PMID- 37860114
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240210
IS  - 1663-9812 (Print)
IS  - 1663-9812 (Electronic)
IS  - 1663-9812 (Linking)
VI  - 14
DP  - 2023
TI  - Revealing the dynamic landscape of drug-drug interactions through network 
      analysis.
PG  - 1211491
LID - 10.3389/fphar.2023.1211491 [doi]
LID - 1211491
AB  - 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.
CI  - Copyright (c) 2023 Jeong, Malin, Nelson, Su, Li and Chen.
FAU - Jeong, Eugene
AU  - Jeong E
AD  - Department of Biomedical Informatics, School of Medicine, Vanderbilt University 
      Medical Center, Nashville, TN, United States.
FAU - Malin, Bradley
AU  - Malin B
AD  - Department of Biomedical Informatics, School of Medicine, Vanderbilt University 
      Medical Center, Nashville, TN, United States.
AD  - Department of Biostatistics, School of Medicine, Vanderbilt University Medical 
      Center, Nashville, TN, United States.
AD  - Department of Computer Science, School of Engineering, Vanderbilt University, 
      Nashville, TN, United States.
FAU - Nelson, Scott D
AU  - Nelson SD
AD  - Department of Biomedical Informatics, School of Medicine, Vanderbilt University 
      Medical Center, Nashville, TN, United States.
FAU - Su, Yu
AU  - Su Y
AD  - Department of Computer Science and Engineering, College of Engineering, The Ohio 
      State University, Columbus, OH, United States.
FAU - Li, Lang
AU  - Li L
AD  - Department of Biomedical Informatics, College of Medicine, The Ohio State 
      University, Columbus, OH, United States.
FAU - Chen, You
AU  - Chen Y
AD  - Department of Biomedical Informatics, School of Medicine, Vanderbilt University 
      Medical Center, Nashville, TN, United States.
AD  - Department of Computer Science, School of Engineering, Vanderbilt University, 
      Nashville, TN, United States.
LA  - eng
GR  - R01 LM014199/LM/NLM NIH HHS/United States
GR  - T15 LM007450/LM/NLM NIH HHS/United States
PT  - Journal Article
DEP - 20231003
PL  - Switzerland
TA  - Front Pharmacol
JT  - Frontiers in pharmacology
JID - 101548923
PMC - PMC10583566
OTO - NOTNLM
OT  - natural language Processing
OT  - network analysis
OT  - pharmacodynamic drug-drug interaction
OT  - pharmacokinetic drug-drug interaction
OT  - research trend
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/10/20 06:42
MHDA- 2023/10/20 06:43
PMCR- 2023/10/03
CRDT- 2023/10/20 04:27
PHST- 2023/04/24 00:00 [received]
PHST- 2023/09/18 00:00 [accepted]
PHST- 2023/10/20 06:43 [medline]
PHST- 2023/10/20 06:42 [pubmed]
PHST- 2023/10/20 04:27 [entrez]
PHST- 2023/10/03 00:00 [pmc-release]
AID - 1211491 [pii]
AID - 10.3389/fphar.2023.1211491 [doi]
PST - epublish
SO  - Front Pharmacol. 2023 Oct 3;14:1211491. doi: 10.3389/fphar.2023.1211491. 
      eCollection 2023.