American journal of pharmacogenomics : genomics-related research in drug development and clinical practice最新文献

The recently amplified pace of development in the technologies to study both normal and aberrant cellular physiology has allowed for a transition from the traditional reductionist approaches to global interrogations of human biology. This transformation has created the anticipation that we will soon more effectively treat or contain most types of diseases through a 'systems-based' approach to understanding and correcting the underlying etiology of these processes. However, to accomplish these goals, we must first have a more comprehensive understanding of all the elements involved in human cellular physiology, as well as why and how they interact. With the vast number of biological components that have and are being discovered, creating methods with modern computational techniques to better organize biological elements is the next requisite step in this process. This article aims to articulate the importance of the organization of chemical elements into a periodic table had on the conversion of chemistry into a quantitative, translatable science, as well as how we can apply the lessons learned in that transition to the current transformation taking place in biology.

Membrane glycoprotein (GP) IIb/IIIa plays a major role in platelet function; indeed it enables stimulated platelets to bind fibrinogen and related adhesive proteins, a process that is considered key in the development of thrombosis. The gene encoding GPIIIa (ITGB3, also known as GP3A) shows a common platelet antigen polymorphism [PL(A1)/PL(A2); expressed by alleles ITGB3*001 and ITGB3*002] that was variably associated with vascular disease. In 1996, the presence of the PL(A2) allele (ITGB3*001) was first reported to increase the risk of coronary heart disease. Shortly after, the interest in this study was increased by the publication of a case report on the death from myocardial infarction of an Olympic athlete who was found to be homozygous for the PL(A2) allele. Overviews of the published studies on the PL(A1)/PL(A2) polymorphism and coronary risk suggest an influence of the PL(A2) allele on the clinical phenotype and the interaction with other environmental factors. In particular, the strongest effect of the ITGB3 PL(A2) allele was expressed on the risk of occlusion after revascularization procedures, mainly after stent implantation, a condition in which platelet activation is more important as compared with other stenotic mechanisms. In the future, the identification of patients who are particularly responsive to GPIIb/IIIa antagonist therapy (e.g. those with the PL(A2) allele) might help to improve the treatment efficacy in this relatively small population. In a mechanism possibly unrelated to its effect on platelet reactivity to aggregating stimuli, the presence of the PL(A2) allele might influence the antiaggregatory effect of platelet inhibitory drugs such as aspirin (acetylsalicylic acid), clopidogrel, and GPIIb/IIIa antagonists. Although interesting, current data does not yet have direct clinical implications for patient risk identification and drug therapy tailoring. Larger studies are necessary to define the role of the PL(A2) allele in more homogeneous groups where platelet GPIIb/IIIa activation might be particularly relevant.

Background: Data analytic approaches to Affymetrix microarray data include: (a) a covariate model, in which the observed signal is some estimated linear function of perfect match (PM) and mismatch (MM) signals; (b) a difference model [PM-MM]; and (c) a PM-only model, in which MM data is not utilized.

Methods: By decomposing the correlations among the variables in the statistical model and making certain assumptions, we theoretically derive the statistical model that reflects the actual gene expression level under a variety of conditions expected in microarray data.

Results and conclusion: When modeling non-systematic variation, the covariate model provides maximum flexibility and often reflects the actual gene expression levels better than the difference model. However, the PM-only model demonstrates superior power in an overwhelming majority of realistic situations, which provides theoretical support for the current trend to employ PM-only models in microarray data analyzes.

One of the key factors in developing improved medicines lies in understanding the molecular basis of the complex diseases we treat. Investigation of genetic associations with disease utilizing advances in linkage disequilibrium-based whole genome association strategies will provide novel targets for therapy and define relevant pathways contributing to disease pathogenesis. Genetic studies in conjunction with gene expression, proteomic, and metabonomic analyses provide a powerful tool to identify molecular subtypes of disease. Using these molecular data, pharmacogenomics has the potential to impact on the drug discovery and development process at many stages of the pipeline, contributing to both target identification and increased confidence in the therapeutic rationale. This is exemplified by the identified association of 5-lipoxygenase-activating protein (ALOX5AP/FLAP) with increased risk of myocardial infarction, and of the chemokine receptor 5 (CCR5) with HIV infection and therapy. Pharmacogenomics has already been used in oncology to demonstrate that molecular data facilitates assessment of disease heterogeneity, and thus identification of molecular markers of response to drugs such as imatinib mesylate (Gleevec) and trastuzumab (Herceptin). Knowledge of genetic variation in a target allows early assessment of the clinical significance of polymorphism through the appropriate design of preclinical studies and use of relevant animal models. A focussed pharmacogenomic strategy at the preclinical phase of drug development will produce data to inform the pharmacogenomic plan for exploratory and full development of compounds. Opportunities post-approval show the value of large well-characterized data sets for a systematic assessment of the contribution of genetic determinants to adverse drug reactions and efficacy. The availability of genomic samples in large phase IV trials also provides a valuable resource for further understanding the molecular basis of disease heterogeneity, providing data that feeds back into the drug discovery process in target identification and validation for the next generation of improved medicines.

