Spotlight commentary: The value of spontaneous reporting systems to detect (the lack of) clinically relevant drug–drug interactions in clinical practice

Drug–drug interactions (DDIs) represent an important cause of serious adverse drug reactions: this results from various guidelines promoting upfront combined pharmacological approaches and relevant polypharmacy with increasing age. The cardiovascular area, including diabetes, represents an ideal setting to characterize clinically relevant DDIs, since patients with diabetes suffer from comorbidities and are exposed to relevant chronic combination regimens, including lipidlowering drugs (statins), agents for heart failure, myocardial infarction and hypertension. Since DDIs are unlikely to be fully and formally assessed during drug development, the post-marketing phase is pivotal for their timely detection, and pharmacovigilance, through the analysis of spontaneous reporting systems (SRSs) and healthcare databases, might fill this gap. The use of SRSs to detect clinically relevant DDIs was first tested on a limited number of case studies, and then extended to a larger scale without an a priori hypothesis. Different approaches have been explored and validated in the recent past, including the Ω shrinkage measure, the upward variation of disproportionality measures (e.g., the reporting odds ratio), and additive/multiplicative methods. Remarkably, no matter the complexity of the implemented analysis/method, the “signal” will never be more than a hypothesis and remains an alert rather than an alarm. Moreover, there is no consensus on the best disproportionality approach, thus leaving researchers with the challenge of choosing the algorithm likely to achieve the best performance in terms of accuracy (false positives vs false negatives), while taking into account expected reporting biases. Gravel et al. explored the potential association between reporting of myotoxicity and concomitant exposure to statins and sodiumglucose co-transporter 2 inhibitors (SGLT2-is) using the Food and Drug Administration Adverse Event Reporting System (FAERS), one of the largest publicly accessible databases of spontaneous reports. The study stems from a well-conceived rationale, namely plausible (albeit weak) pharmacokinetic/pharmacodynamic basis, as well as preliminary clinical evidence (i.e., case report). Of note, Alkabbani et al. previously published a disproportionality analysis on the same database, thus raising the question of the actual added value of a newer but similar pharmacovigilance study. Reproducibility is trivial in modern pharmacovigilance, especially when dealing with the FAERS archive, which requires extensive data preprocessing and curation (e.g., duplicate removal, standardization and correction of data errors/ misspellings). Large variability was recently found in disproportionality analysis results, thus underlying the importance of planning a priori secondary/sensitivity analysis to increase the robustness of a (lack of) signal, as well as the need to share preregistered protocols and codes/ scripts to foster reproducibility of results. Our personal opinion is that replication studies are welcome, provided that they are carefully planned to offer an additional complementary perspective. This is the case with the work by Gravel et al., who tackled major biases, including stimulated reporting, to increase the robustness of findings. A side-by-side comparison is provided in Table 1, including methodological aspects when planning, conducting, reporting and interpreting a disproportionality analysis. These Received: 19 April 2023 Revised: 3 May 2023 Accepted: 5 May 2023