Modeling of cerium oxide nanoparticles pharmacokinetics

Kasyanova, V. V.; Бажукова, И. Н.

doi:10.1063/5.0032208

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Another PBPK model was constructed for cerium oxide (CeO 2 ) NPs administered intravenously to rats and incorporated the internalization of nanoparticles by phagocytic cells along with the various mechanisms for nanoparticle removal from the body . The model sufficiently depicted the CeO 2 biokinetics, and their absorption by phagocytic cells was found a key factor for nanoparticle biokinetics.…”

Section: Physiologically Based Pharmacokinetic (Pbpk) Modeling Applic...mentioning

confidence: 99%

Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Ozbek,

Genc,

O. Ulgen

2024

ACS Pharmacol. Transl. Sci.

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Nanoparticles (NPs) have been widely used to improve the pharmacokinetic properties and tissue distribution of small molecules such as targeting to a specific tissue of interest, enhancing their systemic circulation, and enlarging their therapeutic properties. NPs have unique and complicated in vivo disposition properties compared to small molecule drugs due to their complex multifunctionality. Physiologically based pharmacokinetic (PBPK) modeling has been a powerful tool in the simulation of the absorption, distribution, metabolism, and elimination (ADME) characteristics of the materials, and it can be used in the characterization and prediction of the systemic disposition, toxicity, efficacy, and target exposure of various types of nanoparticles. In this review, recent advances in PBPK model applications related to the nanoparticles with unique properties, and dispositional features in the biological systems, ADME characteristics, the description of transport processes of nanoparticles in the PBPK model, and the challenges in PBPK model development of nanoparticles are delineated and juxtaposed with those encountered in small molecule models. Nanoparticle related, non-nanoparticle-related, and interspecies-scaling methods applied in PBPK modeling are reviewed. In vitro to in vivo extrapolation (IVIVE) methods being a promising computational tool to provide in vivo predictions from the results of in vitro and in silico studies are discussed. Finally, as a recent advancement ML/AI-based approaches and challenges in PBPK modeling in the estimation of ADME parameters and pharmacokinetic (PK) analysis results are introduced.

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Section: Physiologically Based Pharmacokinetic (Pbpk) Modeling Applic...mentioning

confidence: 99%

Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Ozbek,

Genc,

O. Ulgen

2024

ACS Pharmacol. Transl. Sci.

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“…MATLAB provides a range of mathematical and numerical methods for solving PBPK model equations, parameter estimation, and sensitivity analysis [ 53 , 54 , 55 , 56 , 61 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]. In particular, several MATLAB toolkits can be used for modeling, simulating, and analyzing PBPK systems, namely SimBiology [ 57 , 77 , 78 , 79 , 80 ], PottersWheel [ 81 , 82 ], and IntiQuan IQM Tools [ 70 ]. MATLAB was used to simulate the biodistribution of NPs of different sizes in the range from 46 to 162 nm after intravenous injection into the plasma compartment of rats [ 54 ] ( Figure 2 B).…”

Section: Main Pbpk Modeling Softwarementioning

confidence: 99%

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Kutumova

Akberdin

Kiselev

et al. 2022

IJMS

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Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

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“…The elimination of NPs in vivo is often predicted using physiologically based pharmacokinetic (PBPK) tools. 12 In the last decades, more than 25 different nano-PBPK models have been developed, 13,14 covering metallic, [15][16][17][18] carbon, 19 quantum dots (QD), 20 liposome, 21 polymer 22 and crystal NPs. 23 Many types of NPs exist.…”

Section: Introductionmentioning

confidence: 99%

Clearance of nanoparticles from blood: effects of hydrodynamic size and surface coatings

Lu,

Wang,

Hendriks

et al. 2024

Environ. Sci.: Nano

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Modeling of cerium oxide nanoparticles pharmacokinetics

Cited by 3 publications

References 7 publications

Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Clearance of nanoparticles from blood: effects of hydrodynamic size and surface coatings

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