are tend to be less stable and are difficult to crystallize. Scoring function trained with this data can dock high affinity ligand correctly and also the plausible conformation of the ligands that do not bind will be given.
Various software's used
Over 60 docking and 30 scoring function software's are reported. Some of them are only available and a limited in use( AutoDock, DOCK, FlexX, GOLD, ICM, QXP/Flo+, Surflex). Usually molecule docking implements a part of software packages for molecular design and simulation and more than one search method and scoring functions are provided in order to increase the accuracy of the simulations. The various search methods and scoring functions used in several molecule docking software systems are summarized in the table 1.
It is a collection of automated docking tools, designed to predict how small molecules like drug candidates or substrates bind to a receptor of known 3D structure and combines rapid energy evaluation through precalculated grid of affinity potentials with a variety of search algorithms to find suitable binding positions. AutoDock works in Linux platform. Cygwin is used as a user friendly interface.
The methods used by AutoDock are the 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. Amber force field model is used for the evaluation of the binding positions together with several scoring functions based on the free energy.
There are two main parts of the software and they are
Auto Dock- which performs the docking of the ligand to a sets of grids describing the target proteins.
Auto Grid- which pre calculates the grid.
It also has got capabilities to visualize atomic affinity grids and its graphical user interface Auto Dock Tools(ADT) Supports the analysis of docking results. Among its advantages is the free academic license. So far parallel computation are not supported is its drawback. However having in mind the optimization process is based on simulated annealing and genetic algorithm that are both amenable to parallelization a near linear speed up of the docking procedure can be expected by a parallel implementation on a multicomputer platform. The advantages of Auto Dock 4.2 are:
The docking results are more accurate and reliable.
It can optionally model flexibility in the target macromolecule.
It is very fast and provides high quality predictions of ligand conformations and good correlation between predicted inhibition constants and experimental ones.
AutoDock 4.2 now has a free energy scoring function that is based on a linear regression analysis , the AMBER force field, and an even larger set of diverse protein ligand complexes with known inhibition constants than we used in AutoDock 3.0.
AutoDock has also been shown to be useful in blind docking, where the location of the binding site is not known. Plus, AutoDock is free software and version 4 is distributed under the GNU General Public License; it is easy to obtain too.
1.7. ANTI TUBERCULAR AGENTS:14-23
In earlier days for the treatment of the tuberculosis isoniazid was the main ingredient in drug mixtures. But duo to development of resistance to mycobacterium tuberculosis led to the identification of novel drugs.
One of the leading causes of morbidity and among the Indian population is TB and the causative organism is mycobacterium tuberculosis. Nearly 2 million people especially in developing countries have been infected by this disease according to the survey conducted by World Health Organization (WHO).TB is three types -human, bovine, and avain.
The most potent risk factors that transform latent TB into active form are HIV infection. Thus the incidence of the TB has been increased due to co infection with HIV.
One of the major risk factor in controlling TB is multidrug resistance (MDR-TB). In such cause the use of first line drugs becomes ineffective. This drug resistance due to the failure of adhere to full course of drug therapy.
Extensively drug- resistant tuberculosis (XDR-TB) is the resistance caused by bacteria to the most effective anti TB drugs. This XDR-TB strain develops from the improper management of multidrug- resistance (MDR-TB). This type of tuberculosis can spread from one person to person.
Classification of drugs
First Line (Primary) Antituberculosis drugs:
Isoniazid (INH), Rifampicin, Streptomycin, Pyrazinamide, Ethambutol, Thiacetazone etc
Second Line (Secondary) Antituberculosis drug
Ethionamide, Cycloserine, Para amino salicylic acid, Capreomycin and Kanamycin
Current TB therapy called as DOTS (directly observed treatment, short-course)consists of an initial phase of treatment with 4 drugs, INH, RIF, PZA and EMB, for 2 months daily, followed by treatment with INH and RIF for another 4 months, three times a week.
