Winiwarter Susanne, Middleton Brian, Jones Barry, Courtney Paul, Lindmark Bo, Page Ken M, Clark Alan, Landqvist Claire
Innovative Medicines, Drug Safety and Metabolism, AstraZeneca R&D Mölndal, Pepparedsleden 1, 431 83, Mölndal, Sweden.
Innovative Medicines, Discovery Sciences, AstraZeneca R&D Alderley Park, Macclesfield, Cheshire, SK10 4TF, UK.
J Comput Aided Mol Des. 2015 Sep;29(9):795-807. doi: 10.1007/s10822-015-9836-5. Epub 2015 Feb 20.
We demonstrate here a novel use of statistical tools to study intra- and inter-site assay variability of five early drug metabolism and pharmacokinetics in vitro assays over time. Firstly, a tool for process control is presented. It shows the overall assay variability but allows also the following of changes due to assay adjustments and can additionally highlight other, potentially unexpected variations. Secondly, we define the minimum discriminatory difference/ratio to support projects to understand how experimental values measured at different sites at a given time can be compared. Such discriminatory values are calculated for 3 month periods and followed over time for each assay. Again assay modifications, especially assay harmonization efforts, can be noted. Both the process control tool and the variability estimates are based on the results of control compounds tested every time an assay is run. Variability estimates for a limited set of project compounds were computed as well and found to be comparable. This analysis reinforces the need to consider assay variability in decision making, compound ranking and in silico modeling.
我们在此展示了统计工具的一种新用途，用于研究随着时间推移五种早期药物代谢和药代动力学体外试验的内部和不同站点间的试验变异性。首先，介绍了一种过程控制工具。它显示了整体试验变异性，但也允许跟踪由于试验调整导致的变化，并且还可以突出显示其他潜在的意外变化。其次，我们定义了最小鉴别差异/比率，以支持项目了解在给定时间不同站点测量的实验值如何进行比较。针对每个试验，计算3个月期间的此类鉴别值并随时间跟踪。同样，可以注意到试验修改，特别是试验协调工作。过程控制工具和变异性估计均基于每次运行试验时测试的对照化合物的结果。还计算了一组有限的项目化合物的变异性估计值，发现它们具有可比性。该分析强化了在决策、化合物排名和计算机模拟中考虑试验变异性的必要性。
