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The goal of the STriTuVaD project is to test the reliability of performing at least part of clinical trials of new combinatory therapies for tuberculosis in silico, i.e. through computer modelling and simulation, which would dramatically reduce the costs involved, and ultimately provide more affordable therapies. 

A full-scale clinical trial will be conducted to test two new therapeutic vaccines to be used as adjuvant of standard chemotherapy, to reduce the duration of the therapy and to decrease the chances of relapses. For each of the patients enrolled, a full-scale computer simulation of the disease progression and of the effect of these therapies on it will be realized, in order to predict the duration of the therapy and the incidence of relapses. 

The model predictions will then be compared blindly to the clinical results, in order to quantify the predictive accuracy of this new technology. If such accuracy is found to be sufficiently high, we will explore various future scenarios where the computer model could replace at least part of the animal and clinical testing required to evaluate the efficacy of a new therapy for tuberculosis. 

We hope this will reduce the cost of such new therapies, making them more affordable for patients living in lower-income countries.  

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• Adjuvant therapies for TB treatment

• A project clinical description​

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Adjuvant therapies for TB treatment

 

According to the World Health Organization (WHO), Tuberculosis (TB) is the world’s leading infectious cause of death. It is estimated that, in 2015, 10.4 million people became ill with TB. The WHO End TB Strategy aims to achieve by 2035 the target of reducing mortality for TB by 95%, and incidence by 90%: to achieve such ambitious goals, novel treatment strategies are indispensable. A particularly promising one involves the adjuvant use of therapeutic vaccines, that are specifically designed to stimulate the immune system after a patient has been infected and, in doing so, they amplify the efficacy of standard chemotherapies. To be fair, the use of “therapeutic vaccines” is an old strategy designed by Robert Koch himself, which received significant attention until the onset of chemotherapy.   

But in recent years, this therapeutic option has gained renewed attention. One reason is that environmental stress caused by chemotherapy causes drastic changes in the metabolisms of the Mycobacterium tuberculosis bacilli (Mtb), that become non-replicating (NR), which makes them less vulnerable to the chemotherapy, which drastically increase the time to sputum culture conversion, when the disease is considered inactive. Therapeutic vaccines seem effective in reducing the number of NR-Mtb, hopefully reducing considerably the time to sputum culture conversion, and the frequency of short-term relapse. Also, a shorter time to sputum culture conversion decreases the probability of developing multidrug-resistant (MDR) strain through non-compliance to the therapeutic protocol. 

 

Some of the therapeutic vaccines for TB actually in development are: 

• The first therapeutic vaccine designed with the aim of killing NR-Mtb is the Mycobacterium tuberculosis cells detoxified and liposomed (RUTI) (Cardona PJ, et al, 2005). 

• The H56, developed by the Statens Serum Institute; it uses a fusion protein containing epitopes of ESAT-6 and Ag85B antigens, plus the antigen Rv2660c, which is related to the NR-Mtb (Aagard C, et al, 2011). 

• The ID93/GLA-SE, developed by IDRI (Coler RN et al., 2013), is a combination of M. tuberculosis proteins associated with virulence (Rv2608, Rv3619, and Rv3620) or latency (Rv1813). 

• DAR-901 is a SRL172 inactivated, whole-cell mycobacterial vaccine (Lahey T, et al., 2016).

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One problem all these vaccines and any other innovative combinatory therapy have in common is the potentially huge cost involved with bringing such complex therapies to the market, especially due to the severe regulatory requirements for experimental evidences of safety and efficacy, which involve multiple clinical trials. If the development cost is high, the final price for therapy will be as high, or even too high to be affordable for many patients living in lower-income countries.  

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The goal of the STriTuVaD project is to test the reliability of performing at least part of these clinical tests in silico, i.e. through computer modelling and simulation, which would dramatically reduce the costs involved, and ultimately provide more affordable therapies. 

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A project clinical description 

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The efficacy of RUTI and ID93/GLA-SE as adjuvants to chemotherapy will be tested in comparison to chemotherapy alone in two cohorts, one infected by drug-responsive strains, and one infected with multidrug-resistance strains. The duration of the trial differs for the two cohorts, but in one case patients will be follow-up to 24 months, to observe relapses. The primary endpoint will be the time to conversion in sputum culture, followed by the incidence of short-term relapses. A number of secondary endpoints will also be monitored. 

In parallel, a computer model will be created for each of the patients enrolled in the trial, on the basis of a number of clinical and laboratory information, which will be used to predict the time to conversion and the probability of relapse for each patient. These predictions will be produced blindly of the clinical results; a modelling controller, independent from the model’s developers, will collect the results of the clinical trials and of the model predictions and will compare them. 

If the model demonstrates to be sufficiently accurate in predicting the response of each patient to the specific therapy he or she received, three possible use cases will be investigated:  

• the model is validated with the results of the clinical trial at 6 or 12 months, and then used to predict the outcomes at 24 months; 

• the model is used to replace entirely the clinical trial, to be used in early design stages where multiple candidate therapies are being evaluated, typically on animal models (thus contributing to the animal experimentation reduction agenda); 

• a cohort of virtual patients is generated, and combined using a Bayesian adaptive design of the clinical trial to physical patients, so as to reduce the number of patients required to reach statistical confidence.

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