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Drug discovery and development is a powerful, very long and interdisciplinary process. It can be described as a single dimensional and sequential process that begins from target and lead discovery process, followed by lead optimization and pre-clinical studies for launching a new drug. In pharmaceutical industries, the count of years to present an active drug from discovery stage into the market is nearly 12 to14 years and pricing up to several billion dollars (Apostolakis, 1998).
In practice, drugs were synthesized from a variety of compounds which is generally time consuming as well as many steps involved in vivo biological evaluation and additional examination required for their pharmacokinetic properties, metabolic studies and possible toxicity studies. Systematic development process reduces various failures such as poor pharmacokinetic studies, lack of efficacy, animal toxicity, adverse effects in humans and various miscellaneous factors.
The process of drug discovery has been changed with the arrival of genomics, bioinformatics, proteomics, and effective technologies like, combinatorial chemistry, virtual screening, high throughput screening (HTS), de novo drug design, in vitro studies and in silico studies for pharmacokinetic screening and also for the structure-based drug design (Ewing, 1997).
In silico procedures are very useful in identifying drug targets via bioinformatic tools such as computer software programs. Further it is used to examine the lead structures for potential binding or active sites, produce structurally similar molecules, verifying for its drug likeness properties, dock these active molecules (ligands) with the target enzyme, arrange them according to their binding attractions, and finally optimize the lead molecules to enhance its binding properties (Jones 1997).
In silico drug design pipeline
Nowadays computers and various computational methods are developed for following reasons such as,
To reduce the complexity
Reduce the time consumption
Novel target identification
Various facilities which brings the drug discovery process in a very simplest way are,
High performance computing
Data management software
Major advantages of computation in the drug development process are as follows,
Virtual screening and de novo drug design
In silico pharmacokinetic properties prediction
Improved methods to determine protein-ligand binding.
Currently, various protein targets are available through many newer techniques such as
Bio informatic methods,
The demand is increased for computational methods that can encounter and examine active sites of the lead molecules and propose its possible drug molecules that can bind particularly in these binding sites. Usage of computers at early steps has concurrently reduced the cost and time needed for the drug discovery and development process.
TYPES OF DRUG DESIGN:
STRUCTURE BASED DRUG DESIGN:
It depends on the wisdom of three-dimensional structure of the protein molecule. Practically the structure was initially identified by X-ray crystallography which improves the aptitude to produce new drugs that fight against diseases. The awareness of the three-dimensional protein structure is required for the design of a new compound. Commonly, new compound is arranged atom by atom and the important properties like shape and charge perfects with the active site of the particular protein target to increase their interaction and block the protein function automatically (Glen 1996). (Fig. 1 demonstrates the structural similarity between the lead molecule and the target enzyme's active site).
X-ray crystallography is used to examine the structure of target protein bound to the unknown lead molecule. (Fig. 2 illustrates the binary complex of a lead molecule bound to the target enzyme's active site). The binary complex shows the binding of the molecule with the active site of the given target protein. By obtaining this structural knowledge, lead molecules are redesigned, synthesized, refined and finally examined in a proper way to get a potent drug, intended and optimized for the desired action.
LIGAND BASED DRUG DESIGN
It is otherwise known as indirect drug design. It trusts on the awareness of different new ligand molecules that bind with the target protein molecule. The designed molecules are used to develop a new strategy which explains the individual element responsible for the interaction between ligand and target protein molecule. (Fig. 3 provides information about the ligand features important for target protein interaction, eg: H-bond acceptor region).
Inclusion of these elements into a ligand should enhance the ligand - protein interaction. Moreover a target protein model is produced based on the composite ligand.
Ligand based drug design depends on the information of other molecules which bind to the biological target active site. These molecules are used to extract a suitable model which provides important structural properties of a lead molecule which helps in the binding process with the target molecule (Glen, 1996).
