Good Pharmacovigilance Practices Min Chen, RPh Associate Director, Division of Drug Risk Evaluation Office of Drug Safety, CDER

What is Pharmacovigilance? Is generally regarded as all postapproval scientific and data gathering activities relating to the detection, assessment, understanding, and prevention of adverse events or any other product-related problems This includes the use of pharmacoepidemiologic studies

Good Pharmacovigilance Practice starts by acquiring complete data from spontaneous adverse event reports

Characteristics of a Good Case Report Adverse event(s) details Baseline patient characteristics Therapy details Time to onset of signs or symptoms Diagnosis of the event(s) Clinical course of the event and outcomes Laboratory data Any other relevant information

Good Medication Error Report Product(s) involved Sequence of events leading to the error Work environment in which the error occurred Types of personnel involved with the error Narratives- follow NCC MERP taxonomy (National Coordinating Council for Medication Error Reporting and Prevention)

Case Series Development Includes spontaneous case reports and scientific literature case reports Good database search strategies using MedDRA terminology Case definitions may be developed to provide consistent characterizations of the adverse events, and to facilitate retrieval of all clinically relevant cases

Assessing Causality It is rarely certain whether an event is product induced Features supportive of an association event occurred in the expected timeframe absence of symptoms prior to exposure absence of co-morbid conditions or use of concomitant medications (+) dechallenge, (+) rechallenge event consistent with the established mechanism of action of product Grouping case reports into “probable,” “possible,” and “unlikely” when appropriate

Safety Signals An apparent excess of adverse events associated with a product’s use a single well-documented case report may be viewed as a signal preclinical findings experience with other similar products in the class May be further assessed in terms of magnitude, population at risk, changes in risk over time, biological plausibility and other factors

Data Mining As a Signaling Tool Uses raw case report data and arrays of all drug-adverse events combinations Calculates “expected” (E) number of events for all drugs, then compares each drug’s “observed” (O) to “expected” Various methods may be used to generate relative signal scores (intensity) of O:E May be useful adjunct to routine safety signaling methods Further exploration and validation is needed

Safety Signals May Be... New unlabeled adverse events An observed increase in the severity or specificity of a labeled event An observed increase in the frequency of a labeled event New interactions Confusion with a product’s name, packaging or use, either actual or potential

Understanding Safety Signals Demographics - age, gender, race Effect of exposure duration and dose Relationship between concomitant medications and potential interactions and the risk of event Relationship between co-morbid conditions and the risk of event

Understanding Safety Signals (cont’d) Effects of lot-to-lot variation and differences in product formulation and the risk of the event Potential for an excess of adverse events given the disease being treated Estimates of the magnitude of risk or differences from known background rates

Questions for Public Comment How can the quality of spontaneously reported case reports be improved?

Questions for Public Comment What are possible advantages or disadvantages of applying data mining techniques (e.g., empirical Bayesian techniques, proportional reporting ratios) to spontaneous reports databases for the purpose of identifying safety signals?

Questions for Public Comment What are possible advantages or disadvantages of performing causality assessments at the individual case level?