A Minimal Information Model for Potential Drug-Drug Interactions

Abstract

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

Comparison of potential psychiatric drug interactions in six drug interaction database programs: A replication study after 2 years of updates

“…Monteith et al, 2015;Monteith, Glenn, Geddes, et al, 2016;Quinney, 2019;Tornio et al, 2019;Vilar et al, 2018). There is also increasing recognition of the need to improve how DDI knowledge is standardized and presented for clinical decision making (Hochheiser et al, 2021;McEvoy et al, 2017;Payne et al, 2015;Scheife et al, 2015;Tilson et al, 2016). The results of this study emphasize the need for and importance of international standardization efforts.…”

“…However, there are a myriad of challenges related to the resources and the analytical methods used in data mining including data quality, combining diverse data, sampling bias, algorithm transparency and bias, generalizability, rare occurrences, establishing temporal patterns, and the lack of standardization in defining DDI signals and events (Monteith & Glenn 2019; Monteith et al., 2015; Monteith, Glenn, Geddes, et al., 2016; Quinney, 2019; Tornio et al., 2019; Vilar et al., 2018). There is also increasing recognition of the need to improve how DDI knowledge is standardized and presented for clinical decision making (Hochheiser et al., 2021; McEvoy et al., 2017; Payne et al., 2015; Scheife et al., 2015; Tilson et al., 2016). The results of this study emphasize the need for and importance of international standardization efforts.…”

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

“…The mechanism of DDI was analyzed and identified by a professional team of clinical pharmacists and further classified according to the specific aspects of PK or PD that affect the drug. Finally, the severity of each DDI was reviewed and categorized into different levels: major, moderate, and minor . As defined in the previous publication, “major” indicates that the corresponding DDI is life-threatening or requires medical intervention for reducing serious adverse outcomes, which should be avoided in clinical practice; “moderate” denotes that the studied DDI exacerbates the condition of patients, which asks for a modulation in the dosage/usage of current therapy; “minor” refers to that the studied DDI limits the clinical outcome but does not require modulation in therapy.…”

A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions

“…For example, a rule-based method has been utilized to discover DDIs from numerous collections of unstructured texts [ 20 , 21 ]. Others that have been used include knowledge graphs [ 4 , 10 ], machine learning [ 22 , 23 ], and deep learning [ 24 ].…”

“…Interactions such as toxicity or reduced efficacy may occur when two or more agents are coadministered, necessitating dose adjustment or switching to a different therapeutic intervention. While we may prevent additional DDIs by contraindicating drug pairs for which adverse events have been observed in clinic, it remains necessary to develop new methods to improve our understanding of known, unobserved, and potential DDIs [ 4 ].…”