Summary of Research: Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis.

This article provides a summary of a previously published paper: Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis. The paper reported the results of the CHAMPION-MG trial which investigated the drug ravulizumab in the rare disease, myasthenia gravis. Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis (MP4 594600 KB).

Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis.

BACKGROUND: Generalized myasthenia gravis (gMG) is a rare, chronic, and debilitating autoimmune disease. Activation of the complement system by autoantibodies against the postsynaptic acetylcholine receptor (AChR) leads to destruction of the postsynaptic membrane and disruption of neuromuscular transmission. This trial evaluated ravulizumab, a long-acting inhibitor of terminal complement protein C5, as a treatment for gMG.

Associating Cognition With Amyloid Status Using Partially Ordered Set Analysis.

The presence of brain amyloid-beta positivity is associated with cognitive impairment and dementia, but whether there are specific aspects of cognition that are most linked to amyloid-beta is unclear. Analysis of neuropsychological test data presents challenges since a single test often requires drawing upon multiple cognitive functions to perform well. It can thus be imprecise to link performance on a given test to a specific cognitive function.

Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks.

Motivation: Understanding and predicting an individual's response in a clinical trial is the key to better treatments and cost-: effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies.

GeneTopics--interpretation of gene sets via literature-driven topic models.

Background: Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set.

Comparison of hepatic transcription profiles of locked ribonucleic acid antisense oligonucleotides: evidence of distinct pathways contributing to non-target mediated toxicity in mice.

Development of LNA gapmers, antisense oligonucleotides used for efficient inhibition of target RNA expression, is limited by non-target mediated hepatotoxicity issues. In the present study, we investigated hepatic transcription profiles of mice administered non-toxic and toxic LNA gapmers. After repeated administration, a toxic LNA gapmer (TS-2), but not a non-toxic LNA gapmer (NTS-1), caused hepatocyte necrosis and increased serum alanine aminotransferase levels.

A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions.

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88,629 articles relating over 1,200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity.

Analysis of changes in hepatic gene expression in a murine model of tolerance to acetaminophen hepatotoxicity (autoprotection).

Pretreatment of mice with a low hepatotoxic dose of acetaminophen (APAP) results in resistance to a subsequent, higher dose of APAP. This mouse model, termed APAP autoprotection was used here to identify differentially expressed genes and cellular pathways that could contribute to this development of resistance to hepatotoxicity. Male C57BL/6J mice were pretreated with APAP (400mg/kg) and then challenged 48h later with 600mg APAP/kg.

Molecular causes of transcriptional response: a Bayesian prior knowledge approach.

Motivation: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.

Assessing the translatability of in vivo cardiotoxicity mechanisms to in vitro models using causal reasoning.

Drug-induced cardiac toxicity has been implicated in 31% of drug withdrawals in the USA. The fact that the risk for cardiac-related adverse events goes undetected in preclinical studies for so many drugs underscores the need for better, more predictive in vitro safety screens to be deployed early in the drug discovery process. Unfortunately, many questions remain about the ability to accurately translate findings from simple cellular systems to the mechanisms that drive toxicity in the complex in vivo environment.

Causal reasoning on biological networks: interpreting transcriptional changes.

Motivation: The interpretation of high-throughput datasets has remained one of the central challenges of computational biology over the past decade. Furthermore, as the amount of biological knowledge increases, it becomes more and more difficult to integrate this large body of knowledge in a meaningful manner. In this article, we propose a particular solution to both of these challenges.

Assessing statistical significance in causal graphs.

Background: Causal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes.

Modeling the mechanism of action of a DGAT1 inhibitor using a causal reasoning platform.

Triglyceride accumulation is associated with obesity and type 2 diabetes. Genetic disruption of diacylglycerol acyltransferase 1 (DGAT1), which catalyzes the final reaction of triglyceride synthesis, confers dramatic resistance to high-fat diet induced obesity. Hence, DGAT1 is considered a potential therapeutic target for treating obesity and related metabolic disorders.

