Safety of insulin lispro: pooled data from clinical trials.

The safety of insulin lispro was compared with that of regular human insulin of recombinant DNA origin (Humulin R, Lilly), with special emphasis on the development and progression of the chronic complications of diabetes mellitus in relation to insulin therapy. Ten clinical trials of 3634 patients with type 1 and type 2 diabetes mellitus were analyzed. The primary focus was treatment-emergent adverse events, and the secondary focus was the development and progression of the chronic complications of diabetes. The evaluations were based on pertinent laboratory values, predetermined disease-specific COSTART (coding symbol and thesaurus for adverse event terminology) terms, physician evaluations of patients, and physical examinations. There were no clinically or statistically significant differences in the frequency of treatment-emergent adverse events or progression of retinopathy, neuropathy, or cardiovascular disease reported with each therapy. There was no difference between insulin lispro and Humulin R in the occurrence and progression of kidney disease as measured by changes in serum creatinine levels. Pooled data from clinical studies show that insulin lispro has a safety profile comparable to that of Humulin R.     

Principles of data mining.

Data mining is the discovery of interesting, unexpected or valuable structures in large datasets. As such, it has two rather different aspects. One of these concerns large-scale, 'global' structures, and the aim is to model the shapes, or features of the shapes, of distributions. The other concerns small-scale, 'local' structures, and the aim is to detect these anomalies and decide if they are real or chance occurrences. In the context of signal detection in the pharmaceutical sector, most interest lies in the second of the above two aspects; however, signal detection occurs relative to an assumed background model, therefore, some discussion of the first aspect is also necessary. This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.     

A data mining approach for signal detection and analysis.

The WHO database contains over 2.5 million case reports, analysis of this data set is performed with the intention of signal detection. This paper presents an overview of the quantitative method used to highlight dependencies in this data set. The method Bayesian confidence propagation neural network (BCPNN) is used to highlight dependencies in the data set. The method uses Bayesian statistics implemented in a neural network architecture to analyse all reported drug adverse reaction combinations. This method is now in routine use for drug adverse reaction signal detection. Also this approach has been extended to highlight drug group effects and look for higher order dependencies in the WHO data. Quantitatively unexpectedly strong relationships in the data are highlighted relative to general reporting of suspected adverse effects; these associations are then clinically assessed.     

Perspectives on the use of data mining in pharmaco-vigilance.

In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided.Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.     

Safety evaluation of lipase produced from Rhizopus oryzae: summary of toxicological data

The toxicity of Lipase D, an enzyme preparation, was evaluated in a series of studies. Lipase D selectively hydrolyzes triglycerides of fatty acids. It also catalyzes the interesterification of edible fats and oils. In a 13-week gavage study, Sprague-Dawley rats received Lipase D at levels of 0, 500, 1000, or 2000 mg/kg body wt./day. A dose dependent decrease in urinary pH was observed, but there were no effects on electrolyte balance, kidney weight, or histology of the kidney. The no-observed-adverse-effect level in rats was 1000 mg/kg body wt./day. In common with other enzyme preparations, Lipase D was not genotoxic. Lipase D was tested in the Ames assay, the mouse lymphoma forward mutation assay, and the chromosome aberration assay. Finally, the particular strain of Rhizopus oryzae used to prepare Lipase D was shown to have low to moderate pathogenicity when injected into the tail vein of mice at doses up to 1.3 x 10(6) colony-forming units (CFU) per animal. No effects were observed when mice received up to 2.2 x 10(5) CFU by gavage or in their diets daily for 28 days. The results indicate that this particular strain can be handled using ordinary safety practices current in the fermentation industry. These studies support a conclusion that Lipase D is safe when used as described in the processing of dietary fatty acids and glycerides of fatty acids.     