Scheme of Drug Design
Biologically active target model is designed based on the information of binding molecules. This model is used to develop novel compounds which interact with the biologically active target molecule.
Quantitative structure activity relationship (QSAR) is defined as a correlation between calculated properties of the molecule and it's experimentally determined biological activity was derived. QSAR studies are used to predict the activity of new molecules.
Normally, protein model is designed to get more information about various ligands and its interaction with the target protein. The important target for the rational drug design is a protein molecule which should act as an enzyme. Enzymes catalyze biochemical reactions by reducing the energy level from the substrate molecule into product formation. But, malfunctioning enzymes cause diseases.
The primary aim of rational drug design is to generate an extremely active and selective compound that should bind only to the active site of malfunctioning enzyme. Further it prevents the defective enzyme's function and simultaneously inhibits the progression of the disease.
STEPS INVOLVED IN DRUG DESIGN
In the drug design process, various steps are involved from the target identification till the drug confirmation. These steps produce the geometrical development of a novel drug.
Docking is a method that predicts the possible orientation and binding of one molecule to a target molecule that form a stable complex. Information of the possible orientation in turn is used to predict the strength of binding affinity between two molecules using various scoring functions.
The binding ability between biologically similar molecules (proteins, nucleic acids, carbohydrates and lipids) play a vital role in signal transduction process. The relative orientation of two interacting molecules affect the type of the signal produced (eg. agonist vs. antagonist). Docking is very useful for predicting both the strength as well as type of signal produced by the molecules.
This method is normally used to predict the binding orientation of small molecules to their target proteins. And further it predicts affinity and activity of the selected small molecules. It plays a vital role in rational drug design. Various advanced techniques are directed to improve the docking methods based on the biological and pharmaceutical significance (Kramer, 1997).
The important goal in molecular docking is to find the information about protein - ligand complex geometry. The main problem is optimization where the aim is to reduce the two molecule's intermolecular interaction energy. The protein - ligand complex geometry possibility is very high. Various algorithms are used to get all possible conformations while reducing the computational power for docking calculation. It has the following steps as mentioned below:
Ligand geometry optimization, pH calculation, and rotatable bonds identification
Target protein electrostatic properties calculation and Ligand - binding region identification.
Ligand protein interaction and calculation of Intermolecular energies by a proper scoring function
Accurate interpretation, a high quality representation of the geometry is needed.
Important tool for small molecule lead discovery is the availability of the protein's three dimensional structure along with continuing various advances in docking and scoring methods.
The main goal of docking programs is to target the active biological conformations quickly as well as limiting large amount of search space.
Recognition of the ligand's correct binding geometry in the binding site (Binding Mode)
Similar ligands may bind at extremely different orientations in the active site.
TWO MAIN TASKS OF DOCKING TOOLS:
Sampling of conformational (Ligand) space
Scoring protein-ligand complexes.
Recently other docking programs have also been reported such DREAM++, QS Dock, and Darwin.
INTRODUCING FLEXIBILITY: WHOLE MOLECULE DOCKING
Monte Carlo methods (MC)
Molecular Dynamics (MD)
Genetic Algorithms (GA)
Monte Carlo methods (MC):
The Monte Carlo simulation occupies a special place in the history of molecular modeling. This technique is used to perform the first computer simulation of a molecular system. The expression Monte Carlo simulation seems to be unique and many algorithms are called whenever they contain a stochastic process (random sampling).
In molecular docking the expression of Monte Carlo means importance sampling or Metropolis method. The Metropolis method is actually a Markov chain Monte Carlo method, which produces random move to the system and further it accepts or rejects the move based on Boltzmann probability (Leach, 1997).
The Monte Carlo method plays a vital role in molecular docking but the different kinds of algorithms is very large. The various programs using MC method is as follows Auto Dock, Pro Dock, ICM, MCDOCK, Dock Vision, QXP, Affinity, etc,.
