On 14 December 2018 the STriTuVaD consortium published the technical report “A modelling framework to simulate the dynamics of the human immune system”
Led by Etna Biotech, an innovative R&D company based in the south of Italy, the STriTuVaD consortium coordinates some of the research excellences in Europe. The overall goal of the project is to demonstrate how advanced computer modelling and simulation can be used to reduce the costs of the clinical trials required to test the efficacy of new therapies for tuberculosis.
Tuberculosis (TB) is one of the world’s deadliest infectious diseases (Global WHO TB report 2016): one third of the world’s population, mostly in developing countries, is infected with TB (O'Garra A., et al., 2013). But TB is increasingly becoming problematic for developed countries, due to the increased mobility of the world population and the appearance of several new bacterial strains that are multi-drug resistant (MDR). There is now a growing awareness that TB can only be effectively fought by working globally, starting with countries like India, where the infection is endemic. Once a person presents with the active disease, the most critical issue is the current duration of therapy including high costs of treatment, the increased chances of non-compliance (which increases the probability of developing an MDR strain), and the time the patient is still infectious.
One promising possibility to shorten the duration of the therapy is new host-reaction therapies (HRT) offered in combination with the antibiotic therapy.
The endpoints in the clinical trials for HRTs are time to inactivation and incidence of recurrence. With inactivation, it is possible, in some cases, to have statistically powered evidence for efficacy in a Phase II clinical trial; however, recurrence almost always requires a Phase III clinical trial with thousands of patients involved and huge associated costs.
The STriTuVaD project will extend the Universal Immune System Simulator, developed by Prof Francesco Pappalardo at the University of Catania, to include all relevant determinants of such a clinical trial. The project will establish predictive accuracy against the individual patients recruited in the trial, use it to generate virtual patients and predict their response to the HRT being tested, and then combine them with the observations made on physical patients using a new in silico-augmented clinical trial approach that uses a Bayesian adaptive design. This approach, where found effective, could drastically reduce the cost of innovation in this critical sector of public healthcare and make advanced therapies available at reasonable costs.
Full information on the project can be found here