Analysis of a Compartmental Model of Endogenous Immunoglobulin G Metabolism with Application to Multiple Myeloma
Analysis of a Compartmental Model of Endogenous Immunoglobulin G Metabolism with Application to Multiple Myeloma
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DOI：
10.3389/fphys.2017.00149
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发表时间：
2017-03-17
影响因子：
4
通讯作者：
Chappell, Michael J.
中科院分区：
文献类型：
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作者：
Kendrick, Felicity;Evans, Neil D.;Chappell, Michael J.
免疫球蛋白G（IgG）代谢在文献中受到了广泛关注，原因有两个：（i）IgG稳态受新生儿Fc受体（FcRn）调节，通过pH依赖性和可饱和的再循环过程，这是一个有趣的生物系统;（ii）IgG-FcRn相互作用可用作延长治疗性单克隆抗体（主要基于IgG）血浆半衰期的方法。一个研究较少的问题是内源性IgG代谢在IgG型多发性骨髓瘤中的重要性。在多发性骨髓瘤中，血清单克隆免疫球蛋白定量在诊断、监测和疗效评估中起着重要作用。为了研究IgG在这种情况下的动力学，需要表征人体内源性IgG代谢的数学模型。许多作者提出了IgG代谢的二室非线性模型，其中使用Michaelis-Menten动力学描述饱和再循环;然而，可能难以从可用的有限实验数据估计模型参数。本研究的目的是分析该模型以及来自人类实验的可用数据，并估计模型参数。为了实现这一目标，我们线性化的模型，并使用几种方法的模型和参数验证：稳定性分析，结构可识别性分析，灵敏度分析的基础上，传统的灵敏度函数和广义灵敏度函数。我们发现，所有的模型参数是可识别的，结构上，并考虑到参数的相关性，当几种类型的模型输出用于参数估计。基于这些分析，我们估计参数值从有限的可用数据，并将它们与以前公布的参数值进行比较。最后，我们展示了如何将该模型应用于未来的IgG型多发性骨髓瘤的治疗效果的研究与治疗过程中的血清单克隆IgG反应的模拟。
Immunoglobulin G (IgG) metabolism has received much attention in the literature for two reasons: (i) IgG homeostasis is regulated by the neonatal Fc receptor (FcRn), by a pH-dependent and saturable recycling process, which presents an interesting biological system; (ii) the IgG-FcRn interaction may be exploitable as a means for extending the plasma half-life of therapeutic monoclonal antibodies, which are primarily IgG-based. A less-studied problem is the importance of endogenous IgG metabolism in IgG multiple myeloma. In multiple myeloma, quantification of serum monoclonal immunoglobulin plays an important role in diagnosis, monitoring and response assessment. In order to investigate the dynamics of IgG in this setting, a mathematical model characterizing the metabolism of endogenous IgG in humans is required. A number of authors have proposed a two-compartment nonlinear model of IgG metabolism in which saturable recycling is described using Michaelis-Menten kinetics; however it may be difficult to estimate the model parameters from the limited experimental data that are available. The purpose of this study is to analyse the model alongside the available data from experiments in humans and estimate the model parameters. In order to achieve this aim we linearize the model and use several methods of model and parameter validation: stability analysis, structural identifiability analysis, and sensitivity analysis based on traditional sensitivity functions and generalized sensitivity functions. We find that all model parameters are identifiable, structurally and taking into account parameter correlations, when several types of model output are used for parameter estimation. Based on these analyses we estimate parameter values from the limited available data and compare them with previously published parameter values. Finally we show how the model can be applied in future studies of treatment effectiveness in IgG multiple myeloma with simulations of serum monoclonal IgG responses during treatment.