Pharmacogenomics in Clinical Therapeutics

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Current research has clearly established that a range of treatments may be greatly improved through genetic profiling of individual patients before certain therapies begin. Aimed at clinicians, pharmacologists, and clinical laboratory professionals this book discusses the important role of laboratory tests in the practice of personalized medicine.

An important new book, Pharmacogenomics in Clinical Therapeutics serves as a guide to the basic principles of pharmacogenomics in a range of common therapeutic areas and as the basis of the personalized medicine of the future. Langman Editor Amitava Dasgupta, Ph. Loralie J.

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Pharmacogenomics in Clinical Therapeutics

These cookies allow you to explore OverDrive services and use our core features. Without these cookies, we can't provide services to you. Skip to Main Content. Langman Amitava Dasgupta. First published: 24 February About this book Pharmacogenomics is the basis of personalized medicine which will be the medicine of the future.

Through both reducing the numbers of adverse drug reactions and improving the use of existing drugs in targeted populations, pharmacogenomics represents a real advance on traditional therapeutic drug monitoring. Export Citation s. Export Citation. Plain Text. Citation file or direct import. For help, please view the citation help.

For instance, NSAIDs such as aspirin and ibuprofen inhibit COX proteins, preventing the conversion of arachidonic acid to prostoglandin H 2 PGH 2 and blocking the downstream production of other prostoglandins, which mediate inflammation and pain response. While in the case of VKORC1, pharmacogenomic variation is observed at the direct site of action of warfarin, variation in downstream receptors can also influence the effect of drugs on the body.

Pharmacogenomics: a key component of personalized therapy | Genome Medicine | Full Text

While calumenin's effects are downstream of the direct interaction between VKORC1 and warfarin, variants in calumenin are also associated with differences in warfarin dosage [25]. Pharmacogenomic research aims to identify the genes and gene variants involved in the interaction between a drug and the body. For any of the pharmacogenomic applications discussed below, there exist methods for discovering relevant genes and variants typically single nucleotide polymorphisms, or SNPs related to drug response.

Alternatively, analysis of other sources of data, including expression or biochemical data, may provide additional gene candidates. Once candidate variants are identified, further computational and experimental follow-up may be required to fully characterize all the genes and pathways involved in the drug's progression through the body. In a GWAS, hundreds of thousands or millions of SNPs representing regions of the genome with the most inter-individual variation are probed on a DNA microarray for each individual in a set of cases and controls Figure 2A.

In the case of a continuous independent variable, such as drug dose, a likelihood-ratio test or a Wald test is applied to measure whether there is a significantly different dose between the two groups of genotypes. The two most important considerations for the design of any pharmacogenomic study include the selection of representative genetic markers, as well as phenotypically well-characterized patients including cases and controls. The first of these, design of a suitable genotyping array, is technically easy and inexpensive, though the exact design can depend on the desired balance between unbiased genome-wide studies and a targeted SNP panel see below.

As in any trait-association study, the second consideration: the selection, characterization, and covariate identification of cases and controls provides a significant challenge.

Clinical Pharmacogenomics Testing - Module 7, Session 4

SNP-based GWAS methods are effective when there is a strong signal from some SNP for the size of the study that is, when there is good separation between genotypes for the cases and controls. However, under this stringent independence model, weaker signals may be lost among the noise that plagues genetic association studies. Thus, combining data from multiple SNPs in a single gene can boost power and decrease the number of hypotheses for multiple hypothesis correction [26].

Alternatively, if we have prior information about the drug's mechanism of action, we can create targeted SNP panels, limited to genes in the drug target's pathway, to decrease the hypothesis space [27]. As the price of high-throughput sequencing continues to fall, many investigators are turning to exome or whole genome sequencing to discover genetic factors of drug response.


Such technologies have the advantage of remaining unbiased in SNP discovery, detecting less common and even personal mutations, and capturing larger-scale information, including copy number variants CNVs and structural variants SVs. Because different populations have different linkage structures and a different series of polymorphisms, platforms that are optimized for one population may not be the best choice for another.

