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Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles
Nanomaterials, Volume: 11, Issue: 10, Start page: 2606
Swansea University Authors:
Huw Summers , Paul Rees
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DOI (Published version): 10.3390/nano11102606
Abstract
Nanoparticle drug delivery vehicles introduce multiple pharmacokinetic processes, with the delivery, accumulation, and stability of the therapeutic molecule influenced by nanoscale processes. Therefore, considering the complexity of the multiple interactions, the use of data-driven models has critic...
Published in: | Nanomaterials |
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ISSN: | 2079-4991 |
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2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa58254 |
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Therefore, considering the complexity of the multiple interactions, the use of data-driven models has critical importance in understanding the interplay between controlling processes. We demonstrate data simulation techniques to reproduce the time-dependent dose of trimethyl chitosan nanoparticles in an ND7/23 neuronal cell line, used as an in vitro model of native peripheral sensory neurons. Derived analytical expressions of the mean dose per cell accurately capture the pharmacokinetics by including a declining delivery rate and an intracellular particle degradation process. Comparison with experiment indicates a supply time constant, τ = 2 h. and a degradation rate constant, b = 0.71 h−1. Modeling the dose heterogeneity uses simulated data distributions, with time dependence incorporated by transforming data-bin values. 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2022-10-31T17:31:09.2201894 v2 58254 2021-10-05 Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2021-10-05 MEDE Nanoparticle drug delivery vehicles introduce multiple pharmacokinetic processes, with the delivery, accumulation, and stability of the therapeutic molecule influenced by nanoscale processes. Therefore, considering the complexity of the multiple interactions, the use of data-driven models has critical importance in understanding the interplay between controlling processes. We demonstrate data simulation techniques to reproduce the time-dependent dose of trimethyl chitosan nanoparticles in an ND7/23 neuronal cell line, used as an in vitro model of native peripheral sensory neurons. Derived analytical expressions of the mean dose per cell accurately capture the pharmacokinetics by including a declining delivery rate and an intracellular particle degradation process. Comparison with experiment indicates a supply time constant, τ = 2 h. and a degradation rate constant, b = 0.71 h−1. Modeling the dose heterogeneity uses simulated data distributions, with time dependence incorporated by transforming data-bin values. The simulations mimic the dynamic nature of cell-to-cell dose variation and explain the observed trend of increasing numbers of high-dose cells at early time points, followed by a shift in distribution peak to lower dose between 4 to 8 h and a static dose profile beyond 8 h. Journal Article Nanomaterials 11 10 2606 MDPI AG 2079-4991 nanoparticle dosimetry; pharmacokinetics; imaging flow cytometry; nanomedicine; drug delivery; data-driven models 3 10 2021 2021-10-03 10.3390/nano11102606 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) This research was funded by Portuguese funds through FCT/MCTES in the framework of the projects UID/BIM/04293/2013, UIDB/04293/2020, SFRH/BD/137073/2018, PTDC/CTMNAN/115124/2009, and by the UK Engineering and Physical Sciences Research Council under project EP/ /N013506/1 2022-10-31T17:31:09.2201894 2021-10-05T13:00:05.0527358 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Huw Summers 0000-0002-0898-5612 1 Carla P. Gomes 2 Aida Varela-Moreira 3 Ana P. Spencer 4 Maria Gomez-Lazaro 5 Ana P. Pêgo 6 Paul Rees 0000-0002-7715-6914 7 58254__21094__633ac85aad7a45159bd0cd9b9dc90f4a.pdf 58254.pdf 2021-10-05T13:03:53.0657885 Output 14117035 application/pdf Version of Record true © 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/ |
title |
Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles |
spellingShingle |
Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles Huw Summers Paul Rees |
title_short |
Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles |
title_full |
Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles |
title_fullStr |
Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles |
title_full_unstemmed |
Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles |
title_sort |
Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles |
author_id_str_mv |
a61c15e220837ebfa52648c143769427 537a2fe031a796a3bde99679ee8c24f5 |
author_id_fullname_str_mv |
a61c15e220837ebfa52648c143769427_***_Huw Summers 537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees |
author |
Huw Summers Paul Rees |
author2 |
Huw Summers Carla P. Gomes Aida Varela-Moreira Ana P. Spencer Maria Gomez-Lazaro Ana P. Pêgo Paul Rees |
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Journal article |
container_title |
Nanomaterials |
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11 |
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10 |
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2606 |
publishDate |
2021 |
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Swansea University |
issn |
2079-4991 |
doi_str_mv |
10.3390/nano11102606 |
publisher |
MDPI AG |
college_str |
Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering |
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description |
Nanoparticle drug delivery vehicles introduce multiple pharmacokinetic processes, with the delivery, accumulation, and stability of the therapeutic molecule influenced by nanoscale processes. Therefore, considering the complexity of the multiple interactions, the use of data-driven models has critical importance in understanding the interplay between controlling processes. We demonstrate data simulation techniques to reproduce the time-dependent dose of trimethyl chitosan nanoparticles in an ND7/23 neuronal cell line, used as an in vitro model of native peripheral sensory neurons. Derived analytical expressions of the mean dose per cell accurately capture the pharmacokinetics by including a declining delivery rate and an intracellular particle degradation process. Comparison with experiment indicates a supply time constant, τ = 2 h. and a degradation rate constant, b = 0.71 h−1. Modeling the dose heterogeneity uses simulated data distributions, with time dependence incorporated by transforming data-bin values. The simulations mimic the dynamic nature of cell-to-cell dose variation and explain the observed trend of increasing numbers of high-dose cells at early time points, followed by a shift in distribution peak to lower dose between 4 to 8 h and a static dose profile beyond 8 h. |
published_date |
2021-10-03T04:14:38Z |
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1763753988106223616 |
score |
11.021648 |