Regression control chart with unknown parameters for detection of out-of-trend results in pharmaceutical on-going stability studies.
Regression control chart with unknown parameters for detection of out-of-trend results in pharmaceutical on-going stability studies.
复制标题
具有未知参数的回归控制图，用于检测药物持续稳定性研究中的异常结果。
DOI：
10.1016/j.jpba.2020.113375
复制
发表时间：
2020
影响因子：
3.4
通讯作者：
S. Kemény
中科院分区：
文献类型：
--
作者：
Máté Mihalovits;S. Kemény
制药行业正在进行的稳定性研究的主要目的是检测当药物已经为患者生产时，是否符合有关有效期的监管要求。也就是说，产品的估计有效期与药品注册期间声明的有效期一致。监测的工艺是制剂的某些属性（例如pH值）随时间的变化，根据监测结果确定给定批次的估计有效期。稳定性研究中的趋势外（OOT）数据点扭曲了估计的趋势，导致有效期估计值失真。未检测到的OOT点可能导致高估有效期，这可能导致药品质量不可接受，并持续分发给患者。本文将回归控制图法应用于药物稳定性研究，以检测研究中的OOT点。挑战在于样本量很小。通常，在一项研究中，收集的数据限于8-10个点。该过程的参数（真回归线和残差方差）不能视为已知，估计参数的不确定性较大。因此，通常使用的回归控制图，利用休哈特方法，其中的参数是已知的，可以不使用。本文提出的方法考虑了参数的不确定性。还介绍了所提出的方法对不同ANCOVA模型的适应性-例如a）每个批次具有相同的真实截距和斜率，B）每个批次具有相同的斜率但截距不同，c）批次也具有不同的截距和斜率。当所提出的方法使用从ANCOVA测试中获得的信息时，OOT检测的统计功效增加，即OOT检测更有效。
The main purpose of on-going stability studies in pharmaceutical industry is to test whether the regulatory requirement regarding the shelf life is fulfilled when the drug is already produced for patients. That is, the estimated shelf life of the products agrees with the shelf life claimed during the registration of the drug. The monitored process is the change in certain attributes (e.g.pH) of the drug products over time and the estimated shelf life for a given batch is determined based on the results of the monitoring. Out–of-trend (OOT) data points in stability studies distort the estimated trend, which results in distorted estimation of shelf life. Undetected OOT points could lead to overestimated shelf life, which may results in drug products with non-acceptable quality, continuously distributed to patients. In this paper, the regression control chart method is adapted for pharmaceutical stability studies to detect OOT points within the studies. The challenge is that the sample size is small. Usually, in a study the collected data is limited to 8–10 points. The parameters (true regression line and residual variance) of the process cannot be taken as known and the uncertainty of the estimated parameters is rather large. Therefore, the generally used regression control chart that utilizes the Shewhart method in which the parameters are known, may not be used. The proposed method in this paper takes the uncertainty of the parameters into account. Also adaptations of the proposed method for different ANCOVA models – such as a) every batch has the same true intercept and slope, b) every batch has the same slope but the intercepts differ, c) batches have different intercepts and slopes as well – are presented. When the proposed method employs the information obtained from the ANCOVA test, the statistical power of OOT detection is increased,i.e.the OOT detection is more effective.