Molecular Dynamics (MD):
This method is based on the principle of Newton's equations of motions. Discovering the global minimum energy of a docked complex is very difficult because the rugged hyper surface of a biological matter (Leach 1997). The problem is now approached using various standard optimization algorithms such as,
Direct searches: potential function, crude optimization of small molecules is only suitable and far away from the minimum.
Gradient methods: First derivative of the potential function, low convergence near the minimum, initial optimization is recommended.
E.g. Steepest descend
Conjugate-gradient methods: Search influences history, search direction, better convergence, high computational efforts.
Second derivative methods: High efficient convergence.
Least squares methods: Good convergence but computationally high expensive.
Nowadays combinations of the methods are used. For eg. a combination of gradient method and conjugate-gradient method is used. Gradient method is used for initial optimization and conjugate-gradient method is used when nearing the minimum.
Genetic Algorithms (GA):
Genetic algorithms and evolutionary programs are widely suitable for solving docking issues because of their usefulness in managing the complex optimization issues. The important goal of genetic algorithms is estimate the population of possible solutions through genetic operators such as mutation, crossovers and migrations into a final population and optimizing a already defined fit function (Leach 1997).
The genetic algorithms process starts with encoding the variables. Here the degrees of freedom are denoted into "genetic code" e.g. binary strings, after that a random initial population of solutions is generated. Genetic operators are then applied to this population to provide a new population. This new population is then scored and ranked as "the survival of the fittest".
The probabilities of getting to the next iteration round are depending on their score. The size of the population is constant, and then good solutions will occupy the population automatically. It should be denoted that genetic algorithms are well suitable for parallel computing methods. Some of the programs which using GAs are GOLD, Auto Dock, DIVALI and DARWIN.
AutoDock 4.2 is an automated docking tool used to dock the ligands and the target enzyme based on their binding affinity. It is generated to predict how the small molecules (ligands) bind to a target receptor of known three dimensional structure. AutoDock 4.2 works on the principles of Monte carlo and Simulated annealing in combination with genetic algorithm, that is used for the global optimization.
Fig. 1. Therapeutic drug molecule docked to the protein receptor (HIV-1 protease).
The therapeutic drug molecule fits tightly in to the binding site of the HIV protease receptor and blocks the abnormal protein function.
Nowadays lots of docking methods are used in the academic purpose and industrial research purpose. Most commonly used programs for docking are as follows,
The ligand starts the search process randomly outside the binding site by expressing the values for rotations, translations and its internal degrees of freedom. Finally it reaches in to the bound conformation. Distinction between docked conformations is calculated by the scoring function of the molecules. AutoDock is able to use Monte Carlo method (MC) or simulated annealing method (SA) or both in the search process (MacKerell 1998).
The current implemented release is Lamarkian genetic algorithm (LGA), in which a traditional GA is used for the global search and is combined with a local search procedure. The new LGA is able to manage the ligands with a larger number of degrees of freedom than SA or traditional GA.
FlexX and Dock
FlexX and Dock methods are used as an incremental construction algorithm which attempts to redevelop the already bound ligand thereby first placing a rigid molecule in the active binding site and thereafter using a suitable algorithm to add some fragments and finally complete the ligand structure.
Even though these programs are more effective than AutoDock, they require some energy calculations. The main disadvantage is that it is very difficult to choose the anchor fragment. Also the selected algorithm may lead to errors resulting from initial incorrect choices that promote missing final conformations of lower energy (Rarey1999).
It is further classified into two main programs.
AutoDock: Docking of the ligand to fixed grids already described in the target protein was performed.
AutoGrid: Precalculates the grid parameters.
In addition AutoDock is able to imagine the atomic affinity grids and also graphical user interface. It is helpful to guide the organic synthetic chemists to design better binders. AutoDock Tools (ADT) supports the analyses of the docking results and is available free for academic license.
Its main drawback is that it does not support parallel computations. The optimization process is based upon simulated annealing and genetic algorithm (Morris, 1999).
