Ubrogepant and rimegepant: signal detection using spontaneous reports of adverse events from the Food and Drug Administration Adverse Event Reporting System

Battini, Vera; Carnovale, Carla; Clementi, Emilio; Sessa, Maurizio

doi:10.1080/14740338.2023.2223958

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…Finally, the investigation of methodological aspects represents an important goal of DA. While in the recent past the vast majority of efforts were directed in demonstrating/handling of reporting biases (Arnaud et al, 2016), in the current era of artificial intelligence ongoing challenges are represented by automation in detection, assessment and prioritization of safety signals through machine-learning algorithms and natural language processing (Ball and Dal Pan, 2022;Battini et al, 2023a;Al-Azzawi et al, 2023).…”

Section: Rationale For Conducting a Disproportionality Analysismentioning

confidence: 99%

Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems

Cutroneo,

Sartori,

Tuccori

et al. 2024

Front. Drug Saf. Regul.

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Spontaneous reporting systems remain pivotal for post-marketing surveillance and disproportionality analysis (DA) represents a recognized approach for early signal detection. Although DAs cannot be used per se as a standalone approach to assess a drug-related risk and cannot replace clinical judgment in the individual patient, their role remain irreplaceable for rapid detection of rare and unpredictable adverse drug reactions with strong drug-attributable component (e.g., designated medical events), especially when developed by a multidisciplinary team and combined with a careful case-by-case analysis (individual inspection of reports for causality assessment or to uncover reporting patterns and clinical features). In the recent past, a remarkable increase in publications of pharmacovigilance studies using DAs was observed, albeit the quality was debated: several publications contained “spin”, namely, misinterpretation of results to infer causality, calculate incidence, or provide risk stratification, which may ultimately result in unjustified alarm. The development of dedicated Guidelines by the international READUS-PV project (https://readus-statement.org/) will allow reproducible and transparent publication of accurate DAs, thus supporting their real transferability and exploitation by regulators and clinicians. This review offered a perspective on methodological aspects (and understanding) of DAs, their rationale, design, reporting, and interpretation.

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Section: Rationale For Conducting a Disproportionality Analysismentioning

confidence: 99%

Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems

Cutroneo,

Sartori,

Tuccori

et al. 2024

Front. Drug Saf. Regul.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, in Michele's study, safety concerns associated with each type of chimeric antigen receptor T-cell therapy were identified through the application of data mining techniques to the FAERS database [ 11 ]. Similarly, Battini's academic research updated the safety profiles of ubrogepant and rimegepant, filling a significant knowledge gap [ 12 ]. Therefore, employing the FAERS database as a key tool for assessing the real-world safety of eteplirsen shows remarkable feasibility and holds profound clinical significance.…”

Section: Introductionmentioning

confidence: 99%

Adverse events associated with eteplirsen: A disproportionality analysis using the 2016–2023 FAERS data

Dai,

Wang,

Zhang

et al. 2024

Heliyon

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DARCVAA: A Deep Neural Networks Based Framework for Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis

Sinruwng,

Yogita,

Pal

et al. 2024

SN COMPUT. SCI.

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Ubrogepant and rimegepant: signal detection using spontaneous reports of adverse events from the Food and Drug Administration Adverse Event Reporting System

Cited by 9 publications

References 46 publications

Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems

Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems

Adverse events associated with eteplirsen: A disproportionality analysis using the 2016–2023 FAERS data

DARCVAA: A Deep Neural Networks Based Framework for Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis

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