Doctors need to use a complex range of drug combinations to cure many infectious diseases. A good example is tuberculosis (TB). Standard treatment involves four different anti-TB drugs, given in combination over six months - this is a long time to take drugs that cause a range of side effects. Many people do not complete the course of treatment, meaning that their TB may not be cured. They may also develop resistance to TB drugs.
Up to now, recommendations about the amount of drugs and length of treatment that should be offered to people with TB have been based mainly on observational experience, supported by expert opinions.
If we could shorten the length of time that people have to take anti-TB drugs, they are more likely to complete the course of treatment and respond to the drugs effectively. Across a range of health conditions, the use of mathematical models (i.e. nonlinear mixed-effects models) is becoming increasingly common. These models help experts to make recommendations about the dose of drugs that should be offered, and how long this treatment should be offered for. However, at the moment there is no such model available for the treatment of TB.
This research proposal aims to address this problem. It will bring together two different types of data to develop a mathematical modelling framework:
- drug concentrations in the blood and lungs of human beings
- data from innovative in-vitro experiments, which can mimic the antibacterial drug effect in a human lung.
Because of this, a variety of nonlinear mixed-effects models and statistical methods will need to be tested to develop the most reliable framework. This modelling framework can then be used to make recommendations about the level of anti-TB drugs which should be offered, and how long these should be offered for, personalised for patient populations (i.e. children and elderly).
Eventually this will translate into personalised TB treatments, thus improving the quality of life for patients and saving healthcare time and resources.