The various advantages of AutoDock 4.2 are,
The docking results are more accurate and highly reliable
Optional model for flexibility in the target macromolecule
Very fast and gives excellent predictions of ligand conformations and valid correlations common to predicted inhibition constants.
AutoDock 4.2 has a free energy scoring function that is dependent on linear regression, AMBER force field and also a higher set of diverse protein ligand complexes with already known inhibition constants than utilized in AutoDock 3.0.
It is useful in blind docking, where the particular location of the binding site is not identified.
AutoDock 4.2 is operated in Linux platform but also can be operated in windows platform with the support of cygwin as an user friendly interface.
AutoDock has various applications in
X - ray crystallography
Structure based drug design
Lead optimization process
Chemical mechanism studies
Combinatorial library design
Xanthine oxidase is a highly versatile enzyme that is normally distributed among species from bacteria to man and within the various tissues of mammals. It is a class of enzymes known as molybdenum iron - sulphur flavin hydroxylases. Xanthine oxidase is the enzyme which catalyses the hydroxylation of purines, particularly conversion of xanthine in to uric acid.
It is one of the major enzymes which is involved in the catabolism of purine nucleotides. It converts hypoxanthine to xanthine and xanthine in to uric acid. The uric acid produced from xanthine oxidase contributes to the antioxidant capacity of the blood. The reduction of oxygen (O2) and hydrogen peroxide (H2O2) the xanthine oxidase catalysis has been proposed as a central mechanism of oxidase injury in some situations (Silva et al., 2005).
Hypoxanthine + O2 + H2O ® Xanthine + H2 O2 + O2
Xanthine + O2 + H2 O ® Uric acid + H2 O2
Xanthine oxidase causes gout and is responsible for oxidative damage to living tissues. It undergoes oxidation of hypoxanthine into xanthine and further it converts xanthine to uric acid yielding super oxide radical and raised the oxidation level in the organism. The dynamic form of xanthine oxidase is a homodimer of molecular weight 290Kd with each of the monomer independently catalyzing the reaction.
Each subunit contains,
One molybdopterin cofactor,
Two distinct [2Fe-2S] centers,
One FAD cofactor.
ROLE OF XANTHINE OXIDASE IN GOUT
Xanthine oxidase (XO) and xanthine dehydrogenase (XDH) are interconvertible forms of xanthine oxidoreductase (XOR). These enzymes are flavoproteins containing molybdopterin composed of 2 identical subunits of nearly 140 kDa. XOR is present throughout several organs like liver, lung, brain, kidney and the plasma.
Physiologically, XO and XDH take part in multiple biochemical reactions involving the hydroxylation of different pterins, purines, and aromatic heterocycles, and participate in the detoxification of xenobiotics and endogenous molecules. The primary role of oxidoreductase is the conversion of hypoxanthine to xanthine and xanthine in to uric acid. Inherited XOR deficiency leads to xanthineuria and multiple organ failure syndrome which leads to the accumulation of xanthine in different tissues (Borges et al., 2002).
PATHWAY OF PURINE CATABOLISM
Uric acid is the chief end product of purine catabolism, either from exogenous (dietary) or endogenous origin. In men, higher apes and other mammals uric acid is degraded to allantoin by means of the enzyme uricase, which lacks in primates. Almost all tissues contain enzymes capable of breaking nucleoprotein into nucleoside, which can be oxidized to uric acid. In animals other than mammals, uric acid is further degraded to urea and glyoxalic acid. Uric acid is mainly excreted in urine, to a lesser extent in digestive fluid, and in small amounts in sweat and saliva.
A portion is destroyed by bacterial action in the intestine. This intestinal uricolysis give rise to urea and ammonia, which are absorbed and excreted by the kidneys. Under conditions of normal production and removal, the body contains a readily miscible uric acid pool. The normal uric acid content of serum is 2.5 to 7.0 mg/dl for adult males and 1.5 to 6 mg/dl for premenopausal women. Supersaturation of uric acid causes the disease gout which is common in men. Only about 5% of gouty patients are women and most of them are menopausal. Hyperuricaemia is due to over production, decreased destruction and renal excretion of uric acid (Deb, 1992).
