Association between UGT1A1*28*28 genotype and lung cancer in the Japanese population.

BACKGROUND: Lung cancer is the leading cause of cancer death and is closely linked to tobacco smoking. Genetic polymorphisms in genes that encode enzymes involved in metabolizing tobacco carcinogens could affect an individual's risk for lung cancer. While polymorphism of UDP-glucuronosyltransferase1A1 (UGT1A1) is involved in detoxification of benzo(a)pyrene-7,8-dihydrodiol(-), a major tobacco carcinogen, the association between UGT1A1 genotype and lung cancer has not been examined. METHODS: We retrieved the clinical data of 5,285 patients who underwent systemic chemotherapy at Kyoto University Hospital. A total of 765 patients (194 lung cancer patients and 671 patients with other malignancies) with UGT1A1 genotyping data were included in this analysis. We used logistic regression with recessive, dominant, and additive models to identify differences in genotype frequencies between lung cancer and other malignancies. RESULTS: In the recessive model, UGT1A1*28*28 genotype was significantly associated with lung cancer compared to other malignancies (odds ratio 5.3, P = 0.0083). Among lung cancer patients with a smoking history, squamous cell carcinoma was significantly predominant in patients with UGT1A1*28*28 compared to those with other UGT1A1 genotypes (P = 0.024). CONCLUSION: This is the first study to demonstrate a significant association between the homozygous UGT1A1*28 genotype and lung cancer.

Data mining of the public version of the FDA Adverse Event Reporting System.

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses.

Adverse event profile of tigecycline: data mining of the public version of the U.S. Food and Drug Administration adverse event reporting system.

The recent emergence of multidrug-resistant pathogens and/or pharmacokinetics-pharmacodynamics considerations may result in off-label use of a certain class of antibacterials, including tigecycline. This study was performed to clarify the safety profile of tigecycline in the user-derived manner and to compare it with the prescribing information provided by the manufacturer. Numerous spontaneous adverse event reports (AERs) submitted to the U.S. Food and Drug Administration (FDA) were analyzed after a revision of arbitrary drug names and the deletion of duplicated submissions. Standardized official pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Based on 22017956 co-occurrences, i.e., drug-adverse event pairs, found in 1644220 AERs from 2004 to 2009, 248 adverse events were suggested as tigecycline-associated ones. Adverse events with a relatively high frequency included nausea, vomiting, pancreatitis, hepatic failure, hypoglycemia, and increase in levels of alanine aminotransferase, bilirubin, alkaline phosphatase, aspartate aminotransferase, and gamma-glutamyltransferase. It is noted that cholestasis, jaundice, an increase in International Normalized Ratio, and Stevens-Johnson syndrome were also, although they were infrequent. The adverse events suggested were in agreement with information provided by the manufacturer, suggesting that off-label use hardly results in unexpected adverse events, presumably due to usage with extreme caution.

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

BACKGROUND: DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. RESULTS: GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. CONCLUSION: GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at http://cgs.pharm.kyoto-u.ac.jp/services/network.

GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update.

G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery.

Alpha(2B)-adrenergic receptor deletion polymorphism associates with autonomic nervous system activity in young healthy Japanese.

Several polymorphisms of genes involved in autonomic nervous system (ANS) function have been reported to affect metabolic regulation. We have investigated the association of an alpha(2B)-adrenergic receptor (alpha(2B)AR) three-amino acid deletion polymorphism with ANS activity in young healthy subjects by means of electrocardiogram R-R interval power spectral analysis. Three hundred eighty-one young healthy Japanese males (mean +/- SE, 20.6 +/- 0.1 yr) were studied. One hundred sixty-eight (44.1%) were homozygotes of Long allele (Long/Long), 162 (42.5%) were heterozygotes (Long/Short), and 51 (13.4%) were homozygotes of Short allele (Short/Short). The allele frequency of Short allele was 0.35. No significant differences were observed in any of the characteristics investigated: body mass index, plasma glucose, plasma insulin, or family history of diabetes and obesity. In R-R spectral analysis of heart rate variability, carriers of Short/Short had significantly greater low frequency and very low frequency than Long/Long, as well as a higher sympathetic nervous system index. These findings suggest that the alpha(2B)AR deletion polymorphism might result in metabolic disorder by altering ANS function.

Beneficial effect of T-1095, a selective inhibitor of renal Na+-glucose cotransporters, on metabolic index and insulin secretion in spontaneously diabetic GK rats.

1. To investigate the pharmacological effects of T-1095, this novel derivative of phlorizin was administered to GK rats for 8 weeks. T-1095 treatment significantly lowered plasma glucose and glycosylated haemoglobin (HbA1c) levels, but did not significantly affect bodyweight. 2. T-1095 treatment did not affect 3.3 mmol/L glucose-induced insulin secretion in the isolated perfused pancreas of GK rats. 3. The peak insulin release in T-1095-treated GK rats was significantly higher during the first phase than in untreated GK rats (3-4 min after beginning 16.7 mmol/L glucose perfusion). The total amount of insulin secreted during the first phase in T-1095-treated GK rats was significantly higher than in untreated GK rats (35.3 +/- 1.4 vs. 27.3 +/- 2.5 ng in T-1095-treated compared with untreated rats, respectively). 4. During the second phase, insulin release in T-1095-treated GK rats was somewhat higher than in untreated GK rats (7-30 min after beginning 16.7 mmol/L glucose perfusion). The total amount of insulin secreted during the second phase in T-1095-treated GK rats was significantly higher than in untreated GK rats (88.2 +/- 6.1 vs. 68.1 +/- 5.7 ng, respectively). 5. The total amount of insulin secreted during perfusion in T-1095-treated GK rats was significantly higher than in untreated GK rats (123.5 +/- 7.3 vs. 95.4 +/- 7.7 ng, respectively). 6. These data show that the metabolic indices, plasma glucose and HbA1c levels and insulin secretion are significantly improved by T-1095 treatment in GK rats, which are spontaneously diabetic rats, suggesting its usefulness as a novel oral therapeutic antidiabetic agent.