在存在相关输入的情况下,使用δ敏感性指数进行两阶段全局敏感性分析:基于动态能量预算理论的肿瘤生长抑制模型的应用。

IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2023-10-01 Epub Date: 2023-07-09 DOI:10.1007/s10928-023-09872-w
Alessandro De Carlo, Elena Maria Tosca, Nicola Melillo, Paolo Magni
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引用次数: 0

摘要

全局灵敏度分析(GSA)评估模型参数的可变性和/或不确定性对给定模型输出的影响。GSA可用于评估药效学模型推断的质量。事实上,由于数据的稀疏性,模型参数可能会受到高(估计)不确定性的影响。模型参数之间的独立性是GSA方法的一个常见假设。然而,忽略参数之间的(已知的)相关性可能会改变模型预测,然后改变GSA结果。为了解决这个问题,本文提出了一种基于δ指数的新的两阶段GSA技术,该技术在存在相关参数的情况下也得到了很好的定义。在第一步中,忽略统计相关性来识别产生因果效应的参数。在第二步中引入相关性,以考虑模型输出的真实分布,并研究由于相关性结构引起的“间接”影响。基于动态能量预算理论,将所提出的两阶段GSA策略应用于临床前宿主肿瘤生长抑制模型作为案例研究。目的是评估模型参数估计的不确定性(包括相关性)对关键模型衍生指标的影响:肿瘤根除的药物阈值浓度、肿瘤体积倍增时间和评估药物疗效-毒性权衡的新指标。这种方法允许根据参数对输出的影响对参数进行排序,判断参数主要是产生因果效应还是“间接”效应。因此,可以确定必须减少的不确定性,以获得对感兴趣的输出的稳健预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory.

Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed, model parameters can be affected by high (estimation) uncertainty due to the sparsity of data. Independence between model parameters is a common assumption of GSA methods. However, ignoring (known) correlations between parameters may alter model predictions and, then, GSA results. To address this issue, a novel two-stages GSA technique based on the δ index, which is well-defined also in presence of correlated parameters, is here proposed. In the first step, statistical dependencies are neglected to identify parameters exerting causal effects. Correlations are introduced in the second step to consider the real distribution of the model output and investigate also the 'indirect' effects due to the correlation structure. The proposed two-stages GSA strategy was applied, as case study, to a preclinical tumor-in-host-growth inhibition model based on the Dynamic Energy Budget theory. The aim is to evaluate the impact of the model parameter estimate uncertainty (including correlations) on key model-derived metrics: the drug threshold concentration for tumor eradication, the tumor volume doubling time and a new index evaluating the drug efficacy-toxicity trade-off. This approach allowed to rank parameters according to their impact on the output, discerning whether a parameter mainly exerts a causal or 'indirect' effect. Thus, it was possible to identify uncertainties that should be necessarily reduced to obtain robust predictions for the outputs of interest.

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来源期刊
CiteScore
4.90
自引率
4.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Broadly speaking, the Journal of Pharmacokinetics and Pharmacodynamics covers the area of pharmacometrics. The journal is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in drug development, clinical care, and the understanding of drug action. The journal publishes on a variety of topics related to pharmacometrics, including, but not limited to, clinical, experimental, and theoretical papers examining the kinetics of drug disposition and effects of drug action in humans, animals, in vitro, or in silico; modeling and simulation methodology, including optimal design; precision medicine; systems pharmacology; and mathematical pharmacology (including computational biology, bioengineering, and biophysics related to pharmacology, pharmacokinetics, orpharmacodynamics). Clinical papers that include population pharmacokinetic-pharmacodynamic relationships are welcome. The journal actively invites and promotes up-and-coming areas of pharmacometric research, such as real-world evidence, quality of life analyses, and artificial intelligence. The Journal of Pharmacokinetics and Pharmacodynamics is an official journal of the International Society of Pharmacometrics.
期刊最新文献
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