Tuberculosis is an infectious disease that spreads through the air.

When one is infected, the disease can remain dormant for long time, with little or no symptoms, but when the infection becomes active, it is severely debilitating and - if not properly cured - even lethal.

Tuberculosis affects millions of people worldwide, more frequently in lower-middle income countries like India, but it also represents a potential public health threat for other countries, due to the increased mobility and the more frequent appearance of mutations of the disease, that are resistant to all standard therapies. For these and other reasons, we desperately need better therapies

The cost of developing new drugs grew exponentially in the last 30 years, for a number of reasons: one study suggests that it may take today as much as 2.9 billion dollars to develop, test for safety and efficacy, and commercialise a new drug. If we want to develop better therapies that can be sold at prices that also lower-income countries can afford, we need to find better ways to test new therapies


The goal of the STriTuVaD project is to develop new technologies that make possible to test the efficacy of new therapies at least partly in a computer simulation, rather than through expensive and ethically questionable pre-clinical studies on animals, or clinical studies on patients. 

These technologies are called “In Silico Trials”.

What is Tuberculosis?

Tuberculosis (TB) was for a long time a well-known condition in Europe, but nowadays, probably only few people know what TB is. 

Tuberculosis is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. The infection is highly contagious, and can be transmitted through airborne contagion, that is through the aerosol of saliva droplets people produce when sneezing, coughing, or sometimes also talking or singing.

When someone is infected, the bacteria tend to form in the lungs small spheres inside which the bacteria remain protected from the immune system, waiting for an opportunity to proliferate. This usually happens when the infected person weakens, due to other diseases or simply due to aging.


When this happens the disease becomes active, and manifests with the typical signs of a severe infection: high fever, coughing up blood or mucus, weight loss and loss of appetite, general weakness, etc. 

Why we hear rarely about TB in Europe?

TB is as old as humanity: experts believe it originated more than 150 million years ago. 

By the 18th century it was epidemic in Europe, and it killed roughly 1% of the population, with a higher prevalence of young people (from which the popular name of “robber of youth”), but it was during the industrial revolution that the impact of TB dramatically increased: in 1838, up to a third of English tradesmen and employees died of TB. At the peak of its diffusion in Europe and America, TB caused 25% of all deaths. 

In 1882, Robert Koch presented to the Society of Physiology in Berlin the isolation of the tubercle bacillus, for which he received the Nobel Prize in 1905. In the following decades, Pirquet and Mantoux developed a simple test to identify infected subjects, and then Albert Calmette and Camille Guérin developed the first vaccine, known as Bacillus Calmette–Guérin (BCG) vaccine.

In the following years, through an aggressive programme of isolation of the infected patients in sanatoriums and mandatory vaccination, TB was almost entirely eradicated in Europe.

In the period 2008−2012, the TB notification rates in EU/EEA range between 10 and 20 case per each 100,000 inhabitants. 

Worldwide instead, TB is still a huge health problem. 

According to the World Health Organization, TB is still one of the top ten causes of death worldwide.  In 2017, 10 million people fell ill with TB and 1.6 million died from the disease. In India, for example, there are roughly 200,000 deaths per years caused by TB; of the 9.6 million cases worldwide in 2012, 2.2 million were located in India.  

Today, TB is a dramatic problem mostly for developing countries, but things could change tomorrow. 

In USA, already in the late 90s there was a resurgence of TB.


The most alarming danger is something called Multi-Drug Resistant (MDR) TB. 

MDR-TB strains appear when patients with TB are treated improperly: if the antimicrobial therapy is interrupted too early, or if the drug has insufficient potency (for example because it was poorly preserved), there will be a selection of the bacteria carrying mutations that make them more resistant to drugs. These patients remain infectious, and when the disease manifests again, standard drugs will not work anymore. 

MDR-TB is lethal in 50 to 80% of the cases, and there is the concrete risk that current vaccines may not provide protection from these strains of the disease.

Why do we need better therapies


The probability that an MDR-TB develops is proportional to the duration of the antimicrobial therapy, and inversely proportional to its efficacy: the more effective the TB therapies are and the more quickly they work, the more unlikely are the chances that an MDR-TB strain survives and diffuses.  

One very promising line of research is the use of vaccine-like treatments as adjuvants of standard first line therapies.

Preliminary evidences suggest that, in a heavily infected organism, the inoculation of the vaccine can strengthen the immune response, that augments the antimicrobial effect of the first line therapies, reducing the duration of the treatment required for the full resolution of the active infection, and reducing the probability of relapses. 

Why do we need better ways to test new therapies


According to a recent study (DiMasi, 2016), the costs of drug development have increased exponentially in the last decades, reaching the absurd amount of US$ 2.9 billion to bring a new blockbuster drug to the market. While these figures have been questioned, there's one thing all authors agree on: the cost of development, and especially the cost for clinical trials, contribute massively to the final cost of a drug. If we could reduce the cost of development while retaining the same level of safety and reliability, we would dramatically reduce the final price of drugs. 

