Linear-In-Flux-Expressions Methodology: Toward a Robust Mathematical Framework for Quantitative Systems Pharmacology Simulators.

Gene regulation and systems biology Pub Date : 2017-07-26 eCollection Date: 2017-01-01 DOI:10.1177/1177625017711414
Sean T McQuade, Ruth E Abrams, Jeffrey S Barrett, Benedetto Piccoli, Karim Azer
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引用次数: 13

Abstract

Quantitative Systems Pharmacology (QSP) modeling is increasingly used as a quantitative tool for advancing mechanistic hypotheses on the mechanism of action of a drug, and its pharmacological effect in relevant disease phenotypes, to enable linking the right drug to the right patient. Application of QSP models relies on creation of virtual populations for simulating scenarios of interest. Creation of virtual populations requires 2 important steps, namely, identification of a subset of model parameters that can be associated with a phenotype of disease and development of a sampling strategy from identified distributions of these parameters. We improve on existing sampling methodologies by providing a means of representing the structural relationship across model parameters and describing propagation of variability in the model. This gives a robust, systematic method for creating a virtual population. We have developed the Linear-In-Flux-Expressions (LIFE) method to simulate variability in patient pharmacokinetics and pharmacodynamics using relationships between parameters at baseline to create a virtual population. We demonstrate the importance of this methodology on a model of cholesterol metabolism. The LIFE methodology brings us a step closer toward improved QSP simulators through enhanced capture of the observed variability in drug and disease clinical data.

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线性通量表达式方法学:走向定量系统药理学模拟器的鲁棒数学框架。
定量系统药理学(QSP)建模越来越多地被用作一种定量工具,用于推进药物作用机制的机制假设,及其在相关疾病表型中的药理作用,从而将正确的药物与正确的患者联系起来。QSP模型的应用依赖于创建虚拟种群来模拟感兴趣的场景。创建虚拟种群需要两个重要步骤,即确定可能与疾病表型相关的模型参数子集,并从已确定的这些参数分布中制定抽样策略。我们通过提供一种表示模型参数之间的结构关系和描述模型中可变性传播的方法来改进现有的抽样方法。这为创建虚拟人口提供了一个健壮的、系统的方法。我们开发了线性通量表达式(LIFE)方法来模拟患者药代动力学和药效学的变异性,使用基线参数之间的关系来创建虚拟人群。我们证明了这种方法对胆固醇代谢模型的重要性。LIFE方法通过增强对药物和疾病临床数据中观察到的变异性的捕获,使我们更接近于改进QSP模拟器。
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