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Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy
PURPOSE: Pharmacogenetic counselling is a complex task and requires the efforts of an interdisciplinary team, which cannot be implemented in most cases. Therefore, simple rules could help to minimize the risk of medications incompatible with each other or with frequent genetic variants. PATIENTS AND...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9394521/ https://www.ncbi.nlm.nih.gov/pubmed/36004008 http://dx.doi.org/10.2147/PGPM.S368606 |
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author | Salamone, Sandro Spirito, Sara Simmaco, Maurizio Unger, Marius Preissner, Saskia Gohlke, Björn-Oliver Eckert, Andreas Preissner, Robert |
author_facet | Salamone, Sandro Spirito, Sara Simmaco, Maurizio Unger, Marius Preissner, Saskia Gohlke, Björn-Oliver Eckert, Andreas Preissner, Robert |
author_sort | Salamone, Sandro |
collection | PubMed |
description | PURPOSE: Pharmacogenetic counselling is a complex task and requires the efforts of an interdisciplinary team, which cannot be implemented in most cases. Therefore, simple rules could help to minimize the risk of medications incompatible with each other or with frequent genetic variants. PATIENTS AND METHODS: One hundred and eighty-four multi-morbid Caucasian patients suffering from side effects or inefficient therapy were enrolled and genotyped. Their medication was analyzed by a team of specialists using Drug-PIN(®) (medication support system) and individual recommendations for 34 drug classes were generated. RESULTS: In each of the critical drug classes, 50% of the drugs cannot be recommended to be prescribed in typical drug cocktails. PPIs and SSRI/SNRIs represent the most critical drug classes without showing a single favorable drug. Among the well-tolerated drugs (not recommended for less than 5% of the patients) are metamizole, celecoxib, olmesartan and famotidine. For each drug class, a ranking of active ingredients according to their suitability is presented. CONCLUSION: Genotyping and its profound analysis are not available in many settings today. The consideration of frequent alterations of metabolic elimination routes and drug–drug–gene interactions by using simple rankings can help to avoid many incompatibilities, side effects and inefficient therapies. |
format | Online Article Text |
id | pubmed-9394521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-93945212022-08-23 Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy Salamone, Sandro Spirito, Sara Simmaco, Maurizio Unger, Marius Preissner, Saskia Gohlke, Björn-Oliver Eckert, Andreas Preissner, Robert Pharmgenomics Pers Med Original Research PURPOSE: Pharmacogenetic counselling is a complex task and requires the efforts of an interdisciplinary team, which cannot be implemented in most cases. Therefore, simple rules could help to minimize the risk of medications incompatible with each other or with frequent genetic variants. PATIENTS AND METHODS: One hundred and eighty-four multi-morbid Caucasian patients suffering from side effects or inefficient therapy were enrolled and genotyped. Their medication was analyzed by a team of specialists using Drug-PIN(®) (medication support system) and individual recommendations for 34 drug classes were generated. RESULTS: In each of the critical drug classes, 50% of the drugs cannot be recommended to be prescribed in typical drug cocktails. PPIs and SSRI/SNRIs represent the most critical drug classes without showing a single favorable drug. Among the well-tolerated drugs (not recommended for less than 5% of the patients) are metamizole, celecoxib, olmesartan and famotidine. For each drug class, a ranking of active ingredients according to their suitability is presented. CONCLUSION: Genotyping and its profound analysis are not available in many settings today. The consideration of frequent alterations of metabolic elimination routes and drug–drug–gene interactions by using simple rankings can help to avoid many incompatibilities, side effects and inefficient therapies. Dove 2022-08-18 /pmc/articles/PMC9394521/ /pubmed/36004008 http://dx.doi.org/10.2147/PGPM.S368606 Text en © 2022 Salamone et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Salamone, Sandro Spirito, Sara Simmaco, Maurizio Unger, Marius Preissner, Saskia Gohlke, Björn-Oliver Eckert, Andreas Preissner, Robert Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy |
title | Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy |
title_full | Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy |
title_fullStr | Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy |
title_full_unstemmed | Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy |
title_short | Prescription Advice Based on Data of Drug-Drug-Gene Interaction of Patients with Polypharmacy |
title_sort | prescription advice based on data of drug-drug-gene interaction of patients with polypharmacy |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9394521/ https://www.ncbi.nlm.nih.gov/pubmed/36004008 http://dx.doi.org/10.2147/PGPM.S368606 |
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