The medical dictionary for regulatory activities (MedDRA)

Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles

Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles. We downloaded 13,855 full-text articles from the Journal of Oncology (JCO) published between 1983 and 2013. We developed text classification, relationship extraction, signaling filtering, and signal prioritization algorithms to extract drug-SE pairs from downloaded articles. We extracted a total of 26,264 drug-SE pairs with an average precision of 0.405, a recall of 0.899, and an F1 score of 0.465. We show that side effect knowledge from JCO articles is largely complementary to that from the US Food and Drug Administration (FDA) drug labels. Through integrative correlation analysis, we show that targeted drug-associated side effects positively correlate with their gene targets and disease indications. In conclusion, this unique database that we built from a large number of high-profile oncological articles could facilitate the development of computational models to understand toxic effects associated with targeted anticancer drugs.

Ranking adverse drug reactions with crowdsourcing

BACKGROUND

There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events.

OBJECTIVE

The intent of the study was to rank ADRs according to severity.

METHODS

We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs.

RESULTS

There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy.

CONCLUSIONS

ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.

Hepatotoxicity associated with agomelatine and other antidepressants: Disproportionality analysis using pooled pharmacovigilance data from the Uppsala Monitoring Centre

Since its marketing approval, the attention to the hepatic side-effect profile of the antidepressant agomelatine (AGM) has gradually increased. Several cases of severe hepatotoxic adverse drug reactions (ADR) have been reported and the European Medicines Agency has released a safety warning regarding AGM-associated hepatotoxicity. However, there are insufficient data for an adequate safety assessment of AGM-related hepatotoxicity. Therefore, we performed a quantitative signal detection analysis using pharmacovigilance data from the Uppsala Monitoring Centre from the WHO that records ADR data from worldwide sources; we calculated reporting odds ratios (ROR) as measures for disproportionality within a case/non-case approach for AGM and several other antidepressants. AGM was statistically associated with an increased risk of hepatotoxicity (ROR 6.4 [95%CI 5.7-7.2]) as well as both positive controls: amineptine (ROR 38.4 [95%CI 33.8-43.6]) and nefazodone (ROR 3.2 [95%CI 3.0-3.5]). Following amineptine, AGM was associated with the second highest ROR, followed by tianeptine (ROR 4.4 [95%CI 3.6-5.3]), mianserin (ROR 3.6 [95%CI 3.3-3.9]), and nefazodone. These results support the hypothesis that AGM is associated with relevant hepatotoxicity. However, the used data and applied method do not allow a quantitative evaluation of hepatotoxicity or assessment of substance-specific differences regarding the extent of hepatotoxicity.

Self-medication with over-the-counter and prescribed drugs causing adverse-drug-reaction-related hospital admissions: results of a prospective, long-term multi-centre study

BACKGROUND

Self-medication, including both the use of over-the-counter (OTC) drugs and the use of formerly prescribed drugs taken without a current physician's recommendation, is a public health concern; however, little data exist regarding the actual risk.

OBJECTIVE

We aimed to analyse self-medication-related adverse drug reactions (ADRs) leading to hospitalisation.

METHODS

In a multi-centre, observational study covering a hospital catchment area of approximately 500,000 inhabitants, we analysed self-medication-related ADRs leading to hospital admissions in internal medicine departments. Data of patients with ADRs were comprehensively documented, and ADR causality was assessed using Bégaud's algorithm. The included ADRs occurred between January 2000 and December 2008 and were assessed to be at least 'possibly' drug related.

RESULTS

Of 6,887 patients with ADRs, self-medication was involved in 266 (3.9 %) patients. In 143 (53.8 %) of these patients, ADRs were due to OTC drugs. Formerly prescribed drugs and potential OTC drugs accounted for the remaining ADRs. Most self-medication-related ADRs occurred in women aged 70-79 years and in men aged 60-69 years. Self-medication-related ADRs were predominantly gastrointestinal complaints caused by non-steroidal anti-inflammatory drugs (most frequently OTC acetylsalicylic acid [ASA, aspirin]). In 102 (38.3 %) of the patients with self-medication-related ADRs, a relevant drug-drug interaction (DDI), occurring between a self-medication and a prescribed medication, was present (most frequently ASA taken as an OTC drug and prescribed diclofenac).

CONCLUSION

In the general population, self-medication plays a limited role in ADRs leading to hospitalisation. However, prevention strategies focused on elderly patients and patients receiving interacting prescribed drugs would improve patient safety.

Determinants of torsades de pointes in older patients with drug-associated long QT syndrome: a case-control study

OBJECTIVE

Many elderly patients are routinely exposed to drugs that may prolong the cardiac QT interval and cause Torsades de pointes (TdP). However, predictors of TdP in patients with drug-associated long QT syndrome (LQTS) are not fully understood, especially in the geriatric population. The objective of this study was to identify risk factors of TdP in elderly patients with drug-associated LQTS.

METHODS

In this retrospective, case-control study, documented reports of drug-associated LQTS plus TdP (n = 125) and LQTS without TdP (n = 81) in patients ≥65 years of age were retrieved from the French Pharmacovigilance Database over a 10-year period. Available clinical, biological, and drug therapy data were compared in the two groups and logistic regression was performed to identify significant predictors of TdP.

