Structure Based Drug Discovery

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X-ray and cryo-EM structures of inhibitor-bound cytochrome bc₁ complexes for structure-based drug discovery

X-ray and cryo-EM structures of inhibitor-bound cytochrome bc₁ complexes for structure-based drug discovery

Structure-based drug-design (SBDD) programmes are reliant upon high-resolution structures of the target protein being solved (Renaud et al., 2016; Anderson, 2003). When complexed with their natural substrate or an inhibitor mole- cule, they can provide essential information for the design of new compounds which are highly selective for their target. Previously, this has been underpinned by X-ray crystallo- graphy; however, owing to the recent advances in single- particle cryo-electron microscopy (cryo-EM), an increasing number of high-resolution structures have been determined, therefore the technique has the potential to play a role in SBDD programmes (Rawson et al., 2017). For example, a 2.3 A ˚ resolution cryo-EM structure of inhibitor-bound human p97 ATPase identified an allosteric inhibition mechanism that enables a structural basis for cancer drug design (Banerjee et al., 2016). Cryo-EM is particularly useful for more challenging targets such as large macromolecular complexes, viruses and membrane proteins, as illustrated by the Fab–RV-B14 complex (2.26 A ˚ resolution; Dong et al., 2017) and the membrane protein TRPV1 (2.9 A ˚ resolution; Gao et al., 2016). Membrane proteins remain challenging to study using X-ray crystallo- graphy as they face hurdles in overexpression, the quantity of highly purified protein produced and the quality of the crystals obtained (Carpenter et al., 2008). By requiring less sample for structural characterization (micrograms instead of milli- grams), cryo-EM can overcome these issues (Rawson et al., 2016). Moreover, the structure of the mammalian mitochon- drial respirasome supercomplex has recently been determined to an overall resolution of 4.0 A ˚ , which highlights how the individual components in the electron-transport chain interact with one another, which had not previously been seen using X-ray crystallography (Wu et al., 2016; Guo et al., 2017).
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Structure based drug discovery for designing leads for the non toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis

Structure based drug discovery for designing leads for the non toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis

4) Rv0321 (dcd) The gene is involved in the intercon- version of dCTP and dUTP and did not have a reported GSK inhibitor. Therefore, OSDDChem database was screened against the target to identify the top 100 com- pounds exhibiting highest binding energy, better than the NS (docking score = − 9.9).Clustering was carried out for the top ranked compounds, leading to the generation of a pharmacophore model, with survival score of 3.43 (Addi- tional file 1: Figure S6a). In order to validate the quality of the generated pharmacophore model, clinically approved Tb drug Rifampicin showed a two-feature mapping with good fit value of 4.74.A molecular library (~ 1000 com- pounds) was generated using various databases, based on the best structural and pharmacophore similarities. The best binding affinity was obtained for ChEMBL533912 with ΔG score of − 9.3 kcal/mol (Table 4). The lead com- pound showed hydrogen bond interactions between NH of propanamide flanked in the flurophenyl with Tyr162. Nitrogen atom in the 1, 2, 4 triazol ring showed interac- tions with Ala167 and Ser161 with an interatomic dis- tance of 3.5 Å each respectively.
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Discovery and validation of potential drug targets based on the phylogenetic evolution of GPCRs

Discovery and validation of potential drug targets based on the phylogenetic evolution of GPCRs

GPCRs are a large superfamily of membrane bound signaling proteins that are involved in the regulation of a wide range of physiological functions and constitute the most common target for therapeutic intervention [70]. GPCRs are among the most important drug targets for the pharmaceutical industry. Knowledge of the three- dimensional structure of a protein is of utmost impor- tance for drug discovery, as it serves as the basis for the identification of novel ligands by means of computa- tional or in silico techniques, such as de novo design and virtual screening. 25% of the small molecule drugs ap- proved in 2006 were discovered through structure-based drug discovery (SBDD) [70]. Consequently, target iden- tification is a critical step following the discovery of small molecules that elicit a biological phenotype. There are a serial of technologies and approaches applied in new drug targets and biomarker identification, such as proteomics technology, systems biology approach, mi- croRNA technology, and computational methods. Su- gahara et al. have identified a large number of candi- dates for the target proteins specific to β1,4-galactosyl- transferase-I (β4GalT-I) by comparative analysis of β4- GalT-I-deleted and wild-type mice using the LC/MS- based technique with the isotope-coded glycosylation site-specific tagging (IGOT) of lectin-captured N-gly- copeptides [71]. Their approach to identify the target proteins in a proteome-scale offers common features and trends in the target proteins, which facilitate understand- ing of the mechanism that controls assembly of a par- ticular glycan motif on specific proteins. Research on microRNAs (miRNAs) is a promising new research, providing novel insights into the pathogenesis of some diseases, biomarker identification, and treatment. The short (approximately 22 nucleotides), endogenous, widely distributed, single-stranded RNAs target both Mrna
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Investigating ethnopharmacology based natural product leads for antimalarial drug discovery

