Molecular dynamics simulation insights into the cellular uptake of elastic nanoparticles through human pulmonary surfactant

Abstract

The interaction between inhaled nanoparticles (NPs) and the pulmonary surfactant (PS) monolayer has drawn significant attention due to its potential in drug delivery design and application for respiratory therapeutics in active and passive cellular uptake pathways. Even though much attention has been given to explore the interaction between NPs and the PS monolayer, the effects of the NP elasticity on the translocation across the PS monolayer have not been thoroughly studied. Here, we performed a series of coarse-grained (CG) molecular dynamics simulations to study active or passive cellular uptake pathways of three NPs with different elasticities through a PS monolayer. The differences between active and passive pathways underly the enhanced targeting ability by ligand-receptor interaction (L-R interaction). In the active or passive cellular uptake pathways, it is found that the increase in stiffness level leads to a higher penetrability of NPs at the same time range. The soft NP has always been withheld inside the PS monolayer due to the lowest level of elasticity, while the other two types of NPs penetrate through the PS monolayer as the simulation progresses toward the end. The NPs in the active cellular uptake pathways take a longer time to penetrate the PS monolayer, resulting in a longer average penetration distance of approximately 40.55% and a higher average number of contacts, approximately 36.11%, than passive cellular uptake pathways, due to the L-R interaction. Moreover, it demonstrates that NPs in active cellular uptake pathways have a significantly higher targeting ability with the PS monolayer. We conclude that the level of NP elasticities has a substantial link to the penetrability in active or passive cellular uptake pathways. These results provide valuable insights into drug delivery and nanoprobe design for inhaled NPs within the lungs.

Fig. 1. The initial configuration the of simulation system. (a) Illustration of CG-NP elasticity adjustment by the variation of spring constant (k) has been shown. The increment trend of spring constant (k) in kJ mol⁻¹ nm⁻² leads to higher stiffness of CG-NP. The diameter of all three CG-NPs is 11.8 nm. (b) Coarse-grained surfactant lipids (DPPC, POPG, and CHOL) with the framework of MARTINI forcefield. (c) Detailed of the simulation box with information provided. The water molecules are shown in light green, sodium cations in yellow, while chloride anions in light cyan. (d) Top, side and bottom views of pulmonary surfactant monolayer.

Fig. 2. Side and top views of simulation systems for active cellular uptake of a 11.8 nm-in-diameter CG-NP (yellow). (a) The soft CG-NP with spring constant (k) equal 1 kJ mol⁻¹ nm⁻², (b) intermediate CG-NP with spring constant (k) equal 1000 kJ mol⁻¹ nm⁻², and (c) stiff CG-NP with spring constant (k) equal 2000 kJ mol⁻¹ nm⁻². Water and ions have been removed for greater visualization.

Fig. 3. Side and top views of simulation systems for passive cellular uptake of a 11.8 nm-in-diameter CG-NP (yellow). (a) The soft CG-NP with spring constant (k) equal 1 kJ mol⁻¹ nm⁻², (b) intermediate CG-NP with spring constant (k) equal 1000 kJ mol⁻¹ nm⁻², and (c) stiff CG-NP with spring constant (k) equal 2000 kJ mol⁻¹ nm⁻². Water and ions have been removed for greater visualization.

Fig. 4. (a) The time evolution of distance between COM of CG-NPs and COM of pulmonary surfactant monolayer with active/passive cellular uptake. (b) The radial distribution function (RDF) of active cellular uptake (dot) and passive cellular uptake (solid line) between COM of CG-NP beads and COM of pulmonary surfactant monolayer for 150 ns. (c) Number of contacts of all three types of CG-NP in all simulations between CG-NP and pulmonary surfactant monolayer.

Fig. 5. Area per lipid of (a) active and (b) passive cellular uptake simulation systems.

Fig. 6. Average area per lipid (nm²) of active (sapphire)–passive (red) cellular uptake of all variation of CG-NP in all simulation systems.