RESULTS

The uncorrected QT interval was significantly longer in patients with TdP than in patients without TdP (577 ± 79 vs. 519 ± 68 ms; p = 0.0001). The number of drugs with a known risk of TdP administered to each patient was not a predictor of arrhythmia, nor was female gender. Logistic regression analysis identified the uncorrected QT interval as the only significant predictor of TdP. The receiver operating characteristic curve analysis was characterized by an area under the curve of 0.77 (95 % confidence interval 0.64-0.88) and a QT cutoff of 550 ms.

CONCLUSION

The uncorrected QT interval was significantly associated with the probability of TdP in elderly patients with acquired, drug-associated LQTS.

Ferric maltol is effective in correcting iron deficiency anemia in patients with inflammatory bowel disease: results from a phase-3 clinical trial program

BACKGROUND

Iron deficiency anemia (IDA) is frequently seen in inflammatory bowel disease. Traditionally, oral iron supplementation is linked to extensive gastrointestinal side effects and possible disease exacerbation. This multicenter phase-3 study tested the efficacy and safety of ferric maltol, a complex of ferric (Fe) iron with maltol (3-hydroxy-2-methyl-4-pyrone), as a novel oral iron therapy for IDA.

METHODS

Adult patients with quiescent or mild-to-moderate ulcerative colitis or Crohn's disease, mild-to-moderate IDA (9.5-12.0 g/dL and 9.5-13.0 g/dL in females and males, respectively), and documented failure on previous oral ferrous products received oral ferric maltol capsules (30 mg twice a day) or identical placebo for 12 weeks according to a randomized, double-blind, placebo-controlled study design. The primary efficacy endpoint was change in hemoglobin (Hb) from baseline to week 12. Safety and tolerability were assessed.

RESULTS

Of 329 patients screened, 128 received randomized therapy (64 ferric maltol-treated and 64 placebo-treated patients) and comprised the intent-to-treat efficacy analysis: 55 ferric maltol patients (86%) and 53 placebo patients (83%) completed the trial. Significant improvements in Hb were observed with ferric maltol versus placebo at weeks 4, 8, and 12: mean (SE) 1.04 (0.11) g/dL, 1.76 (0.15) g/dL, and 2.25 (0.19) g/dL, respectively (P < 0.0001 at all time-points; analysis of covariance). Hb was normalized in two-thirds of patients by week 12. The safety profile of ferric maltol was comparable with placebo, with no impact on inflammatory bowel disease severity.

CONCLUSIONS

Ferric maltol provided rapid clinically meaningful improvements in Hb and showed a favorable safety profile, suggesting its possible use as an alternative to intravenous iron in IDA inflammatory bowel disease.

GraphSAW: a web-based system for graphical analysis of drug interactions and side effects using pharmaceutical and molecular data

BACKGROUND

Adverse drug reactions are one of the most common causes of death in industrialized Western countries. Nowadays, empirical data from clinical studies for the approval and monitoring of drugs and molecular databases is available.

METHODS

The integration of database information is a promising method for providing well-based knowledge to avoid adverse drug reactions. This paper presents our web-based decision support system GraphSAW which analyzes and evaluates drug interactions and side effects based on data from two commercial and two freely available molecular databases. The system is able to analyze single and combined drug-drug interactions, drug-molecule interactions as well as single and cumulative side effects. In addition, it allows exploring associative networks of drugs, molecules, metabolic pathways, and diseases in an intuitive way. The molecular medication analysis includes the capabilities of the upper features.

RESULTS

A statistical evaluation of the integrated data and top 20 drugs concerning drug interactions and side effects is performed. The results of the data analysis give an overview of all theoretically possible drug interactions and side effects. The evaluation shows a mismatch between pharmaceutical and molecular databases. The concordance of drug interactions was about 12% and 9% of drug side effects. An application case with prescribed data of 11 patients is presented in order to demonstrate the functionality of the system under real conditions. For each patient at least two interactions occured in every medication and about 8% of total diseases were possibly induced by drug therapy.

CONCLUSIONS

GraphSAW (http://tunicata.techfak.uni-bielefeld.de/graphsaw/) is meant to be a web-based system for health professionals and researchers. GraphSAW provides comprehensive drug-related knowledge and an improved medication analysis which may support efforts to reduce the risk of medication errors and numerous drastic side effects.

Safety and feasibility of Irreversible Electroporation (IRE) in patients with locally advanced pancreatic cancer: results of a prospective study

PURPOSE

To evaluate the safety of the NanoKnife Low Energy Direct Current (LEDC) System (Irreversible Electroporation, IRE) in order to treat patients with unresectable pancreatic adenocarcinoma.

METHODS

Prospective, nonrandomized, single-center clinical evaluation of ten patients with a cytohystological diagnosis of unresectable locally advanced pancreatic cancer (LAPC) that was no further responsive to standard treatments. The primary outcome was the rate of procedure-related abdominal complications. The secondary endpoints included the evaluation of the short-term efficacy of IRE through the evaluation of tumor reduction at imaging and biological tumor response as shown by CA 19-9, clinical assessments and patient quality of life.

RESULTS

Ten patients (5 males, 5 females) were enrolled, with a median age of 66 and median tumor size of 30 mm. All patients were treated successfully with a median procedure time of 79.5 min. Two procedure-related complications were described in one patient (10%): a pancreatic abscess with a pancreoduodenal fistula. Three patients had early progression of disease: one patient developed pulmonary metastases 30 days post-IRE and two patients had liver metastases 60 days after the procedure. We registered an overall survival of 7.5 months (range: 2.9-15.9).

