Innovations in information and communication technology infuse all branches of science, including life sciences. Nevertheless, healthcare is historically slow in adopting technological innovation, compared with other industrial sectors. In recent years, new approaches in modelling and simulation have started to provide important insights in biomedicine, opening the way for their potential use in the reduction, refinement and partial substitution of both animal and human experimentation. In light of this evidence, the European Parliament and the United States Congress made similar recommendations to their respective regulators to allow wider use of modelling and simulation within the regulatory process. In the context of in silico medicine, the term 'in silico clinical trials' refers to the development of patient-specific models to form virtual cohorts for testing the safety and/or efficacy of new drugs and of new medical devices. Moreover, it could be envisaged that a virtual set of patients could complement a clinical trial (reducing the number of enrolled patients and improving statistical significance), and/or advise clinical decisions. This article will review the current state of in silico clinical trials and outline directions for a full-scale adoption of patient-specific modelling and simulation in the regulatory evaluation of biomedical products. In particular, we will focus on the development of vaccine therapies, which represents, in our opinion, an ideal target for this innovative approach.

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Cancer vaccine feasibility would benefit from reducing the number and duration of vaccinations without diminishing efficacy. However, the duration of in vivo studies and the huge number of possible variations in vaccination protocols have discouraged their optimization. In this study, we employed an established mouse model of preventive vaccination using HER-2/neu transgenic mice (BALB-neuT) to validate in silico-designed protocols that reduce the number of vaccinations and optimize efficacy. With biological training, the in silico model captured the overall in vivo behavior and highlighted certain critical issues. First, although vaccinations could be reduced in number without sacrificing efficacy, the intensity of early vaccinations was a key determinant of long-term tumor prevention needed for predictive utility in the model. Second, after vaccinations ended, older mice exhibited more rapid tumor onset and sharper decline in antibody levels than young mice, emphasizing immune aging as a key variable in models of vaccine protocols for elderly individuals. Long-term studies confirmed predictions of in silico modeling in which an immune plateau phase, once reached, could be maintained with a reduced number of vaccinations. Furthermore, that rapid priming in young mice is required for long-term antitumor protection, and that the accuracy of mathematical modeling of early immune responses is critical. Finally, that the design and modeling of cancer vaccines and vaccination protocols must take into account the progressive aging of the immune system, by striving to boost immune responses in elderly hosts. Our results show that an integrated in vivo-in silico approach could improve both mathematical and biological models of cancer immunoprevention. Cancer Res; 70(20); 7755-63. ©2010 AACR. Major FindingsLong-term in vivo testing of vaccinations designed in silico yielded three major findings. As predicted in silico, many vaccinations of the Chronic protocol are redundant in the immune plateau phase and can be avoided. A rapid priming of young mice is required for long-term protection from tumor onset, and the accuracy of mathematical modeling of early immune responses is critical. Finally, design and modeling of cancer vaccines and vaccination protocols must take into account the progressive aging of the immune system, and strive to boost antitumor immune responses in elderly hosts.

http://www.dmi.unict.it/CIG/suppdata_bioinf.html.