Background: The past two decades have seen the appearance of new infectious diseases and the reemergence of old diseases previously thought to be under control. At the same time, the effectiveness of the existing antibacterials is rapidly decreasing due to the spread of multidrug-resistant pathogens.

Aim: The aim of this study was to the identify candidate molecular targets (e.g. enzymes) within essential metabolic pathways specific to a significant subset of bacterial pathogens as the first step in the rational design of new antibacterial drugs.

Methods: We constructed a dataset of phylogenomic profiles (vectors that encode the similarity, measured by BLAST scores, of a gene across many species) for a series of 31 pathogenic bacteria of interest with 1073 genes taken from the reference organisms Escherichia coli and Mycobacterium tuberculosis. We applied Bayesian Decomposition, a matrix decomposition algorithm, to identify functional metabolic units comprising overlapping sets of genes in this dataset.

Results: Although no information on phylogeny was provided to the system, Bayesian Decomposition retrieved the known bacteria phylogenic relationships on the basis of the proteins necessary for survival. In addition, a set of genes required by all bacteria was identified, as well as components and enzymes specific to subsets of bacteria.

Conclusion: The use of phylogenomic profiles and Bayesian Decomposition provide important insights for the design of new antibacterial therapeutics.

A new diagnostic tool must pass three major tests before it is adopted for routine clinical use. First, the tool must be robust and reproducible; second, the clinical value of the tool must be proven, i.e. the tool should reliably trigger a clinical decision that results in patient benefit; and, third, the clinical community has to be convinced of the need for this tool and the benefits it affords. Another factor that can influence the adoption of new tools relates to the cost and the vagaries of insurance reimbursement. The Cancer Diagnosis Program (CDP) of the US National Cancer Institute (NCI) launched the Program for the Assessment of Clinical Cancer Tests (PACCT) in 2000 to develop a process for moving the results of new technologies and new understanding of cancer biology more efficiently and effectively into clinical practice. PACCT has developed an algorithm that incorporates the iterative nature of assay development into an evaluation process that includes developers and end users. The effective introduction of new tests into clinical practice has been hampered by a series of common problems that are best described using examples of successes and failures. The successful application of the PACCT algorithm is described in the discussion of the recent development of the OncotypeDX assay and plan for a prospective trial of this assay by the NCI-supported Clinical Trials Cooperative Groups. The assay uses reverse transcription (RT)-PCR evaluation of a set of 16 genes that were shown to strongly associate with the risk of recurrence of breast cancer in women who presented with early stage disease (hormone responsive, and no involvement of the auxiliary lymph nodes). The test is highly reproducible. It provides information to aid the physician and patient in making important clinical decisions, including the aggressiveness of the therapy that should be recommended. A trial is planned to test whether OncotypeDX can be used as a standalone trigger for specific treatment decisions. The problems that have been encountered and have delayed the development of other diagnostic tools are exemplified in the development of tests for human epidermal growth factor receptor (HER2) overexpression, for predictors of response to epidermal growth factor receptor inhibitors, and for the detection of residual disease following chemotherapy.

Genomics and proteomics technologies have created a paradigm shift in the drug discovery process, with bioinformatics having a key role in the exploitation of genomic, transcriptomic, and proteomic data to gain insights into the molecular mechanisms that underlie disease and to identify potential drug targets. We discuss the current state of the art for some of the bioinformatic approaches to identifying drug targets, including identifying new members of successful target classes and their functions, predicting disease relevant genes, and constructing gene networks and protein interaction networks. In addition, we introduce drug target discovery using the strategy of systems biology, and discuss some of the data resources for the identification of drug targets. Although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires an understanding of the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work.

