{"title":"Discordance in Drug-Drug Interaction Alerts for Antidotes: Comparative Analysis of Electronic Databases and Interpretive Insights from AI Tools.","authors":"Thitipon Yaowaluk, Supawit Tangpanithandee, Pinnakarn Techapichetvanich, Phisit Khemawoot","doi":"10.2147/DDDT.S543827","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":11290,"journal":{"name":"Drug Design, Development and Therapy","volume":"19 ","pages":"7427-7443"},"PeriodicalIF":5.1000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12399100/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Design, Development and Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/DDDT.S543827","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
Drug Design, Development and Therapy is an international, peer-reviewed, open access journal that spans the spectrum of drug design, discovery and development through to clinical applications.
The journal is characterized by the rapid reporting of high-quality original research, reviews, expert opinions, commentary and clinical studies in all therapeutic areas.
Specific topics covered by the journal include:
Drug target identification and validation
Phenotypic screening and target deconvolution
Biochemical analyses of drug targets and their pathways
New methods or relevant applications in molecular/drug design and computer-aided drug discovery*
Design, synthesis, and biological evaluation of novel biologically active compounds (including diagnostics or chemical probes)
Structural or molecular biological studies elucidating molecular recognition processes
Fragment-based drug discovery
Pharmaceutical/red biotechnology
Isolation, structural characterization, (bio)synthesis, bioengineering and pharmacological evaluation of natural products**
Distribution, pharmacokinetics and metabolic transformations of drugs or biologically active compounds in drug development
Drug delivery and formulation (design and characterization of dosage forms, release mechanisms and in vivo testing)
Preclinical development studies
Translational animal models
Mechanisms of action and signalling pathways
Toxicology
Gene therapy, cell therapy and immunotherapy
Personalized medicine and pharmacogenomics
Clinical drug evaluation
Patient safety and sustained use of medicines.
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