Perturbational profiling of nanomaterial biologic activity

Abstract

Our understanding of the biologic effects (including toxicity) of nanomaterials is incomplete. In vivo animal studies remain the gold standard; however, widespread testing remains impractical, and the development of in vitro assays that correlate with in vivo activity has proven challenging. Here, we demonstrate the feasibility of analyzing in vitro nanomaterial activity in a generalizable, systematic fashion. We assessed nanoparticle effects in a multidimensional manner, using multiple cell types and multiple assays that reflect different aspects of cellular physiology. Hierarchical clustering of these data identifies nanomaterials with similar patterns of biologic activity across a broad sampling of cellular contexts, as opposed to extrapolating from results of a single in vitro assay. We show that this approach yields robust and detailed structure-activity relationships. Furthermore, a subset of nanoparticles were tested in mice, and nanoparticles with similar activity profiles in vitro exert similar effects on monocyte number in vivo. These data suggest a strategy of multidimensional characterization of nanomaterials in vitro that can inform the design of novel nanomaterials and guide studies of in vivo activity.cluster analysis ͉ molecular imaging ͉ nanoparticles T he expanding use of nanomaterials has spurred interest in defining their biologic effects (1). Traditionally, the in vivo biologic and toxic effects of nanomaterials have been revealed via animal studies. For instance, single-wall carbon nanotubes cause pulmonary granulomas upon intratracheal instillation in rats and mice (2, 3). Although extremely informative, animal studies are costly and labor-intensive and thus ill-suited to systematically explore the sheer number of potential nanomaterial variables that can influence in vivo activity (including size, core material, coating, surface functionalization, and nanoscale and physicochemical properties). In vitro assays in cultured cells, although unlikely to substitute for animal studies, could help dissect structure-activity relationships and suggest nanomaterials likely to have favorable in vivo activity (4).Although numerous studies have used cultured cell models to examine nanomaterial toxicity, extrapolating from in vitro to in vivo activity remains challenging. In addition to the complexities of in vivo pharmacokinetics and bioavailability, cellular phenotypes (such as the repertoire of expressed proteins) can change significantly during in vitro cell culture (5); furthermore, nanomaterials may show significant in vitro toxicity in one cell-based assay but not others (6). Most commonly, these in vitro efforts have evaluated nanomaterials based on a single cell line and by using a limited number of phenotypes (often a single assay). This makes any conclusions critically dependent on the particular choice of cell model and assay and offers a relatively narrow view of the potentially pleiotropic ways in which a nanomaterial can modulate living systems.We sought to develop a generalizable systema...

The Impact of Toxicity Testing Costs on Nanomaterial Regulation

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(Q)SAR modelling of nanomaterial toxicity: A critical review

“…Therefore, the use of practical and rapid assessment platforms, such as high throughput screening method, for toxicity screening of ENMs would provide several benefits in terms of time and cost reductions. High throughput screening systems, which are capable of rapidly assessing multiple toxicants in multiple cell lines (at multiple doses), have already been used for assessing hazard potential of ENMs Harris, et al, 2013;Rallo, et al, 2011;Shaw, et al, 2008). We believe that HTS data will be extremely useful in near future for establishing nano-(Q)SAR models and identifying the parameters that are responsible for the toxicity of ENMs, as they include comprehensive toxicological information.…”

“…It can visually display the causal relationships between nanomaterials' physicochemical descriptors and the toxicity endpoints, handle limited datasets, and allow investigators to interactively make analysis with the help of the interactive functions and multiple colours built in software tools. To provide an example, the data generated by (Shaw, et al, 2008) are scaled, displayed and coloured (Fig. 8) using a parallel coordinates graph produced by C Visual Explorer (CVE) tool.…”

Current situation on the availability of nanostructure–biological activity data

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