CONCLUSIONS

IRE is a safe procedure in patients with LAPC and may represent a new technological option in the treatment and multimodality management of this disease.

Identifying adverse drug reactions and drug-induced diseases using network-based drug mapping

Drugs are essential for the prevention and treatment of diseases. However, co-administration of multiple drugs may cause serious adverse drug reactions, which are usually known but sometimes unknown. Package inserts of prescription drugs are supposed to contain risks and side effects, but such information is not necessarily complete. At the core of efforts to improve prescription quality, there is reliance on the extent and quality of information used for decision of a medical doctor. To address this on-going need, GraphSAW provides users a comprehensive view on drug-related pharmacological and molecular information. The features of GraphSAW allow users to analyze drug cocktails for adverse drug reactions and drug-induced diseases. Network visualization by drug mapping enables exploring associative networks of drugs, pathways, and diseases to fully understand effects of drugs in an intuitive way. GraphSAW is meant to be a platform and starting point for health professionals and researchers for educational and scientific research in order to achieve substantial improvements in patient safety.

Renal effects of the angiotensin receptor neprilysin inhibitor LCZ696 in patients with heart failure and preserved ejection fraction

BACKGROUND

Increases in serum creatinine with renin-angiotensin-aldosterone system (RAAS) inhibitors can lead to unnecessary discontinuation of these agents. The dual-acting angiotensin receptor neprilysin inhibitor LCZ696 improves clinical outcome patients with heart failure with reduced ejection fraction, and pilot data suggest potential benefit in heart failure with preserved ejection fraction (HFpEF). The effects of LCZ696 on renal function have not been assessed.

METHODS AND RESULTS

A total of 301 HFpEF patients were randomly assigned to LCZ696 or valsartan in the PARAMOUNT trial. We studied renal function [creatinine, estimated glomerular filtration rate (eGFR), cystatin C, and urinary albumin to creatinine ratio (UACR)] at baseline, 12 weeks, and after 36 weeks of treatment. Worsening renal function (WRF) was determined as an serum creatinine increase of >0.3 mg/dL and/or >25% between two time-points. Mean eGFR at baseline was 65.4 ± 20.4 mL/min per 1.73 m(2) . The eGFR declined less in the LCZ696 group than in the valsartan group (-1.5 vs. -5.2 mL/min per 1.73 m(2) ; P = 0.002). The incidence of WRF was lower in the LCZ696 group (12%) than in the valsartan group (18%) at any time-point, but this difference was not statistically significant (P = 0.18). Over 36 weeks, the geometric mean of UACR increased in the LCZ696 group (2.4-2.9 mg/mmol), whereas it remained stable in the valsartan group (2.1-2.0 mg/mmol; P for difference between groups = 0.016).

CONCLUSION

In patients with HFpEF, therapy with LCZ696 for 36 weeks was associated with preservation of eGFR compared with valsartan therapy, but an increase in UACR.

Primary results from the cervical dystonia patient registry for observation of onabotulinumtoxina efficacy (CD PROBE)

BACKGROUND

The Cervical Dystonia Patient Registry for Observation of OnabotulinumtoxinA Efficacy (CD PROBE; NCT00836017) is a prospective, observational, multicenter, real-world registry designed to assess the safety, effectiveness, and treatment utilization following multiple treatments of onabotulinumtoxinA.

METHODS

Subjects were naïve to botulinum toxin, new to practice, or had not received toxin in ≥ 16 weeks if in a clinical trial. Dosages and treatment intervals varied due to the real-world design. Descriptive and inferential statistics evaluated changes over 3 treatments.

RESULTS

1046 subjects enrolled. Subjects were 74.4% female, 63.5% toxin-naïve, mean age 58.0 ± 14.7 years. The mean dose over 2481 treatment sessions was 189. 8 ± 87.1U, with average treatment intervals of 14.6 and 15.1 weeks. The mean Toronto Western Spasmodic Torticollis Rating Scale Total score in subjects who completed all assessments (n=479) decreased from 39.2 at baseline to 27.1 at final visit (P<.0001). A high percentage of physicians reported improvement in Clinician Global Impression of Change after initial assessment; this significantly increased at final assessment (n=479, 91.2% vs 95.0%; P<.0001). Similarly, a high percentage of subjects reported improvement in Patient Global Impression of Change after initial assessment, which significantly increased at final assessment (n=470, 83.0% vs 91.7%; P<.0001). Significant reductions in all Cervical Dystonia Impact Profile-58 scores were observed (n=407). Overall, 26.2% of subjects reported adverse events, including muscular weakness (7.0%) and dysphagia (6.4%).

CONCLUSIONS

Results indicate robust improvement in clinical ratings and excellent tolerability following onabotulinumtoxinA treatment of CD.

In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases

BACKGROUND

Near universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System.

RESULTS

This report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more "Th17 T-cell subtype" genes in RA than the other AIDs, and more "Chemokine plus Receptors" genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS.

CONCLUSIONS

Results showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.

