Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms

Yin, Ling; Figler, Bryan; Huang, Hong; Tu, Yi-Cheng; Ju, Wang; Cheng, Feng

doi:10.1371/journal.pone.0173548

Cited by 30 publications

(19 citation statements)

References 35 publications

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“…Included in these studies are approaches to use various kinds of machine learning including linear kernels (e.g., Support Vector Machines) [18,19,32], non-linear kernels (e.g., Graph Models) [22], random forest [16], various neural network architectures [17,21,26,33], advanced use of linguistic, parts of speech and linguistic features [19,23], unsupervised topical models [25], and semantic features from terminologies or ontologies [16,27,32,35]. In general, the goal of these sophisticated approaches is to accurately extract PDDI data from the large body of scientific literature.…”

Section: Comparison Of the Results With Prior Workmentioning

confidence: 99%

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey (Preprint)

Grizzle¹,

Horn²,

Collins³

et al. 2018

Preprint

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Background: Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well-documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated. Objective: The purpose of this study was to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence. Methods: A convenience sample of 70 drug information experts, including compendia editors, knowledge base vendors, and clinicians were invited via email to complete a survey on identifying PDDI evidence. An online survey was created and included questions addressing: 1) development and conduct of searches; 2) resources used e.g., databases, compendia, search engines etc.; 3) types of keywords used to search for specific PDDI information; 4) study types included and excluded in searches; and 5) search terms used. Search strategy questions focused on six topics of PDDI information: 1) that a PDDI exists; 2) seriousness; 3) clinical consequences; 4) management options; 5) mechanism; and 6) health outcomes. Results: Twenty participants (response rate 29%) completed the survey. The majority (85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists with 60% having over ten years' experience. Over half (55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants develop (90%) and conduct (95%) search strategies without librarian assistance. PubMed (100%) and Google Scholar (55%) are most commonly searched for articles, followed by Google Web Search (35%) and EMBASE (15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20; 45%), Micromedex (8/20; 40%), Drugs@FDA (17/20; 85%), and DailyMed (13/20; 65%). Facts and Comparisons was the most commonly used compendia (8/20; 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify existence of PDDIs and determine their mechanism than for the other four attributes (seriousness, consequences, management, and health outcomes). With respect to types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not. Conclusions:The results suggest that drug information experts use various keyword strategies and various database and web resources depending on the PDDI evidence they are seeking to identify.

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Section: Comparison Of the Results With Prior Workmentioning

confidence: 99%

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey (Preprint)

Grizzle¹,

Horn²,

Collins³

et al. 2018

Preprint

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“…Generally, for the prediction of drug-drug interactions associated to CYP inhibition, various in silico approaches and web based computational tools have been reported in literature [134][135][136][137][138][139]. These include WhichCyp [134], vNN Web Server [136], admetSAR [140] and yet other freely available tools based on classification models for the prediction of CYP inhibition potential associated with new chemical entities.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

Kiani

Jabeen

2019

Computation

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The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.

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“…We replaced the cited-by with the similar journals associated with a given journal and applied it to inspect the association between cited-by and similar journals among Both articles [35,36] had applied SNA, and medical subject headings (MESH) terms based on PubMed articles to characterize interesting phenomena of the research.…”

Section: What This Knowledge Adds To What We Already Knewmentioning

confidence: 99%

Mobile-Friendly Dashboards to Display Associations Between Cited-by and Similar Journals on Mobile Health Research: A Bibliometric Study (Preprint)

Chien¹,

Wang²,

Shao³

et al. 2018

Preprint

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Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms

Cited by 30 publications

References 35 publications

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey (Preprint)

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey (Preprint)

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

Mobile-Friendly Dashboards to Display Associations Between Cited-by and Similar Journals on Mobile Health Research: A Bibliometric Study (Preprint)

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