Computational Indicator Approach for Assessment of Nanotoxicity of Two-Dimensional Nanomaterials

Abstract

The increasing growth in the development of various novel nanomaterials and their biomedical applications has drawn increasing attention to their biological safety and potential health impact. The most commonly used methods for nanomaterial toxicity assessment are based on laboratory experiments. In recent years, with the aid of computer modeling and data science, several in silico methods for the cytotoxicity prediction of nanomaterials have been developed. An affordable, cost-effective numerical modeling approach thus can reduce the need for in vitro and in vivo testing and predict the properties of designed or developed nanomaterials. We propose here a new in silico method for rapid cytotoxicity assessment of two-dimensional nanomaterials of arbitrary chemical composition by using free energy analysis and molecular dynamics simulations, which can be expressed by a computational indicator of nanotoxicity (CIN_2D). We applied this approach to five well-known two-dimensional nanomaterials promising for biomedical applications: graphene, graphene oxide, layered double hydroxide, aloohene, and hexagonal boron nitride nanosheets. The results corroborate the available laboratory biosafety data for these nanomaterials, supporting the applicability of the developed method for predictive nanotoxicity assessment of two-dimensional nanomaterials.

Keywords: cytotoxicity prediction; in silico models; lipid extraction; membrane disruption; two-dimensional nanomaterials.

Figure 1

Upper estimate of the g₀ obtained by calculating the potential of mean force during the extraction of a single lipid (series 1a, 1b) and a group of two (series 2) and three (series 3) adjacent lipids: (a) Obtained PMF profiles for series 1–3, the right-hand end points of the curves correspond to the values of $\frac{1}{N}{g}_N$ (N = 1, …, 3). (b) Four characteristic configurations of a periodic fragment of lipid membrane during the extraction of a group of three lipids (simulation series 3). Different lipids in the extracted group are represented by green, purple, and pink colors; atoms of lipids remaining in the bilayer: C—cyan, O—red, N—blue, P—brown, and H—white, water is not shown.

Figure 2

The principal idea of the computational indicator of nanotoxicity (CIN_2D) for two-dimensional nanomaterials. The energy of interaction with the surface of the studied nanomaterial (yellow) is estimated separately for the head (red) and tail (blue) parts of the lipid. The quantitative indicator CIN_2D is calculated according to its definition formula. The CIN_2D value is then compared with the value of $g_0$: if CIN_2D exceeds $g_0$, the nanomaterial would impair the structural and functional integrity of the cell membrane during interaction and, thus, is cytotoxic; if CIN_2D below $g_0$, the potential toxicity cannot be predicted based on this model, and other independent studies need to be performed to confirm its safety.

Figure 3

Free energy change profiles for adsorption–desorption of the lipid head (red) and tail (blue) parts on graphene (a) and graphene oxide (b) nanosheets. Inserts correspond to molecular configurations in the adsorbed state in local free energy minima (red and blue arrows for head and tail parts, respectively). A standard deviation corridor is depicted around each profile by half-transparent filling of the same color. Color code: graphene carbon (gray), lipid carbon (cyan), oxygen (red), hydrogen (white), nitrogen (blue), and phosphorus (mustard). Water is not shown.

Figure 4

Free energy change profiles for adsorption–desorption of the head (red) and tail (blue) parts of the lipid on the Mg/Al-LDH nanosheet (a) and aloohene flat domain (b). Inserts correspond to the adsorbed states of the head part at the ∆G_h local minima (red arrows): M1 for Mg/Al-LDH (configuration in the M2 state is in Supplementary Figure S1) and M for aloohene (see Supplementary Table S1). Adsorption of lipid tails was not observed and the free energy infinitely increases (blue curves) when the lipid tail part gets closer to the hydroxides surfaces. Color code: magnesium (purple), aluminium (cyan), chlorine ions (transparent); other colors are the same as in Figure 3.

Figure 5

Free energy change profiles for adsorption–desorption of the head (red) and tail (blue) parts of the POPC lipid on boron nitride nanosheets. Results obtained for two sets of partial atomic charges: PAC-I ± 1.05 e (a) and PAC-II ± 0.5 e (b). Color code: boron (green), nitrogen (blue); other colors are the same as in Figure 3. Alternative views of the lipid tail part in the adsorbed state are depicted in Supplementary Figure S2.

Figure 6

CIN_2D diagram $g_t$ versus $g_h$ comprises four regions: “no interaction” (blue region, schematically), “adsorption” of nanosheet by membrane (green region), “insertion” (yellow) and “lipid extraction” (white region). Ellipses for aloohene and Mg/Al-LDH nanosheets are inside the adsorption region (green ellipses). Pristine graphene and both models of BNN (red ellipses) are inside the lipid-extraction region, where CIN_2D is larger than $g_0$ that means these nanosheets are cytotoxic. Graphene oxide nanosheet (yellow ellipse), having chemical composition C₁₆O₂(OH)₂(COOH)₁, is inside the “insertion” region.