Modeling of nanomaterial toxicity using a model averaging approach

Abstract

Nanomaterials can be found in many applications, from daily products to the healthcare industry. Since human can be exposed to nanomaterials through many ways, it is necessary to study the nanomaterials, especially their potential adverse effects on humans. This research was conducted under the European’s Union H2020 NanoInformaTIX project and focused on the dose-response analysis of nanomaterials’ toxicity. This research, focusing on the data of in vitro studies, aimed to model the relationship between the amount of administered nanomaterial and the possible toxic response on the cells using nonlinear models for the dose-response data. The data used as an example consisted of 65 data of nanomaterials which was differentiated by the cell types, on which the Likelihood ratio test was first applied to identify significant monotone trend. Dose-response model fitting was then conducted on the 14 data subsets with significant monotone trends. Several nonlinear models such as the Three-, Four-, and Five-parameter Log-logistic model, Weibull model, and Gompertz model were fitted on the data. As an illustration, the analysis of NM-110 (zinc oxide, uncoated) and NM-102 (titanium dioxide, anatase) was presented. For the NM-102 (titanium dioxide, anatase), the best model was the Weibull model according to the value of the AIC, with the value of ED50 equals 22.710 (95% C.I, 3.584–41.836). The model average estimate of the ED50 was also calculated by taking into account all fitted models, which was equal to 20.725.