Investigating ethnopharmacology based natural product leads for antimalarial drug discovery

Studies using X-ray crystallography, nuclear magnetic resonance technology (NMR) and spectroscopic techniques have shown haemozoin and β-haematin to be identical in structure and solubility profile (Slater et al., 1991). Hence β-haematin formation has been extensively studied in vitro to aid in the understanding of haemozoin formation and aggregation. Monomeric haem aggregates in acidic conditions, pH < 4.5. These aggregates are soluble in sodium bicarbonate buffers and DMSO. Interestingly, β-haematin is a higly insoluble substance and can only be dissolved in sodium hydroxide solution. This differential solubility has been invaluable for the separation of formed β-haematin in a reaction from unreacted haem aggregates. Haem can be converted to β-haematin, although certain conditions have to be met for this chemical synthesis to occur. Simply incubating haematin in glacial acetic acid at high temperatures of 70-80°C for 18 h leads to β-haematin formation, with a reaction yield of about 40-50% (Bohle et al., 1993). Several different reactions have been used by different teams for chemical synthesis of β-haematin. Egan et al., 1994 proposed that the reaction is spontaneous in the malaria parasite. He suggested that for the reaction to be spontaneous, there are certain pre-requisites namely pH (acidic), temperature (60-65°C) and certain buffer conditions, in most cases, acetate buffers ranging from 0.1- 4.5 M. These conditions are certainly not physiological. Although β-haematin is identical to haemozoin, its formation cannot be achieved under the same physiological conditions as with haemozoin. This has led much research in the field. Further work has shown that β- haematin formation in vitro can be achieved in aqueous acidic conditions under physiological temperature only in the presence of certain biological factors. These factors include the parasite lysate, histidine rich proteins, preformed haemozoin or β-haematin and certain unsaturated lipids (Fitch, 2004; Tripathi & Tekwani,1999; Dorn et al., 1998; Fitch et al., 1999).
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The Success of Natural Products in Drug Discovery

The Success of Natural Products in Drug Discovery

key technology enabling the efficient generation of large screening libraries for the needs of high-throughput screening. However, now, after two decades of combi- natorial chemistry, it has been pointed out that despite the increased efficiency in chemical synthesis, no in- crease in lead or drug candidates have been reached. This has led to analysis of chemical characteristics of combi- natorial chemistry products, compared to existing drugs and/or natural products. The chemo-informatics concept chemical diversity, depicted as distribution of com- pounds in the chemical space based on their physico- chemical characteristics, is often used to describe the difference between the combinatorial chemistry libraries and natural products. The synthetic, combinatorial library compounds seem to cover only a limited and quite uni- form chemical space, whereas existing drugs and par- ticularly natural products, exhibit much greater chemical diversity, distributing more evenly to the chemical space. The most prominent differences between natural prod- ucts and compounds in combinatorial chemistry libraries are the number of chiral centers (much higher in natural compounds), structure rigidity (higher in natural com- pounds) and number of aromatic moieties (higher in combinatorial chemistry libraries). Other chemical dif- ferences between these two groups include the nature of heteroatoms (O and N enriched in natural products, and S and halogen atoms more often present in synthetic com- pounds), as well as level of non-aromatic unsaturation (higher in natural products). As both structure rigidity and chirality are both well-established factors in medi- cinal chemistry known to enhance compounds specificity and efficacy as a drug, it has been suggested that natural products compare favorable to today’s combinatorial chemistry libraries as potential lead molecules.
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Molecular dynamics simulations and drug discovery