Analysis of chimerism after allogeneic hematopoietic cell transplantation is important for assessing engraftment and the early detection of graft failure. In addition, the monitoring of minimal residual disease and early detection of imminent relapse has also become an important issue. Novel transplant procedures, for example dose-reduced conditioning protocols, rely on chimerism analysis to guide intervention, i.e. the reduction of immunosuppression or infusion of donor lymphocytes. During the last 30 years, several methods for the analysis of chimerism after hematopoietic cell transplantation have been published. Currently, fluorescent in situ hybridization (XY-FISH) analysis of sex chromosomes after transplantation from a sex-mismatched donor or analysis of polymorphic DNA sequences, i.e. short tandem repeats (STR) or variable number of tandem repeats (VNTR), are the most widely used procedures used in the assessment of chimerism. Two major diagnostic fields can be defined for chimerism analysis: the period of engraftment and the detection of minimal residual disease. Although STR-PCR and FISH analysis are very useful in the diagnosis of engraftment and graft failure, they are only of limited use in the monitoring of minimal residual disease, largely because of its limited level of sensitivity (1-5% for the minor population). Several novel procedures to improve this level of detection have been reported in recent years. One focus has been the use of real-time PCR techniques based on analysis of the Y-chromosome or, more recently, single nucleotide polymorphism (SNPs). These procedures combine quantitative analysis with high sensitivity (10(-4) to 10(-6)), and hold great potential for the future. In addition, the combination of cell sorting based on leukemia-specific immunophenotype and STR-PCR has been successfully used for minimal residual disease detection. First clinical data using these procedures indicate that intervention (e.g. the reduction of immunosuppression or donor lymphocyte infusion) may be effective in the minimal residual disease situation, even in high risk diseases like acute myeloid leukemia and acute lymphoblastic leukemia. The optimal timing of these diagnostic interventions is a critical issue and has to be further optimized. Whether this will ultimately improve the survival of patients with leukemia after transplantation has to be shown in prospective studies.

Background: Previous studies of vitamin D binding protein (VDBP, also known as group-specific component, Gc, encoded by the GC gene) have implicated two gene variants, GC*2 and GC*1F, as possible contributors with chronic obstructive pulmonary disease (COPD) protection and susceptibility, respectively. The objective of this study was to examine the association of VDBP to different subtypes of COPD.

Study design: The association of the various GC genotypes to the COPD phenotype was examined in Icelandic COPD patients who were followed by pulmonary physicians at the University Hospital of Iceland.

Methods: All patients were genotyped for the known alleles of the GC gene. The single nucleotide polymorphisms (SNPs) were identified by a restriction fragment length polymorphism procedure. Study power was estimated based on allele frequencies of the variants, and risk ratios were calculated from the prevalence of genotypes in the affected group divided by its prevalence in the control population. Statistical analyses were performed using the 2-tailed Fisher's Exact Test and chi(2) test, where appropriate.

Patient group: One hundred and two COPD patients and 183 controls, together with 46 asthma patients and 48 patients with chronic mucous hypersecretion (CMH) were examined.

Main outcome measure and results: The results demonstrate similar allele and genotype frequencies of GC in COPD patients overall and healthy controls. However, there was a higher prevalence of genotypes carrying a GC*1F allele and lower prevalence of genotypes with a GC*2 allele in the CMH patients than in controls. This difference was most notable in the homozygous form: 8.3% vs 1.1% for the GC*1F/*1F, and 0.0% vs 7.6% for the GC*2/*2 genotypes, respectively. When controlled for smoking, only the non-smoking CMH patients demonstrated a significantly altered frequency of the GC*1F/*1F genotype (p = 0.0001). The prevalence of the GC*2/*2 genotype was also significantly lower in patients with bronchial hypersecretion with airflow obstruction compared with the control group (2.9% vs 7.6%). Taken together, these results demonstrate that the GC*1F and GC*2 alleles are associated with sputum hypersecretion in individuals who are at increased risk of developing COPD.

Genetic diversity contributes to both disease susceptibility and variability in response to drugs. However, it has proven difficult to isolate genes that underlie common complex disease, and genetic variations that influence clinical responses to drugs remain largely uncovered. The candidate gene approach to uncover genetic variations that contribute to disease susceptibility or variations in response to common drugs has not met expectations. Although the sib-pair linkage approach has certain theoretical advantages in dealing with common/complex disease, success has been slow in coming. Meanwhile family studies including siblings, cousins and second cousins, and studies in well-defined founder populations, have increasingly gained popularity and enabled scientists to map and isolate genes for common complex disease, such as schizophrenia and asthma. The latter method has generated new hope that this approach may also be effective in mapping genes that regulate drug response. Indeed, there is compelling evidence that corticosteroid sensitivity is a mapable trait in patients with asthma. Collectively, these studies support the value of leveraging information available within population-based data systems to map and isolate genes for common complex disease and drug response.