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

Migraine is a common complex disorder that affects a large portion of the population and thus incurs a substantial economic burden on society. The disorder is characterized by recurrent headaches that are unilateral and usually accompanied by nausea, vomiting, photophobia, and phonophobia. The range of clinical characteristics is broad and there is evidence of comorbidity with other neurological diseases, complicating both the diagnosis and management of the disorder. Although the class of drugs known as the triptans (serotonin 5-HT(1B/1D) agonists) has been shown to be effective in treating a significant number of patients with migraine, treatment may in the future be further enhanced by identifying drugs that selectively target molecular mechanisms causing susceptibility to the disease.Genetically, migraine is a complex familial disorder in which the severity and susceptibility of individuals is most likely governed by several genes that may be different among families. Identification of the genomic variants involved in genetic predisposition to migraine should facilitate the development of more effective diagnostic and therapeutic applications. Genetic profiling, combined with our knowledge of therapeutic response to drugs, should enable the development of specific, individually-tailored treatment.

Retinopathy of prematurity (ROP) is an ischemia-induced proliferative retinopathy, which affects premature infants with low birth weight. It is a leading cause of visual impairment and blindness in children, and shares pathophysiological characteristics with other common ocular diseases such as diabetic retinopathy, central vein occlusion, and age-related macular degeneration. Pathologically similar inherited diseases such as Norrie disease suggest a possible genetic component in the susceptibility to ROP. The process of retinal neovascularization in ROP and in animal models of oxygen-induced retinopathy is complex, and involves angiogenic factors, such as vascular endothelial growth factor, and basement membrane components. Potential medical therapies for ROP, including modulators of angiogenic factors, inhibitors of basement membrane changes, endogenous inhibitors such as pigment epithelium derived factor, and anti-inflammatory drugs, have shown efficacy against neovascularization in several animal models. Some of these therapies are in clinical trials now for diabetic retinopathy and age-related macular degeneration, and in the future may prove efficacious for the treatment of ROP.

Predicting which patients with rheumatoid arthritis (RA), at presentation, are likely to suffer a severe disease course based on genotype data would be a major clinical advance. It would ensure that patients at highest risk of a severe outcome could be targeted with early aggressive therapies. With a better understanding of interactions between genotype and drug response it would be possible to prescribe treatments most likely to be efficacious and safe for specific patient subgroups. While a clear genetic component has been demonstrated in RA severity, the identification of genetic factors poses a challenge to researchers in the field. Initiatives such as the SNP Consortium and advances in genotyping technology have facilitated the investigation of genetic factors in both disease susceptibility and severity. However, several other factors, such as the availability of suitable longitudinal cohorts, definition of outcome measures, study design, selection of genetic markers, and statistical power, will all contribute to the likely success of genetic studies. Several strategies that have been applied in the pursuit of genetic predictors of clinical outcome in RA. While some encouraging results have been generated, it has so far been difficult to quantify the predictive value of genetic markers and extrapolate the results from genetic studies to clinic patients. Establishing high quality prospective inception cohorts, a more systemic approach to defining suitable outcome measures, and understanding the effects of treatment, will be critical to the eventual identification of good predictive genetic markers.

Better than gene sequencing or quantitative amplification, proteomics tools allow the study of tumor phenotype. Indeed, most current prognostic tests in cancer (carcinoembryonary antigen [CEA], prostate-specific antigen [PSA], CA 19-1, CA 125, alpha-fetoprotein [AFP], etc.) are based on the detection and quantification of single proteins in body fluids. However, a common characteristic of these tests is their relatively low predictive value, so that they are usually complemented with other procedures such as biopsy and/or endoscopy. Recently, improved analytical and bioinformatics tools have driven the attention on pattern recognition approaches rather then single-marker tests for prognostic forecasting. It is expected that predicting metastasization on the basis of tumoral protein patterns will soon be a reality. However, currently available technologies either limit the number of proteins that can be analyzed simultaneously or they are expensive, difficult, and time-consuming. Moreover, the tools adapted for expression proteomics might not be the same as those for prognostic studies that require investigation of protein function over time. We believe that clinical proteomics research designed within a precise clinical and pathology framework should be strongly supported, since many prognostic factors are determined not by the tumor itself, but by the patient, the treatment and the environment.

