Yaowaluk Thitipon, Tangpanithandee Supawit, Techapichetvanich Pinnakarn, Khemawoot Phisit
Drug Information Service and Siriraj Poison Control Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand.
Drug Des Devel Ther. 2025 Aug 27;19:7427-7443. doi: 10.2147/DDDT.S543827. eCollection 2025.
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.
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.
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.
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.
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.
药物相互作用（DDIs）是一个关键的临床问题，尤其是在使用多种药物（包括解毒剂）时。尽管解毒剂具有挽救生命的潜力，但它们可能与其他药物发生有害相互作用。然而，很少有研究专门调查涉及解毒剂的药物相互作用。
本研究使用两个广泛使用的电子数据库评估常用药物与解毒剂之间潜在的药物相互作用，并利用人工智能（AI）评估这些平台之间的一致性。
使用ClinCalc DrugStats数据库（2022年）中的50种常用药物以及加利福尼亚中毒控制中心报告的主要解毒剂进行描述性分析。使用Micromedex和WebMD作为电子数据库，ChatGPT和谷歌Gemini作为代表性的人工智能来评估潜在的相互作用。记录药物相互作用的严重程度级别和文档质量，并使用kappa统计量分析数据库/人工智能的一致性。
总体而言，数据库共识别出154对潜在的药物相互作用（Micromedex：100对，WebMD：118对）。两个数据库均将19种药物相互作用归类为严重。数据库之间的总体一致性较差（kappa = -0.126，p = 0.008），表明在药物相互作用严重程度分类方面存在显著差异。与严重药物相互作用相关的主要机制包括血清素综合征和QT间期延长，亚甲蓝和精神科药物是严重药物相互作用的主要促成因素。在评估两个数据库中的19种严重药物相互作用时，人工智能模型在数据库不一致的情况下通常与更严重的评级一致。人工智能模型的共识通常得到以严重程度为导向的理由支持，这突出表明这是一种解决不一致的药物相互作用信息的保守方法。
已识别出常用药物与解毒剂之间存在大量潜在的药物相互作用，两个数据库与人工智能之间存在明显的不一致。这凸显了统一药物信息系统中药物相互作用评估标准以及提高临床医生对数据库间变异性认识的必要性。纳入全面的药物相互作用筛查和共同决策对于确保患者安全有效的护理至关重要。
