Improved magnetic drug targeting with maximized magnetic forces and limited particle spreading.

BACKGROUND: In magnetic drug targeting (MDT), micro- or nanoparticles are injected into the human body to locally deliver therapeutics. These magnetic particles can be guided from a distance by external magnetic fields and gradients from electromagnets. PURPOSE: During the particles' movement through the vascular network, they are affected by magnetic forces, fluid (drag) forces, particle interactions, diffusion, etc. Adequate targeting is hindered when drag forces overcome the magnetic forces and particles present in vessels are carried away from the targeted region. Moreover, the magnetic force directions and diffusion mechanisms can cause particles to scatter, while they should remain together for an effective targeting performance. In this work, these adverse effects are tackled using optimization methods. METHODS: We formulate an optimization problem with respect to the currents in surrounding electromagnets that aims to maximize the magnetic force on a particle along a predefined direction. A boundary on the magnetic force divergence is introduced as a constraint to limit particle spreading. We also consider particles to be moved from an initial to a target location in a finite-time interval. To this end dynamic optimization is applied. RESULTS: Simulations for particles in a bifurcated vessel show an increase of particle speed by 20% and a successful movement towards the targeted regions without spreading. For the dynamic optimization, simulation results demonstrate that particle collections are accurately guided with 10 times less scattering and 10 times more particles at the target than without the divergence constraint. CONCLUSIONS: The proposed methods significantly improve the steering and capturing of particles in a region of interest. They are applicable to any magnetic drug targeting configuration with electromagnets.

Electromotive Enhanced Drug Administration in Oncology: Principles, Evidence, Current and Emerging Applications.

Local-regional administration of cytotoxic drugs is an important adjunct to systemic chemotherapy amongst cancer patients. It allows for targeted delivery of agents at high concentration to target sites while minimizing systemic side effects. Despite the pharmacokinetic advantages of the local-regional approach, drug transport into tumor nodules remains limited due to the biophysical properties of these tissues. Electromotive enhanced drug administration (EMDA) represents a potential solution to overcome challenges in local drug transport by applying electric currents. Through electrokinetic phenomena of electromigration, electroosmosis and electroporation, electric currents have been shown to improve drug penetration and distribution in a wide variety of clinical applications. Amongst patients with non-muscular invasive bladder cancer (NMIBC) and basal and squamous cell skin cancers, EMDA has been successfully adopted and proven efficacious in several pre-clinical and clinical studies. Its application in ophthalmological and other conditions has also been explored. This review provides an overview of the underlying principles and factors that govern EMDA and discusses its application in cancer patients. We also discuss novel EMDA approaches in pre-clinical studies and explore future opportunities of developments in this field.

Advanced analysis of magnetic nanoflower measurements to leverage their use in biomedicine.

Magnetic nanoparticles are an important asset in many biomedical applications ranging from the local heating of tumours to targeted drug delivery towards diseased sites. Recently, magnetic nanoflowers showed a remarkable heating performance in hyperthermia experiments thanks to their complex structure leading to a broad range of magnetic dynamics. To grasp their full potential and to better understand the origin of this unexpected heating performance, we propose the use of Kaczmarz' algorithm in interpreting magnetic characterisation measurements. It has the advantage that no a priori assumptions need to be made on the particle size distribution, contrasting current magnetic interpretation methods that often assume a lognormal size distribution. Both approaches are compared on DC magnetometry, magnetorelaxometry and AC susceptibility characterisation measurements of the nanoflowers. We report that the lognormal distribution parameters vary significantly between data sets, whereas Kaczmarz' approach achieves a consistent and accurate characterisation for all measurement sets. Additionally, we introduce a methodology to use Kaczmarz' approach on distinct measurement data sets simultaneously. It has the advantage that the strengths of the individual characterisation techniques are combined and their weaknesses reduced, further improving characterisation accuracy. Our findings are important for biomedical applications as Kaczmarz' algorithm allows to pinpoint multiple, smaller peaks in the nanostructure's size distribution compared to the monomodal lognormal distribution. The smaller peaks permit to fine-tune biomedical applications with respect to these peaks to e.g. boost heating or to reduce blurring effects in images. Furthermore, the Kaczmarz algorithm allows for a standardised data analysis for a broad range of magnetic nanoparticle samples. Thus, our approach can improve the safety and efficiency of biomedical applications of magnetic nanoparticles, paving the way towards their clinical use.

Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery.

Magnetic drug targeting (MDT) is an application in the field of targeted drug delivery in which magnetic (nano)particles act as drug carriers. The particles can be steered toward specific regions in the human body by adapting the currents of external (electro)magnets. Accurate models of particle movement and control algorithms for the electromagnet currents are two of the many requirements to ensure effective drug targeting. In this work, a control approach for the currents is presented, based on an underlying physical model that describes the dynamics of particles in a liquid in terms of their concentration in each point in space. Using this model, the control algorithm determines the currents generating the magnetic fields that maximize the particle concentration in spots of interest over a period of time. Such an approach is computationally only feasible thanks to our innovative combination of model order reduction with the method of direct multiple shooting. Simulation results of an in-vitro targeting setup demonstrated that a particle collection can be successfully guided toward the targeted spot with limited dispersion through a surrounding liquid. As now present and future particle behavior can be taken into account, and non-stationary surrounding liquids can be dealt with, a more precise and flexible targeting is achieved compared to existing MDT methods. This proves that the presented methodology can bring MDT closer to its clinical application. Moreover, the developed model is compatible with state-of-the-art imaging methods, paving the way for theranostic platforms that combine both therapy as well as diagnostics.

Simultaneous Coercivity and Size Determination of Magnetic Nanoparticles.

Magnetic nanoparticles are increasingly employed in biomedical applications such as disease detection and tumor treatment. To ensure a safe and efficient operation of these applications, a noninvasive and accurate characterization of the particles is required. In this work, a magnetic characterization technique is presented in which the particles are excited by specific pulsed time-varying magnetic fields. This way, we can selectively excite nanoparticles of a given size so that the resulting measurement gives direct information on the size distribution without the need for any a priori assumptions or complex postprocessing procedures to decompose the measurement signal. This contrasts state-of-the-art magnetic characterization techniques. The possibility to selectively excite certain particle types opens up perspectives in "multicolor" particle imaging, where different particle types need to be imaged independently within one sample. Moreover, the presented methodology allows one to simultaneously determine the size-dependent coercivity of the particles. This is not only a valuable structure-property relation from a fundamental point of view, it is also practically relevant to optimize applications like magnetic particle hyperthermia. We numerically demonstrate that the novel characterization technique can accurately reconstruct several particle size distributions and is able to retrieve the coercivity-size relation of the particles. The developed technique advances current magnetic nanoparticle characterization possibilities and opens up exciting pathways for biomedical applications and particle imaging procedures.

Dynamical Magnetic Response of Iron Oxide Nanoparticles Inside Live Cells.

Magnetic nanoparticles exposed to alternating magnetic fields have shown a great potential acting as magnetic hyperthermia mediators for cancer treatment. However, a dramatic and unexplained reduction of the nanoparticle magnetic heating efficiency has been evidenced when nanoparticles are located inside cells or tissues. Recent studies suggest the enhancement of nanoparticle clustering and/or immobilization after interaction with cells as possible causes, although a quantitative description of the influence of biological matrices on the magnetic response of magnetic nanoparticles under AC magnetic fields is still lacking. Here, we studied the effect of cell internalization on the dynamical magnetic response of iron oxide nanoparticles (IONPs). AC magnetometry and magnetic susceptibility measurements of two magnetic core sizes (11 and 21 nm) underscored differences in the dynamical magnetic response following cell uptake with effects more pronounced for larger sizes. Two methodologies have been employed for experimentally determining the magnetic heat losses of magnetic nanoparticles inside live cells without risking their viability as well as the suitability of magnetic nanostructures for in vitro hyperthermia studies. Our experimental results-supported by theoretical calculations-reveal that the enhancement of intracellular IONP clustering mainly drives the cell internalization effects rather than intracellular IONP immobilization. Understanding the effects related to the nanoparticle transit into live cells on their magnetic response will allow the design of nanostructures containing magnetic nanoparticles whose dynamical magnetic response will remain invariable in any biological environments, allowing sustained and predictable in vivo heating efficiency.