A randomized phase 1 pharmacokinetic trial comparing the potential biosimilar PF-05280014 with trastuzumab in healthy volunteers (REFLECTIONS B327-01)

AIMS

The pharmacokinetic (PK) similarity between PF-05280014, a proposed trastuzumab biosimilar, trastuzumab sourced from European Union (trastuzumab-EU) or from United States (trastuzumab-US) was evaluated. Safety and immunogenicity were also assessed.

METHODS

In this phase 1, double-blind trial (NCT01603264), 105 healthy male volunteers were randomized 1:1:1 to receive a single 6 mg kg(-1) intravenous dose of PF-05280014, trastuzumab-EU, or trastuzumab-US, and evaluated for 70 days. Drug concentration-time data were analyzed by non-compartmental methods. PK similarity for the comparisons of PF-05280014 to each of trastuzumab-EU and trastuzumab-US, and trastuzumab-EU to trastuzumab-US were determined using the standard 80.00% to 125.00% bioequivalence criteria.

RESULTS

Baseline demographics for the 101 subjects evaluable for PK were similar across all arms. The three products exhibited similar PK profiles with target-mediated disposition. The 90% CIs for the ratios of Cmax , AUC (0 , t last) and AUC(0,∞) were within 80.00% to 125.00% for all three pairwise comparisons. Adverse events (AEs) were similar across all arms with treatment-related AEs reported by 71.4%, 68.6% and 65.7% subjects in the PF-05280014, trastuzumab-EU, and trastuzumab-US arms, respectively. The most common AEs were infusion-related reactions, headache, chills, pyrexia and nausea. The AE term 'pyrexia' was numerically greater in the PF-05280014 arm. All post-dose samples, except 1, tested negative for anti-drug antibodies (ADA).

CONCLUSIONS

This study demonstrates PK similarity among PF-05280014, trastuzumab-EU and trastuzumab-US. The safety and immunogenicity profiles observed for the three products in this study are consistent with previous reports for trastuzumab.

Emergency contraceptive pill safety profile. Comparison of the results of a follow-up study to those coming from spontaneous reporting

BACKGROUND

The emergency contraceptive pill (ECP) containing levonorgestrel is dispensed without a prescription in Spain since 2009. An easy access could diminish unwanted pregnancies; however, there is a risk of misuse and, in any case, of developing some adverse events. The aim of the present study is to further learn the adverse effects of this ECP.

METHODS

An ad hoc follow-up study was carried out in three community pharmacies in a city of Central Spain; the sample was composed of those women asking for the ECP; they were interviewed by telephone after at least a month since the last menses. We completed the safety profile obtained with that coming from spontaneous reporting in Spain.

RESULTS

Out of 139 women surveyed, 113 developed any adverse event--two considered as severe; the most frequently reported events were menstrual disturbances, which accounted for 21% of all events. Through spontaneous reporting, 36 cases of whatever adverse events related to levonorgestrel as ECP were identified. Twenty-five cases were considered as severe. Both types of reaction and severity were significantly different in the follow-up study and in the spontaneous reporting. Some of the reactions identified, such as miscarriage, febrile neutropenia, and porphyria, are not included in the Summary of Product Characteristics.

CONCLUSIONS

Levonorgestrel as an ECP is mostly safe. Attention should be paid to some severe events and particularly to those risk factors for them to appear. Combining spontaneous reporting with an ad hoc follow-up study, the whole safety profile of a given medication can be obtained.

ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms

Adverse drug reactions (ADRs) are noxious and unexpected effects during normal drug therapy. They have caused significant clinical burden and been responsible for a large portion of new drug development failure. Molecular understanding and in silico evaluation of drug (or candidate) safety in laboratory is thus so desired, and unfortunately has been largely hindered by misuse of ADR terms. The growing impact of bioinformatics and systems biology in toxicological research also requires a specialized ADR term system that works beyond a simple glossary. Adverse Drug Reaction Classification System (ADReCS; http://bioinf.xmu.edu.cn/ADReCS) is a comprehensive ADR ontology database that provides not only ADR standardization but also hierarchical classification of ADR terms. The ADR terms were pre-assigned with unique digital IDs and at the same time were well organized into a four-level ADR hierarchy tree for building an ADR–ADR relation. Currently, the database covers 6544 standard ADR terms and 34 796 synonyms. It also incorporates information of 1355 single active ingredient drugs and 134 022 drug–ADR pairs. In summary, ADReCS offers an opportunity for direct computation on ADR terms and also provides clues to mining common features underlying ADRs.

Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles

Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications.

Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events

Purpose: New pharmacovigilance methods are needed as a consequence of the morbidity caused by drugs. We exploit fine-grained drug related adverse event information extracted by text mining from electronic medical records (EMRs) to stratify patients based on their adverse events and to determine adverse event co-occurrences.

Methods: We analyzed the similarity of adverse event profiles of 2347 patients extracted from EMRs from a mental health center in Denmark. The patients were clustered based on their adverse event profiles and the similarities were presented as a network. The set of adverse events in each main patient cluster was evaluated. Co-occurrences of adverse events in patients (p-value < 0.01) were identified and presented as well.

Results: We found that each cluster of patients typically had a most distinguishing adverse event. Examination of the co-occurrences of adverse events in patients led to the identification of potentially interesting adverse event correlations that may be further investigated as well as provide further patient stratification opportunities.

