Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

Zhang, Pengyue; Wu, Hui; Chiang, Chien Wei; Wang, Lei; Binkheder, Samar; Zeng, Donglin; Quinney, Sara K.; Li, Lang

doi:10.1002/psp4.12267

Cited by 15 publications

(10 citation statements)

References 86 publications

(188 reference statements)

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“…To deal with this problem, data scientists in the field use both manual and automatic methods. The former involve manual curation by experts for each context, leading to the development of context-specific lexica and corpora, such as scientific literature reporting pharmacokinetics (83,84) or pharmacogenetics (85,86) studies, tweets mentioning medication intake (87), or Instagram user timelines annotated with standardized drug names and symptoms (3). There is also a corpus for comparative pharmacovigilance comprising 1,000 tweets and 1,000 PubMed sentences, with entities such as drugs, diseases, and symptoms (88).…”

Section: Pharmacovigilancementioning

confidence: 99%

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Correia

Wood

Bollen

et al. 2020

Annu. Rev. Biomed. Data Sci.

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Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

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Section: Pharmacovigilancementioning

confidence: 99%

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Correia

Wood

Bollen

et al. 2020

Annu. Rev. Biomed. Data Sci.

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“…To establish causation between DDIs and risk of ADE, studies must incorporate additional methods. Some researchers have utilized pathway analyses to develop drug‐gene‐drug links among potential DDIs . In vitro exploration of potential DDI mechanisms, such as cytochrome P450 or transporter induction or inhibition, coupled with pharmacokinetic in vitro in vivo extrapolation approaches may also be employed …”

Section: Alternative Approaches To Support Findings From Clinical Datamentioning

confidence: 99%

“…Other studies have applied natural language processing and machine learning algorithms to published literature, social media (e.g., Instagram and Twitter), or biological and chemical databases (e.g., KEGG, DrugBank, and PubChem) to predict DDIs. For a more detailed description of data and bioinformatics approaches utilized in translational DDI research, the reader is directed to recently published reviews …”

mentioning

confidence: 99%

Opportunities and Challenges of Using Big Data to Detect Drug‐Drug Interaction Risk

Quinney

2019

Clin Pharma and Therapeutics

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“…Drug-drug interactions (DDIs), a major cause of adverse drug events (ADEs), are a serious global health concern, and a severe detriment to public health. In fact, over 500,000 serious medical complications per year, a portion of which are fatal, result from multiple drug consumption [1]. The most common cause of ADEs is DDIs, and more than three-fourths of American elderly citizens take two or more drugs per day [2].…”

Section: Introductionmentioning

confidence: 99%

Mining and visualizing high-order directional drug interaction effects using the FAERS database

Yao

Tsang

Sun

et al. 2020

BMC Med Inform Decis Mak

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Background Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs. Methods We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset. Results Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs. Conclusions We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care. Availability and implementation http://lishenlab.com/d3i/explorer.html

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Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

Cited by 15 publications

References 86 publications

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Opportunities and Challenges of Using Big Data to Detect Drug‐Drug Interaction Risk

Mining and visualizing high-order directional drug interaction effects using the FAERS database

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