Quantitative model selection for enhanced magnetic nanoparticle imaging in magnetorelaxometry.

PURPOSE: The performance of an increasing number of biomedical applications is dependent on the accurate knowledge of the spatial magnetic nanoparticle (MNP) distribution in the body. Magnetorelaxometry (MRX) imaging is a promising and noninvasive technique for the reconstruction of this distribution. To date, no accurate and quantitative measure is available to compare and optimize different MRX imaging models and setups independent of the MNP distribution. In this paper, the authors employ statistical parameters to develop quantitative MRX imaging models. Using these models, a straightforward optimization of setups and models is possible resulting in improved MNP reconstructions. METHODS: A MRX imaging setup is considered with different coil configurations, each corresponding to a MRX imaging model. The models can be represented by a sensitivity matrix. These are compared by employing the matrices as inputs to statistical parameters such as conditional entropy and mutual information (MI). These parameters determine the best model to reconstruct the MNP amount for each volume-element (voxel) in the sample. The matrix is transformed by multiplying the columns with different weightings depending on the performance of the MRX imaging model with respect to the other models. This transformed matrix is compared to the original sensitivity matrix without weightings. RESULTS: Compared to the original sensitivity matrix, an increased numerical stability and improved noise robustness for the transformed sensitivity matrix are observed. The reconstruction of the MNP shows improvements: a correlation to the actual MNP distribution of 99.2%, whereas the original matrix only had 82.5%. By selecting the MRX models with the smallest MI, the authors are able to reduce the measurement time by 65% and still obtain an improved imaging accuracy and noise robustness. The statistical parameters allow a direct measure of the relative information content within the setup such that the optimal voxel size for the MRX setup is determined to be between 5 and 15 mm, while other sizes show a significant change in the statistical parameters. CONCLUSIONS: The use of statistical parameters in MRX imaging models results in quantitative models which can optimize MRX setups in a very fast and elegant way such that improved MNP imaging can be realized. Finally, the presented measure allows to quantitatively and accurately compare different MRX models and setups independent of the MNP distribution.

Robustness assessment of 1-d electron paramagnetic resonance for improved magnetic nanoparticle reconstructions.

Electron paramagnetic resonance (EPR) is a sensitive measurement technique which can be used to recover the 1-D spatial distribution of magnetic nanoparticles (MNP) noninvasively. This can be achieved by solving an inverse problem that requires a numerical model for interpreting the EPR measurement data. This paper assesses the robustness of this technique by including different types of errors such as setup errors, measurement errors, and sample positioning errors in the numerical model. The impact of each error is estimated for different spatial MNP distributions. Additionally, our error models are validated by comparing the simulated impact of errors to the impact on lab EPR measurements. Furthermore, we improve the solution of the inverse problem by introducing a combination of truncated singular value decomposition and nonnegative least squares. This combination enables to recover both smooth and discontinuous MNP distributions. From this analysis, conclusions are drawn to improve MNP reconstructions with EPR and to state requirements for using EPR as a 2-D and 3-D imaging technique for MNP.

Vinamax: a macrospin simulation tool for magnetic nanoparticles.

We present Vinamax, a simulation tool for nanoparticles that aims at simulating magnetization dynamics on very large timescales. To this end, each individual nanoparticle is approximated by a macrospin. Vinamax numerically solves the Landau-Lifshitz equation by adopting a dipole approximation method, while temperature effects can be taken into account with two stochastic methods. It describes the influence of demagnetizing and anisotropy fields on magnetic nanoparticles at finite temperatures in a space- and time-dependent externally applied field. Vinamax can be used in biomedical research where nanoparticle imaging techniques are under development, e.g., to validate other higher-level models and study their limitations.