Gout is a common metabolic disorder in human, characterized by an elevated serum uric acid level, resulting in the deposition of urate crystals in the joints and kidneys, causing inflammation as well as gouty arthritis and uric acid nephrolithiasis. Lactate production is more in synovial tissue and also in leukocytes associated with inflammatory process, and this produces a local reduction in pH that increases accumulation of uric acid. Accumulation of urate crystals leads to hyperuricaemia (Roberts and Marrow, 2001).
Hyperuricaemia does not always leads to gout, but gout is always preceeded by hyperuricaemia. Around 70% of gout patients have an over production of uric acid, while about 30% have problems in eliminating it from their body.
The pain in the joints may decrease in several days but recur at uneven intervals. Subsequent attacks usually have longer duration. Men, people with kidney disease, postmenopausal women, diabetes mellitus, obesity or sickle cell anemia have higher chances of getting gout. Some drug therapy that interferes with uric acid excretion also leads to gout (Broadhurst, 1999).
Sudden onset of severe inflammation in small joints (commonest is meta tarsophalangeal joints of great toe) due to the precipitation of urate crystals in the joints space, the joint become red, swollen and extremely painful. Prompt treatment is necessary.
When pain and stiffness persists in a joint, gout has become chronic. It is characterized by hyperuricaemia, tophi (chalk like stones under the pinna, eyelids, nose, around the joints and other places) and urate stones in the kidney. Chronic gouty arthritis may cause progressive disability and permanent deformities (Broadhurst, 1999).
FREE RADICAL PRODUCTION INDUCED BY XANTHINE OXIDASE
A free radical may be defined as a molecule or a molecular fragment containing one or more unpaired electrons in its outermost orbit and is capable of independent existence. It is usually represented by the superscript (R.). Free radicals may be formed either by reduction of molecules by electron transfer or by the hemolytic cleavage of covalent bonds. Both these reactions may be enzymatic or non enzymatic.
Free radicals like hydroxyl (OH.), superoxide anion (O.2), and non-free radical species such as hydrogen peroxide (H2O2), singlet oxygen (O.)are different forms of activated oxygen, which are, collectively called as reactive oxygen species (ROS). The generation of ROS proceeds to a variety of pathophysiological disorders such as cancer, inflammation, gout, cataract, diabetes, etc. (Senevirathe et al., 2006).
Generation of superoxide radicals and hydrogen peroxide occur as follows:
Hypoxanthine + O2 ® Xanthine + O2-. + H2O2
Xanthine + O2 ® Uric acid + O2-. + H2O2
Fig.2 XO AND ISCHEMIA/REPERFUSION INJURY
XO is a source of oxygen derived free radicals. Both XO and XDH catalyze the removal of hydrogen from the substrate using oxygen as hydrogen acceptor, and it thus get reduced. During reoxygenation (i.e. reperfusion phase) it reacts with molecular oxygen, thereby releasing superoxide anion radicals, hydrogen peroxide, and further hydroxyl radicals (Borges et al., 2002).
Enzyme-H2 + 2O2 ® Enzyme + 2H+ + 2O2 .-
2O2 .- + 2H+ ® O2 + H2O2
Enzyme- H2 + O2 ¦ Enzyme + H2O2
H2O2 can be converted to free hydroxyl radicals
Fe2+ + H2O2 ® Fe 3+ + OH- + OH.
XO pathway is the common way in the oxidative injury, particularly after ischemia induced reperfusion (Pacher et al., 2006).
