Suppr超能文献

组合药物基因组学算法可预测重度抑郁症患者舍曲林的代谢情况。

Combinatorial pharmacogenomic algorithm is predictive of sertraline metabolism in patients with major depressive disorder.

机构信息

University of Michigan Eisenberg Family Comprehensive Depression Center and Department of Psychiatry, and National Network of Depression Centers, 4250 Plymouth Rd, Ann Arbor, MI 48109.

Myriad Neuroscience, 6960 Cintas Blvd, Mason, OH 45040.

出版信息

Psychiatry Res. 2022 Feb;308:114354. doi: 10.1016/j.psychres.2021.114354. Epub 2021 Dec 22.

Abstract

Pharmacogenomic testing can be used to guide medication selection in patients with major depressive disorder (MDD). Currently, there is no consensus on which gene or genes to consider in medication management. Here, we assessed the clinical validity of the combinatorial pharmacogenomic algorithm to predict sertraline blood levels in a subset of patients enrolled in the Genomics Used to Improve DEpression Decisions (GUIDED) trial. Patients who reported taking sertraline within ≤2 weeks of the screening blood draw were included. All patients received combinatorial pharmacogenomic testing, which included a weighted assessment of individual phenotypes for multiple pharmacokinetic genes relevant for sertraline (CYP2C19, CYP2B6, and CYP3A4). Sertraline blood levels were compared between phenotypes based on: 1) the pharmacokinetic portion of the combinatorial pharmacogenomic algorithm, and 2) individual genes. When evaluated separately, individual genes (for CYP2C19 and CYP2B6) and the combinatorial algorithm were significant predictors of sertraline blood levels. However, in multivariate analyses that included individual genes and the combinatorial pharmacogenomic algorithm, only the combinatorial pharmacogenomic algorithm remained a significant predictor of sertraline blood levels. These findings support the clinical validity of the combinatorial pharmacogenomic algorithm, in that it is a superior predictor of sertraline blood levels compared to individual genes.

摘要

药物基因组学检测可用于指导重度抑郁症(MDD)患者的药物选择。目前,对于药物管理中应考虑哪些基因尚无共识。在这里,我们评估了组合式药物基因组算法在基因组学用于改善抑郁决策(GUIDED)试验中一部分入组患者中预测舍曲林血药浓度的临床有效性。报告在筛选采血后 2 周内服用舍曲林的患者被纳入研究。所有患者均接受了组合式药物基因组学检测,该检测包括对多个与舍曲林相关的药物代谢基因(CYP2C19、CYP2B6 和 CYP3A4)的个体表型进行加权评估。根据以下两种方法对表型进行舍曲林血药浓度比较:1)组合式药物基因组算法的药物代谢部分,和 2)个别基因。单独评估时,个别基因(CYP2C19 和 CYP2B6)和组合算法是舍曲林血药浓度的显著预测因子。然而,在包括个体基因和组合式药物基因组学算法的多变量分析中,只有组合式药物基因组学算法仍然是舍曲林血药浓度的显著预测因子。这些发现支持了组合式药物基因组学算法的临床有效性,因为它是舍曲林血药浓度的优越预测因子,优于个别基因。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验