ArticleFrontiers in pharmacology2025
A comparison of five different drug-drug interaction checkers for selective serotonin reuptake inhibitors.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Selective serotonin reuptake inhibitors (SSRIs) are widely prescribed for depression and anxiety, but their potential for drug-drug interactions (DDIs) poses significant risks, particularly given their influence on cytochrome P450 enzymes. Variability in identifying and classifying these interactions among drug interaction checkers (ICs) can complicate clinical decision-making and compromise patient safety. This study aims to compare five widely used ICs in identifying DDIs related to SSRIs, highlighting discrepancies in DDI identification and severity classification to inform best practices. Methods: A comparative study was conducted using five popular ICs (Micromedex, Lexi-Interact, Epocrates, Medscape, and Drugs.com) to evaluate their performance in identifying SSRIs-related DDIs. Data on drug-SSRIs interaction pairs were extracted over 2 weeks in 2025. Descriptive analysis was used to quantify potential interactions and their severity. Gwet's AC1 coefficient was employed to assess agreement among all five ICs and to compare groups of four- and two-pair sets of ICs. Results: A total of 1,190 potentially interacting drugs with fluoxetine (FXT) were reported, 1,129 for fluvoxamine (FVM), 1,131 for citalopram (CIT), 1,084 for paroxetine (PAR), 1,206 for sertraline (SER) and 1,146 for escitalopram (ESC). The agreement among all five ICs was notably low, with Gwet's AC1 values ranging from 0.16 to 0.24 across different SSRIs. Similarly, it was poor in 4 and 2 sets analyses. The level of agreement among the ICs in classifying the severity of potential DDIs or restricting DDIs identified as severe was poor, also in 4 and 2 sets analysis. Conclusion: The findings reveal substantial discrepancies in the identification and severity categorization of SSRIs-related DDIs among ICs, underscoring the challenges faced by healthcare providers in ensuring safe prescribing practices. The study advocates for the standardization of IC databases and severity criteria to enhance consistency and reliability.
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