Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models.
Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models.
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DOI：
10.1002/psp4.12063
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发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Musante CJ
中科院分区：
文献类型：
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作者：
Allen RJ;Rieger TR;Musante CJ
定量系统药理学模型从机制上描述生物系统和药物治疗对系统行为的影响。由于这些模型很少能从现有数据中识别出来，因此可以对生理参数中的不确定性进行采样，以创建模型的替代参数化，有时称为“虚拟患者”。为了再现临床人群的统计数据，通常对虚拟患者进行加权，形成反映临床队列基线特征的虚拟人群。在这里，我们介绍了一种新的技术来有效地生成虚拟患者，并从这个集合中演示了如何选择一个与观察到的数据相匹配的虚拟人群，而不需要加权。这种方法通过降低虚假的虚拟患者在虚拟人群中被过度代表的风险，提高了模型预测的信心。
Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed “virtual patients.” In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.