The Impact of Label Changes (Boxed Warning) on Real-World Febuxostat Utilization in Patients with Gout: A Cross-Sectional Drug Utilization Study.

INTRODUCTION: This drug utilization study evaluated the impact of 2019 label changes on real-world febuxostat utilization among patients with gout. We describe the numbers and proportions of patients initiating febuxostat as new users (allopurinol-naïve) or prevalent new users (prior allopurinol use) and data on febuxostat users with established cardiovascular disease (CVD) morbidities before, during, and after the 2019 label changes. METHODS: This descriptive, non-interventional, cross-sectional study used data from two large administrative claims databases in the United States, the IQVIA PharMetrics Plus database and the Optum Research Database (ORD). The study population included patients with gout initiating febuxostat on or after June 1, 2016. Data were collected on febuxostat and allopurinol use, established CVD morbidities, comorbidities of interest, concomitant medications, and patient demographics. RESULTS: In both databases, the total number of febuxostat users and proportion of patients who initiated febuxostat as new users both decreased during the study period. Of 13,848 patients in the PharMetrics Plus cohort, 42.7% were new users of febuxostat and 57.3% were prevalent new users. In the ORD cohort, 40.5% of the 10,198 patients were new users and 59.5% were prevalent new users. The most common established CVD morbidities in the 12 months prior to initiation of febuxostat were diabetes mellitus, ischemic heart disease, and heart failure/cardiomyopathy. CONCLUSIONS: Although the benefit-risk profile for febuxostat is considered favorable for the treatment of hyperuricemia in certain patients with gout, real-world febuxostat utilization decreased during the study period, presumably in response to the label change.

Food webs for three burn severities after wildfire in the Eldorado National Forest, California.

Wildfire dynamics are changing around the world and understanding their effects on ecological communities and landscapes is urgent and important. We report detailed food webs for unburned, low-to-moderate and high severity burned habitats three years post-fire in the Eldorado National Forest, California. The cumulative cross-habitat food web contains 3,084 ontogenetic stages (nodes) or plant parts comprising 849 species (including 107 primary producers, 634 invertebrates, 94 vertebrates). There were 178,655 trophic interactions between these nodes. We provide information on taxonomy, body size, biomass density and trophic interactions under each of the three burn conditions. We detail 19 sampling methods deployed across 27 sites (nine in each burn condition) used to estimate the richness, body size, abundance and biomass density estimates in the node lists. We provide the R code and raw data to estimate summarized node densities and assign trophic links.

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

PDON: Parkinson's disease ontology for representation and modeling of the Parkinson's disease knowledge domain.

BACKGROUND: Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism. METHODS: The Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology. RESULTS: The Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson's disease. CONCLUSIONS: Parkinson's disease ontology delivers the knowledge domain of Parkinson's disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson's-related computable models. A practical version of the Parkinson's disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON .

Relative stabilities of IgG1 and IgG4 Fab domains: influence of the light-heavy interchain disulfide bond architecture.

The stability of therapeutic antibodies is a prime pharmaceutical concern. In this work we examined thermal stability differences between human IgG1 and IgG4 Fab domains containing the same variable regions using the thermofluor assay. It was found that the IgG1 Fab domain is up to 11°C more stable than the IgG4 Fab domain containing the same variable region. We investigated the cause of this difference with the aim of developing a molecule with the enhanced stability of the IgG1 Fab and the biological properties of an IgG4 Fc. We found that replacing the seven residues, which differ between IgG1 C(H) 1 and IgG4 C(H) 1 domains, while retaining the native IgG1 light-heavy interchain disulfide (L-H) bond, did not affect thermal stability. Introducing the IgG1 type L-H interchain disulfide bond (DSB) into the IgG4 Fab resulted in an increase in thermal stability to levels observed in the IgG1 Fab with the same variable region. Conversely, replacement of the IgG1 L-H interchain DSB with the IgG4 type L-H interchain DSB reduced the thermal stability. We utilized the increased stability of the IgG1 Fab and designed a hybrid antibody with an IgG1 C(H) 1 linked to an IgG4 Fc via an IgG1 hinge. This construct has the expected biophysical properties of both the IgG4 Fc and IgG1 Fab domains and may therefore be a pharmaceutically relevant format.

BuchneraBASE: a post-genomic resource for Buchnera sp. APS.

SUMMARY: BuchneraBASE is a bioinformatic research tool for the genome of the symbiotic bacterium Buchnera sp. APS that includes an improved genome annotation, comparative information about related insect symbiont genomes and a complete mapping of metabolic reactions to an Escherichia coli in silico model. The database is designed to accommodate genome-wide post-genomic datasets that are becoming available for this organism. AVAILABILITY: BuchneraBASE is available at http://www.buchnera.org/.