Oral Dipyridamole-Associated Circulatory Collapse

Jolma, Pasi; Ollikainen, Jyrki; Uurto, Ilkka

doi:10.1016/j.jstrokecerebrovasdis.2018.08.009

Cited by 3 publications

(1 citation statement)

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“…On another SOC "Gastr", short for "Gastrointestinal disorders", the gene PDE3 A and its polymorphism have been reported to cause nausea, especially one mutation rs12305038 [28]. This mutation is in our list and could be linked to the well-predicted compounds, sildenafil and dipyridamole, known to cause nausea [29,30].…”

Section: Mutation Analysismentioning

confidence: 99%

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks

Dafniet,

Taboureau

2024

Molecular Informatics

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Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome‐wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR‐drug‐target‐mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR‐drug‐target‐mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.

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Section: Mutation Analysismentioning

confidence: 99%