Discordance in Drug–Drug Interaction Alerts for Antidotes: Comparative Analysis of Electronic Databases and Interpretive Insights from AI Tools
2
Issued Date
2025-01-01
Resource Type
eISSN
11778881
Scopus ID
2-s2.0-105014202859
Journal Title
Drug Design Development and Therapy
Volume
19
Start Page
7427
End Page
7443
Rights Holder(s)
SCOPUS
Bibliographic Citation
Drug Design Development and Therapy Vol.19 (2025) , 7427-7443
Suggested Citation
Yaowaluk T., Tangpanithandee S., Techapichetvanich P., Khemawoot P. Discordance in Drug–Drug Interaction Alerts for Antidotes: Comparative Analysis of Electronic Databases and Interpretive Insights from AI Tools. Drug Design Development and Therapy Vol.19 (2025) , 7427-7443. 7443. doi:10.2147/DDDT.S543827 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111906
Title
Discordance in Drug–Drug Interaction Alerts for Antidotes: Comparative Analysis of Electronic Databases and Interpretive Insights from AI Tools
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Drug-drug interactions (DDIs) are a critical clinical concern, especially when administering multiple medications, including antidotes. Despite their lifesaving potential, antidotes may interact harmfully with other drugs. However, few studies have specifically investigated DDIs involving antidotes. Purpose: This study evaluated potential DDIs between commonly prescribed medications and antidotes using two widely used electronic databases, along with artificial intelligence (AI) to assess the concordance between these platforms. Materials and Methods: A descriptive analysis was conducted using 50 frequently prescribed medications from the ClinCalc DrugStats Database (2022) and major antidotes as reported by California Poison Control Center. Potential interactions were assessed using Micromedex and WebMD as electronic databases, and ChatGPT and Google Gemini as representative AI. DDI severity levels and documentation quality were recorded, and database/AI agreement was analyzed using the kappa statistic. Results: Overall, 154 potential DDI pairs were identified by the databases (Micromedex: 100, WebMD: 118). Nineteen DDIs were classified as severe by both databases. The overall agreement between databases was poor (kappa = −0.126, p = 0.008), indicating significant discrepancies in DDI severity classification. The main mechanisms associated with severe DDIs included serotonin syndrome and QT prolongation, with methylene blue and psychiatric medications being major contributors to severe DDIs. When evaluating the 19 severe DDIs from both databases, the AI models generally aligned with the more severe rating in cases of database discordance. The AI models’ consensus was often supported by severity-oriented justifications, highlighting this as a conservative approach to resolving discordant DDI information. Conclusion: Numerous potential DDIs between prescribed drugs and antidotes were identified, with notable inconsistencies between the two databases and AI. This underscores the need to harmonize DDI evaluation criteria across drug information systems and promote clinicians’ awareness of inter-database variability. Incorporating comprehensive DDI screening and shared decision-making is essential to ensure safe and effective patient care.