P376: Microdeletion and microduplication of 15q11.2-q13 share a neurodevelopmental phenotype
Notice bibliographique
Résumé
Introduction: Interpatient variability in response to pharmacotherapy can significantly affect treatment outcomes and the management of cardiovascular events.Commonly used antiplatelet agents such as clopidogrel (Plavix), prasugrel (Effient), and ticagrelor (Brilinta) help reduce thrombotic events.Clopidogrel, in particular, requires activation via the CYP2C19 enzyme, which is responsible for metabolizing approximately 10% of all drugs, including clopidogrel and prasugrel.Genetic variations in CYP2C19, such as the *2 or *3 alleles, can result in a loss-of-function phenotype, leading to a diminished response to clopidogrel.This reduced efficacy is associated with an increased risk of adverse cardiovascular events.In contrast, CYP2C19 variants do not decrease the effectiveness of prasugrel, which is a more potent antiplatelet agent compared to clopidogrel, though it carries a higher risk of bleeding.Both CYP2C19 and CYP3A4 enzymes play crucial roles in determining the effectiveness of these antiplatelet agents.While clopidogrel and prasugrel are primarily influenced by CYP2C19 metabolism, ticagrelor, a newer class of antiplatelet drug, is predominantly metabolized by CYP3A4, which can also impact its therapeutic response.Methods: PMC Diagnostics (PMCDx) utilizes pharmacogenetic (PGx) testing as a tool to personalize the management of cardiovascular disease, particularly in the context of antiplatelet medications.We have conducted PGx testing on 54 participants from diverse ethnic backgrounds, including, but not limited to, White, African American, and Asian populations.The pharmacogenetic responses to three antiplatelet medications-clopidogrel (Plavix), prasugrel (Effient), and ticagrelor (Brilinta)were evaluated by analyzing the metabolic activity of the CYP2C19 and CYP3A4 enzymes.Clinical management guidelines based on PGx findings for these medications have been established by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 7 and PharmGKB.Results: PMCDx studied 54 clinical samples.The results showed that 3 patients showed poor metabolizers, and 15 patients showed intermediate metabolizers, a total of 33% of patients have an increased risk of pharmacotherapy failure.Clopidogrel should be avoided to these patients.There are 10 cases, 18.5% of the patients in this study showed one or two decreased function alleles detected in CYP3A4.Ticagrelor (Brilinta) may present with higher plasma concentrations of the active medication, thus an increased risk of side effects may occur.This medication should be either avoided or the patient's medication response should be monitored to the guide dosing.Conclusion: Implementing population-wide screening for CYP2C19 and CYP3A4 variants not only identifies individuals who are poor or intermediate metabolizers but also facilitates tailored treatment strategies.The significance of the pharmacogenetics testing lies in its potential to enhance medication adherence, optimize therapeutic regimens, and ultimately improve patient outcomes while reducing healthcare costs associated with adverse drug reactions and ineffective therapies.Integrating pharmacogenetics into routine clinical practice represents a crucial advancement in the precision medicine landscape, promising enhanced efficacy in cardiovascular disease management.The use of pharmacogenetic testing can serve as a foundational element in developing a more personalized and effective approach to treating cardiovascular disease.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».