Clinical Pharmacology & DMPK, AstraZeneca R&D Södertälje, Uppsala, Sweden.
Br J Clin Pharmacol. 2011 Jun;71(6):899-906. doi: 10.1111/j.1365-2125.2011.03924.x.
• Modelling has been used to describe the pain relief and dropout for a few non-steroidal anti-inflammatory drugs.
• This study shows the relationship between dose, plasma concentration, pain intensity and dropout for naproxen and naproxcinod. It also extends previous models by using a visual analogue scale for pain intensity instead of modelling pain relief on a categorical scale, and shows the value of including informative dropout in the simulations for visual predictive checks.
To describe pain intensity (PI) measured on a visual analogue scale (VAS) and dropout due to request for rescue medication after administration of naproxcinod, naproxen or placebo in 242 patients after wisdom tooth removal. METHODS Non-linear mixed effects modelling was used to describe the plasma concentrations of naproxen, either formed from naproxcinod or from naproxen itself, and their relationship to PI and dropout. Goodness of fit was assessed by simultaneous simulations of PI and dropout.
Baseline PI for the typical patient was 52.7 mm. The PI was influenced by placebo effects, using an exponential model, and by naproxen concentrations using a sigmoid E(max) model. Typical maximal placebo effect was a decrease in PI by 20.2%, with an onset rate constant of 0.237 h(-1). EC(50) was 0.135 µmol l(-1). A Weibull time-to-event model was used for the dropout, where the hazard was dependent on the predicted PI and by the PI at baseline. Since the dropout was not at random, it was necessary to include the simulated dropout in visual predictive checks (VPC) of PI.
This model describes the relationship between drug effects, PI and the likelihood of dropout after naproxcinod, naproxen and placebo administration. The model provides an opportunity to describe the effects of other doses or formulations, after dental extraction. VPC created by simultaneous simulations of PI and dropout provides a good way of assessing the goodness of fit when there is informative dropout.
描述 242 名智齿切除术后患者使用萘普辛诺德、萘普生或安慰剂后，使用视觉模拟量表（VAS）测量的疼痛强度（PI）和因请求救援药物而导致的停药情况。
使用非线性混合效应模型来描述萘普生（无论是由萘普辛诺德形成还是由萘普生本身形成）的血浆浓度及其与 PI 和停药的关系。通过同时模拟 PI 和停药来评估拟合优度。
典型患者的基线 PI 为 52.7mm。PI 受安慰剂效应影响，采用指数模型，受萘普生浓度影响，采用 Sigmoid E（max）模型。典型的最大安慰剂效应是 PI 下降 20.2%，起始速率常数为 0.237 h（-1）。EC（50）为 0.135µmol l（-1）。使用威布尔时间事件模型来描述停药情况，其中风险取决于预测的 PI 和基线时的 PI。由于停药不是随机的，因此需要在 PI 的视觉预测检查（VPC）中包含模拟的停药。
该模型描述了奈普生、萘普辛诺德和安慰剂给药后药物作用、PI 和停药可能性之间的关系。该模型为描述拔牙后其他剂量或制剂的作用提供了机会。通过同时模拟 PI 和停药来创建 VPC，可以在存在信息性停药时提供一种很好的评估拟合优度的方法。
