Larsen Jens Borggaard, Hoffmann-Lücke Elke, Aaslo Per Hersom, Jørgensen Niklas Rye, Greibe Eva
The Datasupport Centre for Personalised Medicine, Region South, DK 5000 Odense, Denmark.
Department of Clinical Biochemistry, Aarhus University Hospital, DK 8200 Aarhus, Denmark.
Pharmaceutics. 2023 Feb 16;15(2):673. doi: 10.3390/pharmaceutics15020673.
Therapeutic drug monitoring is a tool for optimising the pharmacological treatment of diseases where the therapeutic effect is difficult to measure or monitor. Therapeutic reference ranges and dose-effect relation are the main requirements for this drug titration tool. Defining and updating therapeutic reference ranges are difficult, and there is no standardised method for the calculation and clinical qualification of these. The study presents a basic model for validating and selecting routine laboratory data. The programmed algorithm was applied on data sets of antidepressants and antipsychotics from three public hospitals in Denmark. Therapeutic analytical ranges were compared with the published therapeutic reference ranges by the Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) and in additional literature. For most of the drugs, the calculated therapeutic analytical ranges showed good concordance between the laboratories and to published therapeutic reference ranges. The exceptions were flupentixol, haloperidol, paroxetine, perphenazine, and venlafaxine + o-desmethyl-venlafaxine (total plasma concentration), where the range was considerably higher for the laboratory data, while the calculated range of desipramine, sertraline, ziprasidone, and zuclopenthixol was considerably lower. In most cases, we identified additional literature supporting our data, highlighting the need of a critical re-examination of current therapeutic reference ranges in Denmark. An automated approach can aid in the evaluation of current and future therapeutic reference ranges by providing additional information based on big data from multiple laboratories.
治疗药物监测是一种优化疾病药物治疗的工具，适用于治疗效果难以测量或监测的情况。治疗参考范围和剂量-效应关系是这种药物滴定工具的主要要求。定义和更新治疗参考范围很困难，并且对于这些范围的计算和临床验证没有标准化方法。该研究提出了一个用于验证和选择常规实验室数据的基本模型。将编程算法应用于丹麦三家公立医院的抗抑郁药和抗精神病药数据集。将治疗分析范围与由神经精神药理学和药物精神病学工作小组（AGNP）发布的治疗参考范围以及其他文献中的范围进行比较。对于大多数药物，计算得出的治疗分析范围在各实验室之间以及与已发布的治疗参考范围之间显示出良好的一致性。例外情况是三氟噻吨、氟哌啶醇、帕罗西汀、奋乃静以及文拉法辛+O-去甲基文拉法辛（总血浆浓度），这些药物的实验室数据范围明显更高，而地昔帕明、舍曲林、齐拉西酮和氯普噻吨的计算范围则明显更低。在大多数情况下，我们找到了支持我们数据的其他文献，这突出表明丹麦需要对当前的治疗参考范围进行批判性重新审视。一种自动化方法可以通过基于多个实验室的大数据提供额外信息，来帮助评估当前和未来的治疗参考范围。
