Claycamp H Gregg, Kona Ravikanth, Fahmy Raafat, Hoag Stephen W
Office of Compliance, FDA Center for Drug Evaluation and Research, Silver Spring, MD, USA.
Office of New Animal Drug Evaluation, Food and Drug Administration, Rockville, MD, USA.
AAPS PharmSciTech. 2016 Apr;17(2):233-44. doi: 10.1208/s12249-015-0349-2. Epub 2015 Jul 23.
Qualitative risk assessment methods are often used as the first step to determining design space boundaries; however, quantitative assessments of risk with respect to the design space, i.e., calculating the probability of failure for a given severity, are needed to fully characterize design space boundaries. Quantitative risk assessment methods in design and operational spaces are a significant aid to evaluating proposed design space boundaries. The goal of this paper is to demonstrate a relatively simple strategy for design space definition using a simplified Bayesian Monte Carlo simulation. This paper builds on a previous paper that used failure mode and effects analysis (FMEA) qualitative risk assessment and Plackett-Burman design of experiments to identity the critical quality attributes. The results show that the sequential use of qualitative and quantitative risk assessments can focus the design of experiments on a reduced set of critical material and process parameters that determine a robust design space under conditions of limited laboratory experimentation. This approach provides a strategy by which the degree of risk associated with each known parameter can be calculated and allocates resources in a manner that manages risk to an acceptable level.
定性风险评估方法通常被用作确定设计空间边界的第一步；然而，要全面界定设计空间边界，需要对设计空间进行定量风险评估，即计算给定严重程度下的失效概率。设计和操作空间中的定量风险评估方法对评估拟议的设计空间边界有很大帮助。本文的目的是展示一种使用简化贝叶斯蒙特卡罗模拟进行设计空间定义的相对简单策略。本文建立在之前一篇论文的基础上，该论文使用失效模式与效应分析（FMEA）定性风险评估和Plackett-Burman实验设计来识别关键质量属性。结果表明，定性和定量风险评估的顺序使用可以将实验设计聚焦于一组经过缩减的关键材料和工艺参数，这些参数在有限的实验室实验条件下确定了一个稳健的设计空间。这种方法提供了一种策略，通过该策略可以计算与每个已知参数相关的风险程度，并以将风险控制在可接受水平的方式分配资源。