TREATMENT OPTIONS FOR GOUT
Xanthine oxidase inhibitors are much useful, since they possess lesser side effects compared to uricosuric and anti inflammatory agents. Allopurinol is the main drug which is clinically utilized XOI, but also suffers from many side effects such as hyper sensitivity syndrome, Stevens Johnson syndrome and renal toxicity (Burk et al., 2006). Thus, there is a need to produce compounds with XOI activity with lesser side effects compared to allopurinol. Flavonoids and polyphenols have been reported to possess XOI activity (Lio et al., 1985).
Gout requires immediate medication with NSAIDs or colchicines. Corticosteroids are indicated in refractory cases and those not tolerating NSAIDs or colchicines. In addition, chronic gout requires treatment with uricosuric drugs or uric acid synthesis inhibitors or by both. Commonly, there are three aspects in the treatment of gout,
Pain relievers such as acetaminophen or other potent analgesics.
NSAIDs, colchicines or corticosteroids are used to reduce joint inflammation.
Reducing the level of uric acid in blood by either or both of the following,
Xanthine oxidase inhibitors eg. allopurinol.
Allopurinol was primarily synthesized as a trial to develop new antineoplastic agent in the mid 1950s by Falco, but it was identified that it has inhibitory activity on XO, thus decreasing serum uric acid level. In 1966, allopurinol was approved by FDA for the management of gout and remains a corner stone. Allopurinol is rapidly oxidized by XO in vivo to oxypurinol, which is an active metabolite and also inhibits XO. At lower concentrations, allopurinol acts as a competitive inhibitor of the enzyme, whereas at higher concentrations, it acts as a non competitive inhibitor (Pacher et al., 2006).
Allopurinol is quickly absorbed and peak plasma concentrations are reached within 30 to 60 min, upon oral administration with a half life of about 2-3 h (Pacher et al., 2006). The most common side effects of allopurinol are gastrointestinal distress, hypersensitivity syndrome, Stevens-Johnson syndrome and renal toxicity.
FLAVONOIDS AS INHIBITORS OF XANTHINE OXIDASE
Flavonoids are a group of natural products consisting of various biological and pharmacological activities like antibacterial, antiviral, antioxidant and antimutagenic effects and also inhibit several enzymes. It has been reported that flavonoids inhibit XO enzyme and possess superoxide anion scavenging activities. Thus, flavonoids can be used as a promising remedy in gout and ischemia by decreasing uric acid and superoxide concentrations in human tissues (Cos et al., 1998).
It is estimated that about 2 % of carbon photosynthesized by plants is converted into flavonoids. Most of the tannins are flavonoid derivatives. Thus, flavonoids constitute the largest group of naturally occuring phenols. Flavonoid aglycones occur in a variety of structural forms. All flavonoids contain 15 carbon atoms in their fundemental nucleus and these are organized in a C6-C3-C6 configuration. The flavonoids are more related by normal biosynthetic pathway which in corporate precursors from both "Shikimate" and "Acetate-Malonate" pathways.
Flavonoids commonly occur as flavonoid O-glycosides, where its hydroxyl group is bonded to a sugar molecule. Sugars may also be C- linked to flavonoids as they are directly attached to the benzene nucleus. Optically active flavonoids include flavanones, dihydroflavonols, catechins, pterocarpans, caretenoids, and some biflavonoids. Flavonoids are the characteristic constituents of the green plants. They occur mostly in all parts of the plants, especially in angiosperms (Markham, 1982).
Flavonoid aglycones are broadly classified into:
Flavones - Chrysin, Apigenin, Luteolin,Tricin, Baicalein, Acacetin, Scutellarein, Hispidulin, Chrysoeriol, Diosmetin, Tricetin.
Flavonols - Kaempferol, Quercetin, Myricetin, Galangin, Fisetin, Kaempferide, Robinetin, Herbacetin, Rhamnetin, Isorhamnetin, Quercetagetin, Gossypetin.
Anthocyanidins - Apigenidin, Luteolinidin, Pelargonidin, Cyanidin, Peonidin, Delphinidin, Petunidin, Malvidin.
