Enrico Mombelli

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Research interests

Modelling and evaluating structure-toxicity relationships in regulatory contexts

Chemicals can interact with biological targets (e.g., enzymes, nuclear receptors, ion channels) and perturb their physiological function. Such a perturbation can subsequently result in an adverse outcome at the individual and population levels.

The toxicological profile of chemicals can be experimentally assessed thanks to in vivo (animal testing) or in vitro (experiments on tissue culture cells) methods. These two approaches tend to be time-consuming and expensive. In addition, in the case of in vivo experiments, the use of animals in toxicology raises ethical concerns and it can usually be carried out only as a last resort.

In silico methods (computational modelling) are an alternative to the two aforementioned approaches which are cost-convenient and do not involve experiments on animals. Among all the available in silico tools that can be used to characterize the toxicological effects of chemicals, QSAR (Quantitative Structure-Activity Relationships) models and “read-across” approaches can be used in a regulatory context in order to provide scientific evidence on the toxicological profile of chemicals.

QSAR modeling and read-across approaches are both based on a common hypothesis: “similar chemicals elicit similar adverse outcomes”. Both approaches describe the effect that a change in chemical structure has on the toxicological potency of the chemicals of interest.

QSAR models are often defined by means of multivariate statistical methods and as a function of several tenths or hundreds of chemicals. On the other hand, “read-across” methods tend to be ad hoc approaches that identify a local (i.e. limited to a well-defined chemical class) and mechanistic structure-toxicity trend as a function of fewer chemicals.

My main scientific interest is in defining new structure-activity relationships and in characterizing already existing QSAR models for the prediction of toxicological effects in regulatory contexts.

Recent publications

• • Mombelli E., Raitano, G. Benfenati, E. (2016). In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results. Methods Mol Biol. 1425:87-105.

  • Benfenati, E., Belli, M., Borges, T., Casimiro, E. Mombelli E., Petoumeno, M.I, Papparella, M., Paris,P., Raitano, G. (2016). Results of a Round-Robin exercise on read-across. SAR QSAR Environ Res. 27: 371-384.

  • Souissi, Y., Kinani, S., Bouchonnet,S., Bourcier, S., Malosse, C., Sablier, M., Creusot, N., Mombelli, E. Ait-Aissa, S. (2014). Photolysis of estrone generates estrogenic photoproducts with higher activity than the parent compound. Environ Sci Pollut R. 21: 7818-7827.

  • Winkler, D., Mombelli, E., Pietroiusti, A., Tran, L., Worth, A., Fadeel, B. McCall, M.J. (2013). Applying quantitative structure-activity relationships approaches to nanotoxicology: current status and future potential. Toxicology. 313: 15-23.

  • Tebby, C., Mombelli, E., (2013). Modelling Structure Activity Landscapes with Cliffs: a Kernel Regression-Based Approach. Mol Inform. 32:609:623.

  • Tebby, C., Mombelli, E., (2013). A Kernel-Based Method for Assessing Uncertainty on Individual QSAR Predictions. Mol Inform. 10:741:751.