Individual Case Safety Report Replication: An Analysis of Case Reporting Transmission Networks.

Introduction: The basis of pharmacovigilance is provided by the exchange of Individual Case Safety Reports (ICSRs) between the recipient of the original report and other interested parties, which include Marketing Authorization Holders (MAHs) and Health Authorities (HAs). Different regulators have different reporting requirements for report transmission. This results in replication of each ICSR that will exist in multiple locations.

Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions.

Introduction: Causality assessment of individual case safety reports (ICSRs) is an important step in pharmacovigilance case-level review and aims to establish a position on whether a patient's exposure to a drug is causally related to the patient experiencing an untoward adverse event. There are many different approaches for case causality adjudication, including the use of expert opinions and algorithmic frameworks; however, a great deal of variability exists between assessment methods, products, therapeutic classes, individual physicians, change of process and conventions over time, and other factors.

Objective: The objective of this study was to develop a machine learning-based model that can predict the likelihood of a causal association of an observed drug-reaction combination in an ICSR.

Investigating Overlap in Signals from EVDAS, FAERS, and VigiBase.

Introduction: The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and VigiBase are two established databases for safety monitoring of medicinal products, recently complemented with the EudraVigilance Data Analysis System (EVDAS).

Objective: Signals of disproportionate reporting (SDRs) can characterize the reporting profile of a drug, accounting for the distribution of all drugs and all events in the database. This study aims to quantify the redundancy among the three databases when characterized by two disproportionality-based analyses (DPA).

Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR.

Over a period of 3 years, the European Union's Innovative Medicines Initiative WEB-RADR project has explored the value of social media (i.e., information exchanged through the internet, typically via online social networks) for identifying adverse events as well as for safety signal detection.

Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project.

Introduction And Objective: Social media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events.

Methods: Performance was assessed using a reference set by Harpaz et al.

Regulatory Definitions and Good Pharmacovigilance Practices in Social Media: Challenges and Recommendations.

Social media presents new challenges to the biopharmaceutical industry for conducting pharmacovigilance activities. The authors reviewed worldwide regulatory guidance documents related to monitoring of adverse events posted on social media sites and identified gaps in current regulatory definitions for pharmacovigilance. Points to consider for addressing these gaps are made to offer standards for industry consideration and a potential framework for guidance from global health authorities.