Molecular dynamics simulations and drug discovery

Molecular dynamics simulations are excellent tools for identifying such sites [39-41]. For example, in 2004 Schames et al. [39] performed a molecular dynamics simu lation of HIV integrase, a drug target that had not seemed amenable to structure-based drug design. The simulations revealed a previously unidentified trench that was not evident from any of the available crystal struc- tures. X-ray crystallography later demonstrated that known inhibitors do in fact bind in this cryptic trench, as predicted. These results led to new experimental studies at Merck & Co. [42]; further development ultimately resulted in production of the highly effective antiretro- viral drug raltegravir, the first US Food and Drug Admin- is tration-approved HIV integrase inhibitor.
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Structure-based drug design: aiming for a perfect fit.

Structure-based drug design: aiming for a perfect fit.

Although fragments typically bind with a low potency, they form efficient interactions with the protein target and thus provide attractive starting points for inhibitor design. Nowadays, a plethora of different methods are being used to identify initial fragment hits [17]. They include the traditional biophysical ones such as nuclear magnetic resonance (NMR), surface plasmon resonance (SPR), differential scanning fluorimetry (DSF) — also known as thermal shift assay (TSA), and X-ray crystallography, together with more recent ones like microscale thermophoresis (MST) and mass spectrometry [16,18]. In addition, successful fragment screens have been carried out using in vitro biochemical assays [19]. To optimise a fragment hit into a potent lead molecule, a structure-guided approach is critical. Although there are examples in which fragment hits have been optimised using a combination of biophysical and computational methods [20], we are unaware of FBDD campaigns that have not had some contribution from structural data during the hit-to-lead and/or lead optimisation stages of drug discovery.
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Structure-Based Discovery of Inhibitors Against MurE in Methicillin-Resistant Staphylococcus Aureus

Structure-Based Discovery of Inhibitors Against MurE in Methicillin-Resistant Staphylococcus Aureus

In this study, a pharmacophore based on the structure of MurE Sa was generated and used to screen a total of 7,144 approved and experimental drugs from DrugBank database. The top hits were then docked to the enzyme target and their binding energies were calculated. Four compounds displayed greater inhibitory potential against MurE ligase compared to Fosfomycin, a drug known to act on structurally related MurA ligase. Three of the top hits possess a phosphate group and complement the defined binding site in MurE, which encompasses an ATP-binding pocket that sits between domains 2 and 3 of the enzyme. The topmost hit was an experimental drug, 4-Phospho-L-Threonic acid (DB01756), which is a monosaccharide phosphate that reportedly acts
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Novel Approach for Drug Discovery

Novel Approach for Drug Discovery

clustering) are based on a lattice of pairwise distances between taxa (or any sort of items): The goal is to build a tree with branch lengths so that the distances between the leaves in that tree are as close as conceivable to the input distances. In the event that we hold the structure (topology) of the tree fixed, in some relevant cases (e.g., conventional slightest squares) the optimal values for the branch lengths can be communicated utilizing simple combinatorial formulae. Here we characterize a general shape for these formulae and demonstrate that they all have two alluring properties: First, the normal tree reconstruction approaches (slightest squares, least evolution), when utilized as a part of mix with these formulae, are ensured to derive the right tree when sufficiently given data (consistency); second, the branch lengths of all the simple (nearest neighbor exchange) rearrangements of a tree can be calculated, optimally, in quadratic time in the span of the tree, in this way permitting the efficient application of hill climbing heuristics. The study presented here is a continuation of that by Mihaescu and Pachter on branch length estimation. The emphasis here is on the inference of the tree itself and on giving a basis to novel algorithms to reconstruct trees from distances.
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Breakthroughs in Computational Approaches for Drug Discovery

