Table1_Revealing the dynamic landscape of drug-drug interactions through network analysis.xlsx

Introduction: The landscape of drug-drug interactions (DDIs) has evolved significantly over the past 60 years, necessitating a retrospective analysis to identify research trends and under-explored areas. While methodologies like bibliometric analysis provide valuable quantitative perspectives on DDI research, they have not successfully delineated the complex interrelations between drugs. Understanding these intricate relationships is essential for deciphering the evolving architecture and progressive transformation of DDI research structures over time. We utilize network analysis to unearth the multifaceted relationships between drugs, offering a richer, more nuanced comprehension of shifts in research focus within the DDI landscape.Methods: This groundbreaking investigation employs natural language processing, techniques, specifically Named Entity Recognition (NER) via ScispaCy, and the information extraction model, SciFive, to extract pharmacokinetic (PK) and pharmacodynamic (PD) DDI evidence from PubMed articles spanning January 1962 to July 2023. It reveals key trends and patterns through an innovative network analysis approach. Static network analysis is deployed to discern structural patterns in DDI research, while evolving network analysis is employed to monitor changes in the DDI research trend structures over time.Results: Our compelling results shed light on the scale-free characteristics of pharmacokinetic, pharmacodynamic, and their combined networks, exhibiting power law exponent values of 2.5, 2.82, and 2.46, respectively. In these networks, a select few drugs serve as central hubs, engaging in extensive interactions with a multitude of other drugs. Interestingly, the networks conform to a densification power law, illustrating that the number of DDIs grows exponentially as new drugs are added to the DDI network. Notably, we discovered that drugs connected in PK and PD networks predominantly belong to the same categories defined by the Anatomical Therapeutic Chemical (ATC) classification system, with fewer interactions observed between drugs from different categories.Discussion: The finding suggests that PK and PD DDIs between drugs from different ATC categories have not been studied as extensively as those between drugs within the same categories. By unearthing these hidden patterns, our study paves the way for a deeper understanding of the DDI landscape, providing valuable information for future DDI research, clinical practice, and drug development focus areas.

引言：药物相互作用（DDI）的领域在过去60年间经历了显著的演变，这迫切需要回顾性分析以识别研究趋势和未充分开发的领域。虽然如文献计量学分析等方法为DDI研究提供了有价值的定量视角，但它们尚未成功描绘出药物之间复杂的相互关系。理解这些错综复杂的关系对于解读DDI研究结构随时间的演变和渐进性转变至关重要。本研究采用网络分析方法，揭示药物之间多层面的关系，从而对DDI领域中研究重点的转变提供更为丰富、更为细腻的理解。
方法：这项开创性的研究运用自然语言处理技术，特别是通过ScispaCy进行的命名实体识别（NER）和SciFive信息提取模型，从1962年1月至2023年7月的PubMed文章中提取药代动力学（PK）和药效学（PD）DDI证据。通过创新的网络分析方法揭示了关键趋势和模式。静态网络分析被用于辨别DDI研究中的结构模式，而动态网络分析则被用于监测DDI研究趋势结构随时间的变化。
结果：我们引人注目的结果揭示了药代动力学、药效学及其结合网络的规模无界特性，分别表现出2.5、2.82和2.46的幂律指数值。在这些网络中，少数药物作为中心枢纽，与众多其他药物进行广泛的相互作用。有趣的是，这些网络遵循密度幂律，表明随着新药物被添加到DDI网络中，DDI的数量呈指数增长。值得注意的是，我们发现PK和PD网络中相互连接的药物主要属于由解剖学治疗化学（ATC）分类系统定义的同一类别，不同类别之间的药物相互作用观察较少。
讨论：这一发现表明，来自不同ATC类别的药物之间的PK和PD DDIs尚未像同一类别内的药物相互作用那样得到广泛研究。通过揭示这些隐藏的模式，我们的研究为更深入理解DDI领域铺平了道路，为未来的DDI研究、临床实践和药物开发重点领域提供了宝贵的信息。