Conclusions: We have demonstrated the feasibility of a novel approach in pharmacovigilance to stratify patients based on fine-grained adverse event profiles, which also makes it possible to identify adverse event correlations. Used on larger data sets, this data-driven method has the potential to reveal unknown patterns concerning adverse event occurrences.

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs)

Background

Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs.

Principal Findings

In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper.

Conclusion

Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

Detecting miRNA Mentions and Relations in Biomedical Literature

INTRODUCTION

MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy.

MOTIVATION

Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far.

RESULTS

The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93.  Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks.

AVAILABILITY

The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.

Detecting miRNA Mentions and Relations in Biomedical Literature

INTRODUCTION

MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy.

MOTIVATION

Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity has motivated the need for an improvised framework. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far.

RESULTS

The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93.  Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks.

AVAILABILITY

The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.

Detecting miRNA Mentions and Relations in Biomedical Literature

Introduction: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. Motivation: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. Results: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93.  Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. Availability: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.

Identifying Similar Cases in Document Networks Using Cross-Reference Structures

Our objective was to explore the creation of document networks based on different thresholds of shared information and different clustering algorithms on those networks to identify document clusters describing similar clinical cases. We created networks from vaccine adverse event report sets using seven approaches for linking reports. We then applied three clustering algorithms [visualization of similarities (VOS), Louvain, k-means] to these networks and evaluated their ability to identify known clusters. The report sets included one simulated set and three sets from the Vaccine Adverse Event Reporting System; each was split into training and testing subsets. Training subsets were used to estimate parameter values for the clustering algorithms and testing subsets to evaluate clusters. We created the networks by linking reports based on shared information in the form either of individual Medical Dictionary for Regulatory Activities Preferred Terms (PTs) or of dyads, triplets, quadruplets, quintuplets, and sextuplets of PTs; we created another network by weighting the single PT network connections by Lin's information theoretic approach to similarity. We then repeated this entire process using networks based on text mining output rather than structured data. We evaluated report clustering using recall, precision, and f-measure. The VOS algorithm outperformed Louvain and k-means in general. The best weighting scheme appeared to be related to the complexity of the known cluster. For example, singleton weighting performed best for an intussusception cluster driven by a single PT. We observed marginal differences between the code- and textual-based clustering. In conclusion, our approach supported identification of similar nodes in a document network.

Is faster safer? Cluster versus short conventional subcutaneous allergen immunotherapy

AIM

Few studies have compared cluster immunotherapy and conventional administration regimens. The aim of this study was to establish the safety profile of these different regimens in patients with allergic respiratory diseases who received index-of-reactivity (IR)-standardized allergen extracts by the subcutaneous route.

MATERIALS & METHODS

The safety of subcutaneous immunotherapy (SCIT), administered by means of a 4-week cluster titration schedule (cluster-SCIT) or by an 8-week short conventional titration schedule (SC-SCIT), both with a target dose of 8 IR, was assessed in a retrospective, observational, multicenter study.

RESULTS

A total of 658 patients (339 cluster-SCIT and 319 SC-SCIT) were recruited from 92 sites in Spain. Injection site reactions occurred in 25.1 and 27.3% of patients treated with cluster-SCIT and SC-SCIT, respectively. Systemic reactions (European Academy of Allergy and Clinical Immunology criteria) were reported for 0.2% of doses and 1.5% of patients with cluster-SCIT, and 0.7% of doses and 4.4% of patients with SC-SCIT. Most reactions were mild and there were no grade 3 or 4 systemic reactions. No life-threatening systemic reactions, anaphylactic shock, or adverse events leading to therapy discontinuation were reported.

CONCLUSION

The safety profile of the cluster regimen supports the use of accelerated SCIT schedules with IR-standardized allergen extracts compared with short conventional schedules, particularly if similar extracts and application methods are used.

OAE: The Ontology of Adverse Events

BACKGROUND

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health.

DESCRIPTION

The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term 'adverse event' denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. For example, using the data extracted from the Vaccine Adverse Event Reporting System (VAERS), OAE was used to analyse vaccine adverse events associated with the administrations of different types of influenza vaccines. OAE has also been used to represent and classify the vaccine adverse events cited in package inserts of FDA-licensed human vaccines in the USA.

CONCLUSION

OAE is a biomedical ontology that logically defines and classifies various adverse events occurring after medical interventions. OAE has successfully been applied in several adverse event studies. The OAE ontological framework provides a platform for systematic representation and analysis of adverse events and of the factors (e.g., vaccinee age) important for determining their clinical outcomes.

Hepatotoxicity related to agomelatine and other new antidepressants: a case/noncase approach with information from the Portuguese, French, Spanish, and Italian pharmacovigilance systems

Antidepressants have been associated with a low incidence of idiosyncratic hepatic injury. Some of them, nefazodone or amineptine, were observed to induce severe hepatic injury and withdrawn from the market. Recently, some cases of this severe condition have been reported in association with agomelatine use. Therefore, the objective of this study is to learn the risk of hepatic damage with agomelatine as compared with other new antidepressants. We took data from the Spanish, French, Italian, and Portuguese pharmacovigilance system databases. A case/noncase approach to assess the strength of the association between whichever antidepressant and hepatotoxicity was performed; cases were defined as reports of hepatotoxicity; noncases were reports of all reactions other than hepatotoxicity. Exposure was the recording of a new antidepressant in a report, whether or not it was suspected of causing the reaction. During the period surveyed, 3300 cases of hepatotoxicity were collected for the antidepressants assessed. They represent 10.3% of all cases collected for these drugs; the corresponding figure for all drugs was 6.0%. Meanwhile, 63 cases of hepatotoxicity associated with agomelatine were collected since its introduction until the end of the period studied; they account for a percentage of 14.6. Agomelatine was statistically associated with hepatotoxicity in Spain [reporting odds ratio (ROR), 4.9 (95% confidence interval [CI], 2.4-9.7)], France (ROR, 2.4 [95% CI, 1.5-3.7]), and Italy (ROR, 5.1 [95% CI, 1.7-14.0]). Current results support the idea of agomelatine to be related to a higher hepatotoxicity risk. Physicians should consider early discontinuation if the condition is suspected; health authorities should promptly explore the best regulatory actions to be taken.

Exploitation of semantic methods to cluster pharmacovigilance terms

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. This activity is usually performed within dedicated databases (national, European, international...), in which the ADRs declared for patients are usually coded with a specific controlled terminology MedDRA (Medical Dictionary for Drug Regulatory Activities). Traditionally, the detection of adverse drug reactions is performed with data mining algorithms, while more recently the groupings of close ADR terms are also being exploited. The Standardized MedDRA Queries (SMQs) have become a standard in pharmacovigilance. They are created manually by international boards of experts with the objective to group together the MedDRA terms related to a given safety topic. Within the MedDRA version 13, 84 SMQs exist, although several important safety topics are not yet covered. The objective of our work is to propose an automatic method for assisting the creation of SMQs using the clustering of semantically close MedDRA terms. The experimented method relies on semantic approaches: semantic distance and similarity algorithms, terminology structuring methods and term clustering. The obtained results indicate that the proposed unsupervised methods appear to be complementary for this task, they can generate subsets of the existing SMQs and make this process systematic and less time consuming.

Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data

Drug-induced liver injury (DILI) is one of the most common drug-induced adverse events (AEs) leading to life-threatening conditions such as acute liver failure. It has also been recognized as the single most common cause of safety-related post-market withdrawals or warnings. Efforts to develop new predictive methods to assess the likelihood of a drug being a hepatotoxicant have been challenging due to the complexity and idiosyncrasy of clinical manifestations of DILI. The FDA adverse event reporting system (AERS) contains post-market data that depict the morbidity of AEs. Here, we developed a scalable approach to construct a hepatotoxicity database using post-market data for the purpose of quantitative structure-activity relationship (QSAR) modeling. A set of 2029 unique and modelable drug entities with 13,555 drug-AE combinations was extracted from the AERS database using 37 hepatotoxicity-related query preferred terms (PTs). In order to determine the optimal classification scheme to partition positive from negative drugs, a manually-curated DILI calibration set composed of 105 negatives and 177 positives was developed based on the published literature. The final classification scheme combines hepatotoxicity-related PT data with supporting information that optimize the predictive performance across the calibration set. Data for other toxicological endpoints related to liver injury such as liver enzyme abnormalities, cholestasis, and bile duct disorders, were also extracted and classified. Collectively, these datasets can be used to generate a battery of QSAR models that assess a drug's potential to cause DILI.

Subthalamic nucleus deep brain stimulation in early stage Parkinson's disease

BACKGROUND

Deep brain stimulation (DBS) is an effective and approved therapy for advanced Parkinson's disease (PD), and a recent study suggests efficacy in mid-stage disease. This manuscript reports the results of a pilot trial investigating preliminary safety and tolerability of DBS in early PD.

METHODS

Thirty subjects with idiopathic PD (Hoehn & Yahr Stage II off medication), age 50-75, on medication ≥6 months but ≤4 years, and without motor fluctuations or dyskinesias were randomized to optimal drug therapy (ODT) (n = 15) or DBS + ODT (n = 15). Co-primary endpoints were the time to reach a 4-point worsening from baseline in the UPDRS-III off therapy and the change in levodopa equivalent daily dose from baseline to 24 months.

RESULTS

As hypothesized, the mean UPDRS total and part III scores were not significantly different on or off therapy at 24 months. Medication requirements in the DBS + ODT group were lower at all time points with a maximal difference at 18 months. With a few exceptions, differences in neuropsychological functioning were not significant. Two subjects in the DBS + ODT group suffered serious adverse events; remaining adverse events were mild or transient.

CONCLUSIONS

This study demonstrates that subjects with early stage PD will enroll in and complete trials testing invasive therapies and provides preliminary evidence that DBS is well tolerated in early PD. The results of this trial provide the data necessary to design a large, phase III, double-blind, multicenter trial investigating the safety and efficacy of DBS in early PD.

A randomized, double-blind, placebo-controlled crossover study of α4β 2* nicotinic acetylcholine receptor agonist AZD1446 (TC-6683) in adults with attention-deficit/hyperactivity disorder

RATIONALE

Stimulation of nicotinic cholinergic systems has been shown to alleviate ADHD symptoms and to improve cognitive performance. AZD1446 is a selective α4β2* nicotinic acetylcholine receptor agonist with potential effect on the symptoms of ADHD.

OBJECTIVES

The purpose of this study is to evaluate the efficacy, safety, and pharmacokinetics of AZD1446 in adults with ADHD treated for 2 weeks.

