Nanomaterials hold great promise for medical, technological and economical benefits. Knowledge concerning the toxicological properties of these novel materials is typically lacking. At the same time, it is becoming evident that some nanomaterials could have a toxic potential in humans and the environment. Animal based systems lack the needed capacity to cope with the abundance of novel nanomaterials being produced, and thus we have to employ in vitro methods with high throughput to manage the rush logistically and use high content readouts wherever needed in order to gain more depth of information. Towards this end, high throughput screening (HTS) and high content screening (HCS) approaches can be used to speed up the safety analysis on a scale that commensurate with the rate of expansion of new materials and new properties. The insights gained from HTS/HCS should aid in our understanding of the tenets of nanomaterial hazard at biological level as well as asset the development of safe-by-design approaches. This review aims to provide a comprehensive introduction to the HTS/HCS methodology employed for safety assessment of engineered nanomaterials (ENMs), including data analysis and prediction of potentially hazardous material properties. Given the current pace of nanomaterial development, HTS/HCS is a potentially effective means of keeping up with the rapid progress in this field – we have literally no time to lose.

Pathogenesis of Bacillus anthracis is associated with the production of lethal toxin (LT), which activates the murine Nalp1b/Nlrp1b inflammasome and induces caspase-1–dependent pyroptotic death in macrophages and dendritic cells. In this study, we investigated the effect of allelic variation of Nlrp1b on the outcome of LT challenge and infection by B. anthracis spores. Nlrp1b allelic variation did not alter the kinetics or pathology of end-stage disease induced by purified LT, suggesting that, in contrast to previous reports, macrophage lysis does not contribute directly to LT-mediated pathology. However, animals expressing a LT-sensitive allele of Nlrp1b showed an early inflammatory response to LT and increased resistance to infection by B. anthracis. Data presented here support a model whereby LT-mediated activation of Nlrp1b and subsequent lysis of macrophages is not a mechanism used by B. anthracis to promote virulence, but rather a protective host-mediated innate immune response.

Significance Bacterial and viral infections are a significant public health burden. To corrupt normal host cellular functions, many bacterial toxins and all viruses must gain entry to host cells, a process that exploits the host’s own cellular machinery. In this study, we use high-throughput technologies to screen for chemical inhibitors of bacterial toxin and viral entry. We report the discovery of a small molecule that inhibits several viruses and bacterial toxins. In addition to the therapeutic potential, this compound represents a powerful probe for dissecting the mechanisms of mammalian membrane trafficking processes.

The response of a murine macrophage cell line exposed to a library of seven metal and metal oxide nanoparticles was evaluated via High Throughput Screening (HTS) assay employing luciferase-reporters for ten independent toxicity-related signaling pathways. Similarities of toxicity response among the nanoparticles were identified via Self-Organizing Map (SOM) analysis. This analysis, applied to the HTS data, quantified the significance of the signaling pathway responses (SPRs) of the cell population exposed to nanomaterials relative to a population of untreated cells, using the Strictly Standardized Mean Difference (SSMD). Given the high dimensionality of the data and relatively small dataset the validity of the SOM clusters was established via a consensus clustering technique. Analysis of the SPR signatures revealed two cluster groups corresponding to (i) sub-lethal pro-inflammatory responses to Al2O3, Au, Ag, SiO2 nanoparticles possibly related to ROS generation, and (ii) lethal genotoxic responses due to exposure to ZnO and Pt nanoparticles at a concentration range of 25 μg/mL-100 μg/mL at 12 h exposure. In addition to identifying and visualizing clusters and quantifying similarity measures, the SOM approach can aid in developing predictive quantitative-structure relations; however, this would require significantly larger datasets generated from combinatorial libraries of engineered nanoparticles.