Laser-induced convection shifts size distributions in nanoparticle tracking analysis

Toward Resolving Heterogeneous Mixtures of Nanocarriers in Drug Delivery Systems through Light Scattering and Machine Learning

Nanocarriers (NCs) have emerged as a revolutionary approach in targeted drug delivery, promising to enhance drug efficacy and reduce toxicity through precise targeting and controlled release mechanisms. Despite their potential, the clinical adoption of NCs is hindered by challenges in their physicochemical characterization, essential for ensuring drug safety, efficacy, and quality control. Traditional characterization methods, such as dynamic light scattering and nanoparticle tracking analysis, offer limited insights, primarily focusing on particle size and concentration, while techniques like high-performance liquid chromatography and mass spectrometry are hampered by extensive sample preparation, high costs, and potential sample degradation. Addressing these limitations, this work presents a cost-effective methodology leveraging light scattering and optical forces, combined with machine learning algorithms, to characterize polydisperse nanoparticle mixtures, including lipid-based NCs. We prove that our approach provides quantification of the relative concentration of complex nanoparticle suspensions by detecting changes in refractive index and polydispersity without extensive sample preparation or destruction, offering a high-throughput solution for NC characterization in drug delivery systems. Experimental validation demonstrates the method's efficacy in characterizing commercially available synthetic nanoparticles and Doxoves, a liposomal formulation of Doxorubicin used in cancer treatment, marking a significant advancement toward reliable, noninvasive characterization techniques that can accelerate the clinical translation of nanocarrier-based therapeutics.

Determining nanorod dimensions in dispersion with size anisotropy nanoparticle tracking analysis

Control over nanorod dimensions is critical to their application, requiring fast, robust characterisation of their volume and aspect ratio whilst in their working medium. Here, we present an extension of Nanoparticle Tracking Analysis which determines the aspect ratio of nanoparticles from the polarisation state of scattered light in addition to a hydrodynamic diameter from Brownian motion. These data, in principle, permit the determination of nanorod dimensions of any composition using Nanoparticle Tracking Analysis. The results are compared with transmission electron microscopy and show that this technique can additionally determine the aggregation state of the nanorod dispersion if single nanorod dimensions are determined with a complementary technique. We also show it is possible to differentiate nanoparticles of similar hydrodynamic diameter by their depolarised scattering. Finally, we assess the ability of the technique to output nanorod dimensions and suggest ways to further improve the approach. This technique will enable rapid characterisation of nanorods in suspension, which are important tools for nanotechnology.