Synergistic Entry of Individual Nanoparticles into Mammalian Cells Driven by Free Energy Decline and Regulated by Their Sizes

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.

Energy and Speed Landscapes of the Membrane Internalization Behavior of Soft Nanoparticles

The cellular endocytosis of nanoparticles (NPs) is a fundamental biological process with significant potential in biomedical applications. However, a comprehensive understanding of the mechanistic aspects of endocytosis and the impact of particle properties on this process remains elusive. In this study, we investigated the membrane-wrapping behavior of soft NPs (SNPs) with varying rigidities using theoretical calculations. Our findings reveal that the membrane-wrapping process of SNPs involves a complex energy change including the possible existence of an energy barrier; moreover, it is found that the location and height of this barrier strongly depend on the mechanistic properties of the NPs and membranes. Additionally, by considering force balance in the membrane-wrapping process, we calculated the speed at which NP is internalized by the membrane, showing a nonmonotonic dependence on particle rigidity and/or wrapping degree. These phenomena can be attributed to competition between different energy components associated with NP-membrane binding, membrane tension, and deformations occurring during SNP-membrane interaction processes. Our results contribute to a deeper understanding of cellular-level endocytosis mechanisms and offer potential applications for soft NPs in biomedicine.

Thermal-controlled cellular uptake of "hot" nanoparticles

Nanoparticles (NPs) have shown immense potential in the field of biomedical applications, particularly in NP-based photothermal therapy, which offers a remote-controlled approach to achieve precise temperature control for site-specific heating and sub-cellular tumor treatment. However, the molecular mechanisms underlying related cellular activities, such as the cellular uptake behavior of irradiated NPs in photothermal effects, remain elusive. In this study, we conducted a thorough investigation of the interaction between an irradiated NP with elevated temperature (ranging from 270 to 360 K) and a model bilayer membrane composed of DPPC or DOPC using nonequilibrium coarse-grained molecular dynamics simulations with the implicit-solvent Dry Martini force field. We observe that the interaction between a "hot" NP and a membrane is thermally regulated. In addition, the wrapping of membranes around NPs exhibits a strong dependence on the temperature of the irradiated NP, demonstrating a step-like change in behavior. This membrane wrapping effect is attributed to the heat conduction between NPs and membrane lipids, which occurs almost simultaneously with the membrane deformation and wrapping of NPs during the NP-membrane interaction process. Especially, during the process of heat conduction, a gel-to-fluid phase transition of the membrane may occur, which plays a crucial role in determining the deformation behavior of the membrane. Moreover, it is found that the membrane lipids in the two leaflets exhibit obvious and asymmetric molecular-level responses to heat flux, characterized by significant changes in packing states (e.g., the order parameter of lipid tails and area per lipid) and possible interdigitation between lipids. Furthermore, the thermal-controlled wrapping effect is tightly linked to the properties of NPs (e.g., size, NP-lipid affinity) and lipid species. Our findings are valuable for comprehending the thermal-regulated cellular internalization of NPs and offer insights into devising strategies to precisely modulate NP endocytosis by exploiting the interplay between heating and NP properties.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Membrane-Specific Binding of 4 nm Lipid Nanoparticles Mediated by an Entropy-Driven Interaction Mechanism

Lipid nanoparticles (LNPs) are a leading biomimetic drug delivery platform due to their distinctive advantages and highly tunable formulations. A mechanistic understanding of the interaction between LNPs and cell membranes is essential for developing the cell-targeted carriers for precision medicine. Here the interactions between sub 10 nm cationic LNPs (cLNPs; e.g., 4 nm in size) and varying model cell membranes are systematically investigated using molecular dynamics simulations. We find that the membrane-binding behavior of cLNPs is governed by a two-step mechanism that is initiated by direct contact followed by a more crucial lipid exchange (dissociation of cLNP's coating lipids and subsequent flip and intercalation into the membrane). Importantly, our simulations demonstrate that the membrane binding of cLNPs is an entropy-driven process, which thus enables cLNPs to differentiate between membranes having different lipid compositions (e.g., the outer and inner membranes of bacteria vs the red blood cell membranes). Accordingly, the possible strategies to drive the membrane-targeting behaviors of cLNPs, which mainly depend on the entropy change in the complicated entropy-enthalpy competition of the cLNP-membrane interaction process, are investigated. Our work unveils the molecular mechanism underlying the membrane selectivity of cLNPs and provides useful hints to develop cLNPs as membrane-targeting agents for precision medicine.

Co-administration of Transportan Peptide Enhances the Cellular Entry of Liposomes in the Bystander Manner Both In Vitro and In Vivo

Liposomes have been widely used as a drug delivery vector. One way to further improve its therapeutic efficacy is to increase the cell entry efficiency. Covalent conjugation with cell-penetrating peptides (CPPs) and other types of ligands has been the mainstream strategy to tackle this issue. Although efficient, it requires additional chemical modifications on liposomes, which is undesirable for clinical translation. Our previous study showed that the transportan (TP) peptide, an amphiphilic CPP, was able to increase the cellular uptake of co-administered, but not covalently coupled, metallic nanoparticles (NPs). Termed bystander uptake, this process represents a simpler method to increase the cell entry of NPs without chemical modifications. Here, we extended our efforts to liposomes. Our results showed that co-administration with the TP peptide improved the internalization of liposome into a variety of cell lines in vitro. This effect was also observed in primary cells, ex vivo tumor slices, and in vivo tumor tissues. On the other hand, this peptide-assisted liposome internalization did not apply to cationic CPPs, which were the main inducers for bystander uptake in previous studies. We also found that TP-assisted bystander uptake of liposome is receptor dependent, and its activity is more sensitive to the inhibitors of the macropinocytosis pathway, underlining the potential cell entry mechanism. Overall, our study provides a simple strategy based on TP co-administration to increase the cell entry of liposomes, which may open up new avenues to apply TP peptides in nanotherapeutics.