Isoflavones - Diadzein, Formonetin, Genistein, Babtigenin, Biochanin, Aorobol, Tectorigenin.
Flavonones - Naringenin, Hesperedin, Pinocembrin, Liquiritigenin, Sakuranetin, Eriodictyol.
Dihydroflavonols - Pinobaksin, Aromadendron, Fusitin, Taxifolin.
Biflavonoids - Agathisflavone, Cupressuflavone, Amentoflavone, Ginkgetin, Sciadopitysin, Robustaflavone, Hinokiflavone, Ochnaflavone.
Chalcones - Isoliquiritigenin, Chalconaringenin, Butein, Okanin.
Aurones - Sulphuretin, Auresidin, Martimetin, Leptosidin.
Flavonoids are sub classs of polyphenolic compounds. They generally consist of two aromatic rings, each containing atleast one hydroxyl group, which are connected through three carbon bridge and become part of a six membered hetero cyclic ring. In addition, flavonoids are classified into subclasses according to the connection of an aromatic ring to the hetero cyclic ring, as well as the oxidation state and functional groups of the hetero cyclic ring. Within each subclass, individual compounds are characterised by specific hydroxylation and conjucation patterns.
Flavonoids are widespread in plants and they are important in contributing the ¬‚avour and color of many fruits and vegetables. They are often bound with sugar moiety to raise their water-solubility. Many of them are familiar possess pharmacological and biological activities such as antioxidative, antiviral, anti bacterial and antimutagenic effects, as well as known to be strong inhibitors of various enzymes like xanthine oxidase, lipooxigenase, cyclooxygenase, and phosphoinositide 3-kinase.
Natural polyphenols are divided into various classes depending upon their basic chemical structure which extend from simple molecules to complex polymerized compounds. Coumarin consists of Î±-pyrone rings and fused benzene, this is a vital group of lower molecular weight phenolics and it is commonly used for the prevention and treatment of venous thromboembolism, myocardial infarction and strokes.
Many flavonoids in food also occur as large molecules (tannins). These include condensed tannins (proanthrocyanidins), derived tannins and hydrolysable tannins. For proanthrocyanidins, three subclasses have been identified in foods. Monomers are linked through peculiar carbon - carbon and ether linkages to design polymers.
Flavonoids are greatly scattered in nature, although not uniformly. As a result, specific groups of foods are often rich sources of one or more subclasses of these polyphenols. The polyphenolic structure of flavonoids and tannins are extremely sensitive to oxidative enzymes and cooking conditions.
USES OF FLAVONOIDS
Epidemiological proof proposed a reversed connection between dietary intake of flavonoids and cardiovascular risk. The greater the quantity of flavonoids in the diet, lesser is the risk for heart diseases. The biological activities of flavonoids are partially attributed to their antioxidative effects. All the collected proof from epidemiological and experimental studies exhibits that there is a low risk of degenerative diseases, cardiovascular diseases, hypertension, cataract, stroke and in particular cancers in people with a high intake of fruits and vegetables. These protective effect is assumed to be associated mainly with the antioxidant activities of either individual or interacting bioactive components present in the fruits and vegetables and with other biochemical and physical characteristics of the identified and unknown bioactive components (Velickovic et al., 2007).
Most of the flavonoids have anti inflammatory properties. Therefore the consumption of flavonoids could be appropriate in medical conditions involving inflammation. Flavonoids are the excellent antioxidants when compared to other compounds. Extracts from onion and different flavonoids activate the cellular antioxidant system. Onion extract and quercetin were able to increase the intra cellular concentration of glutathione by nearly 50 %.
Diets rich in flavonoids including quercetin or catechin cause endothelium dependent vasorelaxation. It dilates the blood vessels and this could be advantageous in vascular diseases such as claudication and heart diseases (Miller et al., 1984).
