From the Department of Psychiatry and Behavioral Sciences and the Center for Therapeutic Innovation, University of Miami Miller School of Medicine, Miami; Butler Hospital and the Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, R.I.; the Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison; the Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif.; Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif.; the Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta; the Department of Psychiatry, Massachusetts General Hospital, Charlestown; and the Center on Aging, University of Miami Leonard M. Miller School of Medicine, Miami.
Am J Psychiatry. 2018 Sep 1;175(9):873-886. doi: 10.1176/appi.ajp.2018.17111282. Epub 2018 Apr 25.
The accrual and analysis of genomic sequencing data have identified specific genetic variants that are associated with major depressive disorder. Moreover, substantial investigations have been devoted to identifying gene-drug interactions that affect the response to antidepressant medications by modulating their pharmacokinetic or pharmacodynamic properties. Despite these advances, individual responses to antidepressants, as well as the unpredictability of adverse side effects, leave clinicians with an imprecise prescribing strategy that often relies on trial and error. These limitations have spawned several combinatorial pharmacogenetic testing products that are marketed to physicians. Typically, combinatorial pharmacogenetic decision support tools use algorithms to integrate multiple genetic variants and assemble the results into an easily interpretable report to guide prescribing of antidepressants and other psychotropic medications. The authors review the evidence base for several combinatorial pharmacogenetic decision support tools whose potential utility has been evaluated in clinical settings. They find that, at present, there are insufficient data to support the widespread use of combinatorial pharmacogenetic testing in clinical practice, although there are clinical situations in which the technology may be informative, particularly in predicting side effects.
基因组测序数据的积累和分析已经确定了与重度抑郁症相关的特定遗传变异。此外，大量研究致力于识别基因-药物相互作用，通过调节其药代动力学或药效学特性来影响抗抑郁药物的反应。尽管取得了这些进展，但个体对抗抑郁药的反应以及不良反应的不可预测性，使得临床医生的处方策略不够精确，往往依赖于反复试验。这些局限性催生了几种组合式药物遗传学检测产品，这些产品推向了医生市场。通常，组合式药物遗传学决策支持工具使用算法整合多个遗传变异，并将结果组合成一个易于解释的报告，以指导抗抑郁药和其他精神药物的处方。作者回顾了几种组合式药物遗传学决策支持工具的证据基础，这些工具的潜在效用已经在临床环境中得到了评估。他们发现，目前，没有足够的数据支持在临床实践中广泛使用组合式药物遗传学检测，尽管在某些临床情况下，该技术可能具有信息性，特别是在预测副作用方面。
