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Cited by 351

The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.

Physiologically Based Pharmacokinetic Modeling of Nanoparticles

Li, Mingguang, Al-Jamal, Khuloud T., Kostarelos, Kostas, Reineke, Joshua·2010·ACS Nano

Abstract

Rapid expansion of nanoparticle research demands new technologies that will enable better interpretation of experimental data and assistance in the rational design of future nanoparticles. The use of physiologically based pharmacokinetic (PBPK) models may serve as powerful tools to meet these needs. PBPK models have been successfully applied for the study of the absorption, distribution, metabolism, and excretion (ADME) of small molecules, such as drugs. Preliminary application of PBPK models to nanoparticles illustrated their potential usefulness for nanoparticle ADME research. However, due to the differences between nanoparticles and small molecules, modifications are needed to build appropriate PBPK models for nanoparticles. This review is divided into two sections, with the first discussing nanoparticle ADME research, emphasizing the interaction of nanoparticles with living systems, including transportation kinetics across biobarriers. In the second section, the basic principles of PBPK model development are introduced, and research pertaining to PBPK models of nanoparticles is reviewed. Factors that need to be considered for developing PBPK models for nanoparticles are also discussed. Finally, perspective applications of nanoparticle PBPK models are summarized.

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Metadata

Authors
Li, M., Al-Jamal, K. T., Kostarelos, K., & Reineke, J.
Venue
ACS Nano
Published
2010-10-14
Volume
4
Issue
11
Pages
6303-6317
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2031560835
Cited by
351
References
120
FWCI
8.42
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.