DNA methylation is an epigenetic phenomenon influencing the normal function of DNA and its scaffolding proteins. Especially in cancer, aberrant methylation patterns may contribute to the disease process by the induction of point mutations, activation of inactive genes through hypomethylation of promoters, and transcriptional inactivation through a complex interplay with histone acetylation and other inhibitory mechanisms. Aberrant methylation patterns have been evaluated as tools in the management of patients with cancer. The predictive value, the therapeutic manipulation and the prognostic significance of aberrantly methylated gene loci have been tested in hematological as well as in solid neoplasias in adults and children. A seemingly insurmountable wealth of data has been generated, however, data on clinical associations are sometimes presented in an almost incautious fashion. Nevertheless, some genes like p15INK4B in myelodysplastic syndrome (MDS) and p16INK4A in some lung cancer subtypes have been shown to confer a certain prognosis. In selected cases the data have been confirmed by independent studies. Assays have been developed that can be used by almost any clinical laboratory (e.g. methylation-specific PCR) for the rapid and affordable screening of tumors for aberrant methylation. The study of aberrant methylation patterns has successfully entered the arena of relevant clinical applications. Importantly, methylation does not only hold the potential for being 'just another' biomarker, but also, as it can be reverted chemically, it is a phenomenon that holds great promise for therapeutic exploitation.

Genetic factors play an important role in the determination of bone mass and osteoporosis. A number of candidate genes have been implicated in osteoporosis, including genes encoding type 1 collagen, vitamin D receptor, estrogen receptor-alpha (ERalpha), and others. A number of association studies have been performed with single nucleotide polymorphisms in the ERalpha gene to assess their relation with bone mineral density in pre- and postmenopausal women, as well as the rate of bone loss after menopause and skeletal response to estrogen administration. The polymorphisms studied thus far mostly involved intronic polymorphisms in intron 1. Other less frequently studied polymorphisms include those in exons 1, 4, and 8. Although most studies demonstrated associations with various bone-related parameters, the results are still disputed. Assessing genetic factors including ERalpha polymorphisms, if their significances are confirmed, can be helpful in targeting preventive measures to individuals with higher risk of developing osteoporosis and render the preventive effort more cost-effective. Moreover, pharmacogenetically, it may help identify postmenopausal women who tend to have better skeletal responses after estrogen replacement. It is not known, however, if patients who possess favorable polymorphisms in terms of skeletal responsiveness will also have an undesirably higher risk of adverse effects. This issue needs to be further investigated before clinical decisions based on the balance between benefits and risks can be made.

Pharmacogenomics promises to have an important impact on the major health problems of the developing world, especially on neglected infectious diseases such as malaria, tuberculosis, and HIV/AIDS. Its capacity to identify new targets for drug development, together with its potential application in identifying populations who will respond favorably to a particular drug, gives it a unique place as a technology to bridge the genomics divide between rich and poor nations. To realize its true potential, however, significant scientific, legal, ethical, political, and economic challenges need to be overcome. For this to occur, an innovative global approach based on strong collaboration between industry, academia, non-governmental, and international organizations will be required. Simultaneously, more equitable and active participation from developing country researchers themselves is critical in overcoming these challenges.

Alcoholism is a complex psychiatric disorder that has high heritability (50-60%) and is relatively common; in the US the lifetime prevalence of alcohol dependence is 20% in men and 8% in women. Current psychosocial and pharmacological therapies have relatively modest effects. Treatment is complicated by the fact that alcoholism is often co-morbid with other disorders, including anxiety, depression, and antisocial personality disorder. Approximately 80% of alcoholics smoke cigarettes and there is considerable genetic overlap between nicotine and alcohol addiction. Convergent evidence supports the classification of alcoholics into two broad categories: type 1 - later onset with feelings of anxiety, guilt, and high harm avoidance; and type 2 - early age of onset, usually men, impulsive, antisocial, and with low levels of brain serotonin. The pharmacogenomics of alcohol response is well established; genetic variants for the principal enzymes of alcohol metabolism influence drinking behavior and protect against alcoholism. Vulnerability to alcoholism is likely to be due to multiple interacting genetic loci of small to modest effects. First-line therapeutic targets for alcoholism are neurotransmitter pathway genes implicated in alcohol use. Of particular interest are the 'reward pathway' (serotonin, dopamine, GABA, glutamate, and beta endorphin) and the behavioral stress response system (corticotrophin-releasing factor and neuropeptide Y). Common functional polymorphisms in these genes are likely to be predictive (although each with small effect) of individualized pharmacological responses. Genetic studies, including case-control association studies and genome wide linkage studies, have identified associations between alcoholism and common functional polymorphisms in several candidate genes. Meanwhile, the current pharmacological therapies for alcoholism are effective in some alcoholics but not all. Some progress has been made in elucidating the pharmacogenomic responses to these drugs, particularly in the context of the type 1/type 2 classification system for alcoholics.