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抗抑郁药物遗传学的全面特征:对重度抑郁症研究的系统评价

Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder.

作者信息

Grant Caroline W, Delaney Karina, Jackson Linsey E, Bobo Justin, Hassett Leslie C, Wang Liewei, Weinshilboum Richard M, Croarkin Paul E, Gentry Melanie T, Moyer Ann M, Athreya Arjun P

机构信息

Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.

Department of Clinical and Translational Sciences, Mayo Clinic, Rochester, Minnesota, USA.

出版信息

Clin Transl Sci. 2025 Jun;18(6):e70255. doi: 10.1111/cts.70255.

DOI:10.1111/cts.70255
PMID:40465332
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12135885/
Abstract

Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/methodologies, and outcomes investigated in the context of MDD. The secondary aim was to identify clinical genetic panels indicated for antidepressant prescribing and summarize their genes and variants. Screening of N = 5793 articles yielded N = 390 for inclusion, largely comprising adult (≥ 18 years) populations. Top-studied variants identified in the search were discussed and compared with those represented on the N = 34 clinical genetic panels that were identified. Summarization of articles revealed sources of heterogeneity across studies and low rates of replicability of pharmacogenetic associations. Heterogeneity was present in outcome definitions, treatment regimens, and differential inclusion of mediating variables in analyses. Efficacy outcomes (i.e., response, remission) were studied at greater frequency than adverse-event outcomes. Studies that used advanced analytical approaches, such as machine learning, to integrate variants with complimentary biological datatypes were fewer in number but achieved higher rates of significant associations with treatment outcomes than candidate variant approaches. As large biological datasets become more prevalent, machine learning will be an increasingly valuable tool for parsing the complexity of antidepressant response. This review provides valuable context and considerations surrounding pharmacogenetic associations in MDD which will help inform future research and translation efforts for guiding antidepressant care.

摘要

药物遗传学是一种很有前景的策略,有助于为重度抑郁症(MDD)患者提供个性化护理。目前正在进行研究,以确定预测抗抑郁治疗效果的最佳基因标记。本系统评价的主要目的是总结抗抑郁药物遗传学研究,以增进对MDD背景下所研究的基因、变异、数据类型/方法以及结果的理解。次要目的是确定适用于抗抑郁药物处方的临床基因检测板,并总结其基因和变异。对5793篇文章进行筛选后,纳入了390篇文章,主要包括成人(≥18岁)人群。对搜索中确定的研究最多的变异进行了讨论,并与在确定的34个临床基因检测板上所代表的变异进行了比较。文章总结揭示了各研究之间的异质性来源以及药物遗传学关联的低重复性。异质性存在于结果定义、治疗方案以及分析中介变量的差异纳入中。对疗效结果(即反应、缓解)的研究频率高于不良事件结果。使用先进分析方法(如机器学习)将变异与补充生物数据类型整合的研究数量较少,但与治疗结果的显著关联率高于候选变异方法。随着大型生物数据集越来越普遍,机器学习将成为解析抗抑郁反应复杂性的越来越有价值的工具。本综述提供了围绕MDD药物遗传学关联的有价值背景和考量,这将有助于为指导抗抑郁护理的未来研究和转化工作提供信息。

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Effects of cannabidiol and Δ-tetrahydrocannabinol on cytochrome P450 enzymes: a systematic review.大麻二酚和Δ-四氢大麻酚对细胞色素 P450 酶的影响:系统评价。
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The role of pharmacogenetics in the treatment of major depressive disorder: a critical review.药物遗传学在重度抑郁症治疗中的作用:一项批判性综述。
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