Lessons learned from annotation of VAERS reports on adverse events following influenza vaccination and related to Guillain-Barré syndrome.

BACKGROUND: Vaccine Adverse Events ReportingSystem (VAERS) is a promising resource of tracking adverse events following immunization. Medical Dictionary for Regulatory Activities (MedDRA) terminology used for coding adverse events in VAERS reports has several limitations. We focus on developing an automated system for semantic extraction of adverse events following vaccination and their temporal relationships for a better understanding of VAERS data and its integration into other applications. The aim of the present studyis to summarize the lessons learned during the initial phase of this project in annotating adverse events following influenza vaccination and related to Guillain-Barré syndrome (GBS). We emphasize on identifying the limitations of VAERS and MedDRA. RESULTS: We collected 282 VAERS reports documented between 1990 and 2016 and shortlisted those with at least 1,100 characters in the report. We used a subset of 50 reports for the preliminary investigation and annotated all adverse events following influenza vaccination by mapping to representative MedDRA terms. Associated time expressions were annotated when available. We used 16 System Organ Class (SOC) level MedDRA terms to map GBS related adverse events and expanded some SOC terms to Lowest Level Terms (LLT) for granular representation. We annotated three broad categories of events such as problems, clinical investigations, and treatments/procedures. The inter-annotator agreement of events achieved was 86%. Incomplete reports, typographical errors, lack of clarity and coherence, repeated texts, unavailability of associated temporal information, difficulty to interpret due to incorrect grammar, use of generalized terms to describe adverse events / symptoms, uncommon abbreviations, difficulty annotating multiple events with a conjunction / common phrase, irrelevant historical events and coexisting events were some of the challenges encountered. Some of the limitations we noted are in agreement with previous reports. CONCLUSIONS: We reported the challenges encountered and lessons learned during annotation of adverse events in VAERS reports following influenza vaccination and related to GBS. Though the challenges may be due to the inevitable limitations of public reporting systems and widely reported limitations of MedDRA, we emphasize the need to understand these limitations and extraction of other supportive information for a better understanding of adverse events following vaccination.

Evaluating MedDRA-to-ICD terminology mappings.

BACKGROUND: In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage. RESULTS: The identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD. CONCLUSION: Some of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS.

Immune checkpoint inhibitors and potential risk of thromboembolic events: Analysis of the WHO global database of individual case safety reports.

Introduction: Thromboembolic events with the use of immune checkpoint inhibitors (ICIs) in patients with cancer have been reported in few studies. However, the detailed profile of these cases remains mostly uncertain. Method: A descriptive analysis of Thromboembolic events associated with ICIs retrieved from the VigiBase, between 1967 to November 2020. We extracted the data using the terms of 'pulmonary embolism' OR 'deep vein thrombosis' OR 'acute coronary syndrome' OR 'myocardial infarction' OR 'ischemic stroke' (preferred term (PT) (MedDRA). Results: We included 161 cases from 26 countries in our descriptive analysis. Patients' ages were reported in 141 (87.6%) cases, with a median of 68 years (interquartile range 61-74), and 63.4% of the patients were male. Indications for ICIs were reported in 151 (93.8%) cases, as follows: lung cancer (n = 85, 52.8%), renal cell carcinoma (n = 24, 14.9%), melanoma (n = 20, 12.4%), urethral carcinoma (n = 12, 7.45%), breast cancer (n = 4, 2.48%), adenocarcinoma of the gastroesophageal junction (n = 3, 1.9%), gastric cancer (n = 2, 1.24%), and skin cancer (n = 1, 0.62%). Nivolumab was reported as a suspected drug in 76 cases (47%), pembrolizumab in 46 cases (28.5%), atezolizumab in 21 cases (13%), durvalumab in 14 cases (8.6%), and avelumab in four cases (2.4%).The time to onset of thromboembolic events was reported in 127 (78.8%) cases. Most of these patients (n = 109, 85.8%) reported thromboembolic events within the first six months. The causality assessment of included cases showed that 50.3% of reported thromboembolic events were possibly related to the suspected reported medication, 13.7% were probably related, 13% were unlikely to be related, and 23% were not assessable due to insufficient information. Conclusion: This study demonstrates a possible association between the use of ICIs and thromboembolic events. Further epidemiological studies are needed to assess this association and to elucidate the underlying mechanism.

Vaccine adverse event enrichment tests.