Flavonoids are capable of inhibiting blockades in the arteries, the flavonoids quercetin and related compounds may be able to protect against the development of certain types of heart disease and related circulatory disorders. These flavonoid compounds are also helpful in that they strengthen and maintain the integrity of various blood vessels in the body in a variety of ways precluding the onset of diseases.
The advantages of flavonoid rich diet was reported in recent studies conducted in Finland and Netherlands. In these tests, people who generally consumed greater quantity of flavonoids, particularly quercetin, in the diet had a lesser risk of heart diseases or stroke, as it was decreased by 50% in the tested women and by a factor of 23% in the tested men.
Another important use of the flavonoids such as quercetin and the other polyphenolic compounds is their promising role as anticancer or anti-carcinogenic agents. In the clinical studies, the lowest rate for stomach, pancreatic, lung and breast cancer was recorded in people who consumed large amounts of these flavonoids in their diet.
Moreover, the intake of high quantities of the soy based flavonoid genistein could also help in combating breast cancer. It is also relied to reduce hot flushes in women by acting on the estrogen receptors in the body and thus being a potential remedy for hormonal imbalances (Zhou et al., 2007).
Quercetin possesses anti-inflammatory activity, and also provides relief from allergic reactions including hay fever, chronic asthma and sinusitis. This flavonoid compound inhibits all the allergic reactions and effectively decreases the inflammation affecting the airways and the lungs. Actually, its effectiveness as an anti-inflammatory agent is excellent for the treatment of minor problems like skin disorders such as eczema, and related disorders. In addition, it is useful for the treatment of inflammatory disorders affecting the muscles and the joints like chronic gout, rheumatoid arthritis and related disorders.
Flavonoids are advantageous as supplements as they strengthen the blood vessels and citrus flavonoids are in particular very helpful in repairing problems like chronic hemorrhoids and varicose veins (Mo et al., 2007).
The rate at which the enzymes carry out its work is known as enzyme kinetics. Enzyme kinetics mainly depends upon the concentration of substrate.
Graded concentrations of substrate [S] are prepared.
At the time zero, constant amount of the enzyme solution was added.
After that within few minutes, measure the concentration of the product formed initially. If the formed product absorbs the light, then it is evaluated by spectrophotometer.
When the amount of substrate is in substantial the excess to the amount of enzyme, the rate observed is known as initial velocity of Vi.
Line weaver-Burk plot analysis
The plot is used to know about the mode of the enzyme inhibition. It is the double reciprocal plot and it is graphical representation of the equation of the enzyme kinetics. It was described by Hans Line weaver and Dean Burk in the year 1934.
Plotting Vi as a key of [S],
At lesser values of [S], the initial velocity, Vi, increases along with increasing [S].
If the [S] is increases, the gains in Vi level is not changed (rectangular hyperbola formation is take placed).
The asymptote stand for the highest velocity of the reaction, designated Vmax
The concentration of the substrate that yields a Vi that is one-half of Vmax is designated the Michaelis-Menten constant, Km
Km is an indirect measure of the affinity of binding between the enzyme and its substrate. The lesser Km value, the greater the affinity. So the lower the concentration of the substrate necessary to achieve a given rate (John et al., 2005).
Enzymes can be inhibited
In the competitive inhibition, the substrate and the inhibitor compete each other for binding to the same active site.
In non competitive inhibition, the inhibitor binds somewhere else on the enzyme molecule reducing its efficacy.
The difference may be estimated by plotting enzyme activity with and without the inhibitor present.
In case of a competitive inhibitor, it takes a large amount of substrate concentration to achieve the same velocities that were reached in its absence. Therefore Vmax able to reach if adequate substrate is present, one-half of the Vmax requires a higher [S] than before and thus Km is larger.
In this inhibition, enzyme molecules bound by the inhibitor are taken out and thus,
enzyme rate is decreased for all values of the [S]
Vmax and one-half Vmax but
Km stays unchanged due to the active site of enzyme molecules that have not been inhibited is unchanged.