Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles
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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/11/10/2606 |
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| author | Huw D. Summers Carla P. Gomes Aida Varela-Moreira Ana P. Spencer Maria Gomez-Lazaro Ana P. Pêgo Paul Rees |
| author_facet | Huw D. Summers Carla P. Gomes Aida Varela-Moreira Ana P. Spencer Maria Gomez-Lazaro Ana P. Pêgo Paul Rees |
| author_sort | Huw D. Summers |
| collection | DOAJ |
| 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<sup>−1</sup>. 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. |
| first_indexed | 2024-03-10T06:18:59Z |
| format | Article |
| id | doaj.art-330175154cfa481caeaf369fe0074dc9 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-10T06:18:59Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-330175154cfa481caeaf369fe0074dc92023-11-22T19:23:54ZengMDPI AGNanomaterials2079-49912021-10-011110260610.3390/nano11102606Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based NanoparticlesHuw D. Summers0Carla P. Gomes1Aida Varela-Moreira2Ana P. Spencer3Maria Gomez-Lazaro4Ana P. Pêgo5Paul Rees6Department of Biomedical Engineering, Swansea University, Swansea SA1 8QQ, UKi3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugali3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugali3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugali3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugali3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, PortugalDepartment of Biomedical Engineering, Swansea University, Swansea SA1 8QQ, UKNanoparticle 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<sup>−1</sup>. 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.https://www.mdpi.com/2079-4991/11/10/2606nanoparticle dosimetrypharmacokineticsimaging flow cytometrynanomedicinedrug deliverydata-driven models |
| spellingShingle | Huw D. Summers Carla P. Gomes Aida Varela-Moreira Ana P. Spencer Maria Gomez-Lazaro Ana P. Pêgo Paul Rees Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles Nanomaterials nanoparticle dosimetry pharmacokinetics imaging flow cytometry nanomedicine drug delivery data-driven models |
| title | 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_short | 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 |
| topic | nanoparticle dosimetry pharmacokinetics imaging flow cytometry nanomedicine drug delivery data-driven models |
| url | https://www.mdpi.com/2079-4991/11/10/2606 |
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