Size-Dependent Endocytosis of Nanoparticles

By Sulin Zhang, Ju Li, G. Lykotrafitis, Gang Bao, Subra Suresh. Advances in Materials, 2009

995 citations recorded by the source corpus.

Recent advances in nanotechnology have stimulated novel applications in biomedicine where nanoparticles (NPs) are used to achieve drug delivery and photodynamic therapy. In chemotherapeutic cancer treatment, tumor-specific drug delivery is a topic of considerable research interest for achieving enhanced therapeutic efficacy and for mitigating adverse side effects. Most anticancer agents are incapable of distinguishing between benign and malignant cells, and consequently cause systematic toxicity during cancer treatment. Owing to their small size, ligand-coated NPs can be efficiently directed toward, and subsequently internalized by tumor cells through ligand–receptor recognition and interaction (see Fig. 1), thereby offering an effective approach for specific targeting of tumor cells. For example, branching dendrimers have recently been identified as potential candidates for site-specific drug carriers.[2] NPs have also been exploited in other biomedical applications such as bioimaging[3,4] and biosensing.[5,6] It has been demonstrated that florescent quantum dots are efficient in tumor cell imaging, recognition, and tracking,[3,4] and that gold NPs are capable of detecting small proteins.[5,6] To enable rational design of such NP-based agents, it is essential to understand the underlying mechanisms that govern the transmembrane transport and invagination of NPs in biological cells. In this communication, we present a thermodynamic model for receptor-mediated endocytosis of ligand-coated NPs. We identify an optimal NP radius at which the cellular uptake reaches a maximum of several thousand at physiologically relevant parameters, and we show that the cellular uptake of NPs is regulated by membrane tension, and can be elaborately controlled by particle size. The optimal NP radius for endocytosis is on the order of 25−30 nm, which is in good agreement with prior estimates.[7]

Subfields: Biomaterials, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/0ad6ef00592b3a8e05250d2b129839800142d980

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

  1. Ya-nan Chang
  2. Mingyi Zhang
  3. Abuzer Alp Yetisgin
  4. Lin Xia
  5. Jun Zhang
  6. Ali Kosar
  7. Ozlem Kutlu
  8. Sibel Çetinel

Citations in the corpus