METHOD

This was a randomized, double-blind, placebo-controlled crossover trial. Participants were 79 adults with ADHD, grouped according to their use of nicotine-containing products. Nicotine non-users received placebo and two of three AZD1446 treatment regimens (80 mg tid, 80 mg qd, 10 mg tid). Nicotine users received placebo, AZD1446 80 mg tid and 80 mg qd. Efficacy measures included the Conners' Adult ADHD Rating Scale and cognitive measures of immediate and delayed verbal episodic memory, learning, attention, working memory, executive functioning, and spatial problem solving (CogState computerized test battery).

RESULTS

There was no significant effect of AZD1446 on any of the clinical scores irrespective of dose, schedule, or concomitant use of nicotine products. A statistically significant improvement was seen on the Groton Maze Learning Task, a measure of executive functioning, in nicotine non-users after treatment with AZD1446 80 mg qd.

CONCLUSIONS

AZD1446 was well tolerated, but did not significantly improve ADHD symptoms after 2 weeks of treatment compared to placebo. While the present study does not support the therapeutic utility of AZD1446 in ADHD, its potential pro-cognitive effects remain to be explored in other neuropsychiatric disorders.

Building a common pediatric research terminology for accelerating child health research

Longitudinal observational clinical data on pediatric patients in electronic format is becoming widely available. A new era of multi-institutional data networks that study pediatric diseases and outcomes across disparate health delivery models and care settings are also enabling an innovative collaborative rapid improvement paradigm called the Learning Health System. However, the potential alignment of routine clinical care, observational clinical research, pragmatic clinical trials, and health systems improvement requires a data infrastructure capable of combining information from systems and workflows that historically have been isolated from each other. Removing barriers to integrating and reusing data collected in different settings will permit new opportunities to develop a more complete picture of a patient's care and to leverage data from related research studies. One key barrier is the lack of a common terminology that provides uniform definitions and descriptions of clinical observations and data. A well-characterized terminology ensures a common meaning and supports data reuse and integration. A common terminology allows studies to build upon previous findings and to reuse data collection tools and data management processes. We present the current state of terminology harmonization and describe a governance structure and mechanism for coordinating the development of a common pediatric research terminology that links to clinical terminologies and can be used to align existing terminologies. By reducing the barriers between clinical care and clinical research, a Learning Health System can leverage and reuse not only its own data resources but also broader extant data resources.

Acute adverse reactions to iopromide vs iomeprol: a retrospective analysis of spontaneous reporting from a radiology department

OBJECTIVE

To compare the safety of iopromide and iomeprol use in a hospital that switched from the former to the latter and found an apparent increase in the number (and a different profile) of adverse reactions reported for iomeprol, putting the safety of its use into question.

METHODS

This was a retrospective study of cases of acute reactions to iopromide and iomeprol reported in two successive time periods. Data from examinations using iopromide (62539 CT scans and 10348 urography scans) and iomeprol (34308 CT scans and 2846 urography scans) were obtained from the computer system of the hospital.

RESULTS

For each period, 154 cases of reactions were reported for iopromide and 86 for iomeprol, being severe in 10 (6.5%) patients for iopromide vs 17 (19.8%) patients for iomeprol; a statistically significant difference of p<0.003 was recorded. The most frequent adverse reactions (%/%) for iopromide/iomeprol were urticaria (29.1/17.2), pruritus (22.6/15.6), upper respiratory tract signs and symptoms (12.1/16.7), oedema (4.3/0), erythemas (3.4/5.0), nausea or vomiting (1.2/11.7) and chest pain (0/3.9) (p<0.0001 for the global comparison). The distribution of the reactions (%/%) by System Organ Class for iopromide/iomeprol was skin (56.7/41.1), respiratory (19.2/26.7), vascular (6.8/2.2), general (5.3/7.2), gastrointestinal (4.6/15.0) and others (7.4/7.9) (p<0.0002 for the global comparison).

CONCLUSION

Adverse reactions were more severe for iomeprol. Skin and vascular reactions with no chest pain were more frequent for iopromide, whereas gastrointestinal reactions were more frequent for iomeprol.

ADVANCES IN KNOWLEDGE

Comparative studies of media contrast safety are scarce and summary information on product characteristics is insufficient. This study showed the differences in severity and profile of adverse reactions between iopromide and iomeprol.

Translation of off-target effects: prediction of ADRs by integrated experimental and computational approach

Adverse drug reactions (ADRs) are associated with most drugs, often discovered late in drug development and sometimes only during extended course of clinical use.

From concept representations to ontologies: a paradigm shift in health informatics?

Objectives

This work aims at uncovering challenges in biomedical knowledge representation research by providing an understanding of what was historically called "medical concept representation" and used as the name for a working group of the International Medical Informatics Association.

Methods

Bibliometrics, text mining, and a social media survey compare the research done in this area between two periods, before and after 2000.

Results

Both the opinion of socially active groups of researchers and the interpretation of bibliometric data since 1988 suggest that the focus of research has moved from "medical concept representation" to "medical ontologies".

Conclusions

It remains debatable whether the observed change amounts to a paradigm shift or whether it simply reflects changes in naming, following the natural evolution of ontology research and engineering activities in the 1990s. The availability of powerful tools to handle ontologies devoted to certain areas of biomedicine has not resulted in a large-scale breakthrough beyond advances in basic research.

