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Parameter identification for a model of neonatal Fc receptor-mediated recycling of endogenous immunoglobulin G in humans
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Kendrick, Felicity, Evans, Neil D., Berlanga, Oscar, Harding, Stephen J. and Chappell, Michael J. (2019) Parameter identification for a model of neonatal Fc receptor-mediated recycling of endogenous immunoglobulin G in humans. Frontiers in Immunology, 10 . doi:10.3389/fimmu.2019.00674 ISSN 1664-3224.
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WRAP-parameter-identification-model-neonatal-fc-receptor-mediated-recycling-endogenous-immunoglobulin-humans-Evans-2019.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1883Kb) | Preview |
Official URL: http://dx.doi.org/10.3389/fimmu.2019.00674
Abstract
Salvage of endogenous immunoglobulin G (IgG) by the neonatal Fc receptor (FcRn) is implicated in many clinical areas, including therapeutic monoclonal antibody kinetics, patient monitoring in IgG multiple myeloma, and antibody-mediated transplant rejection. There is a clear clinical need for a fully parameterized model of FcRn-mediated recycling of endogenous IgG to allow for predictive modeling, with the potential for optimizing therapeutic regimens for better patient outcomes. In this paper we study a mechanism-based model incorporating nonlinear FcRn-IgG binding kinetics. The aim of this study is to determine whether parameter values can be estimated using the limited in vivo human data, available in the literature, from studies of the kinetics of radiolabeled IgG in humans. We derive mathematical descriptions of the experimental observations—timecourse data and fractional catabolic rate (FCR) data—based on the underlying physiological model. Structural identifiability analyses are performed to determine which, if any, of the parameters are unique with respect to the observations. Structurally identifiable parameters are then estimated from the data. It is found that parameter values estimated from timecourse data are not robust, suggesting that the model complexity is not supported by the available data. Based upon the structural identifiability analyses, a new expression for the FCR is derived. This expression is fitted to the FCR data to estimate unknown parameter values. Using these parameter estimates, the plasma IgG response is simulated under clinical conditions. Finally a suggestion is made for a reduced-order model based upon the newly derived expression for the FCR. The reduced-order model is used to predict the plasma IgG response, which is compared with the original four-compartment model, showing good agreement. This paper shows how techniques for compartmental model analysis—structural identifiability analysis, linearization, and reparameterization—can be used to ensure robust parameter identification.
| Item Type: | Journal Article | ||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Journal or Publication Title: | Frontiers in Immunology | ||||||
| Publisher: | Frontiers Media S.A. | ||||||
| ISSN: | 1664-3224 | ||||||
| Official Date: | 8 April 2019 | ||||||
| Dates: |
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| Volume: | 10 | ||||||
| DOI: | 10.3389/fimmu.2019.00674 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 16 July 2019 | ||||||
| Date of first compliant Open Access: | 16 July 2019 |
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