Breakthroughs in Computational Approaches for Drug Discovery

are: e-Pharmacophores, implemented in Maestro suite (Schrödinger, LLC, New York, NY), LigandScout (Inte: Ligand GmbH, Vienna, Austria), Catalyst, implemented in Discovery Studio (Accelrys, Inc., San Diego, CA, USA) and SB pharmacophore, implemented in Molecular Operating Environment (MOE) (Chemical Computing Group’s (CCG), Montreal, QC, Canada). Among them, the e-Pharmacophores method achieves the advantages of both ligand- and structure-based approaches by generating energetically optimized SB pharmacophores that can be used to rapidly screen billions of compounds. Indeed, SB models are employed in large-scale chemical databases screening procedures. As reported for LB methods, the progress in the experimental procedures and the recent improvements in CPU performances coupled to the availability of large public 3D-chemical libraries, gave a boost to this computational approach. Intriguingly, a relevant advancement in SB pharmacophore modeling is represented by the use of multiple SB pharmacophore models, built employing available crystal structures of the protein of interest in complex with diverse ligands, in VS protocols. The SB models can be used as sequential filtering tools for screening chemical libraries. Alternatively, they can be combined in an inclusive SB pharmacophore model taking into account the most relevant interactions of ligands into the receptor for generating a comprehensive SB pharmacophore [13]. In both ways, multiple SB pharmacophore models can be used in VS or in rational ligand design for identifying novel chemical entities or for optimizing existing hits. Likewise, LB and SB methods can be combined for obtaining more reliable hybrid computational protocols. Following this approach a performance increase in retrieving active molecules for a given target has been observed [2].
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Biological Motif Discovery Algorithm based on Mining Tree Structure

Biological Motif Discovery Algorithm based on Mining Tree Structure

As there are a big growing interest on regulatory element that can lead to understand some virus function, detect new drug, classify spices, or to get many other helpful new knowledge of biology. The researchers have developed many algorithms in order to discover or predict this small part of biochemical molecular, each of these algorithms have different concept, in the way of data representation to the process of discovering to the results.

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The discovery of potent ribosomal S6 kinase inhibitors by high- throughput screening and structure-guided drug design

The discovery of potent ribosomal S6 kinase inhibitors by high- throughput screening and structure-guided drug design

present. Although the S6K1 construct is considerably shorter than the PKA and PKA-S6K1 chimera sequences, the overall tertiary SK61 structure is very similar to the PKA-S6K1 chimera (rmsd 1.33 Å for 247 equivalent Cα atoms, Figure 1B). The most notable differences with the PKA-S6K1 chimera include the disordered αB-helix and partially disordered activation loop and αC-helix, which are consistent with the low activity of the phospho-Thr252 form of the enzyme. The binding-mode of staurosporine to native S6K1 and the PKA-S6K1 chimera is nearly identical, and the amino acid residues lining the respective ATP-binding sites and contacting staurosporine have very similar side chain conformations. The exception is the conformation of the tyrosine residue (Tyr102 in S6K1 and Tyr54 in PKA-S6K1) at the tip of the P-loop, which extends outward in S6K1, but is folded back underneath the P-loop in the PKA-S6K1 chimera (Figure 1D). This could potentially be a cause of differences in inhibitor- binding between native S6K1 and the PKA-S6K1 chimera, but because of the high similarity of the overall kinase domain structures and the very minor differences in side chain conformations within the respective staurosporine- occupied ATP-binding sites, we concluded that the PKA- S6K1 chimera was a suitable, robust and high-resolution surrogate system to guide our structure-based S6K inhibitor design.
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 CURRENT TRENDS IN DRUG DISCOVERY: TARGET IDENTIFICATION TO CLINICAL DEVELOPMENT OF THE DRUG

 CURRENT TRENDS IN DRUG DISCOVERY: TARGET IDENTIFICATION TO CLINICAL DEVELOPMENT OF THE DRUG

It follows the lead finding process. The aim of lead optimization to synthesize lead compounds, new analogs with improved potency, reduce off-target activities, as well as to optimize this with respect to other properties viz. selectivity, metabolic stability, etc. This optimization is accomplished through chemical modification of the hit structure, with modifications chosen by employing structure-activity analysis (SAR) as well as structure-based design if structural information about the target is available 18 .

