Central Institute of Mental Health, Department of Molecular Neuroimaging, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Laboratory Limbach Analytics GmbH, Heidelberg, Germany.
Pharmacopsychiatry. 2023 Mar;56(2):73-80. doi: 10.1055/a-2039-2829. Epub 2023 Mar 21.
Cross sectional therapeutic drug monitoring (TDM) data mining introduces new opportunities for the investigation of medication treatment effects to find optimal therapeutic windows. Medication discontinuation has been proven useful as an objective surrogate marker to assess treatment failure. This study aimed to investigate the treatment effects of escitalopram and pharmacokinetic influences on blood levels using retrospectively assessed data from a TDM database.
Data was collected from 134 patients longitudinally treated with escitalopram for whom TDM was requested to guide drug therapy. Escitalopram metabolism was estimated by the log-transformed dose-corrected concentrations and compared within subpopulations differing in age, gender, renal function, smoking status, body mass index, and comedication.
Patients with a depressive episode who were treated with escitalopram and discontinued the treatment within the hospital stay showed lower serum concentrations compared to patients who continued escitalopram treatment with a concentration of 15 ng/mL separating both groups. Variability was high between individuals and factors influencing blood levels, including dose, sex, and age. Comedication that inhibits cytochrome P450 (CYP) 2C19 isoenzymes were further found to influence escitalopram pharmacokinetics independent of dose, age or sex.
Medication switch is a valuable objective surrogate marker to assess treatment effects under real-world conditions. Of note, treatment discontinuation is not always a cause of insufficient response but may also be related to other factors such as medication side effects. TDM might not only be useful in addressing these issues but titrating drug concentrations into the currently recommended reference range for escitalopram will also increase response in non-responders and avoid treatment failure in underdosed patients.
横断面治疗药物监测(TDM)数据挖掘为研究药物治疗效果、寻找最佳治疗窗口带来了新的机遇。药物停药已被证明是评估治疗失败的一种有用的客观替代标志物。本研究旨在使用 TDM 数据库中回顾性评估的数据,调查艾司西酞普兰的治疗效果和药代动力学对血药浓度的影响。
收集了 134 例接受艾司西酞普兰长期治疗的患者数据,这些患者进行 TDM 以指导药物治疗。通过对数转化剂量校正后的浓度来估计艾司西酞普兰的代谢情况,并在年龄、性别、肾功能、吸烟状况、体重指数和合并用药不同的亚人群中进行比较。
与继续艾司西酞普兰治疗的患者相比,在住院期间停药的抑郁发作患者的血清浓度较低,两组之间的浓度差异为 15ng/ml。个体之间的变异性很大,影响血药浓度的因素包括剂量、性别和年龄。此外,还发现抑制细胞色素 P450(CYP)2C19 同工酶的合并用药独立于剂量、年龄或性别影响艾司西酞普兰的药代动力学。
药物转换是评估真实世界条件下治疗效果的有价值的客观替代标志物。值得注意的是,停药并不总是疗效不足的原因,也可能与药物副作用等其他因素有关。TDM 不仅有助于解决这些问题,而且将药物浓度滴定至艾司西酞普兰目前推荐的参考范围,也将增加无反应者的反应率,并避免低剂量患者的治疗失败。