论文标题
一种潜在的可变方法来说明全球灵敏度分析中相关输入与药理学系统建模的病例的相关输入
A latent variable approach to account for correlated inputs in global sensitivity analysis with cases from pharmacological systems modelling
论文作者
论文摘要
在与候选药物选择有关的药物研究和开发决策中，通常通过建模和仿真（M \＆S）支持功效和安全性。除其他外，基于生理的药代动力学模型用于描述人类的药物吸收，分布和代谢。全球敏感性分析（GSA）正在对药理学M \＆s社区产生兴趣，这是基于模型推断质量评估的重要因素。生理模型通常呈现相关参数。在GSA中包含相关因素和敏感性指数的解释已证明了这些模型的问题。在这里，我们设计并评估了一种潜在变量方法来处理GSA中的相关因素。这种方法通过三个独立因素的因果关系描述了两个模型输入之间的相关性：潜在变量和两个相关参数的唯一方差。然后，使用基于经典方差的方法执行GSA。我们将潜在变量方法应用于一组代数模型和基于生理的药代动力学的案例。然后，我们比较了我们对SOBOL的GSA的方法，假设没有相关性，SOBOL的GSA与群体和Kucherenko方法。相对易于实施和解释使这是针对具有相关输入因素的模型执行GSA的简单方法。
In pharmaceutical research and development decision-making related to drug candidate selection, efficacy and safety is commonly supported through modelling and simulation (M\&S). Among others, physiologically-based pharmacokinetic models are used to describe drug absorption, distribution and metabolism in human. Global sensitivity analysis (GSA) is gaining interest in the pharmacological M\&S community as an important element for quality assessment of model-based inference. Physiological models often present inter-correlated parameters. The inclusion of correlated factors in GSA and the sensitivity indices interpretation has proven an issue for these models. Here we devise and evaluate a latent variable approach for dealing with correlated factors in GSA. This approach describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. Then, GSA is performed with the classical variance-based method. We applied the latent variable approach to a set of algebraic models and a case from physiologically-based pharmacokinetics. Then, we compared our approach to Sobol's GSA assuming no correlations, Sobol's GSA with groups and the Kucherenko approach. The relative ease of implementation and interpretation makes this a simple approach for carrying out GSA for models with correlated input factors.
