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Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making

Potential drug-drug interactions (DDIs) are a core concern across medical decision support systems. Among healthcare practitioners, the common practice for screening these interactions is via computer software. However, as real-world negative reporting is missing, counterexamples that serve as contr...

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Detalles Bibliográficos
Autores principales: Assiri, Abdullah, Noor, Adeeb
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013308/
https://www.ncbi.nlm.nih.gov/pubmed/35437468
http://dx.doi.org/10.1155/2022/8904342
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author Assiri, Abdullah
Noor, Adeeb
author_facet Assiri, Abdullah
Noor, Adeeb
author_sort Assiri, Abdullah
collection PubMed
description Potential drug-drug interactions (DDIs) are a core concern across medical decision support systems. Among healthcare practitioners, the common practice for screening these interactions is via computer software. However, as real-world negative reporting is missing, counterexamples that serve as contradictory evidence may exist. In this study, we have developed an anti-DDI resource, a set of drug combinations having negative reported interactions. This resource was created from a set of the top 200 most-used drugs, resulting in 14365 prospective negative reported DDI pairs. During analysis and filtering, 2110 DDIs (14.69%) were found in publicly free DDI resources, another 11130 (77.48%) were filtered by a rule-based inference engine incorporating ten mechanisms of interaction, and 208 were identified through commercial resources. Additionally, 90 pairs were removed due to recent FDA approvals or being unapplicable in clinical use. The final set of 827 drug pairs represents combinations potentially having negative reported interactions. The anti-DDI resource is intended to provide a distinctly different direction from the state of the art and establish a ground focus more centered on the evaluation and utilization of existing knowledge for performing thorough assessments. Our negative reported DDIs resource shall provide healthcare practitioners with a level of certainty on DDIs that is worth investigating.
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spelling pubmed-90133082022-04-17 Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making Assiri, Abdullah Noor, Adeeb J Healthc Eng Research Article Potential drug-drug interactions (DDIs) are a core concern across medical decision support systems. Among healthcare practitioners, the common practice for screening these interactions is via computer software. However, as real-world negative reporting is missing, counterexamples that serve as contradictory evidence may exist. In this study, we have developed an anti-DDI resource, a set of drug combinations having negative reported interactions. This resource was created from a set of the top 200 most-used drugs, resulting in 14365 prospective negative reported DDI pairs. During analysis and filtering, 2110 DDIs (14.69%) were found in publicly free DDI resources, another 11130 (77.48%) were filtered by a rule-based inference engine incorporating ten mechanisms of interaction, and 208 were identified through commercial resources. Additionally, 90 pairs were removed due to recent FDA approvals or being unapplicable in clinical use. The final set of 827 drug pairs represents combinations potentially having negative reported interactions. The anti-DDI resource is intended to provide a distinctly different direction from the state of the art and establish a ground focus more centered on the evaluation and utilization of existing knowledge for performing thorough assessments. Our negative reported DDIs resource shall provide healthcare practitioners with a level of certainty on DDIs that is worth investigating. Hindawi 2022-04-09 /pmc/articles/PMC9013308/ /pubmed/35437468 http://dx.doi.org/10.1155/2022/8904342 Text en Copyright © 2022 Abdullah Assiri and Adeeb Noor. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Assiri, Abdullah
Noor, Adeeb
Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making
title Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making
title_full Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making
title_fullStr Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making
title_full_unstemmed Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making
title_short Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making
title_sort anti-ddi resource: a dataset for potential negative reported interaction combinations to improve medical research and decision-making
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013308/
https://www.ncbi.nlm.nih.gov/pubmed/35437468
http://dx.doi.org/10.1155/2022/8904342
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