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Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles

Huw Summers Orcid Logo, Carla P. Gomes, Aida Varela-Moreira, Ana P. Spencer, Maria Gomez-Lazaro, Ana P. Pêgo, Paul Rees Orcid Logo

Nanomaterials, Volume: 11, Issue: 10, Start page: 2606

Swansea University Authors: Huw Summers Orcid Logo, Paul Rees Orcid Logo

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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...

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Published in: Nanomaterials
ISSN: 2079-4991
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58254
first_indexed 2021-10-05T12:02:33Z
last_indexed 2023-01-11T14:38:44Z
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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, &#x3C4; = 2 h. and a degradation rate constant, b = 0.71 h&#x2212;1. Modeling the dose heterogeneity uses simulated data distributions, with time dependence incorporated by transforming data-bin values. 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spelling 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 EAAS 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 Engineering and Applied Sciences School COLLEGE CODE EAAS 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
format Journal article
container_title Nanomaterials
container_volume 11
container_issue 10
container_start_page 2606
publishDate 2021
institution 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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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
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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 2021-10-03T20:06:13Z
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