Pharmaceutical  is not the only industry that produces potentially dangerous products, but in all other sectors, the cost of safety testing has been reduced by one order of magnitude by using computer modelling & simulation to explore “in silico” thousands of possible scenarios. In the old days, cars safety was tested by throwing actual cars against walls, but nowadays 99% of these tests are done by computer simulation, and the very few physical tests are done to check that the models’ predictions are indeed accurate (model validation). 

Until 2016, it was impossible to produce primary evidence of safety or efficacy for a new drug by modelling and simulations; all evidence had to be obtained experimentally, on animals or on humans.  Cautious estimates made by looking at other industrial fields suggest that in silico trials - that is the testing of new drugs with computer simulation - could save in the future from 50% to 90% of the development costs.   

This is particularly important for a disease like TB, which affects mostly people living in developing countries, where the final cost of the therapy is a critical factor. In Europe as well, where universal health care models are predominant, there is a growing concern on the financial sustainability due to our aging population, and drugs are one of the main expenditures for most universal health care providers.


In Silico Trials: a layperson description


To explain in silico trials, we will use the UISS model that we plan to use in the StriTuVad project.

When a pathogen invades the body of a patient, it seeds and starts to replicate. In most cases, the immune system detects the presence of such pathogen and specialised cells are directed to search it and destroy it. A sort of race starts then between the rate of replication of the pathogen and the rate of destruction of the pathogen by the immune system (The Immune Response in Tuberculosis). 

The UISS model simulates the pathogen invasion in an anatomical compartment; for TB, such compartment is the respiratory system made of the mouth, the throat, the bronchi, and the lungs.

In the computer model, each bacterium is represented as an autonomous agent which can move, replicate and, if attacked, die. Also, the cells of the immune system are modelled as autonomous agents, who can move, replicate, kill the pathogens, and die.  

Of course, the fight between the pathogen and the immune system is decided by a number of factors, which UISS model accounts for. These include:

  • the genetic structure of the pathogen; 

  • the initial pathogen load (how much pathogens the patient has been exposed initially); 

  • the profile of the patient’s immune system.

One important information about this later factor is the Human Leukocyte Antigen (HLA), a portion of the patient genome that encodes many of the specific proteins that help the immune system to distinguish self (the host) and non-self (harmful agents), hence to recognise the pathogens. 

Knowing all this information about a patient, it is possible to build a patient-specific model capable of predicting how the pathogen will proliferate over a certain time. In the case of TB, the best measure of the pathogen proliferation is probably the content of TB bacteria in the saliva or in the phlegm of the patient; by measuring the bacterial load today, and then measuring it again after some days, wecan calculate a difference that gives us a measurement of the proliferation rate.

UISS model is able to predict such a rate. Comparing, for each patient, the actual proliferation rate against the model prediction, it is possible to measure the predictive accuracy of UISS. 

In some circumstances (for example in the case of a virulent strain of the pathogen) the immune system will not be able to fight the diffusion of the pathogen, and the bacterial load will increase over time.  However, if a patient is treated with an antibacterial drug, the proliferation would slow down considerably, and the immune system will be able again to clear the pathogen from the organism. UISS can also simulate the effect of different drugs and predict how the bacterial load after a given time will change on the basis of the drug used on that patient. Once more, by treating a patient with a drug, and then measuring the change in bacterial load, we can establish the predictive accuracy of UISS for that patient and that specific drug. 

Say that we validate UISS for many patients, i.e. we build many patient-specific models; to some extent we can consider these models as Virtual Patients, who when treated with a drug will respond likely as physical patients. We can then create new virtual patients who are not copies or physical ones, but who have HLA, initial bacterial load, etc. that other patients could have. Now, one can test on these virtual patients a new treatment and see how effective this is, when compared to the current standard of care. Or one can simulate the effect of a standard treatment on special patients’ groups, for example patients with comorbidities, or exposed to a larger than usual bacterial load, or exposed to an MDR-TB strain. 

Every physical patient we add to a physical clinical trial costs on average US$36,000, according to this study. For a phase III clinical trial, one needs at least to enrol 1000 patients per each study. So, to study a new drug it is required $36 million; to study a different dose of the same drug another $36 million are needed; to explore if the drug still works on patients affected by other concomitant pathologies another $36 millions, and so on. 

The main cost of an in silico trial consists of the initial development and the validation of the model. STriTuVaD budget is €5 million, but the UISS model has been under development for years, before the STriTuVaD project was approved. Let us say for the sake of simplicity that the development, validation, and regulatory qualification cost of UISS will be in the end €18 millions.  This is the 50% of the cost of a single trial. But once we have UISS fully validated, we can use it to test any new drug and their effect on a specific population, optimize the drug dosage for each subpopulation, and so much more. In special cases the savings could exceed 90% of the current costs. 

The inner workings of UISS are very difficult to explain.  But the grandparent of UISS is a very simple computer simulation designed by John Conway in 1970, called Game of Life, based on the Cellular Automaton paradigm first proposed by Stanislaw Ulam and John von Neumann while they were working at Los Alamos National Laboratory. 

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