Vaccination safety is critical for individual and public health. Many existing methods have been used to conduct safety studies with the VAERS (Vaccine Adverse Event Reporting System) database. However, these methods frequently identify many adverse event (AE) signals and they are often hard to interpret in a biological context. The AE ontology introduces biologically meaningful structures to the Vaccine Adverse Event Reporting System (VAERS) database by connecting similar AEs, which provides meaningful interpretation for the underlying safety issues. In this paper, we develop rigorous statistical methods to identify "interesting" AE groups by performing AE enrichment analysis. We extend existing gene enrichment tests to perform AE enrichment analysis, while incorporating the special features of the AE data. The proposed methods were evaluated using simulation studies and were further illustrated on two studies using VAERS data. The proposed methods were implemented in R package AEenrich and can be installed from the Comprehensive R Archive Network, CRAN, and source code are available at https://github.com/umich-biostatistics/AEenrich.

Pharmacovigilance network as an additional tool for the surveillance of antimicrobial resistance.

BACKGROUND: The WHO Programme for International Drug Monitoring (PIDM) is a large Pharmacovigilance network of countries sharing Adverse Drug Reaction (ADR) reports. Pharmacovigilance Experts have suggested that antimicrobial resistance (AMR) is an overlooked adverse event. We undertook this study to investigate the potential role of Pharmacovigilance databases in the surveillance of AMR. METHODS: Using the AWaRe (Access, Watch and Reserve) list and the WHO Priority Pathogens List, we established a list of antimicrobials and carried out a VigiBase search via VigiAccess, looking for ADR reports with Preferred Terms (PTs) that contained AMR-relevant information. Identified Terms were matched with codes from the Medical Dictionary for Regulatory Activities (MedDRA Version 21.1). RESULTS: Records on 86 drugs were retrieved with a total of 1 170 751 ADR reports submitted between 1968 and 2018. Seventeen PTs suggesting suspected resistance, ineffectiveness, inappropriate use, or medication error were used to code 15 250 reports. The most frequently used PTs were "Drug Ineffective" (45.6%), "Off label use" (9.5%) and "Pathogen Resistance" (8.9%). A group of six agents (Amoxicillin, Cefalotin, Ciprofloxacin, Clarithromycin, Levofloxacin and Daptomycin) accounted for 38% (n = 5806) of all 15 250 AMR-relevant ADR reports. The PTs most frequently used in 5806 reports were grouped in 4 categories: drug ineffectiveness (62.5%), resistance (19.2%), off-label use (12.1%) and prescription errors (6.2%). CONCLUSION: Our findings suggest that Pharmacovigilance databases could serve as a tool in tracking antimicrobial use and resistance especially in settings where laboratory capacity is still in its development stages. National Pharmacovigilance centers could play a proactive role in stimulating the reporting of AMR-relevant ADRs which can serve as a basis for resistance suspicion alerts. Further studies focusing on the narrative and other clinical pharmacology details in ADR reports are required.

How to interact with medical terminologies? Formative usability evaluations comparing three approaches for supporting the use of MedDRA by pharmacovigilance specialists.

BACKGROUND: Medical terminologies are commonly used in medicine. For instance, to answer a pharmacovigilance question, pharmacovigilance specialists (PVS) search in a pharmacovigilance database for reports in relation to a given drug. To do that, they first need to identify all MedDRA terms that might have been used to code an adverse reaction in the database, but terms may be numerous and difficult to select as they may belong to different parts of the hierarchy. In previous studies, three tools have been developed to help PVS identify and group all relevant MedDRA terms using three different approaches: forms, structured query-builder, and icons. Yet, a poor usability of the tools may increase PVS' workload and reduce their performance. This study aims to evaluate, compare and improve the three tools during two rounds of formative usability evaluation. METHODS: First, a cognitive walkthrough was performed. Based on the design recommendations obtained from this evaluation, designers made modifications to their tools to improve usability. Once this re-engineering phase completed, six PVS took part in a usability test: difficulties, errors and verbalizations during their interaction with the three tools were collected. Their satisfaction was measured through the System Usability Scale. The design recommendations issued from the tests were used to adapt the tools. RESULTS: All tools had usability problems related to the lack of guidance in the graphical user interface (e.g., unintuitive labels). In two tools, the use of the SNOMED CT to find MedDRA terms hampered their use because French PVS were not used to it. For the most obvious and common terms, the icons-based interface would appear to be more useful. For the less frequently used MedDRA terms or those distributed in different parts of the hierarchy, the structured query-builder would be preferable thanks to its great power and flexibility. The form-based tool seems to be a compromise. CONCLUSION: These evaluations made it possible to identify the strengths of each tool but also their weaknesses to address them before further evaluation. Next step is to assess the acceptability of tools and the expressiveness of their results to help identify and group MedDRA terms.