Safety evaluation of phosphodiesterase produced from Penicillium citrinum: summary of toxicological data

The toxicity of Enzyme RP-1, an enzyme preparation used to hydrolyze yeast RNA to produce flavor enhancers for use in the food industry, was evaluated in a series of studies. A 5-week dietary toxicity study in Wistar rats was conducted in which animals received Enzyme RP-1 in feed at concentrations of 0, 500, 2000, or 8000 mg/kg body wt/day. A 13-week dietary toxicity study in Sprague-Dawley rats was conducted in which animals received RP-1 concentrate at 0, 0.125, 0.5, or 2% in their diets. At the highest dose levels in both studies, submandibular glands in the oral cavity were enlarged, an effect attributed to protease activity of the enzyme preparation. The no-observed-effect level in rats in the 13-week study was 0.5%, equivalent to 317 mg/kg body wt/day for males and 346 mg/kg body wt/day for females. Based on estimated dietary exposure to the enzyme preparation, the margin of exposure is estimated to be greater than 38,000. Lack of genotoxic potential was demonstrated by an in vitro reverse mutation assay in Salmonella typhimurium strains TA98, TA100, TA1535, and TA1537 and in Escherichia coli strain WP2uvr and by an in vitro chromosome aberration test in CHL/IU cells derived from fibroblasts from the lungs of Chinese hamsters. Finally, the particular strain of Penicillium citrinum, the fungal strain used to prepare Enzyme RP-1, was shown to have low pathogenicity upon a single injection into the tail vein of rats of viable spores at doses up to 2.8x10(5) colony-forming units per animal. The results of these studies demonstrate that the enzyme preparation may be considered safe to workers and consumers when employed in the production of flavor enhancers from yeast.     

Safety evaluation of lipase produced from Candida rugosa: summary of toxicological data

The toxicity of lipase AY, an enzyme preparation used in lipid hydrolysis to produce flavors, was evaluated in a series of studies. A 13-week dietary toxicity study in Sprague-Dawley (Crj:CD) rats was conducted in which animals received lipase AY in the feed at concentrations of 0, 625, 1250, or 2500 mg/kg body wt. No adverse treatment-related effects were observed. Lack of genotoxic potential was demonstrated by the results of an in vitro reverse mutation assay in Salmonella typhimurium strains TA98, TA100, TA1535, and TA1537 and in Escherichia coli strain WP2 uvrA, by an in vitro forward mutation assay in L5178Y mouse lymphoma cells, and by an in vitro chromosome aberration test in CHL/IU cells derived from fibroblasts of the lungs of Chinese hamsters. Finally, the particular strain of Candida rugosa, the yeast strain used to prepare lipase AY, has been shown to be nonpathogenic upon a single injection into the tail vein of rats of viable spores at doses up to 1.5x10(7) colony-forming units per animal. The results of these studies demonstrate that the enzyme preparation may be considered safe to workers and consumers when employed in the production of flavors from fats.     

Evaluation of docking performance: comparative data on docking algorithms

Docking molecules into their respective 3D macromolecular targets is a widely used method for lead optimization. However, the best known docking algorithms often fail to position the ligand in an orientation close to the experimental binding mode. It was reported recently that consensus scoring enhances the hit rates in a virtual screening experiment. This methodology focused on the top-ranked pose, with the underlying assumption that the orientation/conformation of the docked compound is the most accurate. In an effort to eliminate the scoring function bias, and assess the ability of the docking algorithms to provide solutions similar to the crystallographic modes, we investigated the most known docking programs and evaluated all of the resultant poses. We present the results of an extensive computational study in which five docking programs (FlexX, DOCK, GOLD, LigandFit, Glide) were investigated against 14 protein families (69 targets). Our findings show that some algorithms perform consistently better than others, and a correspondence between the nature of the active site and the best docking algorithm can be found.     

Acute changes in sputum collected from exposed human subjects in mining conditions

Neprilysin (NEP) is a key cell surface peptidase in the maintenance of airway homeostasis and the development of pulmonary disorders. However, little information is available about the effect of particulate matter (PM) on airway NEP. In this controlled human exposure study, changes in induced sputum were measured in 11 subjects at baseline, overshot (OS) mucking, and diesel exhaust (DE) exposure days. Neither OS condition nor DE exposure was found to induce significant changes in total protein, but DE induced significant increases in cell numbers of macrophages and epithelium. Moreover, significant increases in soluble NEP were observed following OS mining dust particulates (0.43?±?0.06 nmol/μg protein/min; p?=?.023) and DE exposure (0.40?±?0.03 nmol/μg protein/min; p?=?.035) when compared with the baseline control (0.30?±?0.04 nmol/μg protein/min), with 42% and 31% average net increase, respectively. Pearson’s correlation analyses indicated that sputum NEP activity was significantly associated with personal exposure product (elemental carbon concentration [mg/m³] × time [min]; C × T). The data suggest that changes in NEP activity may be an early, accurate endpoint for airway epithelial injury and provide a new insight into the mechanism of airway effects following particulate exposure.     

