scholarly journals Harnessing scientific literature reports for pharmacovigilance

2017 ◽  
Vol 26 (01) ◽  
pp. 291-305
Author(s):  
Alfred Sorbello ◽  
Anna Ripple ◽  
Joseph Tonning ◽  
Monica Munoz ◽  
Rashedul Hasan ◽  
...  
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Signal Detection ◽  
User Satisfaction ◽  
Scientific Literature ◽  
Usability Testing ◽  
Analytical Tool ◽  
Drug Event ◽  
Safety Signal ◽  
Safety Signals

Summary Objectives: We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. Methods: A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. Results: All usability test participants cited the tool’s ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool’s automated literature search relative to a manual ‘all fields’ PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. Conclusions: Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

scholarly journals Pediatric Drug Safety Signal Detection: A New Drug–Event Reference Set for Performance Testing of Data-Mining Methods and Systems

Drug Safety ◽  
2015 ◽  
Vol 38 (2) ◽  
pp. 207-217 ◽  
Author(s):  
Osemeke U. Osokogu ◽  
Federica Fregonese ◽  
Carmen Ferrajolo ◽  
Katia Verhamme ◽  
Sandra de Bie ◽  
...  
Keyword(s):  
Data Mining ◽  
Signal Detection ◽  
Drug Safety ◽  
Performance Testing ◽  
Drug Event ◽  
Safety Signal ◽  
Pediatric Drug ◽  
New Drug ◽  
Reference Set ◽  
Mining Methods

Safety Signal Detection in the Drug Development Process

2015 ◽  
pp. 64-89
Author(s):  
Ramin B. Arani ◽  
Antoni F.Z. Wisniewski
Keyword(s):  
Adverse Event ◽  
Drug Development ◽  
Signal Detection ◽  
Safety Information ◽  
Safety Signal ◽  
Drug Development Process ◽  
Safety And Efficacy ◽  
Strength Of Association ◽  
Efficacy Profile ◽  
Complicated Task

Drug development is a complex set of inter-linked processes in which the cumulative understanding of a drug's safety and efficacy profile is shaped during different learning phases. Often, drugs are approved based on limited safety information, for example in highly at risk or rare disease populations. Therefore, post approval, regulatory organizations have mandated proactive surveillance strategies that include the collection of reported adverse events experienced by exposed populations, some of whom may have been on treatment for extended periods of time. Analyzing these accumulating adverse event reports to understand their clinical significance, given the limitations imposed by the methods of data collection, is a complicated task. The aim of this chapter is to provide the readers with a general understanding of safety signal detection and assessment, followed by a description of statistical methods (both classical and Bayesian) typically utilized for quantifying the strength of association between a drug and an adverse event.

scholarly journals Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

2021 ◽  
Author(s):  
Nicholas P. Giangreco ◽  
Nicholas P. Tatonetti
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Drug Effect ◽  
Temporal Trends ◽  
Population Modeling ◽  
Adverse Drug Effect ◽  
Drug Event ◽  
Event Reporting ◽  
Development Stages ◽  
Stratification Method

AbstractBackgroundIdentifying adverse drugs effects (ADEs) in children is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, current data mining methodologies have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to design data mining methodologies to identify and evaluate drug event patterns within observational databases for ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood.ResultsUsing simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child developmental stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages.ConclusionsOur study underscores the opportunity for using population modeling techniques, which leverages drug event reporting across development stages, to identify adverse drug effect risk resulting from ontogenic mechanisms.

scholarly journals Simulating adverse event spontaneous reporting systems as preferential attachment networks

2014 ◽  
Vol 05 (01) ◽  
pp. 206-218 ◽  
Author(s):  
T. Botsis ◽  
R. Ball ◽  
J. Scott
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Signal Detection ◽  
Spontaneous Reporting ◽  
Reporting System ◽  
Preferential Attachment ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Reporting Systems ◽  
Simulation Strategy

SummaryBackground: Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations.Objective: We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time.Methods: We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm.Results: The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold.Discussion: The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches.Conclusion: The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.Citation: Scott J, Botsis T, Ball R. Simulating adverse event spontaneous reporting systems as preferential attachment networks: Application to the Vaccine Adverse Event Reporting System. Appl Clin Inf 2014; 5: 206–218 http://dx.doi.org/10.4338/ACI-2013-11-RA-0097

Adverse Drug Events in the Prevention and Treatment of COVID-19: A Data Mining Study on FDA Adverse Event Reporting System (FAERS) (Preprint)

2021 ◽  
Author(s):  
Qiang Guo ◽  
Shaojun Duan ◽  
Yaxi Liu ◽  
Yinxia Yuan
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Drug Events ◽  
Reporting System ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Event Reporting ◽  
Prevention And Treatment

BACKGROUND In the emergency situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs so as to help health professionals and patients get rid of these risks. OBJECTIVE This pharmacovigilance study aimed to investigate the ADEs of “Hot Drugs” in COVID-19 prevention and treatment based on the data of the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). METHODS FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2021 were retrieved with “Hot Drugs” and frequent ADEs recognized. A combination of support, proportional reporting ratio (PRR) and Chi-square (2) test was applied to detect significant “Hot Drug” & ADE signals by Python programming language on Jupyter notebook. RESULTS 13,178 COVID-19 cases were retrieved with 18 “Hot Drugs” and 312 frequent ADEs on “Preferred Term” (PT) level. 18  312 = 5,616 “Drug & ADE” candidates were formed for further data mining. The algorithm finally produced 219 significant ADE signals associated with 17 “Hot Drugs”and 124 ADEs.Some unexpected ADE signals were observed for chloroquine, ritonavir, tocilizumab, Oxford/AstraZeneca COVID-19 Vaccine and Moderna COVID-19 Vaccine. CONCLUSIONS Data mining is a promising and efficient way to assist pharmacovigilance work and the result of this paper could help timely recognize ADEs in the prevention and treatment of COVID-19.

scholarly journals Adverse Event Trends Associated with Over-the-counter Combination Cold Remedy: Data Mining of the Japanese Adverse Drug Event Report Database

2018 ◽  
Vol 138 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Sayaka Sasaoka ◽  
Haruna Hatahira ◽  
Shiori Hasegawa ◽  
Yumi Motooka ◽  
Akiho Fukuda ◽  
...  
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Drug Event ◽  
Drug Event ◽  
Event Report ◽  
Over The Counter ◽  
Cold Remedy

scholarly journals Adverse Event Trends Associated with OTC Analgesic and Antipyretic Drug: Data Mining of the Japanese Adverse Drug Event Report Database

2017 ◽  
Vol 137 (10) ◽  
pp. 1301-1311 ◽  
Author(s):  
Shiori Hasegawa ◽  
Haruna Hatahira ◽  
Misa Naganuma ◽  
Akari Shimauchi ◽  
Sayaka Sasaoka ◽  
...  
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Drug Event ◽  
Drug Event ◽  
Event Report
Sign in / Sign up

Export Citation Format

Share Document