The Role of Signal Detection in Pharmacovigilance

The World Health Organisation defines pharmacovigilance as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other possible drug-related problems. Signal detection is a core pharmacovigilance activity. The motivation behind exploring signal detection and evaluation process is the timely detection of safety issues, and ultimately a better protection of public health. This thesis aimed to explore the processes of signal detection and evaluation and to inform regulatory decision making by providing evidence-based solutions to some of the existing questions. We investigated methods of detection and alternative data sources. Furthermore, we were interested to find out which characteristics of drugs make them more prone to have safety issues discovered post-marketing and tried to find predictive characteristics and we tried to gain some insight in the prioritisation process. The current work is relevant for all involved stakeholders, as European Medicines Agency (EMA), national regulatory authorities, pharmaceutical industry and, healthcare professionals and researchers

Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification

Introduction: Spontaneous reports of suspected adverse drug reactions (ADRs) can be analyzed to yield additional drug safety evidence for the pediatric population. Signal detection algorithms (SDAs) are required for these analyses; however, the performance of SDAs in the pediatric population specifically is unknown. We tested the performance of two SDAs on pediatric data from the US FDA Adverse Event Reporting System (FAERS) and investigated the impact of age stratification and age adjustment on the performance of SDAs. Methods: We tested the performance of two established SDAs: the proportional reporting ratio (PRR) and the empirical Bayes geometric mean (EBGM) on a pediatric dataset from FAERS (2004–2012). We compared the performance of the SDAs with a published pediatric-specific reference set by calculating diagnostic test-related statistics, including the area under the curve (AUC) of receiver operating characteristics. Impact of age stratification and age-adjustment on the performance of the SDAs was assessed. Age adjustment was performed by pooling (Mantel-Hanszel) stratum-specific estimates. Results: A total of 115,674 pediatric reports (patients aged 0–18 years) comprising 893,587 drug–event combinations (DECs) were analysed. Crude values of the AUC were similar for both SDAs: 0.731 (PRR) and 0.745 (EBGM). Stratification unmasked four DECs, e.g., ‘ibuprofen and thrombocytopenia’. Age adjustment did not improve performance. Conclusion: The performance of the two tested SDAs was similar in the pediatric population. Age adjustment does not improve performance and is therefore not recommended to be performed routinely. Stratification can reveal new associations, and therefore is recommended when either drug use is age-specific or when an age-specific risk is suspected

Imposed registries within the European postmarketing surveillance system: Extended analysis and lessons learned for regulators

Purpose: Building on previous research, we examined whether delayed study start and low patient accrual rates found in 31 postauthorization registry-based studies requested by European Medicines Agency (EMA) are maintained after 2 additional years of follow-up. Method: The registries identified in the previous EMA study and the same methodology were used. The follow-up was extended from June 2015 to November 2017. The information available for the following variables was updated: marketing authorization status, study and registry status, study end date, planned duration, number of patients planned to be enrolled, and actual patients enrolled. Data were collected from several nonpublic in-house sources such as the study protocols, interim and final study reports, risk management plans, and periodic safety update reports. Results: As of November 2017, 10 (32.2%) studies were finalized (vs. 9.7% as of June 2015), 14 (45.2%) were still ongoing (vs. 64.5%). Four of the ongoing studies had patients' accrual lower than 50%. Six of the finalized studies had a delayed completion, with a median delay of 3 years. As of November 2017, the median patients' accrual percentages were 24% for ongoing studies (vs. 8.5%) and 101% for finalized studies (vs. 24%). Conclusion: Overall, the rate of recruitment and timely finalization were improved after 2 years of additional follow-up but show that further work is needed to facilitate use of registry data for regulatory purposes, a work that has started via the EMA registry initiative

Characteristics of drugs safety signals that predict safety related product information update

Purpose: Investigation of drug safety signals is one of the major tasks in pharmacovigilance. Among many potential signals identified, only a few reflect adverse drug reactions requiring regulatory actions, such as product information (PI) update. Limited information is available regarding the signal characteristics that might predict PI update following signal evaluation. The objective of this study was to identify signal characteristics associated with PI updates following signal evaluation by the European Medicines Agency Pharmacovigilance Risk Assessment Committee during 2012 to 2016. Methods: A comparative study was performed based on data from 172 safety signals. Characteristics of signals were extracted from the European Pharmacovigilance Issues Tracking Tool database. Multivariable logistic regression analysis was used to assess the relationship between signal characteristics and the decision to update the PI. Results: Multivariable logistic regression analysis showed that the presence of evidence in multiple types of data sources (adjusted odds ratio [OR] 7.8 95% CI [1.5, 40.1]); mechanistic plausibility of the drug-event association (adjusted OR 3.9 95% CI [1.9, 8.0]); seriousness of the event (adjusted OR 4.2 95% CI [1.3, 13.9]); and age of drugs ≤5 years (adjusted OR 3.9 95% CI [1.2, 12.7]) were associated with the decision to change the PI (P < 0.05). Conclusions: This study identified 4 characteristics of drug safety signals that have shown to be associated with PI changes as outcome of signal evaluation. These characteristics may be used as criteria for selection and prioritization of potential signals that are more likely to necessitate product information updates

Impact of different assumptions on estimates of childhood diseases obtained from health care data: A retrospective cohort study

Purpose: Accurate estimates of disease incidence in children are required to support pediatric drug development. Analysis of electronic health care records (EHR) may yield such estimates but pediatric-specific methods are lacking. We aimed to understand the impact of assumptions regarding duration of disease episode and length of run-in period on incidence estimates from EHRs. Methods: Children aged 0 to 17 years (5-17 years for asthma) registered in the Integrated Primary Care Information database between 2002 and 2014 were studied. We tested the impact of the following: max

Exploratory Study of Signals for Asthma Drugs in Children, Using the EudraVigilance Database of Spontaneous Reports

Introduction: As asthma medications are frequently prescribed for children, knowledge of the safety of these drugs in the paediatric population is important. Although spontaneous reports cannot be used to prove causality of adverse events, they are important in the detection of safety signals. Objective: Our objective was to provide an overview of adverse drug events associated with asthma medications in children from a spontaneous reports database and to identify new signals. Methods: Spontaneous reports concern

Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection

Background and Objective: Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data source