DEVELOPMENT OF AN INTEGRATED IN SILICO STRATEGY FOR THE RISK ASSESSMENT OF CHEMICALS AND THEIR MIXTURES ON DIFFERENT TOXICOLOGICAL OUTCOMES.

Daily, we are exposed to a mixture of multiple chemicals via food intake, inhalation and dermal contact. The risk for health that may result from this depends on how the effects of different chemicals in the mixture combine, and whether there is any synergism or antagonism between them. The number of different combinations of chemicals in mixtures is infinite and an efficient test strategy for mixtures is lacking. Furthermore, there is social pressure to reduce animal testing, which is the current practice in safety testing of chemicals. In this context, computational biochemistry and, more in general, bioinformatics meets all the requirements, and provides the foundation for further in vitro or in vivo studies. Aim of this PhD thesis is the development of an in silico workflow able to prioritize and discriminate chemicals that act as endocrine active substances (EAS), interfere with the retinoic acid pathway during embryo development and/or may cause liver toxicity. From the observation of the molecular initiating event to the description of the adverse outcome pathway, both ligand- and structure-based approaches were integrated with systems biology. Within this framework, (Q)SAR and molecular docking results were mixed into a majority consensus score to rank chemicals and low-mode molecular dynamic simulations were used to study their intrinsic activity, with respect to a specific nuclear receptor. Moreover, a computational approach based on both the transition state and the density functional theories was used to try discriminating a subset of chemicals as inhibitors or substrates of particular enzymes involved in the retinoic acid pathway, computing also their binding free energy values. This information was also included both in the pharmaco-dynamics (PD) and in the physiological based pharmaco-kinetics (PBPK) models. This in silico pipeline, besides being faster, has economic and ethical advantages, reducing both the research costs and the number of involved animals, in agreement with the “3R” principles (Reduction, Refinement and Replacement).