This problem is further complicated by underlying differences in genomes: the effect a given SNP has on drug response may be different or even the opposite because of a hidden interaction with an alternate variant of another gene. Specifically, since many of the original genotyping platforms were developed for Caucasian populations, studies on Africans or Asians will require different approaches. In addition, other sources of data can be used to identify genes involved in drug response, including RNA expression data from microarrays or RNA-Seq experiments from drug-treated samples.

For instance, using expression profiles from patients with a disease of interest, one can identify the genes involved in the progression of the disease and identify potential drug target candidates. Alternatively, expression profiles generated from a drug treated sample compared to control can be used to determine a molecular response to a drug. Ideally, such drug treatment experiments would be done on humans in order to generate organic in vivo physiological response. However, such experiments are unethical for experimental early phase drugs, require significant regulatory approval, and are expensive.

Thus, established cell lines have provided a valuable, lower-cost resource for investigators to generate gene expression profiles. One such effort, the connectivity map CMAP , a publicly available resource of gene expression data of cell lines treated with various small molecules, has been used to compare expression profiles See Figure 1 of [28] to identify metabolite-protein interactions, small molecules with similar binding profiles, and metabolites that may mimic or suppress disease [28].

For instance, this approach predicted gedunin to be an inhibitor of HSP90 due to the similarity between gedunin's expression profile and the profile of known inhibitors. Despite the lack of structural similarity between gedunin and other HSP90 inhibitors, CMAP's predicted result was validated biochemically.


Thus, cell lines can be used as surrogates for individuals, where a cellular phenotype is used as a proxy for the individual's own physiological response based on the cellular expression response to a drug treatment. For instance, one can search for associations between a cell line's genetic makeup and cell viability after drug treatment the IC 50 of drug for each cell lines [29]. Alternatively, similar methods can be used to characterize toxicological response: treating cell lines with a drug and measuring gene expression can suggest genes involved in the drug's toxicity.

Such methods are computationally expensive, as they explore a large search space for each pair of molecule and protein and use molecular dynamics or genetic algorithms to optimize fits. Therefore, molecule docking can be limited to the active site of a protein with a group of molecules to decrease the search space. Alternatively, if a given molecule is previously known to interact with a protein, molecule similarity metrics can be included to suggest similar molecules as protein-binding candidates. In this way, a search limited to ligands that score above a similarity threshold to the known ligand would be much faster than a search through all of PubChem.

While such predictions must still be confirmed through biochemistry such as binding assays , these methods can be used to limit the hypothesis space for drug discovery, prioritizing the expensive, lower-throughput biochemical assays.

  1. Clinical Implementation of Pharmacogenomics - PGRN Hub.
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  3. Pharmacogenomics in Clinical Therapeutics - AbeBooks: .

Various algorithms have been developed for screening ligand-target fits using docking reviewed in [31]. Additionally, methods that incorporate the structure of the ligand along with known interactions can identify patterns of related drug targets [32]. These methods leverage small molecule databases such as PubChem and ChEMBL, which maintain structures and properties of small molecules and ligands, as well as bioassay results of these compounds.

Additionally, a wealth of scientific information is available in the biomedical literature as lower-throughput free text. Thus, text mining techniques such as natural language processing NLP , which exploits sentence syntax to pull structured knowledge from the literature, can be used to mine PubMed and other sources of published information to discover new drug-protein interactions [34].

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  • Once a candidate gene is identified, studying the gene's known genetic networks, cascades, and pathways can help identify other possible candidates that affect drug action. For instance, if a kinase is identified as a drug target, the proteins it phosphorylates and any proteins affected downstream may be relevant to the study of the drug. Additionally, knowledge of biological pathways influencing a disease can aid in the drug discovery process see below: Drug Discovery.


    For a gene-drug relationship of interest, information on the gene's network or pathway can be used to limit the hypothesis space of other analyses and experiments. Additionally, a mechanism of action can be formalized by closing the loop between all the genes involved. The methods discussed thus far provide only computational evidence for potential drug-protein interactions. In order to prove drug-protein interactions and effects, follow-up biochemical methods, such as measuring binding affinity or functional assays, are required to demonstrate a molecule's potential therapeutic activity or to definitively prove an interaction.

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