Read-across predictions of nanoparticle hazard endpoints: a mathematical optimization approach.

In the present study, a novel read-across methodology for the prediction of toxicity related end-points of engineered nanomaterials (ENMs) is developed. The proposed method lies in the interface between the two main read-across approaches, namely the analogue and the grouping methods, and can employ a single criterion or multiple criteria for defining similarities among ENMs. The main advantage of the proposed method is that there is no need of defining a prior read-across hypothesis. Based on the formulation and the solution of a mathematical optimization problem, the method searches over a space of alternative hypotheses, and determines the one providing the most accurate read-across predictions. The procedure is automated and only two parameters are user-defined: the balance between the level of predictive accuracy and the number of predicted samples, and the similarity criteria, which define the neighbors of a target ENM.