Effect of the solvent on the green synthesis of NIR active polymeric nanoparticles and their in vitro photothermal therapeutic validation

Abstract

Nanoparticle-mediated drug delivery has revolutionized nano-therapeutics. It ensures improved biodistribution, longer blood circulation, and improved bioavailability inside the body. The loading efficiency and stability of the drug within the carrier are the major challenges for ideal drug delivery. In this study, we have synthesized indocyanine green (ICG) loaded Poly-L-Lysine (PLL) nanoparticles by a two-step self-assembly process using a green chemistry approach, where water-based solvents were used for fabrication such as phosphate-buffered saline (PBS, pH 7.4), deionized water (DI), and Milli-Q water (MQ). The effect of these solvents on the morphology, stability and loading efficiency of ICG was investigated using UV-visible spectroscopy, fluorescence spectroscopy, scanning electron microscopy, and dynamic light scattering. The results demonstrated that nanoparticles can be fabricated using all the three solvents, however, there was a huge difference between their functional and morphological properties. These functional and morphological properties play important role in their biomedical applications. It was found that PBS-based NPs showed the maximum loading of ICG followed by DI water and MQ water respectively. The PBS suspended ICG-loaded PLL nanoparticles were highly monodispersed with the mean diameter of ∼200 nm and showed highest photothermal efficiency. The green synthesized biocompatible and biodegradable NPs were designed to treat solid tumors via local hyperthermia due to photothermal property of these NPs. The photothermal cytotoxicity assessment of PBS-based PLL-ICG NPs in both 2D and 3D in vitro cultures displayed notable efficacy. Therefore, we conclusively demonstrate that selection of right solvent is crucial to realize the full potential of green-synthesized polymeric nanoparticles. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.