The metrics of MWCNT-induced pulmonary inflammation are dependent on the selected testing regimen.

Convincing evidence suggests that high-surface-activity nano-materials, such as MWCNT, exert two modes of action (MoA), in which one appears to be related to surface activity/area and occurs concurrent with deposition, and the other is related to cumulative lung burden. Pulmonary inflammation induced by the latter mode appears to be dependent on cumulative volumetric lung burden and on whether the accumulated particle displacement volume within the pool of alveolar macrophages is above or below the kinetic lung overload threshold. However, the relative importance and effect of each MoA are still controversial. In addition, the test protocol variables, which may predetermine the leading MoA, have not yet received increased attention. This study compares the respective outcome of previously published repeated-exposure inhalation studies with MWCNT (Nanocyl and Baytube) in rats. Modeling procedures were performed to compare post hoc the equivalence of empirical and modeled outcomes, including critical protocol variables. This comparison provided compelling evidence that the accumulated retained particle displacement volume was the most prominent unifying denominator linking the pulmonary retained volumetric particle dose to inflammogenicity and toxicity. However, conventional study designs may not always be appropriate to unequivocally dissociate the surface area/activity-related acute adversity from the cumulative retention volume-related adversity. Thus, in the absence of adequately designed studies, it may become increasingly challenging to differentiate substance-specific deposition-related acute effects from the more chronic retained cumulative dose-related effects. In summary, this analysis of existing data supports the conclusion that both the deposition and retention-related effects need to be judiciously dissociated to better understand the MoA of heightened concern. This exercise supports the conclusion that hypothesis-based computational study design delivers the highest degree of scientifically important information and may further reduce the number of experimental animals in repeated-exposure inhalation studies with low-toxicity, biopersistent, poorly soluble granular particles.