20 December 2018

Can new drugs be tested with computers? Researchers to explore the future of tuberculosis drug testing

Researchers at the University of Sheffield have launched a new initiative to demonstrate how advanced computer modelling and simulation can be used to reduce the costs of clinical trials testing new therapies for tuberculosis.

Drugs screening

The STriTuVaD consortium, led by Italian research and development company Etna Biotech, includes Dr Miguel Juarez in the School of Mathematics and Statistics at the University of Sheffield, plus colleagues in the Department of Computer Science who are part of the Insigneo Institute for in silico Medicine.

To launch the consortium, STriTuVaD has published a new technical report, 'A modelling framework to simulate the dynamics of the human immune system'.

Tuberculosis is one of the world’s deadliest infectious diseases, with one third of the world's population, mostly in developing countries, carrying the infection. While it is endemic in countries such as India, tuberculosis is a growing problem 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.

Once a person presents with the active disease, the biggest challenges in tackling it are the duration of therapy, high costs of treatment, the increased chances of non-compliance (which increases the probability of developing an multi-drug resistant strain), and the amount of time the patient is still infectious.

A way of potentially shortening the duration of the therapy is by using new host-reaction therapies, in combination with the antibiotic therapy. But clinical trials for such therapies may need to involve thousands of patients and bring huge associated costs.

The STriTuVaD project will use the Universal Immune System Simulator, developed by Prof Francesco Pappalardo at the University of Catania, to gather data from individual trial participants, use it to generate 'virtual patients' and predict their response to the host-reaction therapy being tested. These predictions will be combined with observations made on real patients using a new in silico-augmented clinical trial approach that uses a Bayesian adaptive design.

This approach could drastically reduce the cost of developing tuberculosis therapies and make them available at reasonable costs.

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