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使用有序分类测量法对劳拉西泮的困倦和头晕的药效学进行区分。

Pharmacodynamic differentiation of lorazepam sleepiness and dizziness using an ordered categorical measure.

机构信息

F. Hoffmann-La Roche Inc, Modeling & Simulation, Nutley, NJ 07110, USA.

出版信息

J Pharm Sci. 2010 Aug;99(8):3628-41. doi: 10.1002/jps.22093.

Abstract

Categorical measures of lorazepam sleepiness and dizziness were modeled to identify differences in pharmacodynamic (PD) parameters between these adverse events (AEs). Differences in data-derived PD parameters were compared with relative incidence rates in the drug label (15.7% and 6.9%, respectively). Healthy volunteers (n = 20) received single oral doses of 2 mg lorazepam or placebo in a randomized, double-blind, cross-over fashion. A seven-point categorical scale measuring the intensity of AEs was serially administered over 24 h. The maximum score (MaxS), and area under the effect curve (AUEC) were determined by noncompartmental methods and compared using a paired t-test. Individual scores were modeled using a logistic function implemented in NONMEM. AUEC and MaxS for sleepiness were significantly higher than dizziness (20.35 vs. 9.76, p < 0.01) and (2.35 vs. 1.45, p < 0.01). Model slope estimates were similar for sleepiness and dizziness (0.21 logits x mL/ng vs. 0.19 logits x mL/ng), but baseline logits were significantly higher for sleepiness (-2.81 vs. -4.34 logits). Data-derived PD parameters were in concordance with label incidence rates. The higher intensity of sleepiness may be directly related to baseline (no drug present) while the increase in intensity as a result of drug was relatively similar for both AEs.

摘要

采用分类测量方法评估劳拉西泮引起的困倦和头晕等不良事件,以识别这些不良事件的药效学(PD)参数的差异。比较了数据衍生的 PD 参数与药物标签中相对发生率(分别为 15.7%和 6.9%)的差异。20 名健康志愿者以随机、双盲、交叉方式接受单剂量 2mg 劳拉西泮或安慰剂口服。采用七点分类量表连续评估 24 小时内不良事件的强度。采用非房室模型法确定最大评分(MaxS)和效应曲线下面积(AUEC),并采用配对 t 检验进行比较。采用 NONMEM 中实现的逻辑函数对个体评分进行建模。困倦的 AUEC 和 MaxS 显著高于头晕(20.35 比 9.76,p<0.01)和(2.35 比 1.45,p<0.01)。困倦和头晕的模型斜率估计值相似(0.21 对数单位 x mL/ng 比 0.19 对数单位 x mL/ng),但困倦的基线对数单位显著更高(-2.81 比-4.34 对数单位)。数据衍生的 PD 参数与标签发生率一致。困倦的强度较高可能与基线(无药物存在)直接相关,而两种不良事件药物引起的强度增加则相对相似。

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