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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., (Published by Elsevier Inc.)

Introduction: The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data., Objective: Our objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS)., Methods: A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each 'post with resemblance to an adverse event' (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA ® ) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS., Findings: A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen--skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS., Conclusions: An efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

Our study is a cross-sectional analysis of Maryland poisoning deaths for years 2003 and 2004. We used Classification and Regression Tree (CART) methodology to classify undetermined intent Maryland poisoning deaths as either unintentional or suicidal poisonings. The predictive ability of the selected set of variables (i.e., poisoned in the home or workplace, location type, where poisoned, place of death, poison type, victim race and age, year of death) was extremely good. Of the 301 test cases, only eight were misclassified by the CART regression tree. Of 1,204 undetermined intent poisoning deaths, CART classified 903 as suicides and 301 as unintentional deaths. The major strength of our study is the use of CART to differentiate with a high degree of accuracy between unintentional and suicidal poisoning deaths among Maryland undetermined intent poisoning deaths.

Objective: To assess risk factors for tardive dyskinesia (TD) in spontaneous reports of metoclopramide and TD and evaluate metoclopramide prescribing patterns before and after withdrawal of cisapride from the market in the United States., Design: Retrospective and observational analyses., Setting: International metoclopramide adverse event reports and domestic drug-use data for the continental United States., Patients: Users of metoclopramide for 30 days or more who experienced adverse events reported as TD., Interventions: Analyses of the Food and Drug Administration Adverse Event Reporting System (AERS) and IMS HEALTH data., Main Outcome Measures: Pharmacoepidemiological patterns in AERS reports and utilization data from IMS HEALTH., Results: The case series comprised 87 reports of primarily older (mean+/-SD, 60+/-22 years ) women (67% of all cases). While average metoclopramide daily dose (33+/-14 mg) was within recommended product labeling limits, duration of use was considerably longer (753+/-951 days). Overall, 37% of the reports included concomitant drugs believed to be TD risk factors. Similarly, 18% of the reports noted comorbid diseases that are considered risk factors for development of TD. Metoclopramide utilization decreased following cisapride marketing in 1993 and increased following cisapride withdrawal in 2000. The majority (62%) of metoclopramide prescriptions were intended for women. Intended use overall increased with age and was highest in the seventh and eighth decades, with nearly one quarter of all utilization being in persons older than 70 years., Conclusion: Well-described TD risk factors were common in metoclopramide-associated TD reports. Given the cisapride market withdrawal and associated increased metoclopramide utilization, the incidence of TD may increase accordingly. TD risk factors relative to the intended benefit and duration of use should be considered in metoclopramide prescribing.

Objective: Identify, during the period of criminal anthrax exposures in October 2001, changes in utilization of ciprofloxacin and geographic patterns of any identified variations., Design: Observational., Setting: United States., Patients: Individuals making prescription claims through a pharmacy benefits management company., Interventions: Analysis of AdvancePCS pharmacy claims database., Main Outcome Measures: Percentage change in ciprofloxacin utilization for 2000 and 2001 and, by locale, for September and October 2001., Results: Utilization of ciprofloxacin tablets was significantly lower in calendar year 2001 than in calendar year 2000 (median decline, 10.3%) for all months except October, when utilization of ciprofloxacin increased 9.8%. During the period of anthrax exposures (October 2001 versus September 2001), affected geographic areas, including New York (an increase of 62.5%), some other Mid-Atlantic states, and Florida (28.5%), had some of the highest percentage increases in the rate of ciprofloxacin utilization., Conclusion: Many Americans actively sought prophylaxis with ciprofloxacin during the course of the October 2001 anthrax attack and that utilization was higher in, but not limited to, locales with publicized cases of disease. Pharmacists, clinicians, and public health officials should note that such behavior may be expected in the event of a similar attack and should be familiar with current recommendations for the assessment and management of anthrax exposure.