Advanced in silico characterization of nanomaterials for nanoparticle toxicology

TL;DR

This paper presents an advanced computational approach for characterizing nanomaterials, specifically titanium dioxide nanoparticles, to predict their toxicity in silico, addressing safety testing limitations.

Contribution

It introduces a multiscale computational method for nanomaterial characterization based solely on chemical and structural data to aid toxicity prediction.

Findings

Successful application to titanium dioxide nanoparticles

Potential to predict biological activity from physicochemical properties

Supports nanoinformatics as a safe alternative to experimental testing

Abstract

Nanomaterials possess a wide range of potential applications due to their novel properties compared to bulk matter, but these same properties may represent an unknown risk to health. Experimental safety testing cannot keep pace with the rate at which new nanoparticles are developed and, being lengthy and expensive, often hinders the development of technology. An economic alternative to in vitro and in vivo testing is offered by nanoinformatics, potentially enabling the quantitative relation of the nanomaterial properties to their crucial biological activities. Recent research efforts have demonstrated that such activities can be successfully predicted from the physicochemical characteristics of nanoparticles, especially those related to the bionano interface, by means of statistical models. In this work, as a step towards in silico prediction of toxicity of nanomaterials, an advanced computational characterization of these materials has been proposed and applied to titanium dioxide nanoparticles. The characteristics of nanoparticles and bionano interface are computed using a systematic multiscale approach relying only on information on chemistry and structure of the nanoparticles.