Dataset Information

In Silico before In Vivo: how to Predict the Heating Efficiency of Magnetic Nanoparticles within the Intracellular Space.

ABSTRACT: This work aims to demonstrate the need for in silico design via numerical simulation to produce optimal Fe₃O₄-based magnetic nanoparticles (MNPs) for magnetic hyperthermia by minimizing the impact of intracellular environments on heating efficiency. By including the relevant magnetic parameters, such as magnetic anisotropy and dipolar interactions, into a numerical model, the heating efficiency of as prepared colloids was preserved in the intracellular environment, providing the largest in vitro specific power absorption (SPA) values yet reported. Dipolar interactions due to intracellular agglomeration, which are included in the simulated SPA, were found to be the main cause of changes in the magnetic relaxation dynamics of MNPs under in vitro conditions. These results pave the way for the magnetism-based design of MNPs that can retain their heating efficiency in vivo, thereby improving the outcome of clinical hyperthermia experiments.

SUBMITTER: Sanz B

PROVIDER: S-EPMC5141417 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

In Silico before In Vivo: how to Predict the Heating Efficiency of Magnetic Nanoparticles within the Intracellular Space.

Sanz Beatriz B Calatayud M Pilar MP De Biasi Emilio E Lima Enio E Mansilla Marcelo Vasquez MV Zysler Roberto D RD Ibarra M Ricardo MR Goya Gerardo F GF

Scientific reports 20161207

This work aims to demonstrate the need for in silico design via numerical simulation to produce optimal Fe<sub>3</sub>O<sub>4</sub>-based magnetic nanoparticles (MNPs) for magnetic hyperthermia by minimizing the impact of intracellular environments on heating efficiency. By including the relevant magnetic parameters, such as magnetic anisotropy and dipolar interactions, into a numerical model, the heating efficiency of as prepared colloids was preserved in the intracellular environment, providin ...[more]

PMID: 27924942

Similar Datasets

Project description:Despite its promising therapeutic potential, nanoparticle-mediated magnetic hyperthermia is currently limited to the treatment of localized and relatively accessible cancer tumors because the required therapeutic temperatures above 40 °C can only be achieved by direct intratumoral injection of conventional iron oxide nanoparticles. To realize the true potential of magnetic hyperthermia for cancer treatment, there is an unmet need for nanoparticles with high heating capacity that can efficiently accumulate at tumor sites following systemic administration and generate desirable intratumoral temperatures upon exposure to an alternating magnetic field (AMF). Although there have been many attempts to develop the desired nanoparticles, reported animal studies reveal the challenges associated with reaching therapeutically relevant intratumoral temperatures following systemic administration at clinically relevant doses. Therefore, we developed efficient magnetic nanoclusters with enhanced heating efficiency for systemically delivered magnetic hyperthermia that are composed of cobalt- and manganese-doped, hexagon-shaped iron oxide nanoparticles (CoMn-IONP) encapsulated in biocompatible PEG-PCL (poly(ethylene glycol)- b-poly(ε-caprolactone))-based nanocarriers. Animal studies validated that the developed nanoclusters are nontoxic, efficiently accumulate in ovarian cancer tumors following a single intravenous injection, and elevate intratumoral temperature up to 44 °C upon exposure to safe and tolerable AMF. Moreover, the obtained results confirmed the efficiency of the nanoclusters to generate the required intratumoral temperature after repeated injections and demonstrated that nanocluster-mediated magnetic hyperthermia significantly inhibits cancer growth. In summary, this nanoplatform is a milestone in the development of systemically delivered magnetic hyperthermia for the treatment of cancer tumors that are difficult to access for intratumoral injection.

Dataset Information

In Silico before In Vivo: how to Predict the Heating Efficiency of Magnetic Nanoparticles within the Intracellular Space.

Publications

In Silico before In Vivo: how to Predict the Heating Efficiency of Magnetic Nanoparticles within the Intracellular Space.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets