Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling

Abstract

The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.

Keywords: QNAR modeling; cellular uptake; model predictions; nanomaterial design; nanoparticle library; virtual simulations.

Figure 1

Schematic workflow of virtual GNP (vGNP) development, predictive modeling, and experimental validation.

Figure 2

Gold nanoparticle (GNP) data set. (a) Synthesis of the GNP libraries with a combination of surface ligands for each series. (b) Experimental data of (1) cellular uptake by A549 cells; (2) cellular uptake by HEK293 cells; (3) HO-1 level in A549 cells; and (4) the partition coefficient (logP). The first six series (GNPs 1–29) were designed as dual surface ligand GNPs, and the last series (GNPs 30–34) was designed with single surface ligands. Series are distinguished by colors. Error bars represent the standard deviations (n = 3).

Figure 3

Simulated surface features of the vGNPs. First column: series 1 (GNPs 1–7); second: hydrophobic potentials; third: interaction potential with sodium cation; fourth: interaction potential with dry (hydrophobic) probe; fifth: electrostatic surface associated with hydrophobic interaction atom types; sixth: nonbonded contact preference with hydrophobic ligand atoms.

Figure 4

Principal component analysis of the 41 GNPs based on the 90 chemical descriptors. Dots are GNPs in the modeling set, and star points are those in the external validation set.

Figure 5

Heatmap of the chemical descriptors generated for 34 GNPs. Descriptor values were normalized between 0 and 1.

Figure 6

QNAR model performance in the 10-fold cross-validation (dots) and external validation (stars) results in (a) cellular uptake in A549 cells; (b) cellular uptake in HEK293 cells; (c) HO-1 level in A549 cells; and (d) logP.

Figure 7

Computational profile, design, and experimental validation of seven external nanoparticles. (a) Computationally designed vGNPs; (b) predicted properties and bioactivities of the vGNPs; and (c) experimental validation results.