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Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine
Cancer treatment often lacks individual dose adaptation, contributing to insufficient efficacy and severe side effects. Thus, personalized approaches are highly desired. Although various analytical techniques are established to determine drug levels in preclinical models, they are limited in the aut...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284432/ https://www.ncbi.nlm.nih.gov/pubmed/32366029 http://dx.doi.org/10.3390/pharmaceutics12050413 |
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author | Joseph, Jan F. Gronbach, Leonie García-Miller, Jill Cruz, Leticia M. Wuest, Bernhard Keilholz, Ulrich Zoschke, Christian Parr, Maria K. |
author_facet | Joseph, Jan F. Gronbach, Leonie García-Miller, Jill Cruz, Leticia M. Wuest, Bernhard Keilholz, Ulrich Zoschke, Christian Parr, Maria K. |
author_sort | Joseph, Jan F. |
collection | PubMed |
description | Cancer treatment often lacks individual dose adaptation, contributing to insufficient efficacy and severe side effects. Thus, personalized approaches are highly desired. Although various analytical techniques are established to determine drug levels in preclinical models, they are limited in the automated real-time acquisition of pharmacokinetic profiles. Therefore, an online UHPLC-MS/MS system for quantitation of drug concentrations within 3D tumor oral mucosa models was generated. The integration of sampling ports into the 3D tumor models and their culture inside the autosampler allowed for real-time pharmacokinetic profiling without additional sample preparation. Docetaxel quantitation was validated according to EMA guidelines. The tumor models recapitulated the morphology of head-and-neck cancer and the dose-dependent tumor reduction following docetaxel treatment. The administration of four different docetaxel concentrations resulted in comparable courses of concentration versus time curves for 96 h. In conclusion, this proof-of-concept study demonstrated the feasibility of real-time monitoring of drug levels in 3D tumor models without any sample preparation. The inclusion of patient-derived tumor cells into our models may further optimize the pharmacotherapy of cancer patients by efficiently delivering personalized data of the target tissue. |
format | Online Article Text |
id | pubmed-7284432 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72844322020-08-13 Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine Joseph, Jan F. Gronbach, Leonie García-Miller, Jill Cruz, Leticia M. Wuest, Bernhard Keilholz, Ulrich Zoschke, Christian Parr, Maria K. Pharmaceutics Article Cancer treatment often lacks individual dose adaptation, contributing to insufficient efficacy and severe side effects. Thus, personalized approaches are highly desired. Although various analytical techniques are established to determine drug levels in preclinical models, they are limited in the automated real-time acquisition of pharmacokinetic profiles. Therefore, an online UHPLC-MS/MS system for quantitation of drug concentrations within 3D tumor oral mucosa models was generated. The integration of sampling ports into the 3D tumor models and their culture inside the autosampler allowed for real-time pharmacokinetic profiling without additional sample preparation. Docetaxel quantitation was validated according to EMA guidelines. The tumor models recapitulated the morphology of head-and-neck cancer and the dose-dependent tumor reduction following docetaxel treatment. The administration of four different docetaxel concentrations resulted in comparable courses of concentration versus time curves for 96 h. In conclusion, this proof-of-concept study demonstrated the feasibility of real-time monitoring of drug levels in 3D tumor models without any sample preparation. The inclusion of patient-derived tumor cells into our models may further optimize the pharmacotherapy of cancer patients by efficiently delivering personalized data of the target tissue. MDPI 2020-04-30 /pmc/articles/PMC7284432/ /pubmed/32366029 http://dx.doi.org/10.3390/pharmaceutics12050413 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Joseph, Jan F. Gronbach, Leonie García-Miller, Jill Cruz, Leticia M. Wuest, Bernhard Keilholz, Ulrich Zoschke, Christian Parr, Maria K. Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine |
title | Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine |
title_full | Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine |
title_fullStr | Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine |
title_full_unstemmed | Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine |
title_short | Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine |
title_sort | automated real-time tumor pharmacokinetic profiling in 3d models: a novel approach for personalized medicine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284432/ https://www.ncbi.nlm.nih.gov/pubmed/32366029 http://dx.doi.org/10.3390/pharmaceutics12050413 |
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