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA.

BACKGROUND: Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. RESULTS: This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). CONCLUSIONS: This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Disbiome database: linking the microbiome to disease.

BACKGROUND: Recent research has provided fascinating indications and evidence that the host health is linked to its microbial inhabitants. Due to the development of high-throughput sequencing technologies, more and more data covering microbial composition changes in different disease types are emerging. However, this information is dispersed over a wide variety of medical and biomedical disciplines. DESCRIPTION: Disbiome is a database which collects and presents published microbiota-disease information in a standardized way. The diseases are classified using the MedDRA classification system and the micro-organisms are linked to their NCBI and SILVA taxonomy. Finally, each study included in the Disbiome database is assessed for its reporting quality using a standardized questionnaire. CONCLUSIONS: Disbiome is the first database giving a clear, concise and up-to-date overview of microbial composition differences in diseases, together with the relevant information of the studies published. The strength of this database lies within the combination of the presence of references to other databases, which enables both specific and diverse search strategies within the Disbiome database, and the human annotation which ensures a simple and structured presentation of the available data.

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.

Do antiepileptic drugs increase the risk of infectious diseases? A meta-analysis of placebo-controlled studies.

AIMS: Experimental studies show that some antiepileptic drugs (AEDs) may modify natural immune defences, thus influencing the risk of developing infectious diseases. The aim of this meta-analysis was to explore whether AEDs as a class of drugs or singularly may increase risk of infectious diseases. METHODS: A meta-analysis of all randomized, double-blind, placebo-controlled trials (RCTs) investigating any AED in any condition was performed. All terms that could be coded in the System Organ Classes (SOCs) of infections and infestations using the Medical Dictionary for Regulatory Activities were recorded. Additional subanalyses were performed also pooling together AEDs sharing similar mechanisms of action. RESULTS: Two hundreds and sixty-nine double-blind, placebo-controlled studies were identified and, among them, 127 RCTs with 16 AEDs (brivaracetam, gabapentin, lacosamide, levetiracetam, lamotrigine, oxcarbazepine, perampanel, pregabalin, phenytoin, remacemide, retigabine, rufinamide, tiagabine, topiramate, valproate, zonisamide) reported at least one of 19 symptoms or diseases that could be included in the Medical Dictionary for Regulatory Activities SOC term infections and infestations. These terms were singularly recorded and then pooled together in the SOC term infection and infestation. Topiramate was significantly associated with an increased risk of infection (risk difference = 0.04; 95% confidence interval = 0.01/0.06), while oxcarbazepine was significantly associated with a lower risk (-0.005; -0.09/-0.01). Risk difference of all studies with all AEDs showed a slight, but significantly increased risk of infection (0.01; 0.00/0.002). Levetiracetam and brivaracetam RCTs, when pooled together, were associated with a significantly increased risk of infection (0.03; 0.01/0.05). CONCLUSIONS: Some AEDs are associated with a mild increased risk of infection.

Prospective, systematically recorded mycophenolate safety data in Graves' orbitopathy.

CONTEXT: The antiproliferative mechanism of mycophenolate acid (MPA) suggests a beneficial effect in patients with Graves' orbitopathy (GO). OBJECTIVE: To systematically analyze for the first time adverse events (AEs) during MPA treatment in GO. DESIGN: Prospective longitudinal study. SETTING: Academic tertiary referral center with a joint thyroid-eye clinic. PATIENTS: Fifty-three consecutive, unselected patients with clinically active and moderate-to-severe GO. METHODS: MPA 0.720 g was given once daily for 24-weeks in GO patients. AEs were documented and coded according to the standardized medical dictionary for regulatory activities (MedDRA). AE were followed up and seriousness as defined by ICH-guideline E6 was documented. All AEs were analyzed regarding a possible underlying cause and if not, graded as side effect (SE). RESULTS: Fifty GO patients (93 %) had Graves' disease, 37 (70 %) and 29 (54.7 %) were female and smoker, respectively. Thirty-six patients (68 %) reported at least one AE. A total of 88 AEs were documented, most frequent AEs were insomnia (N = 6), fatigue (N = 5) and optic neuropathy (N = 5), while other AEs occurred in up to three patients (5.6 %), only. In 12 (23 %) patients, at least one SE occurred. All 17 reported SE, i.e. mild infections and gastrointestinal intolerance were within the known safety profile of MPA. No patient dropped MPA medication because of drug-induced SE. Most AEs showed a recovered (76 %) or recovering (16 %) outcome. Seven (13 %) patients were hospitalized, five (62 %) due to optic neuropathy, none of these events was graded as SE. CONCLUSIONS: MedDRA-coded data documented the good tolerance of a moderate MPA dose in GO patients.

