Radu Andrei-Flavius, Radu Ada, Tit Delia Mirela, Bungau Gabriela, Negru Paul Andrei
Doctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, Romania.
Department of Psycho-Neurosciences and Recovery, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania.
Bioengineering (Basel). 2025 Jul 19;12(7):783. doi: 10.3390/bioengineering12070783.
The significant burden of polypharmacy in clinical settings contrasts sharply with the narrow research focus on drug-drug interactions (DDIs), revealing an important gap in understanding the complexity of real-world multi-drug regimens. The present study addresses this gap by conducting a high-resolution, multidimensional bibliometric and network analysis of 19,151 DDI publications indexed in the Web of Science Core Collection (1975-2025). Using advanced tools, including VOSviewer version 1.6.20, Bibliometrix 5.0.0, and AI-enhanced terminology normalization, global research trajectories, knowledge clusters, and collaborative dynamics were systematically mapped. The analysis revealed an exponential growth in publication volume (from 55 in 1990 to 1194 in 2024), with output led by the United States and a marked acceleration in Chinese contributions after 2015. Key pharmacological agents frequently implicated in DDI research included CYP450-dependent drugs such as statins, antiretrovirals, and central nervous system drugs. Thematic clusters evolved from mechanistic toxicity assessments to complex frameworks involving clinical risk management, oncology co-therapies, and pharmacokinetic modeling. The citation impact peaked at 3.93 per year in 2019, reflecting the increasing integration of DDI research into mainstream areas of pharmaceutical science. The findings highlight a shift toward addressing polypharmacy risks in aging populations, supported by novel computational methodologies. This comprehensive assessment offers insights for researchers and academics aiming to navigate the evolving scientific landscape of DDIs and underlines the need for more nuanced system-level approaches to interaction risk assessment. Future studies should aim to incorporate patient-level real-world data, expand bibliometric coverage to underrepresented regions and non-English literature, and integrate pharmacogenomic and time-dependent variables to enhance predictive models of interaction risk. Cross-validation of AI-based approaches against clinical outcomes and prospective cohort data are also needed to bridge the translational gap and support precision dosing in complex therapeutic regimens.
临床环境中多重用药的巨大负担与对药物相互作用（DDIs）的狭隘研究重点形成鲜明对比，这揭示了在理解现实世界中多药治疗方案复杂性方面的一个重要差距。本研究通过对科学引文索引核心合集（1975 - 2025年）中索引的19151篇DDI出版物进行高分辨率、多维度的文献计量和网络分析来填补这一差距。使用包括VOSviewer 1.6.20版、Bibliometrix 5.0.0以及人工智能增强的术语规范化等先进工具，系统地绘制了全球研究轨迹、知识集群和合作动态。分析显示出版物数量呈指数增长（从1990年的55篇增至2024年的1194篇），美国在产出方面领先，2015年后中国的贡献显著加速。DDI研究中经常涉及的关键药物包括细胞色素P450依赖性药物，如他汀类药物、抗逆转录病毒药物和中枢神经系统药物。主题集群从机械毒性评估演变为涉及临床风险管理、肿瘤联合治疗和药代动力学建模的复杂框架。引文影响力在2019年达到每年3.93的峰值，反映出DDI研究越来越多地融入药学主流领域。研究结果突出了在新的计算方法支持下，朝着解决老年人群多重用药风险的转变。这一全面评估为旨在驾驭DDIs不断演变的科学格局的研究人员和学者提供了见解，并强调需要采用更细致入微的系统层面方法来进行相互作用风险评估。未来的研究应旨在纳入患者层面的真实世界数据，将文献计量覆盖范围扩大到代表性不足的地区和非英语文献，并整合药物基因组学和时间依赖性变量以增强相互作用风险预测模型。还需要针对临床结果和前瞻性队列数据对基于人工智能的方法进行交叉验证，以弥合转化差距并支持复杂治疗方案中的精准给药。