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Computational Approaches in Drug Discovery: An Overview

Computational Approaches in Drug Discovery: An Overview

Drug discovery and development process involves a series of events that include target identification and validation, lead identification and optimization, pre-clinical pharmacology and toxicology. Computer-aided drug discovery (CADD) tools can be used to automate and speed up these process and to reduce the research and development cost. Today CADD has become an essential tool in drug development. The bioinformatics research has made available a significant amount of data sources like biological structures, ligand databases, and various computational tools that can be used in various phases of the drug discovery and development pipeline. This paper gives an overview of computational methods used in different stages of drug discovery. In this review, both structure-based and ligand-based drug discovery methods are discussed. Developments in virtual high-throughput screening, prediction of protein–ligand interaction using docking tools are reviewed. Keywords: ADME, Docking, High-throughput screening, lead optimization.
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The development of sialidase inhibitors using structure based drug design

The development of sialidase inhibitors using structure based drug design

quinic acid were used in the discovery of oseltamivir (von Itzstein, 2007). Three key strategies were employed to develop an orally administered NA inhibitor: 1) positioning of the double bond, 2) replacement of the glycerol moiety with a lipophilic group and 3) pro-drug development to improve oral bioavailability (von Itzstein, 2007). The positioning of the double bond is critical to mimic the putative transition state sialosyl cation (von Itzstein, 2007). Movement of the double bond between C2 – C3 to C2 – C7 results in a >32 fold decrease in potency (von Itzstein, 2007). Replacement of the glycerol group was explored to improve lipophilicity while maintaining potency (von Itzstein, 2007). This led to the 3-pentyl ether side chain (von Itzstein, 2007). Structural information from a ligand- protein crystal complex showed an induced fit with changes in the active site upon binding, in particular an unpredicted movement of Glu276 towards Arg224 created a hydrophobic pocket (von Itzstein, 2007). This movement is essential for oseltamivir’s potency allowing accommodation of the branched alkyl ether (Sriwilaijaroen et al., 2016). This change resulted in improved lipophilicity from LogP -4.1 for zanamivir to LogP -2.1 for oseltamivir carboxylate (Lindegardh et al., 2011, Bahrami et al., 2008, Oo et al., 2003). A prodrug strategy was employed, as the addition of this lipophilic group was not sufficient to improve bioavailability despite the improvement in lipophilicity (von Itzstein, 2007). The esterification of the carboxylate to the ethyl ester resulted in a significant change in lipophilicity (LogP = 0.36) (Oo et al., 2003). In vivo endogenous esterases hydrolyse the ester to form the active NA inhibitor, oseltamivir carboxylate (Rautio et al., 2008). Oseltamivir was FDA approved in 1999 and is currently marketed as Tamiflu® (Chand et al., 2005, Lindemann et al., 2010). Both oseltamivir and zanamivir are first generation NA inhibitors (Vavricka et al., 2013).
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Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures

Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures

previous studies in this as well as several other laboratories demonstrated that no correlation exists between leave-one-out (LOO) cross-validated R 2 (q 2 ) for the training set and the correlation coefficient R 2 between the predicted and observed activities for the test set(Golbraikh and Tropsha, 2002; Kubinyi, Hamprecht, and Mietzner, 1998). These findings indicated that in order to obtain QSAR models with high predictive ability, external validation was critical. Thus, each dataset of compounds was divided randomly into external and internal sets. Then, the internal set was divided into multiple chemically diverse training and test sets with a rational approach implemented in our group(Golbraikh and Tropsha, 2002) based on the Sphere Exclusion (SE) algorithm(Snarey et al., 1997). SE is a general procedure that is typically applied to molecules characterized by multiple descriptors of their chemical structures. The entire dataset can then be treated as a collection of points (each point corresponding to an individual compound) in the multidmensional descriptor space. The goal of the SE method is to divide a dataset into two subsets (training and test sets) using a diversity sampling procedure(Golbraikh and Tropsha, 2002).
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PROTEIN INTERACTION NETWORK AND DRUG DISCOVERY

PROTEIN INTERACTION NETWORK AND DRUG DISCOVERY

ABSTRACT: Protein conformation and dynamics are influenced by various factors, including binding of ligands, their physico-chemical properties, etc. Every conformational change is dictated by an array of events, as making and breaking of bonds or change in interaction of protein residues. The three dimensional structure of protein molecules are widely investigated, based on small world network approaches, with an emphasis on different combinations of descriptors affecting the structure which have been tested on studies involving binding in protein ligand complexes and for protein-protein complexes. This application has revealed the benefits and success of the small world network approach which can change the focus from specific interactions in the local environment or to non-local phenomenon. Network analysis of interacting protein upon ligand binding is analysed. A similarity in interaction parameters among residues of the target protein, upon binding of particular ligands, is identified. This method differentiates ligands, on the basis of overall changes in interaction among residues of Target proteins in complex.
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Interferon, viruses and drug discovery