[Automated grouping of terms associated to cardiac valve fibrosis in MedDRA]. / Groupement automatisé de termes liés aux valvulopathies médicamenteuses dans MedDRA.

AIM: To propose an alternative approach for building custom groupings of terms that complements the usual approach based on both hierarchical method (selection of reference groupings in medical dictionary for regulatory activities [MedDRA]) and/or textual method (string search), for case reports extraction from a pharmacovigilance database in response to a safety problem. Here we take cardiac valve fibrosis as an example. METHODS: The list of terms obtained by an automated approach, based on querying ontology of adverse drug reactions (OntoADR), a knowledge base defining MedDRA terms through relationships with systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts, was compared with the reference list consisting of 53 preferred terms obtained by hierarchical and textual method. Two queries were performed on OntoADR by using a dedicated software: OntoADR query tools. Both queries excluded congenital diseases, and included a procedure or an auscultation method performed on cardiac valve structures. Query 1 also considered MedDRA terms related to fibrosis, narrowing or calcification of heart valves, and query 2 MedDRA terms described according to one of these four SNOMED CT terms: "Insufficiency", "Valvular sclerosis", "Heart valve calcification" or "Heart valve stenosis". RESULTS: The reference grouping consisted of 53 MedDRA preferred terms. Our automated method achieved recall of 79% and precision of 100% for query 1 privileging morphological abnormalities, and recall of 100% and precision of 96% for query 2 privileging functional abnormalities. CONCLUSION: An alternative approach to MedDRA reference groupings for building custom groupings is feasible for cardiac valve fibrosis. OntoADR is still in development. Its application to other adverse reactions would require significant work for a knowledge engineer to define every MedDRA term, but such definitions could then be queried as many times as necessary by pharmacovigilance professionals.

Semantic distance-based creation of clusters of pharmacovigilance terms and their evaluation.

BACKGROUND: Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. The detection of adverse drug reactions is performed using statistical algorithms and groupings of ADR terms from the MedDRA (Medical Dictionary for Drug Regulatory Activities) terminology. Standardized MedDRA Queries (SMQs) are the groupings which become a standard for assisting the retrieval and evaluation of MedDRA-coded ADR reports worldwide. Currently 84 SMQs have been created, while several important safety topics are not yet covered. Creation of SMQs is a long and tedious process performed by the experts. It relies on manual analysis of MedDRA in order to find out all the relevant terms to be included in a SMQ. Our objective is to propose an automatic method for assisting the creation of SMQs using the clustering of terms which are semantically similar. METHODS: The experimental method relies on a specific semantic resource, and also on the semantic distance algorithms and clustering approaches. We perform several experiments in order to define the optimal parameters. RESULTS: Our results show that the proposed method can assist the creation of SMQs and make this process faster and systematic. The average performance of the method is precision 59% and recall 26%. The correlation of the results obtained is 0.72 against the medical doctors judgments and 0.78 against the medical coders judgments. CONCLUSIONS: These results and additional evaluation indicate that the generated clusters can be efficiently used for the detection of pharmacovigilance signals, as they provide better signal detection than the existing SMQs.

Cadec: A corpus of adverse drug event annotations.

CSIRO Adverse Drug Event Corpus (Cadec) is a new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs). The corpus is sourced from posts on social media, and contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules. Annotations contain mentions of concepts such as drugs, adverse effects, symptoms, and diseases linked to their corresponding concepts in controlled vocabularies, i.e., SNOMED Clinical Terms and MedDRA. The quality of the annotations is ensured by annotation guidelines, multi-stage annotations, measuring inter-annotator agreement, and final review of the annotations by a clinical terminologist. This corpus is useful for studies in the area of information extraction, or more generally text mining, from social media to detect possible adverse drug reactions from direct patient reports. The corpus is publicly available at https://data.csiro.au.(1).

