Associations of obesity and dengue-associated mortality, acute liver failure and mechanical ventilation in children with dengue shock syndrome.

Dengue-associated complications, including dengue shock syndrome, severe respiratory distress, and pediatric acute liver failure (PALF), are associated with high mortality rates in patients with dengue. There is increasing prevalence of overweight and obesity among children worldwide. Obesity may activate inflammatory mediators, leading to increased capillary permeability and plasma leakage in patients with dengue. Several studies have shown a correlation between obesity and DSS, but did not include dengue fatality or PALF. Therefore, we hypothesized possible associations between obesity and critical dengue-associated clinical outcomes among PICU-admitted children with DSS, including dengue-related mortality, mechanical ventilation (MV) requirements, and dengue-associated PALF. The nutritional status of the participants was assessed using World Health Organization growth charts. A total of 858 participants with complete nutritional data were enrolled in this study. Obesity was significantly associated with risk of severe respiratory failure and MV support (odds ratio = 2.3, 95% CI: 1.31-4.06, P < .01); however, it was not associated with dengue-associated mortality or acute liver failure. Obese pediatric patients with DSS should be closely monitored for severe respiratory distress and the need for high-flow oxygenation support, particularly MV, soon after hospitalization.

RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling.

The US drug labeling document contains essential information on drug efficacy and safety, making it a crucial regulatory resource for Food and Drug Administration (FDA) drug reviewers. Due to its extensive volume and the presence of free-text, conventional text mining analysis have encountered challenges in processing these data. Recent advances in artificial intelligence (AI) for natural language processing (NLP) have provided an unprecedented opportunity to identify key information from drug labeling, thereby enhancing safety reviews and support for regulatory decisions. We developed RxBERT, a Bidirectional Encoder Representations from Transformers (BERT) model pretrained on FDA human prescription drug labeling documents for an enhanced application of drug labeling documents in both research and drug review. RxBERT was derived from BioBERT with further training on human prescription drug labeling documents. RxBERT was demonstrated in several tasks using regulatory datasets, including those involved in the National Institutes of Technology Text Analysis Challenge Dataset (NIST TAC dataset), the FDA Adverse Drug Event Evaluation Dataset (ADE Eval dataset), and the classification of texts from submission packages into labeling sections (US Drug Labeling dataset). For all these tasks, RxBERT reached 86.5 F1-scores in both TAC and ADE Eval classification, respectively, and prediction accuracy of 87% for the US Drug Labeling dataset. Overall, RxBERT was shown to be as competitive or have better performance compared to other NLP approaches such as BERT, BioBERT, etc. In summary, we developed RxBERT, a transformer-based model specific for drug labeling that outperformed the original BERT model. RxBERT has the potential to be used to assist research scientists and FDA reviewers to better process and utilize drug labeling information toward the advancement of drug effectiveness and safety for public health. This proof-of-concept study also demonstrated a potential pathway to customized large language models (LLMs) tailored to the sensitive regulatory documents for internal application.

Feature engineering and machine learning for causality assessment in pharmacovigilance: Lessons learned from application to the FDA Adverse Event Reporting System.

BACKGROUND: Our objective was to support the automated classification of Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) reports for their usefulness in assessing the possibility of a causal relationship between a drug product and an adverse event. METHOD: We used a data set of 326 redacted FAERS reports that was previously annotated using a modified version of the World Health Organization-Uppsala Monitoring Centre criteria for drug causality assessment by a group of SEs at the FDA and supported a similar study on the classification of reports using supervised machine learning and text engineering methods. We explored many potential features, including the incorporation of natural language processing on report text and information from external data sources, for supervised learning and developed models for predicting the classification status of reports. We then evaluated the models on a larger data set of previously unseen reports. RESULTS: The best-performing models achieved recall and F1 scores on both data sets above 0.80 for the identification of assessable reports (i.e. those containing enough information to make an informed causality assessment) and above 0.75 for the identification of reports meeting at least a Possible causality threshold. CONCLUSIONS: Causal inference from FAERS reports depends on many components with complex logical relationships that are yet to be made fully computable. Efforts focused on readily addressable tasks, such as quickly eliminating unassessable reports, fit naturally in SE's thought processes to provide real enhancements for FDA workflows.

ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance.

