Data-driven modeling of the cellular pharmacokinetics of degradable chitosan-based nanoparticles

• Autor(a) principal: Summers, HD
• Data de Publicação: 2021
• Outros Autores: Gomes, CP, Varela-Moreira, A, Spencer, AP, Gomez-Lazaro, M, Pêgo, AP, Rees, P
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/153796
• topic: Data-driven models; Drug delivery; Imaging flow cytometry; Nanomedicine; Nanoparticle dosimetry; Pharmacokinetics

Nanoparticle drug delivery vehicles introduce multiple pharmacokinetic processes, with the delivery, accumulation, and stability of the therapeutic molecule influenced by nanoscale pro-cesses. 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 chi-tosan 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, t = 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.