Background: The antithyroid drugs propylthiouracil and methimazole were introduced for clinical use about 60 yr ago and are estimated to be used in more than 6000 children and adolescents per year in ...the United States. Over the years that these medications have been used, reports of adverse events involving hepatotoxicity have appeared. To date, there has not been a systematic and comparative evaluation of the adverse events associated with antithyroid drug use.
Objective: Our objective was to assess safety and hepatotoxicity profiles of propylthiouracil and methimazole by age in the U.S. Food and Drug Administration’s Adverse Event Reporting System (AERS).
Design: We used the multi-item gamma-Poisson shrinker (MGPS) data mining algorithm to analyze more than 40 yr of safety data in AERS. MGPS uses a Bayesian model to calculate adjusted observed to expected ratios empiric Bayes geometric mean (EBGM) values for every drug-adverse event combination in AERS, focusing on hepatotoxicity events.
Results: MGPS identified higher-than-expected reporting of severe liver injury in pediatric patients treated with propylthiouracil but not with methimazole. Propylthiouracil had a high adjusted reporting ratio for severe liver injury (EBGM 17; 90% confidence interval = 11.5–24.1) in the group less than 17 yr old. The highest EBGM values for methimazole were with mild liver injury in the group 61 yr and older EBGM 4.8 (3.3–6.8), which consisted of cholestasis. Vasculitis was also observed for propylthiouracil in children and adolescents, reaching higher EBGM values than hepatotoxicity signals.
Conclusions: MGPS detects higher-than-expected reporting of severe hepatotoxicity and vasculitis in children and adolescents with propylthiouracil but not with methimazole.
Propylthiouracil is associated with a much greater risk of severe liver injury and vasculitis than methimazole in children and adolescents.
Use of data mining at the Food and Drug Administration Duggirala, Hesha J; Tonning, Joseph M; Smith, Ella ...
Journal of the American Medical Informatics Association : JAMIA,
03/2016, Letnik:
23, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Objectives This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA).
Target audience We address data miners in all sectors, anyone ...interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities.
Scope Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.
Abstract
Easy access to large quantities of accurate health data is required to understand medical and scientific information in real-time; evaluate public health measures before, during, and after ...times of crisis; and prevent medical errors. Introducing a system in the USA that allows for efficient access to such health data and ensures auditability of data facts, while avoiding data silos, will require fundamental changes in current practices. Here, we recommend the implementation of standardized data collection and transmission systems, universal identifiers for individual patients and end users, a reference standard infrastructure to support calibration and integration of laboratory results from equivalent tests, and modernized working practices. Requiring comprehensive and binding standards, rather than incentivizing voluntary and often piecemeal efforts for data exchange, will allow us to achieve the analytical information environment that patients need.
Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety ...surveillance. The development and evaluation of these methodologies requires proper reference benchmarks. While progress has recently been made in developing such benchmarks, our understanding of the performance characteristics of the data mining methodologies is limited because existing benchmarks do not support prospective performance evaluations. We address this shortcoming by providing a reference standard to support prospective performance evaluations. The reference standard was systematically curated from drug labeling revisions, such as new warnings, which were issued and communicated by the US Food and Drug Administration in 2013. The reference standard includes 62 positive test cases and 75 negative controls, and covers 44 drugs and 38 events. We provide usage guidance and empirical support for the reference standard by applying it to analyze two data sources commonly mined for drug safety surveillance.
The large number of adverse‐event reports generated by marketed drugs and devices argues for the application of validated computerized algorithms to supplement traditional methods of detecting ...adverse‐event signals. Difficulties in accurately estimating patient exposure and background rates for a given event in a specific population hinder risk estimation in spontaneous adverse‐event databases. The United States Food and Drug Administration (FDA) is evaluating a Bayesian data mining system called Multi‐item Gamma Poisson Shrinker (MGPS) to enhance the FDA's ability to monitor the safety of drugs, biologics, and vaccines after they have been approved for use. The MGPS computes adjusted higher‐than‐expected reporting relationships between drugs and adverse events across 35 years of data relative to internal background rates. The MGPS can also adjust for random noise by using a model derived from the data, and corrects for temporal trends and confounding related to age, sex, and other variables by stratifying over 900 categories. Signals can then be compared with or used in conjunction with other sources (e.g. clinical trials, general practice databases) to further study the adverse‐event risk. The example of pancreatitis risk with atypical antipsychotics, valproic acid, and valproate is used to discuss the strengths and limitations of MGPS versus traditional methods. Validated data mining techniques offer great promise to enhance pharmacovigilance practices.
Introduction
Statistical signal detection is a crucial tool for rapidly identifying potential risks associated with pharmaceutical products. The unprecedented environment created by the coronavirus ...disease 2019 (COVID-19) pandemic for vaccine surveillance predisposes commonly applied signal detection methodologies to a statistical issue called the masking effect, in which signals for a vaccine of interest are hidden by the presence of other reported vaccines. This masking effect may in turn limit or delay our understanding of the risks associated with new and established vaccines.
Objective
The aim is to investigate the problem of masking in the context of COVID-19 vaccine signal detection, assessing its impact, extent, and root causes.
