Adverse drug reactions and correlations with drug–drug interactions: A retrospective study of reports from 2011 to 2020

Jiang, Huaqiao; Lin, Yanhua; Ren, Weifang; Fang, Zhonghong; Liu, Yujuan; Tan, Xiaofang; Lv, Xiaoqun; Zhang, Ning

doi:10.3389/fphar.2022.923939

Cited by 28 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Drugs are frequently reported to be able to interact with multiple co-administered agents. − Particularly, with the rapidly accumulating number of aging people, there is a significant increase in the incidence of multiple acute/chronic diseases, which greatly promotes the administration of combinatorial therapies and a subsequent threat of DDIs. − Therefore, a comprehensive collection of all drugs that interact with the studied drugs is of great importance to the prevention of potential DDIs, and such full lists of DDIs for each drug were provided in MecDDI. In Figure , the drug page that describes the general information of a drug together with the list of DDIs for this drug is provided.…”

Section: Resultsmentioning

confidence: 99%

MecDDI: Clarified Drug–Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug–Drug Interaction Prediction

Zhang

Zhou

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Drug–drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled “MecDDI” was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: .

show abstract

Section: Resultsmentioning

confidence: 99%

MecDDI: Clarified Drug–Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug–Drug Interaction Prediction

Zhang

Zhou

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…A combination of two antiplatelet agents was frequently implicated in serious ADRs associated with DDIs identified via a spontaneous reporting database from Italy ( Magro et al, 2020 ). In a pharmacovigilance study from China ( Jiang et al, 2022 ), acetylsalicylic acid represented the most common medication implicated in ADRs caused by actual DDIs. The inclusion of a combination of antidepressants belonging to the SSRI and SNRI class with antithrombotics should also be considered.…”

Section: Discussionmentioning

confidence: 99%

Potentially clinically significant drug-drug interactions in older patients admitted to the hospital: A cross-sectional study

Maříková

Vlček

2023

Front. Pharmacol.

View full text Add to dashboard Cite

Background: An international consensus list of potentially clinically significant drug-drug interactions (DDIs) in older people has been recently validated. Our objective was to describe the prevalence and characteristics of drug combinations potentially causing clinically significant DDIs identified in the medication history of older patients admitted to the hospital and the prevalence and characteristics of manifest DDIs–DDIs involved in adverse drug events present at hospital admission, DDIs that contributed to ADE-related hospital admissions, and DDIs involved in drug-related laboratory deviations.Methods: The data were obtained from our previous study that examined the drug-relatedness of hospital admissions to University Hospital Hradec Králové via the department of emergency medicine in the Czech Republic. Patients ≥ 65 years old were included. Drug combinations potentially causing clinically significant DDIs were identified using the international consensus list of potentially clinically significant DDIs in older people.Results: Of the 812 older patients admitted to the hospital, 46% were exposed to drug combinations potentially causing clinically significant DDIs. A combination of medications that affect potassium concentrations accounted for 47% of all drug combinations potentially causing clinically significant DDIs. In 27 cases, potentially clinically significant DDIs were associated with drug-related hospital admissions. In 4 cases, potentially clinically significant DDIs were associated with ADEs that were present at admissions. In 4 cases, the potentially clinically significant DDIs were associated with laboratory deviations. Manifest DDIs that contributed to drug-related hospital admissions most frequently involved antithrombotic agents and central nervous system depressants.Conclusion: The results confirm the findings from the European OPERAM trial, which found that drug combinations potentially causing clinically significant DDIs are very common in older patients. Manifest DDIs were present in 4.3% of older patients admitted to the hospital. In 3.3%, manifest DDIs contributed to drug-related hospital admissions. The difference in the rates of potential and manifest DDIs suggests that if a computerized decision support system is used for alerting potentially clinically significant DDIs in older patients, it needs to be contextualized (e.g., take concomitant medications, doses of medications, laboratory values, and patients’ comorbidities into account).

show abstract

“…extract its drug-flow subgraph Sh,t by DSextract(G, h, t, K, P ) (Algorithm 1); if multiclass DDI prediction then optimize θg and θ k w.r.t. equation (8).…”

Section: B Baselines and Their Codesmentioning

confidence: 99%

“…Identifying DDIs by clinical evidence such as laboratory studies is extremely costly and timeconsuming [6,8]. In recent years, computational techniques especially deep learning approaches are developed to speed up the discovery of potential DDIs.…”

Section: Introductionmentioning

confidence: 99%

Impacts of haze weather on tourist arrivals and destination preference: Analysis based on Baidu Index of 73 scenic spots in Beijing, China

Wang

Xiang-hong

et al. 2020

Journal of Cleaner Production

View full text Add to dashboard Cite

Adverse drug reactions and correlations with drug–drug interactions: A retrospective study of reports from 2011 to 2020

Cited by 28 publications

References 38 publications

MecDDI: Clarified Drug–Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug–Drug Interaction Prediction

MecDDI: Clarified Drug–Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug–Drug Interaction Prediction

Potentially clinically significant drug-drug interactions in older patients admitted to the hospital: A cross-sectional study

Impacts of haze weather on tourist arrivals and destination preference: Analysis based on Baidu Index of 73 scenic spots in Beijing, China

Contact Info

Product

Resources

About