Drug-drug interaction discovery and demystification using Semantic Web technologies

Noor, Adeeb; Assiri, Abdullah; Ayvaz, Serkan; Clark, Connor; Dumontier, Michel

doi:10.1093/jamia/ocw128

Cited by 30 publications

(37 citation statements)

References 54 publications

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“…It is therefore understandable that none are listed as an interaction (leading to a serious bleed) in clopidogrel's label, Micromedex, or Lexicomp. Our automated screening approach did not consider preexisting mechanistic knowledge, which may be a poor predictor of clinically important DDIs because of incomplete knowledge of off‐target drug effects, failure to identify complex multipathway interactions, and traditional overreliance on commonly considered mechanisms, most notably cytochrome P450 (CYP) inhibition. This is exemplified by the fact that some of our DDI signals may have identifiable putative mechanisms (e.g., primidone induces CYP2C19 and other hepatic isozymes), while others do not.…”

Section: Discussionmentioning

confidence: 99%

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real‐World Evidence via Automated High‐Throughput Pharmacoepidemiologic Screening

Leonard

Zhou

Brensinger

et al. 2019

Clin Pharma and Therapeutics

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Few population‐based studies have examined bleeding associated with clopidogrel drug–drug interactions (DDIs). We sought to identify precipitant drugs taken concomitantly with clopidogrel (an object drug) that increased serious bleeding rates. We screened 2000–2015 Optum commercial health insurance claims to identify DDI signals. We performed self‐controlled case series studies for clopidogrel plus precipitant pairs, examining associations with gastrointestinal bleeding or intracranial hemorrhage. To distinguish native bleeding effects of a precipitant, we reexamined associations using pravastatin as a negative control object drug. Among 431 analyses, 28 clopidogrel plus precipitant pairs were statistically significantly positively associated with serious bleeding. Ratios of rate ratios ranged from 1.13–3.94. Among these pairs, 13 were expected given precipitant drugs alone increased and/or were harbingers of serious bleeding. The remaining 15 pairs constituted new DDI signals, none of which are currently listed in two major DDI knowledge bases.

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Section: Discussionmentioning

confidence: 99%

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real‐World Evidence via Automated High‐Throughput Pharmacoepidemiologic Screening

Leonard

Zhou

Brensinger

et al. 2019

Clin Pharma and Therapeutics

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“…For those interactions, measures of similarity among drugs and mechanistic information (Ferdousi et al, 2017), Semantic Web Technologies and Linked Data in the life sciences (Kamdar and Musen, 2017), web data (Fokoue et al, 2016), textual (Abdelaziz et al, 2017;Tari et al, 2010), and interaction networks (Bai and Abernethy, 2013;Park et al, 2015) have been developed and successfully applied to predict DDIs and ways in which drugs interact. Moreover, features that have been used to identify and better understand DDIs and mechanisms of interaction include molecular structure (Vilar et al, 2012), interaction profile fingerprints (Vilar et al, 2013), phenotypic, therapeutic, chemical, and genomic properties (Cheng and Zhao, 2014;Ferdousi et al, 2017), model organism phenotypes (Hoehndorf et al, 2013), multiple interaction mechanisms (Noor et al, 2016), and drug and protein properties (Kamdar and Musen, 2017). These methods can predict and identify different mechanisms of interaction in DDIs.…”

Section: )mentioning

confidence: 99%

“…We broadly distinguish between mechanisms of DDIs due to pharmacokinetic, pharmacodynamic, multiple-pathway, and pharmacogenetic interactions. Specifically, we utilize a rule-based inference engine for drug-drug interaction discovery and demystification (D3) (Noor et al, 2016) to annotate known DDIs with their mechanisms of interaction. D3 applies rules on a knowledge graph to distinguish between five pharmacokinetic mechanisms of interaction: protein binding, metabolic induction, metabolic inhibition, transporter induction, and transporter inhibition.…”

Section: Annotation With Ddi Mechanismsmentioning

confidence: 99%

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D4: Deep Drug-drug interaction Discovery and Demystification

Noor

Liu-Wei

Barnawi

et al. 2020

Preprint

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Motivation: Drug-drug interactions (DDIs) are complex processes which may depend on many clinical and non-clinical factors. Identifying and distinguishing ways in which drugs interact remains a challenge. To minimize DDIs and to personalize treatment based on accurate stratification of patients, it is crucial that mechanisms of interaction can be identified. Most DDIs are a consequence of metabolic mechanisms of interaction, but DDIs with different mechanisms occur less frequently and are therefore more difficult to identify. Results: We developed a method (D4) for computationally identifying potential DDIs and determining whether they interact based on one of eleven mechanisms of interaction. D4 predicts DDIs and their mechanisms through features that are generated through a deep learning approach from phenotypic and functional knowledge about drugs, their side-effects and targets. Our findings indicate that our method is able to identify known DDIs with high accuracy and that D4 can determine mechanisms of interaction. We also identify numerous novel and potential DDIs for each mechanism of interaction and evaluate our predictions using DDIs from adverse event reporting systems.

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“…Experimental results have demonstrated that the proposed approach performs better than other baselines. Other learning approaches have also been proposed to predict new DDIs [11], [20]- [26]. However, the above-mentioned methods are not capable of predicting unknown interactions if data for known DDIs are not available.…”

Section: Introductionmentioning

confidence: 99%

Novel method for the prediction of drug-drug Interaction based on Gene Expression profiles

Taguchi

Turki

2020

Preprint

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The accurate prediction of new interactions between drugs is important for avoiding unknown (mild or severe) adverse reactions to drug combinations. The development of effective in silico methods for evaluating drug interactions based on gene expression data requires an understanding of how various drugs alter gene expression. Current computational methods for the prediction of drug-drug interactions (DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited in the absence of known predictive DDIs. To improve DDIs' interpretation, a recent study has demonstrated strong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervised learning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D. We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. We found that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot always be attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.

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Drug-drug interaction discovery and demystification using Semantic Web technologies

Abstract: The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.

Cited by 30 publications

References 54 publications

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real‐World Evidence via Automated High‐Throughput Pharmacoepidemiologic Screening

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real‐World Evidence via Automated High‐Throughput Pharmacoepidemiologic Screening

D4: Deep Drug-drug interaction Discovery and Demystification

Novel method for the prediction of drug-drug Interaction based on Gene Expression profiles

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