What question did this study set out to answer?

This research aims to interpret safety signals from antiviral therapies used during pregnancy by integrating pharmacovigilance data with mechanistic frameworks.

March 13, 2026Open Access

From Pharmacovigilance Signals to Mechanistic Phenotypes: Integrating ADMET, PK/PD, and Network Context to Interpret Antiviral Safety in Pregnancy

Key Points

This research aims to interpret safety signals from antiviral therapies used during pregnancy by integrating pharmacovigilance data with mechanistic frameworks.
Analyzed pregnancy-related EudraVigilance reports from 2015-2025.
Utilized a network-based pharmacovigilance framework to assess ADR clusters.
Integrated ADMET profiles and PK/PD parameters with polypharmacy metrics.
Performed exploratory statistical and machine-learning analyses.
Phenotype membership accounted for 22.3% of variance in ADMET risk.
Significant differences in ADMET parameters were found across phenotypes.
One phenotype was enriched with 13 over-represented antivirals.
Incidence of serious outcomes increased by ~5% for each additional drug noted.

Abstract

Background: Antiviral therapies are widely used during pregnancy and are generally considered safe, pregnancy-specific severe safety signals continue to be observed in post-marketing pharmacovigilance data. These signals are rarely interpreted within an integrated mechanistic framework. Methods: We analysed pregnancy-related EudraVigilance reports (2015–2025) using a previously network-based pharmacovigilance framework. Established ADR clusters were treated as fixed phenotypes and integrated with in silico ADMET liabilities, literature-derived pregnancy pharmacokinetic/pharmacodynamic (PK/PD) parameters, polypharmacy and co-medication network metrics, and exploratory statistical, machine-learning, and exposure–liability analyses for mechanistic prioritisation. Results: Phenotype membership explained 22.3% of the variance in composite ADMET risk (intraclass correlation coefficient = 0.223; p < 0.001), and all tested ADMET parameters differed significantly across phenotypes (FDR-adjusted p < 10−10). One phenotype showed pronounced enrichment, with 13 antivirals over-represented. Polypharmacy strongly modified seriousness, with odds of serious outcomes increasing by ~5% per additional co-reported active drug (OR 1.05, 95% CI 1.04–1.05). A composite mechanistic vulnerability index showed moderate concordance with empirical burden (Spearman’s ρ = 0.65), while regimen-level prioritisation of drug–drug interactions (DDIs) identified no high-priority combinations. Conclusions: Pregnancy-related antiviral ADRs cluster into reproducible phenotypes driven by mechanistic liability and system-level complexity, supporting mechanistically informed prioritisation and targeted pharmacometric follow-up.

From Pharmacovigilance Signals to Mechanistic Phenotypes: Integrating ADMET, PK/PD, and Network Context to Interpret Antiviral Safety in Pregnancy

Key Points

Abstract

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