Limitations of current drug therapy and need for new drug targets
Factors that contribute to mortality due to TB are; human immunodeficiency virus (HIV) co-infection, drug resistance, lack of patient compliance with chemotherapy, delay in diagnosis, and variable efficacy of BCG vaccine.
Second line drugs are prescribed for MDR-TB along with DOTS therapy. But is very expensive and has side effects. One of the major drawbacks of DOTS therapy is that, it should be administered for at least 6 month. This can lead to patent noncompliance and may become resistance to these drugs. Another problem of current therapy is its ineffectiveness against persistent bacilli, except for RIF and PZA. Even through new advances in the field of biology of mycobacterium tuberculosis such as microarrays and proteomics has been still find out.
Possible drug targets
A large number of genes are being studied for new drug targets using various approaches
Genes involved in cell wall synthesis
Genes involved in dormancy or persistence
Genes of signal transduction
Genes of other metabolic pathways
Distribution of drug targets in emerging tuberculosis therapy is mentioned below;
DISTRIBUTION OF DRUG TARGETS
Fatty acid biosynthesis (FASII) enzymes
Enzymes involved in the bacterial fatty acid biosynthetic pathway are the fatty acid synthase system. One of the major FAS-II enzymes is Mycobacterium Enoyl ACP redactase. It is also called as Inh A, Fab-I, ENR, etc. These are four isoforms, FabI, Fabk,FabL and FabV of ENR. This biosynthsis provides fatty acid, which are used in the formation of essential cellular components of bacteria such as cell envelopes, phospholipids, lipoproteins, lipopolysaccharides or mycolic acids.
In the type I system of mammal's fatty acid synthase is a single large polypeptide composed of several distinct domains. In the type I system of bacteria, the fatty acid synthase components including the acyl carrier protein (ACP) exist as discrete proteins. This difference in organization makes the bacterial, fatty acid biosynthetic enzymes potentially selective anti bacterial targets.
Fatty acid biosynthesis (FASII) pathway in M. tuberculosis:
This patway is include in elongated fatty acid from the Fas I cycle. The acyl carrier protein, AcpM, is involved in elongating fatty acid through the cycle.
Fig 3: Pathway for fatty acid synthesis
AcpM is first condensed with malonyl CoA via FabD. This pathway is then initiated by another condensation reaction involving the long chain acyl-CoA via FabH, a Î²- ketoacyl-AcpM synthase III, thereby liberating CO2. The Î²-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 are carried out by the condensation of another malonyl-AcpM by either of the Î²-ketoacyl-AcpM synthases, KasA or KasB with the growing acyl-AcpM product formed. For every cycle, two carbons are added resulting in a C50-60 fatty acid chain.
1.8. Structural overview of enoyl acyl carrier protein reductase
The NADH-dependent enoyl acyl carrier protein reductase (EACP reductase) encoded by the Mycobacterium gene InhA is a key catalyst in mycolic acid biosynthesis. EACP reductase catalyzes the NADH-dependent reduction of the trans double bond between positions C2 and C3 of fatty acyl substrates. EACP reductase is a member of the short chain dehydrogenase/reductase (SDR) family of enzymes. The main characteristic of this family is a polypeptide backbone in which each subunit consists of a single domain with a central core that contains a Rossmann fold supporting an NADH binding site. several B-helices and C-strands of this fold extend beyond to the NADH binding site, creating a deep crevice for the fatty acyl substrate. this extension is called "substrate binding loop" (residues 196-219).
According to crystallographic and molecular modelling 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. The dual hydrogen bonding network involved the ligands oxygen atom, EACP redactase catalytic residue Tyr 158 and NADH. In addition with hydrogen bonding interactions, Ï€-Ï€ interactions between nicotinamide ring of cofactor NADH and at least one of the rings in the ligand contribute towards inhibitor binding to InhA. By inhibiting Enoyl ACP reductase , 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. This can also result in the loss of integrity of cell structure. This will result in the bacterial death. 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.
