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Key Elements in Adverse Drug Interaction Safety Signals
Linköping University, Department of Medical and Health Sciences, Clinical Pharmacology. Linköping University, Faculty of Health Sciences.
Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
Linköping University, Department of Medical and Health Sciences, Clinical Pharmacology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Clinical Pharmacology.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: Effective surveillance of adverse drug interactions (a problematic drug combination resulting in an adverse drug reaction (ADR)) in large collections of individual case safety reports (ICSRs) requires a combination of expert clinical assessments and efficient algorithms. To date, most methods proposed for adverse drug interaction surveillance focus on disproportionality analysis, although a recent study proposed that the reported clinical and pharmacological information is also useful for systematic screening of adverse drug interactions.

Objective: The purpose of this study is to identify and describe key elements in adverse drug interaction safety signals.

Methods: Altogether 137 case reports from three previously published safety signals of suspected adverse drug interaction were re-evaluated using an operational algorithm for causality analysis of drug interactions; the Drug Interaction Probability Scale (DIPS). Reports in the WHO Global ICSR Database, VigiBase, and their corresponding original files were analysed, examining whether the DIPS elements were registered. The retrieved case information was specified as being listed in the structured fields, free text and, in total. In addition, information not covered by DIPS, such as explicit notifications of a suspected drug interaction by the reporter or the pharmacovigilance centre was also registered.

Results: As expected from the data used in this analysis, the most frequently fulfilled DIPS elements were: objective evidence (such as ADR) of a drug interaction (137 cases; 100%). Other frequent elements were (ranked order) plausible time to onset (53 cases; 38%), and resolution of the ADR after terminating the drug inducing the interaction (10 cases; 7%). Ten cases (7%) fulfilled both a plausible time to onset and resolution of the ADR after stopping the drug. Positive rechallenge was only reported in 3 cases (2%). For 32 cases additional information was reported in free text in the original files that were not available in VigiBase. A suspected drug interaction was noted by the reporter in 47 original cases (35%) and more than 80% of these were assessed as a possible or probable drug interaction according to the DIPS classification. Among cases without notes of suspected interactions were 58 original reports (64%) assessed as possible (56 cases) or probable (2 cases).

Conclusions: A plausible time to onset pattern and resolution of the ADR after withdrawal of the drug inducing the interaction frequently strengthened the suspected causality of a drug interaction. Particularly strong cases were those containing both these key elements. Since this information is often available in structured format, it could potentially be used to automatically highlight strong cases in firstpass screening. Finally, this analysis also demonstrated the importance of free text where particularly relevant clinically details such as timeliness, severity, resolution of the reaction after withdrawal of the drug inducing the interaction, possible alternative causes, and dosage changes are available.

Keyword [en]
Adverse drug interactions, signals, systematic surveillance system, plausible time to onset
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-70847OAI: diva2:442061
Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2011-09-20Bibliographically approved
In thesis
1. Drug interaction surveillance using individual case safety reports
Open this publication in new window or tab >>Drug interaction surveillance using individual case safety reports
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background: Drug interactions resulting in adverse drug reactions (ADRs) represent a major health problem both for individuals and society in general. Post-marketing pharmacovigilance reporting databases with compiled individual case safety reports (ICSRs) have been shown to be particularly useful in the detection of novel drug - ADR combinations, though these reports have not been fully used to detect adverse drug interactions.

Aim: To explore the potential to identify drug interactions using ICSRs and to develop a method to facilitate the detection of adverse drug interaction signals in the WHO Global ICSR Database, VigiBase.

Methods: All six studies included in this thesis are based on ICSRs available in VigiBase. Two studies aimed to characterise drug interactions reported in VigiBase. In the first study we examined if contraindicated drug combinations (given in a reference source of drug interactions) were reported on the individual reports in the database, and in the second study we examined the scientific literature for interaction mechanisms for drug combinations most frequently co-reported as interacting in VigiBase. Two studies were case series analyses where the individual reports were manually reviewed. The two remaining studies aimed to develop a method to facilitate detection of novel adverse drug interactions in VigiBase. One examined what information (referred to as indicators) was reported on ICSRs in VigiBase before the interactions became listed in the literature. In the second methodological study, logistic regression was used to set the relative weights of the indicators to form triage algorithms. Three algorithms (one completely data driven, one semi-automated and one based on clinical knowledge) based on pharmacological and reported clinical information and the relative reporting rate of an ADR with a drug combination were developed. The algorithms were then evaluated against a set of 100 randomly selected case series with potential adverse drug interactions. The algorithm’s performances were then evaluated among DDAs with high coefficients.

Results: Drug interactions classified as contraindicated are reported on the individual reports in VigiBase, although they are not necessarily recognised as interactions when reported. The majority (113/123) of drug combinations suspected for being responsible for an ADR were established drug interactions in the literature. Of the 113 drug interactions 46 (41%) were identified as purely pharmacodynamic; 28 (25%) as pharmacokinetic; 18 (16%) were a mix of both types and for 21 (19%) the mechanism have not yet been identified. Suspicions of a drug interaction explicitly noted by the reporter are much more common for known adverse drug interactions than for drugs not known to interact. The clinical evaluation of the triage algorithms showed that 20 were already known in the literature, 30 were classified as signals and 50 as not signals. The performance of the semi-automated and the clinical algorithm were comparable. In the end the clinical algorithm was chosen. At a relevant level, 38% were of the adverse drug interactions were already known in the literature and of the remaining 80% were classified as signals for this algorithm.

Conclusions: This thesis demonstrated that drug interactions can be identified in large post-marketing pharmacovigilance reporting databases. As both pharmacokinetic and pharmacodynamic interactions were reported on ICSRs the surveillance system should aim to detect both. The proposed triage algorithm had a high performance in comparison to the disproportionality measure alone.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 45 p.
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1252
Adverse drug reactions, adverse drug interaction surveillance, drug interactions, individual case safety reports, postmarketing pharmacovigilance, signal detection
National Category
Medical and Health Sciences
urn:nbn:se:liu:diva-70424 (URN)978-91-7393-106-9 (ISBN)
Public defence
2011-10-06, Nils Holger, Campus US, Linköpings universitet, Linköping, 13:00 (English)
Available from: 2011-09-07 Created: 2011-09-07 Last updated: 2011-09-20Bibliographically approved

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