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Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients

Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for...

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Autores principales: Thiel, Christoph, Cordes, Henrik, Conde, Isabel, Castell, José Vicente, Blank, Lars Mathias, Kuepfer, Lars
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306109/
https://www.ncbi.nlm.nih.gov/pubmed/27161439
http://dx.doi.org/10.1007/s00204-016-1723-x
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author Thiel, Christoph
Cordes, Henrik
Conde, Isabel
Castell, José Vicente
Blank, Lars Mathias
Kuepfer, Lars
author_facet Thiel, Christoph
Cordes, Henrik
Conde, Isabel
Castell, José Vicente
Blank, Lars Mathias
Kuepfer, Lars
author_sort Thiel, Christoph
collection PubMed
description Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration–time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00204-016-1723-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-53061092017-02-24 Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients Thiel, Christoph Cordes, Henrik Conde, Isabel Castell, José Vicente Blank, Lars Mathias Kuepfer, Lars Arch Toxicol In vitro Systems Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration–time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00204-016-1723-x) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2016-05-09 2017 /pmc/articles/PMC5306109/ /pubmed/27161439 http://dx.doi.org/10.1007/s00204-016-1723-x Text en © The Author(s) 2016 Open AccessThis 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 In vitro Systems
Thiel, Christoph
Cordes, Henrik
Conde, Isabel
Castell, José Vicente
Blank, Lars Mathias
Kuepfer, Lars
Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
title Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
title_full Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
title_fullStr Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
title_full_unstemmed Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
title_short Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
title_sort model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients
topic In vitro Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306109/
https://www.ncbi.nlm.nih.gov/pubmed/27161439
http://dx.doi.org/10.1007/s00204-016-1723-x
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