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Model-Based Optimisation of Deferoxamine Chelation Therapy

PURPOSE: Here we show how a model-based approach may be used to provide further insight into the role of clinical and demographic covariates on the progression of iron overload. The therapeutic effect of deferoxamine is used to illustrate the application of disease modelling as a means to characteri...

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Autores principales: Bellanti, Francesco, Del Vecchio, Giovanni C., Putti, Maria C., Cosmi, Carlo, Fotzi, Ilaria, Bakshi, Suruchi D., Danhof, Meindert, Della Pasqua, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709373/
https://www.ncbi.nlm.nih.gov/pubmed/26555666
http://dx.doi.org/10.1007/s11095-015-1805-0
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author Bellanti, Francesco
Del Vecchio, Giovanni C.
Putti, Maria C.
Cosmi, Carlo
Fotzi, Ilaria
Bakshi, Suruchi D.
Danhof, Meindert
Della Pasqua, Oscar
author_facet Bellanti, Francesco
Del Vecchio, Giovanni C.
Putti, Maria C.
Cosmi, Carlo
Fotzi, Ilaria
Bakshi, Suruchi D.
Danhof, Meindert
Della Pasqua, Oscar
author_sort Bellanti, Francesco
collection PubMed
description PURPOSE: Here we show how a model-based approach may be used to provide further insight into the role of clinical and demographic covariates on the progression of iron overload. The therapeutic effect of deferoxamine is used to illustrate the application of disease modelling as a means to characterising treatment response in individual patients. METHODS: Serum ferritin, demographic characteristics and individual treatment data from clinical routine practice on 27 patients affected by β-thalassaemia major were used for the purposes of this analysis. The time course of serum ferritin was described by a hierarchical nonlinear mixed effects model, in which compliance was parameterised as a covariate factor. Modelling and simulation procedures were implemented in NONMEM (7.2.0). RESULTS: A turnover model best described serum ferritin changes over time, with the effect of blood transfusions introduced on the ferritin conversion rate and the effect of deferoxamine on the elimination parameter (Kout) in a proportional manner. The results of the simulations showed that poor quality of execution is preferable over drug holidays; and that independently of the compliance pattern, the therapeutic intervention is not effective if >60% of the doses are missed. CONCLUSIONS: Modelling of ferritin response enables characterisation of the dynamics of iron overload due to chronic transfusion. The approach can be used to support decision making in clinical practice, including personalisation of the dose for existing and novel chelating agents. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-015-1805-0) contains supplementary material, which is available to authorised users.
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spelling pubmed-47093732016-01-19 Model-Based Optimisation of Deferoxamine Chelation Therapy Bellanti, Francesco Del Vecchio, Giovanni C. Putti, Maria C. Cosmi, Carlo Fotzi, Ilaria Bakshi, Suruchi D. Danhof, Meindert Della Pasqua, Oscar Pharm Res Research Paper PURPOSE: Here we show how a model-based approach may be used to provide further insight into the role of clinical and demographic covariates on the progression of iron overload. The therapeutic effect of deferoxamine is used to illustrate the application of disease modelling as a means to characterising treatment response in individual patients. METHODS: Serum ferritin, demographic characteristics and individual treatment data from clinical routine practice on 27 patients affected by β-thalassaemia major were used for the purposes of this analysis. The time course of serum ferritin was described by a hierarchical nonlinear mixed effects model, in which compliance was parameterised as a covariate factor. Modelling and simulation procedures were implemented in NONMEM (7.2.0). RESULTS: A turnover model best described serum ferritin changes over time, with the effect of blood transfusions introduced on the ferritin conversion rate and the effect of deferoxamine on the elimination parameter (Kout) in a proportional manner. The results of the simulations showed that poor quality of execution is preferable over drug holidays; and that independently of the compliance pattern, the therapeutic intervention is not effective if >60% of the doses are missed. CONCLUSIONS: Modelling of ferritin response enables characterisation of the dynamics of iron overload due to chronic transfusion. The approach can be used to support decision making in clinical practice, including personalisation of the dose for existing and novel chelating agents. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-015-1805-0) contains supplementary material, which is available to authorised users. Springer US 2015-11-10 2016 /pmc/articles/PMC4709373/ /pubmed/26555666 http://dx.doi.org/10.1007/s11095-015-1805-0 Text en © The Author(s) 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research Paper
Bellanti, Francesco
Del Vecchio, Giovanni C.
Putti, Maria C.
Cosmi, Carlo
Fotzi, Ilaria
Bakshi, Suruchi D.
Danhof, Meindert
Della Pasqua, Oscar
Model-Based Optimisation of Deferoxamine Chelation Therapy
title Model-Based Optimisation of Deferoxamine Chelation Therapy
title_full Model-Based Optimisation of Deferoxamine Chelation Therapy
title_fullStr Model-Based Optimisation of Deferoxamine Chelation Therapy
title_full_unstemmed Model-Based Optimisation of Deferoxamine Chelation Therapy
title_short Model-Based Optimisation of Deferoxamine Chelation Therapy
title_sort model-based optimisation of deferoxamine chelation therapy
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709373/
https://www.ncbi.nlm.nih.gov/pubmed/26555666
http://dx.doi.org/10.1007/s11095-015-1805-0
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