Grouping of carbonaceous nanomaterials based on association of patterns of inflammatory markers in BAL fluid with adverse outcomes in lungs

Abstract

Carbonaceous nanomaterials (CNMs) are universally being used to make commodities, as they present unique opportunities for development and innovation in the fields of engineering, biotechnology, etc. As technology advances to incorporate CNMs in industry, the potential exposures associated with these particles also increase. CNMs have been found to be associated with substantial pulmonary toxicity, including inflammation, fibrosis, and/or granuloma formation in animal models. This study attempts to categorize the toxicity profiles of various carbon allotropes, in particular, carbon black, different multi-walled carbon nanotubes, graphene-based materials, and their derivatives. Statistical and machine learning-based approaches were used to identify groups of CNMs with similar pulmonary toxicity responses from a panel of proteins measured in bronchoalveolar lavage (BAL) fluid samples and with similar pathological outcomes in the lungs. Thus, grouped particles, based on their pulmonary toxicity profiles, were used to select a small set of proteins that could potentially identify and discriminate between the toxicity profiles associated within each group. Specifically, MDC/CCL22 and MIP-3β/CCL19 were identified as common protein markers associated with both toxicologically distinct groups of CNMs. In addition, the persistent expression of other selected protein markers in BAL fluid from each group suggested their ability to predict toxicity in the lungs, i.e. fibrosis and microgranuloma formation. The advantages of such approaches can have positive implications for further research in toxicity profiling.