Exploring the drug loading mechanism of photoactive inorganic nanocarriers through molecular dynamics simulations

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Engineering Self-Assembled Nanomedicines Composed of Clinically Approved Medicines for Enhanced Tumor Nanotherapy

The traditional nanocarriers are typically constructed to deliver anticancer agents for improving drug bioavailability and enhancing chemotherapeutic efficacy, but this strategy suffers from the critical issue of nanocarrier biosafety that hinders further clinical translation. In this work, a unique nanomedicine (PTX@ICG) has been rationally constructed by combining two clinically approved agents, i.e., paclitaxel (PTX) and indocyanine green (ICG), by a facile ultrasound-assisted self-assembly methodology. The formation of the nanostructure can effectively increase the enrichment of PTX and ICG molecules in the tumor site, and improve the utilization factor of hydrophobic PTX. Moreover, since the molecule interaction in PTX@ICG is mainly Van der Waals forces, the self-assembled structure can be spontaneously dissociated under laser irradiation and release PTX in situ to achieve safe tumor-targeted chemotherapy. Simultaneously, the released ICG can act as photothermic agents for photothermal therapy (PTT), thus combining chemotherapy and PTT to obtain an enhanced tumor nanotherapy via facile self-assembly. The synergistic chemo/photothermal tumor nanotherapy achieved the efficient tumor cell-killing effect and tumor-ablation ability, as systematically demonstrated both in vitro and in vivo. This work provides a distinct paradigm of the self-assembled nanomedicine design for effectively improving the drug bioavailability to achieve high antitumor efficacy.

Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles

Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO₂ nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.