The Growing Role of Signal Management in the Product Life Cycle and How Optimised Pharmacovigilance Operations Can Help

Journal for Clinical Studies
April 2016
Author: Dr. Mitchell Gandelman, Sciformix Corporation

A safety signal suggests a possible causal relationship between an adverse event (AE) and a drug, or a new aspect of a known AE and a drug, which requires some type of further investigation. When a signal is detected, further investigation is warranted to determine whether an actual causal relationship exists1. The entire process of signal management is one of the most crucial steps in PV and is defined in numerous guidelines to various degrees. These include the EMA Guideline on Good Pharmacovigilance Practices (GPV) Module IX – Signal Management2, FDA Guidance for Industry, GPV and Pharmacoepidemiology Assessment3, reports of CIOMS Working Group VIII4, and ICH E2E5.

Signal management includes many processes, such as identifying sources of data, signal detection, prioritisation, evaluation, analysis, and assessment with recommendations for action, and remains at the centre of PV and drug safety. These processes are required for patient safety and by drug regulatory agencies. Tracking and documentation of the activities, decisions, and results is also required. The outcome of the signal management process is directly dependent on the quality of the safety data and, as a result, improvements in the quality of safety data will have a significant impact on the effectiveness of signal management and pharmacovigilance as a whole. The area of safety signal detection has come into focus over the past few years and is growing in importance. It is well accepted that statistical methods of signal detection can flag certain drug-event combinations for in-depth analysis from a medical perspective, potentially leading to confirmation of evidence and identification of a signal. Signal detection can be completed using many methods, each of which has inherent advantages and limitations. New and improved computer-aided statistical methodologies are under evaluation with the anticipation of offering improved sensitivity, specificity and predictive value.

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