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Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS co...

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Autores principales: Yamamoto, Yumi, Välitalo, Pyry A., Huntjens, Dymphy R., Proost, Johannes H., Vermeulen, An, Krauwinkel, Walter, Beukers, Margot W., van den Berg, Dirk‐Jan, Hartman, Robin, Wong, Yin Cheong, Danhof, Meindert, van Hasselt, John G. C., de Lange, Elizabeth C. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702903/
https://www.ncbi.nlm.nih.gov/pubmed/28891201
http://dx.doi.org/10.1002/psp4.12250
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author Yamamoto, Yumi
Välitalo, Pyry A.
Huntjens, Dymphy R.
Proost, Johannes H.
Vermeulen, An
Krauwinkel, Walter
Beukers, Margot W.
van den Berg, Dirk‐Jan
Hartman, Robin
Wong, Yin Cheong
Danhof, Meindert
van Hasselt, John G. C.
de Lange, Elizabeth C. M.
author_facet Yamamoto, Yumi
Välitalo, Pyry A.
Huntjens, Dymphy R.
Proost, Johannes H.
Vermeulen, An
Krauwinkel, Walter
Beukers, Margot W.
van den Berg, Dirk‐Jan
Hartman, Robin
Wong, Yin Cheong
Danhof, Meindert
van Hasselt, John G. C.
de Lange, Elizabeth C. M.
author_sort Yamamoto, Yumi
collection PubMed
description Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development.
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spelling pubmed-57029032017-11-29 Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model Yamamoto, Yumi Välitalo, Pyry A. Huntjens, Dymphy R. Proost, Johannes H. Vermeulen, An Krauwinkel, Walter Beukers, Margot W. van den Berg, Dirk‐Jan Hartman, Robin Wong, Yin Cheong Danhof, Meindert van Hasselt, John G. C. de Lange, Elizabeth C. M. CPT Pharmacometrics Syst Pharmacol Original Articles Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development. John Wiley and Sons Inc. 2017-10-13 2017-11 /pmc/articles/PMC5702903/ /pubmed/28891201 http://dx.doi.org/10.1002/psp4.12250 Text en © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Yamamoto, Yumi
Välitalo, Pyry A.
Huntjens, Dymphy R.
Proost, Johannes H.
Vermeulen, An
Krauwinkel, Walter
Beukers, Margot W.
van den Berg, Dirk‐Jan
Hartman, Robin
Wong, Yin Cheong
Danhof, Meindert
van Hasselt, John G. C.
de Lange, Elizabeth C. M.
Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model
title Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model
title_full Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model
title_fullStr Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model
title_full_unstemmed Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model
title_short Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model
title_sort predicting drug concentration‐time profiles in multiple cns compartments using a comprehensive physiologically‐based pharmacokinetic model
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702903/
https://www.ncbi.nlm.nih.gov/pubmed/28891201
http://dx.doi.org/10.1002/psp4.12250
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