Historically, mathematics has been used extensively in the sciences to describe, explain, and ultimately predict the behaviour of complex systems. Starting from the seventies, models have been widely used also to study complex biological systems in order to understand the fundamental biological mechanisms. Independently from the specific application and the specific modelling/computational techniques, the common denominator in this field is the use of tools coming from the statistics, the mathematics and the artificial intelligence togheter with the biological/physiological knowledge. The integration of these elements allows to derive qualitative information about the phenomena under investigation or to make quantitative prediction of the main variables of a biological system.

Our activities cope with both methodological and applied issues.  Bayesian techniques (and Markov Chain Monte Carlo algorithms), population analysis and deconvolution methods are some examples of our interests and advanced expertise. At the present, the most important application field is the support of drug development and registration (in vitro, preclinical, clinical studies) by a quantitative assessment of drug efficacy and safety. A key objective in this area is to characterize the pharmacokinetic and pharmacodynamic (PK-PD) properties of new drugs, based on pre-clinical and/or clinical data. Therefore, our research activity focuses on the development of PK-PD models to quantitatively describe kinetics, mechanism of action and the effects on relevant endpoints of new compounds currently under investigation. For that we use several software tools such as Matlab, NONMEM, Monolix, WinBUGS. For more information see the Mathematical Modelling page.