Enoyl ACP reductase enzyme inhibitors are slow, tight, slow-tight inhibitors. Slow tight inhibition occurs when the initial enzyme-inhibitor complex EI undergoes isomerisation to a second more tightly held complex, EI*, but the overall inhibition process is reversible. Under these conditions, traditional Michaelis-Menten kinetics gives a false value for Ki, which is time-dependent. The true value of Ki can be obtained through more complex analysis of the on (kon) and off (koff) rate constants for inhibitor association.
Mycobacterium Enoyl ACP reductase inhibitors
Based on the mechanism of inhibition there are two types of Inha inhibitors.
In this type of inhibition, the front-line drug isoniazid (INH) does not directly target InhA. InhA must be activated by KatG, catalytic peroxidise enzyme with dual activating catalase and peroxidaseoxidising 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) adducts.
Resistance to INH may occur due to some mutations in katG. So it is desirable to discover an inhibitor that can overcome this initial activation step and target InhA directly. Thus lead to the development of direct Enoyl ACP reducatase inhibitors.
Based on the virtual screening several direct InhA inhibitors have been recognized.
Triclosan (TCN), broad spectrum antibacterial inhibitor targets InhA without KatG activation.
Earlier it was found that triclosan being a hydrophobic molecule absorbed via diffusion into the bacterial cell wall and resulting in the known specific disruption of the organism's cell wall. Extensive biochemical and structural studies when performed to confirm that triclosan is a specific inhibitor of E.Coli InhA by the genetic analysis of an Escherichia coli strain resistance to triclosan linked to the resistance to FabI gene, which encoded ENR. triclosan can also directly inhibit InhA from Mycobacterium tuberculosis, Mycobacterium smegmatis (encoded by InhA) and Plasmodium falciparum, the malarial parasite ,Staphylococcus aureus, Haemophilus influenzae.
All these InhA inhibitors conserved interactions with the active site of InhA through 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. The ether oxygen of triclosan is necessary for the formation of the stable ENR-triclosan-NAD+ complex, because the replacement of the oxygen by a sulphur atom diminishes the inhibitory activity. Thus these interactions lead to the development of structurally novel InhA direct inhibitors.
1.10. ANTIBACTERIAL agents 24-29
Bacteria's are unicellular organisms. Some bacteria help to digest food, destroy disease-causing cells and provide the needed vitamins to the body. They can be observed under a microscope. They are also used to making healthy foods like yogurt and cheese. But infectious bacteria can cause infections, give off chemicals called toxins, that can damage our body tissue. Examples of bacteria that cause infections include Streptococcus, Staphylococcus and E. coli.
ROLE IN DISEASE
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
Urinary Tract Infections
In Gastroenteritis E. coli bacteria can escape through a perforation of the intestinal tract and enter in to the abdomen, cause peritonitis. E. coli is responsible for approximately 90% of urinary tract infections (UTI) .this bacteria in the faeces can colonize in the urethra and enter into the urinary bladder as well as to the kidneys (causing pyelonephritis).The serotype of Escherichia coli contains a capsular antigen called K1 is responsible for meningitis . In new born's intestines, the colonization of this bacteria in the mother's vagina can enter in to the fetus, leads to Neonatal meningitis.
Antibacterial agents specific to E.coli
Amoxicillin as well as other semi-synthetic penicillins
E. coli is the most common gram-negative bacteria in the intestine flora. Rate of adaptative mutations in E. coli is "on the order of 10-5 per genome per generation. Due to the increasing resistances of important pathogens, antibiotic treatments are of world-wide concern. To overcome this antibiotic resistance, new antibacterial agents have been introduced that operate with distinct mechanisms from the currently available drugs. Identification of new molecular targets of pathogenic strains is an another recent targets to overcome antibiotic resistance.
1.11. Enoyl ACP reductase of Escherichia coli
Enzymes that compromise the fatty acid synthetase (FAS) complex responsible for fatty acid biosynthesis are considered ideal targets for designing new antibacterial agents. enoyl acp reductase is a key enzyme in fatty acid elongation, catalysing the NADH dependent stereo specific reduction of Î±,Î² unsaturated fatty acids bound to the acyl carrier protein. Reviews reveal that the main chain 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.