INTRODUCTION: The US FDA is interested in a tool that would enable pharmacovigilance safety evaluators to automate the identification of adverse drug events (ADEs) mentioned in FDA prescribing information. The MITRE Corporation (MITRE) and the FDA organized a shared task-Adverse Drug Event Evaluation (ADE Eval)-to determine whether the performance of algorithms currently used for natural language processing (NLP) might be good enough for real-world use. OBJECTIVE: ADE Eval was conducted to evaluate a range of NLP techniques for identifying ADEs mentioned in publicly available FDA-approved drug labels (package inserts). It was designed specifically to reflect pharmacovigilance practices within the FDA and model possible pharmacovigilance use cases. METHODS: Pharmacovigilance-specific annotation guidelines and annotated corpora were created. Two metrics modeled the experiences of FDA safety evaluators: one measured the ability of an algorithm to identify correct Medical Dictionary for Regulatory Activities (MedDRA®) terms for the text from the annotated corpora, and the other assessed the quality of evidence extracted from the corpora to support the selected MedDRA® term by measuring the portion of annotated text an algorithm correctly identified. A third metric assessed the cost of correcting system output for subsequent training (averaged, weighted F1-measure for mention finding). RESULTS: In total, 13 teams submitted 23 runs: the top MedDRA® coding F1-measure was 0.79, the top quality score was 0.96, and the top mention-finding F1-measure was 0.89. CONCLUSION: While NLP techniques do not perform at levels that would allow them to be used without intervention, it is now worthwhile exploring making NLP outputs available in human pharmacovigilance workflows.

Complete genome sequence of a novel lytic phage infecting Aeromonas hydrophila, an infectious agent in striped catfish (Pangasianodon hypophthalmus).

The bacteriophage vB_AhM_PVN02 (PVN02), infecting Aeromonas hydrophila, was isolated from a striped catfish pond water sample in Can Tho City, Vietnam. The phage had high lytic activity with a latent period and burst size of approximately 20 min and 105 plaque-forming units per cell, respectively. Observation of the phage by transmission electron microscopy indicated that PVN02 belongs to the family Myoviridae. The genome of PVN02 is a double-stranded linear DNA with a length in 51,668 bp and a content of 52% GC. Among the 64 genes, 16 were predicted to encode proteins with predicted functions. No virulence or antibiotic resistance genes were found in the genome, suggesting it would be a useful biocontrol agent. Classification of the phage based on sequence comparisons, phylogenetic analysis, and gene-sharing networks was carried out, and it was found to be the first representative of a new species within a previously undefined genus in the family Myoviridae. This study confirmed that PVN02 is a novel lytic phage that could potentially be used as an agent to control Aeromonas hydrophila in striped catfish in the Mekong Delta, Vietnam.

Information Visualization Platform for Postmarket Surveillance Decision Support.

INTRODUCTION: The US FDA receives more than 2 million postmarket reports each year. Safety Evaluators (SEs) review these reports, as well as external information, to identify potential safety signals. With the increasing number of reports and the size of external information, more efficient solutions for data integration and decision making are needed. OBJECTIVES: The aim of this study was to develop an interactive decision support application for drug safety surveillance that integrates and visualizes information from postmarket reports, product labels, and biomedical literature. METHODS: We conducted multiple meetings with a group of seven SEs at the FDA to collect the requirements for the Information Visualization Platform (InfoViP). Using infographic design principles, we implemented the InfoViP prototype version as a modern web application using the integrated information collected from the FDA Adverse Event Reporting System, the DailyMed repository, and PubMed. The same group of SEs evaluated the InfoViP prototype functionalities using a simple evaluation form and provided input for potential enhancements. RESULTS: The SEs described their workflows and overall expectations around the automation of time-consuming tasks, including the access to the visualization of external information. We developed a set of wireframes, shared them with the SEs, and finalized the InfoViP design. The InfoViP prototype architecture relied on a javascript and a python-based framework, as well as an existing tool for the processing of free-text information in all sources. This natural language processing tool supported multiple functionalities, especially the construction of time plots for individual postmarket reports and groups of reports. Overall, we received positive comments from the SEs during the InfoViP prototype evaluation and addressed their suggestions in the final version. CONCLUSIONS: The InfoViP system uses context-driven interactive visualizations and informatics tools to assist FDA SEs in synthesizing data from multiple sources for their case series analyses.

The Role of Pseudomonas in Heterotrophic Nitrification: A Case Study on Shrimp Ponds (Litopenaeus vannamei) in Soc Trang Province.

