Post-Marketing Safety Profile of Vortioxetine Using a Cluster Analysis and a Disproportionality Analysis of Global Adverse Event Reports

INTRODUCTION: Vortioxetine, a multimodal serotonergic drug, is widely used as treatment for major depressive disorder. Although on the market since late 2013, the data of the relative safety of vortioxetine, especially compared to selective serotonin reuptake inhibitors, are still scarce. OBJECTIVE: The aim of this study was to explore the adverse event reporting pattern of vortioxetine through a cluster analysis. Furthermore, to compare the adverse event reporting pattern for vortioxetine with that of the selective serotonin reuptake inhibitors. METHODS: Individual case safety reports for vortioxetine in VigiBase up to 1 November, 2019 were subjected to consensus clustering, to identify and describe natural groupings of reports based on their reported adverse events. A vigiPoint exploratory analysis compared vortioxetine to the selective serotonin reuptake inhibitors in terms of relative frequencies for a wide range of covariates, including patient sex and age, reported drugs and adverse events, and reporting country. Important differences were identified using odds ratios with adaptive statistical shrinkage. RESULTS: Thirty-six clusters containing at least five reports were identified and analysed. The two largest clusters included 48% of the vortioxetine reports and appeared to represent gastrointestinal adverse events and hypersensitivity adverse events. Other distinct clusters were related to, respectively, fatigue, aggression/suicidality, convulsion, medication errors, arthralgia/myalgia, increased weight, paraesthesia and anticholinergic effects. Some of these clusters are not labelled for vortioxetine, such as arthralgia/myalgia and paraesthesia, but are known adverse events for selective serotonin reuptake inhibitors. A vigiPoint analysis revealed a higher proportion of reports from consumers and non-health professionals for vortioxetine as well as higher relative reporting rates of gastrointestinal symptoms, pruritus and mood-related symptoms, consistent with the cluster analysis. CONCLUSIONS: A pattern of co-reported adverse events that is consistent with labelled adverse events for vortioxetine and the safety profile for selective serotonin reuptake inhibitors in general was revealed. Clusters of unlabelled adverse events were identified that reflect clinical entities that might represent signals of previously unknown adverse events. More extensive analyses of spontaneous reports may help to further understand the reporting pattern of adverse events. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-021-01139-y

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance

The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement

Background and aim: Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. Methods: We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. Results: Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. Conclusions: The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence

Statistical methods for knowledge discovery in adverse drug reaction surveillance, Matematiska Institutionen 2007, http://urn.kb.se/resolve?urn=urn: nbn:se:su:diva-6764 (last accessed on 28

Abstract Collections of individual case safety reports are the main resource for early discovery of unknown adverse reactions to drugs once they have been introduced to the general public. The data sets involved are complex and based on voluntary submission of reports, but contain pieces of very important information. The aim of this thesis is to propose computationally feasible statistical methods for large-scale knowledge discovery in these data sets. The main contributions are a duplicate detection method that can reliably identify pairs of unexpectedly similar reports and a new measure for highlighting suspected drug-drug interaction. Specifically, we extend the hit-miss model for database record matching with a hit-miss mixture model for scoring numerical record fields and a new method to compensate for strong record field correlations. The extended hit-miss model is implemented for the WHO database and demonstrated to be useful in real world duplicate detection, despite the noisy and incomplete information on individual case safety reports. The Information Component measure of disproportionality has been in routine use since 1998 to screen the WHO database for excessive adverse drug reaction reporting rates. Here, it is further refined. We introduce improved credibility intervals for rare events, post-stratification adjustment for suspected confounders and an extension to higher order associations that allows for simple but robust screening for potential risk factors. A new approach to identifying reporting patterns indicative of drug-drug interaction is also proposed. Finally, we describe how imprecision estimates specific to each prediction of a Bayes classifier may be obtained with the Bayesian bootstrap. Such case-based imprecision estimates allow for better prediction when different types of errors have different associated loss, with a possible application in combining quantitative and clinical filters to highlight drug-ADR pairs for clinical review

Statistical methods for knowledge discovery in adverse drug reaction surveillance

Collections of individual case safety reports are the main resource for early discovery of unknown adverse reactions to drugs once they have been introduced to the general public. The data sets involved are complex and based on voluntary submission of reports, but contain pieces of very important information. The aim of this thesis is to propose computationally feasible statistical methods for large-scale knowledge discovery in these data sets. The main contributions are a duplicate detection method that can reliably identify pairs of unexpectedly similar reports and a new measure for highlighting suspected drug-drug interaction. Specifically, we extend the hit-miss model for database record matching with a hit-miss mixture model for scoring numerical record fields and a new method to compensate for strong record field correlations. The extended hit-miss model is implemented for the WHO database and demonstrated to be useful in real world duplicate detection, despite the noisy and incomplete information on individual case safety reports. The Information Component measure of disproportionality has been in routine use since 1998 to screen the WHO database for excessive adverse drug reaction reporting rates. Here, it is further refined. We introduce improved credibility intervals for rare events, post-stratification adjustment for suspected confounders and an extension to higher order associations that allows for simple but robust screening for potential risk factors. A new approach to identifying reporting patterns indicative of drug-drug interaction is also proposed. Finally, we describe how imprecision estimates specific to each prediction of a Bayes classifier may be obtained with the Bayesian bootstrap. Such case-based imprecision estimates allow for better prediction when different types of errors have different associated loss, with a possible application in combining quantitative and clinical filters to highlight drug-ADR pairs for clinical review