A comparative computational analysis of IFN-alpha pharmacokinetics and its induced cellular response in mice and humans.

IF 3.6 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2025-09-25 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013509

Priyata Kalra, Bastian Kister, Rebekka Fendt, Mario Köster, Julia Pulverer, Sven Sahle, Lars Kuepfer, Ursula Kummer

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Abstract

Drug effects are difficult to investigate in detail in vivo. However, a mechanistic understanding of drug action is clearly beneficial for both pharmaceutical development as well as for optimization of treatment designs. We here established a quantitative systems pharmacology (QSP) mouse model which simultaneously describes whole-body pharmacokinetics of murine IFN-α as well as the cellular pharmacodynamic effect through the antiviral response biomarker Mx2. To this end, a dynamic model of intracellular IFN-α signalling in the JAK/STAT pathway was combined with a whole-body physiologically-based pharmacokinetic model of IFN-α in mice. The pharmacodynamic behaviour of the resulting mouse IFN-α QSP model was first compared to a cellular model of the JAK/STAT pathway to compare in vitro and in vivo drug effects and to identify functional differences. It was found that the in vitro drug effect in the cellular model overestimates the in vivo response in mice at least by a factor of two which is partly due to the missing drug clearance in vitro. Also, the drug responses in the in vitro model were time delayed. Interspecies analyses in murine and a previously published human QSP model of IFN-α next show a similar dynamic behavior. However, our models demonstrate eight to 16-fold stronger response levels in mice than in humans due to more efficient interferon binding. Our analysis supports a mechanistic analysis of both upstream pharmacokinetic as well as downstream pharmacodynamic drug effects through the combination of physiological knowledge and quantitative computational models. The study hence shows potential applications for QSP modelling in terms of study planning, for example by choosing physiologically relevant in vitro concentrations. Also, the QSP model allows inter-species comparisons of the effect strength in specific functional readouts, which in humans are otherwise not possible due to the limited sampling possibilities. We expect QSP modelling to play an increasingly important role in drug development and research in the future.

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小鼠和人体内ifn - α药代动力学及其诱导细胞反应的比较计算分析。

药物在体内的作用很难进行详细的研究。然而，对药物作用的机制理解显然有利于药物开发以及治疗设计的优化。我们建立了定量系统药理学（QSP）小鼠模型，该模型通过抗病毒反应生物标志物Mx2同时描述了小鼠IFN-α的全身药代动力学和细胞药效学效应。为此，我们将JAK/STAT通路中细胞内IFN-α信号的动态模型与小鼠体内基于生理的IFN-α全身药代动力学模型相结合。首先将所得小鼠IFN-α QSP模型的药效学行为与JAK/STAT通路的细胞模型进行比较，比较体外和体内药物作用，并确定功能差异。研究发现，细胞模型中的体外药物效应至少高估了小鼠体内反应的两倍，部分原因是缺乏体外药物清除。此外，体外模型的药物反应有时间延迟。鼠种间分析和先前发表的人类IFN-α QSP模型显示出类似的动态行为。然而，我们的模型显示，由于更有效的干扰素结合，小鼠的反应水平比人类强8到16倍。我们的分析通过结合生理学知识和定量计算模型，支持上游药代动力学和下游药效学药物效应的机制分析。因此，该研究显示了QSP模型在研究计划方面的潜在应用，例如通过选择与生理相关的体外浓度。此外，QSP模型允许物种间比较特定功能读数的效应强度，这在人类中是不可能的，因为有限的采样可能性。我们期望QSP模型在未来的药物开发和研究中发挥越来越重要的作用。

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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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

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