Revealing the dynamic landscape of drug-drug interactions through network analysis.
Revealing the dynamic landscape of drug-drug interactions through network analysis.
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DOI：
10.3389/fphar.2023.1211491
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发表时间：
2023
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
--
作者：
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.
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影响因子：
--
作者：
Bayat Mokhtari R;Homayouni TS;Baluch N;Morgatskaya E;Kumar S;Das B;Yeger H
通讯作者：
Yeger H
DOI：
10.1038/nrd4461
发表时间：
2015-01
期刊：
Nature reviews. Drug discovery
影响因子：
--
作者：
Nigam SK
通讯作者：
Nigam SK
影响因子：
4.7
作者：
Kotlinska-Lemieszek, Aleksandra;Paulsen, Ornulf;Klepstad, Pal
通讯作者：
Klepstad, Pal
影响因子：
6.7
作者：
Niu, Jin;Straubinger, Robert M.;Mager, Donald E.
通讯作者：
Mager, Donald E.
DOI：
10.1126/science.1187816
发表时间：
2010-06-04
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bloom JD;Gong LI;Baltimore D
通讯作者：
Baltimore D