Prospectively recorded and MedDRA-coded safety data of intravenous methylprednisolone therapy in Graves' orbitopathy.

CONTEXT: Safety of intravenous (IV) steroid pulses in patients with Graves' orbitopathy (GO) is still controversial while steroid dose and treatment application have not been finalized. Frequency, severity and characterization of adverse events (AE) were prospectively analyzed. SETTING: Academic referral orbital center with a joint thyroid-eye clinic. PATIENTS: Eighty consecutive and unselected patients with active and severe GO. METHODS: During an established treatment with IV methylprednisolone (cumulative dose 4.5 g) occurring AE were prospectively coded according to the standardized and recognized medical dictionary for regulatory activities (MedDRA). Outcome and severity of AE were documented. AEs judged as at least possibly related to drug treatment were graded as side effect (SE). AEs matching a seriousness criteria as defined by the ICH guideline E6 (good clinical practice) were graded as serious. RESULTS: A total of 38.75% (31/80) of the treated GO patients reported at least one AE while 18 patients (22.5%) reported at least one SE. All SE were within the safety profile of IV methylprednisolone; 31/32 SE (96.87%) were mild-moderate and reversible and only 1/80 patient (1.25%) stopped steroid treatment due to exacerbation of her depression. Most AE were accessory symptoms of the underlying disease and a few only were directly related to IV steroids. Most AEs (90.6%) were graded as mild. Only six patients (7.5%) were hospitalized, three of them due to a dysthyroid optic neuropathy. CONCLUSIONS: Prospective and standardized evaluation with MedDRA and the ICH guideline demonstrated the good pharmacological tolerance and low morbidity of this moderate steroid regimen.

Formalizing MedDRA to support semantic reasoning on adverse drug reaction terms.

Although MedDRA has obvious advantages over previous terminologies for coding adverse drug reactions and discovering potential signals using data mining techniques, its terminological organization constrains users to search terms according to predefined categories. Adding formal definitions to MedDRA would allow retrieval of terms according to a case definition that may correspond to novel categories that are not currently available in the terminology. To achieve semantic reasoning with MedDRA, we have associated formal definitions to MedDRA terms in an OWL file named OntoADR that is the result of our first step for providing an "ontologized" version of MedDRA. MedDRA five-levels original hierarchy was converted into a subsumption tree and formal definitions of MedDRA terms were designed using several methods: mappings to SNOMED-CT, semi-automatic definition algorithms or a fully manual way. This article presents the main steps of OntoADR conception process, its structure and content, and discusses problems and limits raised by this attempt to "ontologize" MedDRA.

Anaphylaxis in Clinical Trials of Sublingual Immunotherapy Tablets.

BACKGROUND: There is no consensus method to identify anaphylaxis in sublingual immunotherapy (SLIT) trials. Standardized Medical Dictionary for Regulatory Activities (MedDRA) queries (SMQs) are standardized groupings of MedDRA terms used in drug safety monitoring. OBJECTIVE: To develop a method to identify potential anaphylaxis in SLIT-tablet trials using SMQ searches and case definitions of anaphylaxis adopted from the National Institute of Allergy and Infectious Disease. METHODS: The SMQ search tool contained 2 criteria including treatment-emergent adverse events (AEs): (1) narrow MedDRA terms related to anaphylaxis and (2) all AEs with broad MedDRA terms from at least 2 of 3 categories (respiratory/skin/cardiovascular) occurring on the same day. Criteria were applied to a pooled data set of all subjects from 48 timothy grass, ragweed, house dust mite, and tree SLIT-tablet trials (SLIT-tablet, N = 8200; placebo, N = 7033). Additional search strategies were any treatment-emergent AE with MedDRA preferred term "hypersensitivity" and epinephrine administrations. Identified potential cases underwent blinded independent medical expert review. Nonanaphylaxis cases were designated local AEs or mild to moderate systemic reactions. RESULTS: Using the SMQ search tool and after subsequent medical review, 8 anaphylaxis cases were identified; 3 were considered treatment-related, resulting in a proportion of anaphylaxis cases/subject of 0.02% (2 of 8200) with SLIT-tablet and 0.01% (1 of 7033) with placebo. One additional anaphylaxis case related to SLIT-tablet was identified by the preferred term "hypersensitivity." The 3 anaphylaxis cases associated with SLIT-tablet treatment were not life-threatening. The epinephrine administration rate was 17 of 8200 (0.2%) with SLIT-tablet treatment and 2 of 7033 (0.03%) with placebo. CONCLUSIONS: SMQ search criteria for identifying potential anaphylaxis related to SLIT were developed. Anaphylaxis was rare for SLIT-tablets.