Flupirtine-induced liver injury--seven cases from the Berlin Case-control Surveillance Study and review of the German spontaneous adverse drug reaction reporting database

PURPOSE

The hepatotoxic potential of the analgesic flupirtine has attracted increased attention over the past years. Recently, risk minimisation measures such as maximum treatment duration of 2 weeks have been requested by the European Medicines Agency (EMA). This study was conducted to further elucidate the clinical pattern of flupirtine-induced liver injury (FILI).

METHODS

Seven FILI patients were ascertained in all Berlin hospitals in the Berlin Case-control Surveillance Study (FAKOS) between 2002 and 2011. Furthermore, we reviewed the severe cases of flupirtine-associated hepatotoxicity included in the adverse drug reaction database of the Federal Institute for Drugs and Medical Devices (BfArM) in Germany from between 1991 and 2012.

RESULTS

All seven FILI patients of FAKOS were hospitalised. Six of them were female, mean age was 58 [corrected] years, and the most common symptoms were fatigue and jaundice. Three patients developed acute liver failure (ALF). Discontinuation of flupirtine invariably led to clinical and laboratory improvement. Review of the BfArM cases (n = 248) showed female sex predominance and high prevalence of jaundice and ALF. Time to onset of symptoms was less than 2 weeks in 9 % of the patients with respective data.

CONCLUSIONS

Our results corroborate previous findings on FILI's clinical pattern and on its potentially severe course. Although the hepatotoxic risk might be higher after the first 2 weeks of treatment, earlier onset of severe FILI cannot be ruled out. Postauthorisation safety studies are needed to evaluate EMA's risk minimisation measures and to quantify flupirtine's risk according to its duration of use.

Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning

OBJECTIVE

Adverse drug reaction (ADR) can have dire consequences. However, our current understanding of the causes of drug-induced toxicity is still limited. Hence it is of paramount importance to determine molecular factors of adverse drug responses so that safer therapies can be designed.

METHODS

We propose a causality analysis model based on structure learning (CASTLE) for identifying factors that contribute significantly to ADRs from an integration of chemical and biological properties of drugs. This study aims to address two major limitations of the existing ADR prediction studies. First, ADR prediction is mostly performed by assessing the correlations between the input features and ADRs, and the identified associations may not indicate causal relations. Second, most predictive models lack biological interpretability.

RESULTS

CASTLE was evaluated in terms of prediction accuracy on 12 organ-specific ADRs using 830 approved drugs. The prediction was carried out by first extracting causal features with structure learning and then applying them to a support vector machine (SVM) for classification. Through rigorous experimental analyses, we observed significant increases in both macro and micro F1 scores compared with the traditional SVM classifier, from 0.88 to 0.89 and 0.74 to 0.81, respectively. Most importantly, identified links between the biological factors and organ-specific drug toxicities were partially supported by evidence in Online Mendelian Inheritance in Man.

CONCLUSIONS

The proposed CASTLE model not only performed better in prediction than the baseline SVM but also produced more interpretable results (ie, biological factors responsible for ADRs), which is critical to discovering molecular activators of ADRs.

The Ontology of Vaccine Adverse Events (OVAE) and its usage in representing and analyzing adverse events associated with US-licensed human vaccines

Background

Licensed human vaccines can induce various adverse events (AE) in vaccinated patients. Due to the involvement of the whole immune system and complex immunological reactions after vaccination, it is difficult to identify the relations among vaccines, adverse events, and human populations in different age groups. Many known vaccine adverse events (VAEs) have been recorded in the package inserts of US-licensed commercial vaccine products. To better represent and analyze VAEs, we developed the Ontology of Vaccine Adverse Events (OVAE) as an extension of the Ontology of Adverse Events (OAE) and the Vaccine Ontology (VO).

Results

Like OAE and VO, OVAE is aligned with the Basic Formal Ontology (BFO). The commercial vaccines and adverse events in OVAE are imported from VO and OAE, respectively. A new population term ‘human vaccinee population’ is generated and used to define VAE occurrence. An OVAE design pattern is developed to link vaccine, adverse event, vaccinee population, age range, and VAE occurrence. OVAE has been used to represent and classify the adverse events recorded in package insert documents of commercial vaccines licensed by the USA Food and Drug Administration (FDA). OVAE currently includes over 1,300 terms, including 87 distinct types of VAEs associated with 63 human vaccines licensed in the USA. For each vaccine, occurrence rates for every VAE in different age groups have been logically represented in OVAE. SPARQL scripts were developed to query and analyze the OVAE knowledge base data. To demonstrate the usage of OVAE, the top 10 vaccines accompanying with the highest numbers of VAEs and the top 10 VAEs most frequently observed among vaccines were identified and analyzed. Asserted and inferred ontology hierarchies classify VAEs in different levels of AE groups. Different VAE occurrences in different age groups were also analyzed.

Conclusions

The ontology-based data representation and integration using the FDA-approved information from the vaccine package insert documents enables the identification of adverse events from vaccination in relation to predefined parts of the population (age groups) and certain groups of vaccines. The resulting ontology-based VAE knowledge base classifies vaccine-specific VAEs and supports better VAE understanding and future rational AE prevention and treatment.