A comprehensive review of computational methods for drug-drug interaction detection
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance,
which provides effective and safe co-prescriptions of multiple drugs. Since laboratory …
which provides effective and safe co-prescriptions of multiple drugs. Since laboratory …
Antifungal drugs TDM: trends and update
Purpose: The increasing burden of invasive fungal infections results in growing challenges
to antifungal (AF) therapeutic drug monitoring (TDM). This review aims to provide an …
to antifungal (AF) therapeutic drug monitoring (TDM). This review aims to provide an …
Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus
(SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead …
(SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead …
A chronological pharmacovigilance network analytics approach for predicting adverse drug events
B Davazdahemami, D Delen - Journal of the American Medical …, 2018 - academic.oup.com
Objectives This study extends prior research by combining a chronological
pharmacovigilance network approach with machine-learning (ML) techniques to predict …
pharmacovigilance network approach with machine-learning (ML) techniques to predict …
Proposing a framework to quantify the potential impact of pharmacokinetic drug–drug interactions caused by a new drug candidate by using real world data about the …
S Dagenais, C Lee, C Cronenberger… - Clinical and …, 2024 - Wiley Online Library
Drug development teams must evaluate the risk/benefit profile of new drug candidates that
perpetrate drug–drug interactions (DDIs). Real‐world data (RWD) can inform this decision …
perpetrate drug–drug interactions (DDIs). Real‐world data (RWD) can inform this decision …
Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System
With increasing trend of polypharmacy, drug‐drug interaction (DDI)‐induced adverse drug
events (ADEs) are considered as a major challenge for clinical practice. As premarketing …
events (ADEs) are considered as a major challenge for clinical practice. As premarketing …
Data‐driven queries between medications and spontaneous preterm birth among 2.5 million pregnancies
Background Our goal was to develop an approach that can systematically identify potential
associations between medication prescribed in pregnancy and spontaneous preterm birth …
associations between medication prescribed in pregnancy and spontaneous preterm birth …
A Case‐Crossover–Based Screening Approach to Identifying Clinically Relevant Drug–Drug Interactions in Electronic Healthcare Data
We sought to develop a semiautomated screening approach using electronic healthcare
data to identify drug–drug interactions (DDIs) that result in clinical outcomes. Using a case …
data to identify drug–drug interactions (DDIs) that result in clinical outcomes. Using a case …
Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources
Abstract Objective Drug–drug interactions (DDIs) can result in adverse and potentially life-
threatening health consequences; however, it is challenging to predict potential DDIs in …
threatening health consequences; however, it is challenging to predict potential DDIs in …
Mining and visualizing high-order directional drug interaction effects using the FAERS database
Abstract 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 …
interactions (DDIs). The use of data mining for detecting effects of drug combinations on …