Safety of immune checkpoint inhibitors: An updated comprehensive disproportionality analysis and meta-analysis.

BACKGROUND: The exact safety profile of Immune checkpoint inhibitors (ICIs) is unclear so far. AIM: The aim of the current study is to analyse the safety profile of ICIs in cancer patients. METHODOLOGY: The updated comprehensive disproportionality analysis of post-marketing data using the FAERS database and meta-analysis of randomized clinical trials (RCTs) was conducted. Disproportionality measures were calculated in terms of PRR associated with chi-square value and ROR with 95% confidence intervals whereas overall estimate measures with 95% CIs, publication bias and heterogeneity were calculated using RevMan 5.4. The GRADE analysis was also done to check the quality of evidence for each outcome. RESULTS: Various novel signals such as cholangitis, encephalitis, anuria, myelosuppression, and cachexia related to different system organ class were identified with ICIs. The sensitivity analysis results have indicated the influence of concomitant drugs on the identified signals. The meta-analysis results have shown a good safety profile of atezolizumab in non-small cell lung cancer (NSCLC) and melanoma, pembrolizumab in gastro-oesophageal cancer, urothelial cancer and head and neck squamous cell carcinoma (HNSCC), nivolumab in HNSCC as compared to the non-ICI group. CONCLUSION: The safety of ICIs is dependent on their types as well as on the types of cancer.

Root causes, clinical effects, and outcomes of unintentional exposures to buprenorphine by young children.

OBJECTIVE: To characterize the rates, root causes, and clinical effects of unintentional exposures to buprenorphine sublingual formulations among young children and to determine whether exposure characteristics differ between formulations. STUDY DESIGN: Unintentional exposures to buprenorphine-containing products among children 28 days to less than 6 years old were collected from the Researched Abuse, Diversion, and Addiction-Related Surveillance System Poison Center Program and Reckitt Benckiser Pharmaceuticals' pharmacovigilance system from October 2009-March 2012. After adjustment for drug availability, negative binomial regression was used to estimate average exposure rates. Root cause assessment was conducted, and an expert clinician panel adjudicated causality and severity of moderate to severe adverse events (AEs). RESULTS: A total of 2380 cases were reviewed, including 4 deaths. Exposures to buprenorphine-naloxone combination film were significantly less frequent than exposures to buprenorphine tablets (rate ratio 3.5 [95% CI, 2.7-4.5]) and buprenorphine-naloxone combination tablets (rate ratio 8.8 [7.2-10.6]). The most commonly identified root causes were medication stored in sight, accessed from a bag or purse, and not stored in the original packaging. Among 536 panel review cases, the most common AEs reported for all formulations were lethargy, respiratory depression, miosis, and vomiting. The highest level AE severity did not differ significantly by formulation. CONCLUSIONS: Unintentional exposure to buprenorphine can cause central nervous system depression, respiratory depression, and death in young children. Exposure rates to film formulations are significantly less than to tablet formulations. Package and storage deficiencies contribute to unintentional exposures in young children.

Bayesian approach for clinical trial safety data using an Ising prior.

In drug safety, development of statistical methods for multiplicity adjustments has exploited potential relationships among adverse events (AEs) according to underlying medical features. Due to the coarseness of the biological features used to group AEs together, which serves as the basis for the adjustment, it is possible that a single adverse event can be simultaneously described by multiple biological features. However, existing methods are limited in that they are not structurally flexible enough to accurately exploit this multi-dimensional characteristic of an adverse event. In order to preserve the complex dependencies present in clinical safety data, a Bayesian approach for modeling the risk differentials of the AEs between the treatment and comparator arms is proposed which provides a more appropriate clinical description of the drug's safety profile. The proposed procedure uses an Ising prior to unite medically related AEs. The proposed method and an existing Bayesian method are applied to a clinical dataset, and the signals from the two methods are presented. Results from a small simulation study are also presented.