Exploratory Signal Detection of Maternal and Perinatal Adverse ART Drug Events in EudraVigilance: Insights from Network and Cluster Analyses

Background: Medication safety in pregnancy, puerperium, and perinatal periods is underexplored because these populations are excluded from clinical trials. EudraVigilance offers post-marketing evidence, but disproportionality analyses focus on isolated drug event pairs and may miss syndromic patterns. We applied a network- and cluster-based framework to EudraVigilance reports on antiviral use in pregnancy to improve surveillance and identify meaningful constellations. Methods: We retrieved all individual case safety reports (ICSRs) from January 2015 to June 2025, including pregnancy, puerperium, or perinatal terms, focusing on suspect antivirals. After parsing terms, disproportionality metrics were computed as a benchmark. A bipartite drug–event network was built and projected to event–event co-occurrence networks; Louvain community detection identified clusters. Clusters were characterized by size, drug mix, seriousness, overlap with disproportionality signals, and stratification across periods. Results: The dataset comprised 106,924 ICSRs and 232,067 unique pairs. Disproportionality yielded 6142 signals, mainly involving antiretrovirals (ritonavir, lamivudine, zidovudine, emtricitabine/tenofovir). Network analysis revealed clusters grouping maternal and fetal/neonatal outcomes (e.g., fetal death, low birth weight), and transplacental transfer, highlighting structures not visible in pairwise analyses. Several clusters combined high-frequency exposures with clinically relevant outcomes, suggesting early-warning potential. Conclusions: Combining disproportionality with network- and cluster-based pharmacovigilance adds value for monitoring pregnancy medication safety. Beyond individual signals, this approach reveals meaningful clusters and “bridge” reactions connecting adverse-event domains, offering a richer framework for perinatal surveillance. Despite spontaneous-reporting limits, findings generate hypotheses for mechanistic and pharmacoepidemiologic follow-up and support network methods as complements to traditional pharmacovigilance.