Interferon, viruses and drug discovery

One of the most commonly used approaches to target elucidation is affinity-chromatography (Titov and Liu, 2012, McFedries et al., 2013). Cell lysates are incubated with affinity-tagged compound followed by standard immunoprecipitation protocols and LC-MS or western blotting to identify the bound proteins (Figure 7.2B). As these approaches rely on the conjugation of a small molecule to an affinity tag such as biotin, or a solid matrix such as agarose, these methods require considerable knowledge of SAR relating to the compound as to not ablate activity once immobilised. To determine the regions of StA-IFN-1 and StA-IFN-4 that may be amenable to conjugation, we tested molecules with high levels of similarity to the parental compounds and fragments of the structures. These experiments suggest that the acetyl indole of StA-IFN-1 and the dichloro pyridazinone of StA-IFN-4 are crucial for their inhibitory activity. Therefore, in order to use affinity chromatography to elucidate the target of StA-IFN-1, a biotin affinity-tag could be attached to the pyrazolone structure. Loss of this group did not ablate the activity of StA-IFN-1, although it was reduced. Similarly, the pyridopyrimidine group of StA-IFN-4 appeared dispensable, and as such a biotin tag could be attached here. In order to maximize the knowledge pertaining to mode of action, SAR and potentiate target elucidation, medicinal chemistry to optimize the potency of StA-IFN-1 and StA-IFN-4 may be necessary before affinity-based approaches to target deconvolution are utilized.
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The Drug Discovery Development for Treatment of Tuberculosis

The Drug Discovery Development for Treatment of Tuberculosis

Tuberculosis (TB) is immensely infectious disease it reaches almost exclusively by airborne transmission. Although the disease can affect any site in the body, it most often affects the lungs. When pulmonary TB infected persons cough, their cough contains miniature droplet nuclei that enclose TB bacteria. These droplets remain suspended in the air for prolong duration. If someone who breathes in this contaminated air, can develop or be infected with TB. The cell wall of Mycobacterium species is essential for its growth and for continued existence in the infected host it is the most essential structural and functional component. In fact, this is the one of the most important drug target for some of the most efficient anti-mycobacterial drug molecules e.g. isoniazid and ethambutol. These chemical entities are known inhibitors of the biogenesis of cell wall which is in turn subjugated by covalently associated mycolic acids, few related arabinogalactan and peptidoglycan (AGP), based mycolic acids are accoladed by glycolipids such as α,α-
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Pharmacogenomics: Applications in Drug Discovery and Pharmacotherapy

Pharmacogenomics: Applications in Drug Discovery and Pharmacotherapy

The Pharmacogenetic testing helps to determine the genotypic and phenotypic differences involved in the pharmacodynamics and pharmacokinetics of drug metabolism. The pharmacogenetics means genetic variations which can effect in drug metabolism and response both interms of therapeutic action and adverse effects. The pharmacogenomics refers that how the genetic composition effects an individuals response to drugs. Molecular alterations in enzymes involved in metabolism lead to the genetic variability in drug response. (Ensom, M. H. et al; 2001). Drugdrug interactions (DDIs) have shown serious effects like adverse drug reactions (ADRs), and in extreme cases lead to death. DDIs have become a major problem particularly in the care of aged patients, as they are often prescribed with broad variety of medications (Routledge, P.A., 2004 ). Recently, ADRs are considered as the fourth leading cause of death in the United States, resulting in 106,000 deaths per year, and the fifth leading cause of illness. Presently, approx. 28% of adults and 17% of children have drug-related ADRs. The pharmacogenetic techniques will become an essential part for the drug monitoring and health management of patients. By using pharmacogenetic methods the patient genotyping can be carried out before drug treatment and helps in minimizing the unfavourable effects (Lundkvist, J., 2004).
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