Methods
Based on data underlying the Vaccine Adverse Event Reporting System, three commonly applied statistical signal detection methodologies, and a more advanced regression-based methodology, we investigate the temporal evolution of signals corresponding to five largely recognized adverse events and two potentially new adverse events.
Results
The results demonstrate that signals of adverse events related to COVID-19 vaccines may be undetected or delayed due to masking when generated by methodologies currently utilized by pharmacovigilance organizations, and that a class of advanced methodologies can partially alleviate the problem. The results indicate that while masking is rare relative to all possible statistical associations, it is much more likely to occur in COVID-19 vaccine signaling, and that its extent, direction, impact, and roots are not static, but rather changing in accordance with the changing nature of data.
Conclusions
Masking is an addressable problem that merits careful consideration, especially in situations such as COVID-19 vaccine safety surveillance and other emergency use authorization products.
This publication describes uniform definitions for cardiovascular and stroke outcomes developed by the Standardized Data Collection for Cardiovascular Trials Initiative and the U.S. Food and Drug ...Administration (FDA). The FDA established the Standardized Data Collection for Cardiovascular Trials Initiative in 2009 to simplify the design and conduct of clinical trials intended to support marketing applications. The writing committee recognizes that these definitions may be used in other types of clinical trials and clinical care processes where appropriate. Use of these definitions at the FDA has enhanced the ability to aggregate data within and across medical product development programs, conduct meta-analyses to evaluate cardiovascular safety, integrate data from multiple trials, and compare effectiveness of drugs and devices. Further study is needed to determine whether prospective data collection using these common definitions improves the design, conduct, and interpretability of the results of clinical trials.
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Since 1998, the US Food and Drug Administration (FDA) has been exploring new automated and rapid Bayesian data mining techniques. These techniques have been used to systematically screen the FDA's ...huge MedWatch database of voluntary reports of adverse drug events for possible events of concern. The data mining method currently being used is the Multi-Item Gamma Poisson Shrinker (MGPS) program that replaced the Gamma Poisson Shrinker (GPS) program we originally used with the legacy database. The MGPS algorithm, the technical aspects of which are summarised in this paper, computes signal scores for pairs, and for higher-order (e.g. triplet, quadruplet) combinations of drugs and events that are significantly more frequent than their pair-wise associations would predict. MGPS generates consistent, redundant, and replicable signals while minimising random patterns. Signals are generated without using external exposure data, adverse event background information, or medical information on adverse drug reactions. The MGPS interface streamlines multiple input-output processes that previously had been manually integrated. The system, however, cannot distinguish between already-known associations and new associations, so the reviewers must filter these events. In addition to detecting possible serious single-drug adverse event problems, MGPS is currently being evaluated to detect possible synergistic interactions between drugs (drug interactions) and adverse events (syndromes), and to detect differences among subgroups defined by gender and by age, such as paediatrics and geriatrics. In the current data, only 3.4% of all 1.2 million drug-event pairs ever reported (with frequencies > or = 1) generate signals lower 95% confidence interval limit of the adjusted ratios of the observed counts over expected (O/E) counts (denoted EB05) of > or = 2. The total frequency count that contributed to signals comprised 23% (2.4 million) of the total number, 10.4 million of drug-event pairs reported, greatly facilitating a more focused follow-up and evaluation. The algorithm provides an objective, systematic view of the data alerting reviewers to critically important, new safety signals. The study of signals detected by current methods, signals stored in the Center for Drug Evaluation and Research's Monitoring Adverse Reports Tracking System, and the signals regarding cerivastatin, a cholesterol-lowering drug voluntarily withdrawn from the market in August 2001, exemplify the potential of data mining to improve early signal detection. The operating characteristics of data mining in detecting early safety signals, exemplified by studying a drug recently well characterised by large clinical trials confirms our experience that the signals generated by data mining have high enough specificity to deserve further investigation. The application of these tools may ultimately improve usage recommendations.
Drug-induced valvular heart disease (VHD) is a serious side effect linked to long-term treatment with 5-hydroxytryptamine (serotonin) receptor 2B (5-HT2B) agonists. Safety assessment for off-target ...pharmacodynamic activity is a common approach used to screen drugs for this undesired property. Such studies include in vitro assays to determine whether the drug is a 5-HT2B agonist, a necessary pharmacological property for development of VHD. Measures of in vitro binding affinity (IC50, Ki) or cellular functional activity (EC50) are often compared to maximum therapeutic free plasma drug levels (fCmax) from which safety margins (SMs) can be derived. However, there is no clear consensus on what constitutes an appropriate SM under various therapeutic conditions of use. The strengths and limitations of SM determinations and current risk assessment methodology are reviewed and evaluated. It is concluded that the use of SMs based on Ki values, or those relative to serotonin (5-HT), appears to be a better predictor than the use of EC50 or EC50/human fCmax values for determining whether known 5-HT2B agonists have resulted in VHD. It is hoped that such a discussion will improve efforts to reduce this preventable serious drug-induced toxicity from occurring and lead to more informed risk assessment strategies.
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