Lebrun Pierre, Giacoletti Katherine, Scherder Tara, Rozet Eric, Boulanger Bruno
a Arlenda S.A. , Liège , Belgium.
J Biopharm Stat. 2015;25(2):247-59. doi: 10.1080/10543406.2014.979197.
The concept of quality by design (QbD) as published in ICH-Q8 is currently one of the most recurrent topics in the pharmaceutical literature. This guideline recommends the use of information and prior knowledge gathered during pharmaceutical development studies to provide a scientific rationale for the manufacturing process of a product and provide guarantee of future quality. This poses several challenges from a statistical standpoint and requires a shift in paradigm from traditional statistical practices. First, to provide "assurance of quality" of future lots implies the need to make predictions regarding the quality given past evidence and data. Second, the quality attributes described in the Q8 guidelines are not always a set of unique, independent measurements. In many cases, these criteria are complicated longitudinal data with successive acceptance criteria over a defined period of time. A common example is a dissolution profile for a modified or extended-release solid dosage form that must fall within acceptance limits at several time points. A Bayesian approach for longitudinal data obtained in various conditions of a design of experiment is provided to elegantly address the ICH-Q8 recommendation to provide assurance of quality and derive a scientifically sound design space.
《国际人用药品注册技术协调会-质量8》（ICH-Q8）中发布的设计质量（QbD）概念是目前药学文献中最常出现的话题之一。本指南建议利用在药物研发研究过程中收集的信息和先验知识，为产品的生产工艺提供科学依据，并确保未来产品质量。从统计学角度来看，这带来了诸多挑战，需要从传统统计方法转变范式。首先，要保证未来批次产品的“质量保证”，就意味着需要根据过去的证据和数据对质量进行预测。其次，Q8指南中描述的质量属性并非总是一组独特、独立的测量值。在许多情况下，这些标准是复杂的纵向数据，在规定时间内有连续的验收标准。一个常见的例子是改良或缓释固体剂型的溶出曲线，该曲线必须在多个时间点落在验收限度内。本文提供了一种贝叶斯方法，用于处理在各种实验设计条件下获得的纵向数据，以巧妙地应对ICH-Q8中关于提供质量保证并得出科学合理的设计空间的建议。