A text mining analysis of medication quality related event reports from community pharmacies

BackgroundMedication errors are estimated to cost $42 billion in annual global treatment costs. Pharmacy-based Patient Safety Organizations (PSO) are tasked with collecting and analyzing incidents, near misses, and unsafe condition reports as one way of engaging pharmacies in quality improvement efforts. Collectively, these reports are referred to as quality related events (QREs). Large-scale analysis of typed narratives from QRE reports across organizations has been a missing component of quality improvement programs.ObjectiveTo identify topics within the components of a proposed medication safety event framework contained in the free-text narrative of QRE reports.MethodsA retrospective, observational analysis of data from a PSOs voluntary reporting system, from January 1, 2011 to December 31, 2014. The dataset contained structured and unstructured data elements. A structural topic model extracted themes from the free-text narrative component of the report. These topics were assigned a human label and mapped onto constructs of the medication safety event framework.ResultsA total of 531,555 QREs were analyzed from 1660 pharmacies. 90.6% were near miss and unsafe condition reports. There were 40 topics generated. There were 29 topics identified as QRE types, 3 were identified as contributing factors, and 5 were related to signals/alerts that an incident or near miss had occurred. One topic each was identified as a recovery step and a quality improvement strategy. One topic was not assigned a human label. Examples of topics labeled included incorrect tapering directions, needing to double-check work, and attention-related contributing factor.ConclusionsThe free-text narrative provided novel information compared to the structured fields of the reports. Topics were mapped onto a proposed medication safety event framework to advance knowledge of medication QREs and identify ways to improve medication safety in community pharmacy. Future work may focus on communicating these topics to the pharmacies to improve medication safety efforts.     

基因导向的华法林给药模型安全性和有效性的系统评价

目的:评价基因导向的华法林给药模型(pharmacogenomics-guided warfarin dosing algorithms)在华法林应用初期的安全性和有效性。方法:检索数据库,纳入随机对照试验(RCT),并评价其方法学质量,提取资料,用Revman 5.0软件进行Meta分析。结果:共纳入5篇文献,其方法学质量、研究人群等存在很大差异,可能存在较高的偏倚风险。Meta分析结果表明:华法林给药初期应用基因导向的华法林给药模型可以降低不良反应发生率[RR=0.52,95%CI(0.28,0.97),P=0.04],但由于存在异质性,故进行亚组分析;在INR治疗范围内的时间[SMD=-0.35,95%CI(-0.51,-0.18),P<0.000 01]、第一次达INR治疗范围的时间[SMD=-1.12,95%CI(-1.36,-0.89),P<0.000 01]、华法林初始剂量和稳定剂量的差值[SMD=-0.33,95%CI(-0.55,-0.11),P=0.003],PG组均要优于C组,且达到统计学意义。结论:给药初期,应用基因导向的华法林给药模型可以提高华法林给药的安全性和有效性。     

Constructive data mining: modeling consumers’ expenditurein Venezuela

Hoover and Perez (1999) advocate a constructive approach to data mining. Thecurrent paper identifies four pejorative senses of data mining and shows how Hoover and Perez’s approach counters each. To assess the benefits of constructive data mining, the current paper applies a data-mining algorithm similar to Hoover and Perez’s to a dataset for Venezuelan consumers’ expenditure. The selected model is economically sensible and statisticallysatisfactory; and it illustrates how data can be highly informative, even with relatively few observations. Limitations to algorithmically based data mining provide opportunities for the researcher to contribute value added in the empirical analysis.