Based on a total of 6,295,650 sequences from the V3 and V4 regions (16S ribosomal RNA), the composition of the microorganism communities in the water of three Litopenaeus vannamei (Decapoda, Whiteleg shrimp; Soc Trang, Vietnam) ponds were identified. Pseudomonas (10-20.29%), Methylophilus (13.26-24.28%), and Flavobacterium (2.6-19.29%) were the most abundant genera. The total ammonia (TAN) concentration (p = 0.025) and temperature (p = 0.015) were significantly correlated with the relative abundance of Pseudomonas in two bacterial communities (ST1, ST4), whereas the predictive functions of microorganism communities based on 16S rRNA gene data was estimated using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUST), which showed that nitrogen metabolism was significantly negatively correlated (p = 0.049) with TAN concentration. The abundance of Pseudomonas and nitrogen metabolism increased with a decrease in TAN concentration. The correlation between TAN concentration and the abundance of Pseudomonas was followed by the isolation, and heterotrophic nitrifying performance analysis was used to confirm our findings. Six Pseudomonas strains capable of heterotrophic nitrification were isolated from the three water samples and showed a complete reduction of 100 mg/L NH4Cl during a 96-h cultivation. These results indicate the potential of applying Pseudomonas in shrimp ponds for water treatment.

Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system.

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.

INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biologic drug product's postmarketing phase. Many AE reports must be reviewed by drug safety experts to identify unlabeled AEs, even if the reported AEs are previously identified, labeled AEs. Integrating the labeling status of drug product AEs into FAERS could increase report triage and review efficiency. Medical Dictionary for Regulatory Activities (MedDRA) is the standard for coding AE terms in FAERS cases. However, drug manufacturers are not required to use MedDRA to describe AEs in product labels. We hypothesized that natural language processing (NLP) tools could assist in automating the extraction and MedDRA mapping of AE terms in drug product labels. MATERIALS AND METHODS: We evaluated the performance of three NLP systems, (ETHER, I2E, MetaMap) for their ability to extract AE terms from drug labels and translate the terms to MedDRA Preferred Terms (PTs). Pharmacovigilance-based annotation guidelines for extracting AE terms from drug labels were developed for this study. We compared each system's output to MedDRA PT AE lists, manually mapped by FDA pharmacovigilance experts using the guidelines, for ten drug product labels known as the "gold standard AE list" (GSL) dataset. Strict time and configuration conditions were imposed in order to test each system's capabilities under conditions of no human intervention and minimal system configuration. Each NLP system's output was evaluated for precision, recall and F measure in comparison to the GSL. A qualitative error analysis (QEA) was conducted to categorize a random sample of each NLP system's false positive and false negative errors. RESULTS: A total of 417, 278, and 250 false positive errors occurred in the ETHER, I2E, and MetaMap outputs, respectively. A total of 100, 80, and 187 false negative errors occurred in ETHER, I2E, and MetaMap outputs, respectively. Precision ranged from 64% to 77%, recall from 64% to 83% and F measure from 67% to 79%. I2E had the highest precision (77%), recall (83%) and F measure (79%). ETHER had the lowest precision (64%). MetaMap had the lowest recall (64%). The QEA found that the most prevalent false positive errors were context errors such as "Context error/General term", "Context error/Instructions or monitoring parameters", "Context error/Medical history preexisting condition underlying condition risk factor or contraindication", and "Context error/AE manifestations or secondary complication". The most prevalent false negative errors were in the "Incomplete or missed extraction" error category. Missing AE terms were typically due to long terms, or terms containing non-contiguous words which do not correspond exactly to MedDRA synonyms. MedDRA mapping errors were a minority of errors for ETHER and I2E but were the most prevalent false positive errors for MetaMap. CONCLUSIONS: The results demonstrate that it may be feasible to use NLP tools to extract and map AE terms to MedDRA PTs. However, the NLP tools we tested would need to be modified or reconfigured to lower the error rates to support their use in a regulatory setting. Tools specific for extracting AE terms from drug labels and mapping the terms to MedDRA PTs may need to be developed to support pharmacovigilance. Conducting research using additional NLP systems on a larger, diverse GSL would also be informative.

A validated enantioselective LC-MS/MS assay for quantification of a major chiral metabolite of an achiral 11-ß-hydroxysteroid-dehydrogenase 1 inhibitor in human plasma: Application to a clinical pharmacokinetic study.

BMS-823778 is a potent 11-ß-hydroxysteroid-dehydrogenase 1 (11ßHSD-1) inhibitor and a potential therapeutic agent for type 2 diabetes mellitus (T2DM). A high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay was developed and validated to enable reliable separation and quantification of both enantiomers of a chiral hydroxy metabolite (BMT-094817) in human plasma. Following liquid-liquid extraction in a 96-well plate format, chromatographic separation of the metabolite enantiomers was achieved by isocratic elution on a Chiralpak IA-3 column. Chromatographic conditions were optimized to ensure separation of both metabolite enantiomers. Metabolite enantiomers and stable isotope-labeled (SIL) internal standards were detected by positive ion electrospray tandem mass spectrometry. The LC-MS/MS assay was validated over a concentration range of 0.200-200ng/mL. Intra- and inter-assay precision values for replicate quality control samples were less than 9.9% for both enantiomers during the assay validation. Mean quality control accuracy values were within ±7.3%. Assay recoveries were high (>75%) and consistent across the assay range. The metabolite enantiomers were stable in human blood for 2h on ice. The analytes were also stable in human plasma for 25h at room temperature, 34days at -20°C and -70°C, and following five freeze-thaw cycles. No interconversion of the metabolite enantiomers was detected under any bioanalytical stress conditions, from blood collection/processing through extracted sample storage. The validated assay was successfully applied to the quantification of both metabolite enantiomers in human plasma in support of a human pharmacokinetic study.