Opposite regulation of cholesterol levels by the phosphatase and hydrolase domains of soluble epoxide hydrolase.

Soluble epoxide hydrolase (sEH) is a bifunctional enzyme with two catalytic domains: a C-terminal epoxide hydrolase domain and an N-terminal phosphatase domain. Epidemiology and animal studies have attributed a variety of cardiovascular and anti-inflammatory effects to the C-terminal epoxide hydrolase domain. The recent association of sEH with cholesterol-related disorders, peroxisome proliferator-activated receptor activity, and the isoprenoid/cholesterol biosynthesis pathway additionally suggest a role of sEH in regulating cholesterol metabolism.

Distribution and expression of soluble epoxide hydrolase in human brain.

Epoxyeicosatrienoic acids (EETs) are cytochrome P450 metabolites of arachidonic acid, which function in the brain to regulate cerebral blood flow and protect against ischemic brain injury. EETs are converted by soluble epoxide hydrolase (sEH) to the corresponding inactive diol metabolites. Previous animal studies have indicated that sEH gene deletion or treatment with sEH inhibitors results in increased levels of EETs and protection against stroke-induced brain damage.

Novel Role of Soluble Epoxide Hydrolase in Regulating Cholesterol in Mammalian Cells.

Soluble epoxide hydrolase (sEH) is becoming an attractive therapeutic target in cardiovascular disease. Recently, known human sEH polymorphisms were associated with elevated plasma cholesterol and atherosclerosis. In this study we evaluated the potential role of sEH in regulating cholesterol metabolism through modulating the levels of fatty acid epoxide substrates and/or their corresponding diol products known to activate peroxisome proliferator activated receptors (PPARs).

Distribution of soluble epoxide hydrolase, cytochrome P450 2C8, 2C9 and 2J2 in human malignant neoplasms.

Soluble epoxide hydrolase (sEH) is a bifunctional enzyme with a C-terminal epoxide hydrolase activity and an N-terminal phosphatase activity. Arachidonic acid epoxides, previously suggested to be involved in apoptosis, oncogenesis and cell proliferation, are generated by cytochrome P450 epoxygenases and are good substrates of the sEH C-terminal domain. In addition, the N-terminal phosphatase domain hydrolyzes isoprenoid mono- and pyrophosphates, which are involved in cell signaling and apoptosis.

Effects of human soluble epoxide hydrolase polymorphisms on isoprenoid phosphate hydrolysis.

Soluble epoxide hydrolase (sEH) is highly expressed in human liver and contains a C-terminal epoxide hydrolase activity and an N-terminal phosphatase activity. Endogenous C-terminal hydrolase substrates include arachidonic acid epoxides, however, data are limited regarding possible endogenous substrates for the N-terminal phosphatase. Possible sEH N-terminal substrates include isoprenoid phosphate precursors of cholesterol biosynthesis and protein isoprenylation.

Cell-specific subcellular localization of soluble epoxide hydrolase in human tissues.

Soluble epoxide hydrolase (sEH) is a phase-I xenobiotic metabolizing enzyme having both an N-terminal phosphatase activity and a C-terminal epoxide hydrolase activity. Endogenous hydrolase substrates include arachidonic acid epoxides, which have been involved in regulating blood pressure and inflammation. The subcellular localization of sEH has been controversial.

Distribution of soluble epoxide hydrolase and of cytochrome P450 2C8, 2C9, and 2J2 in human tissues.

Soluble epoxide hydrolase (sEH) hydrolyzes a wide variety of endogenous and exogenous epoxides. Many of these epoxides are believed to be formed by cytochrome P450 epoxygenases. Here we report the distribution of sEH and cytochrome P450 epoxygenases 2C8, 2C9, and 2J2 by immunohistochemistry.

Polymorphisms in human soluble epoxide hydrolase.

Human soluble epoxide hydrolase (hsEH) metabolizes a variety of epoxides to the corresponding vicinal diols. Arachidonic and linoleic acid epoxides are thought to be endogenous substrates for hsEH. Enzyme activity in humans shows high interindividual variation (e.