The design space (DS) is defined as the combination of materials and process conditions, which provides assurance of quality for a pharmaceutical product. A model-based approach to identify a probability-based DS requires costly simulations across the entire process parameter space (certain) and the uncertain model parameter space. We demonstrate that application of global sensitivity analysis (GSA) can significantly reduce model complexity and reduce computational time for identifying and quantifying DS by screening out non-important uncertain parameters. The novelty of this approach in that the usage of an indicator function which takes only binary values as a model function allows to apply a straightforward GSA based on Sobol’ sensitivity indices and to avoid using more costly Monte Carlo filtering or GSA for constrained problems. We consider an application from the chemical industry to illustrate how this formulation results in model reduction and dramatic reduction of the number of required model runs.

中文翻译：

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全局敏感性分析在概率设计空间识别中的应用

设计空间（DS）被定义为材料和工艺条件的组合，它为药品的质量提供保证。基于模型的方法来识别基于概率的 DS 需要在整个过程参数空间（确定）和不确定的模型参数空间进行昂贵的模拟。我们证明，应用全局敏感性分析（GSA）可以通过筛选出不重要的不确定参数来显着降低模型复杂性并减少识别和量化 DS 的计算时间。这种方法的新颖之处在于，使用仅采用二进制值作为模型函数的指示函数允许应用基于 Sobol 灵敏度指数的直接 GSA，并避免使用成本更高的蒙特卡罗过滤或 GSA 来解决约束问题。我们考虑化学工业的一个应用来说明这种公式如何减少模型并显着减少所需模型运行的数量。