LC-MS/MS determination of apixaban (BMS-562247) and its major metabolite in human plasma: an application of polarity switching and monolithic HPLC column.

BACKGROUND: apixaban (BMS-562247) (Eliquis(®)) is a novel, orally active, selective, direct, reversible inhibitor of the coagulation factor Xa (FXa). A sensitive and reliable method was developed and validated for the measurement of apixaban (BMS-562247) and its major circulating metabolite (BMS-730823) in human citrated plasma for use in clinical testing. METHODOLOGY/RESULTS: A 0.100 ml portion of citrated plasma sample was extracted and analyzed by LC-MS/MS. Run times were approximately 3 min. The lower limit of quantification (LLOQ) was 1.00 ng/ml for BMS-562247 and 5.00 ng/ml for BMS-730823. Intra- and inter-assay precision values for replicate QC control samples were within ≤5.36% for both analytes (≤7.52% at the LLOQ). The accuracy for both analytes was within ±9.00%. CONCLUSION: The method was demonstrated to be sensitive, selective and robust, and was successfully used to support clinical studies.

Cutting edge: apoptosis-regulating signal kinase 1 is required for reactive oxygen species-mediated activation of IFN regulatory factor 3 by lipopolysaccharide.

IFN regulatory factor (IRF) 3 participates in the transcriptional induction of IFN-alpha, IFN-beta, and a subset of IFN-stimulated genes (ISGs) as a result of viral infection. In addition, bacterial cell wall components such as LPS activate IRF3 in a p38-dependent manner. In this study we show that IRF3-mediated ISG induction by LPS requires the production of reactive oxygen species (ROS) by the NADPH-dependent oxidase NOX4. Furthermore, we present evidence that LPS-mediated ROS production leads to activation of apoptosis-regulating-signal kinase (ASK) 1, a MAPK kinase kinase family member capable of activating the MAP kinase 6/p38 axis. ASK1 kinase activity proved essential for IRF3-mediated ISG induction by LPS. Thus, our results presented here suggest a novel role for ROS and ASK1 in the innate immune response as signaling intermediates in the IRF3 activation pathway.

Inhibition of lipopolysaccharide-induced interferon regulatory factor 3 activation and protection from septic shock by hydroxystilbenes.

Interferon regulatory factor 3 (IRF3) mediates the transcriptional induction of interferon-stimulated genes (ISGs) in response to viral and bacterial infections. Here we show that the hydroxystilbene piceatannol inhibits the LPS-mediated activation of IRF3 and subsequent ISG induction. Consequently, piceatannol blocks the LPS-induced up-regulation of critical mediators of the inflammatory response such as interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), intercellular adhesion molecule 1 (ICAM-1), and macrophage chemoattractant protein (MCP-1). Furthermore, the LPS-mediated induction of tissue factor (TF), a cell surface protein responsible for initiating the coagulation cascade, is also inhibited by piceatannol. The effectiveness of piceatannol in blocking both the inflammatory response and the coagulation pathway is evidenced by its ability to confer protection against LPS-induced septic shock in a murine model. Thus, IRF3 appears to be a promising target for pharmacologic intervention in the prevention or treatment of septic shock syndrome.

Cutting edge: anthrax lethal toxin inhibits activation of IFN-regulatory factor 3 by lipopolysaccharide.

IFN-regulatory factor 3 (IRF3) is known to participate in the transcriptional induction of chemokines and cytokines, including IFNs, as a result of viral or bacterial infection. In this study, we demonstrate that the LPS-mediated activation of IRF3 and subsequent induction of chemokine genes or IRF3-responsive reporter constructs are inhibited after exposure of human or murine macrophages to the Bacillus anthracis toxin lethal factor. The inhibitory effect is caused by interference with the activation of the stress-activated protein kinase, p38, due to a proteolytic cleavage of mitogen-activated protein kinase kinase 6, and can be overcome by the ectopic expression of a cleavage-resistant mutant of mitogen-activated protein kinase kinase 6 or a constitutively active IRF3. The lethal factor-mediated inhibition of IRF3 activation and subsequent cytokine production through bacterial membrane components offers Bacillus anthracis an efficient mechanism to evade the innate immune response.