Spatially-resolved PKPD modelling for optimised treatment of central nervous system infection due to Mycobacterium tuberculosis Background and Context  Tuberculosis (TB) is a bacterial infection that causes over a million deaths per year worldwide. TB treatment involves taking a complex cocktail of antibiotics for many months. Although TB infection usually occurs in the lung, other sites of the body can also be affected, such as the brain and spine. TB infection in the brain usually occurs in children and those who have a suppressed immune system. With currently available treatment, one in five patients with TB brain infections die, and over half of those who survive have significant permanent disabilities. One reason for this may be that antibiotics used to treat TB do not get to the areas of the brain where the bacteria are living and growing.    Aims and Objectives  The aim of this research is to understand and describe the concentration of different antibiotics in the brain and spinal fluid compared to the blood. This information will be used along with measurements from previous studies where samples were obtained from human patients to build mathematical models. These models will describe how the concentrations of drugs change in different areas of the brain over time. This can be used to make computer simulations to find drug doses and combinations that will best treat TB infection in the brain. These doses and drug combinations can then be tested in clinical trials. Another problem is that TB from blood and spinal fluid samples grow very slowly (or may not grow at all) in the laboratory. This makes TB a difficult condition to diagnose. This project will also, therefore, investigate how TB can best be diagnosed and monitoring with new tests that have been developed and tested in TB lung infection.    Methods  TB infection involves a complex interaction between the TB bacteria, the immune system, and antibiotic medicines. For this reason, the experiments to study TB treatments will necessarily use infected animals. The use of an in vivo model will allow for tissue samples to be collected that cannot be obtained regularly from human patients or synthesised in the lab (for example brain tissue). Animals will be infected with TB and receive drugs at different doses. A new scanning techniques will be used to show how much of each drug reaches the brain and highlight 'hot spots' (containing high concentrations of drug) and 'cold spots' (containing low drug concentrations). These scans will be compared to those showing where in the brain the TB bacteria are living and growing. Together, this information will be used to build mathematical models that describe the amount of drugs in different areas of the brain and in the spinal fluid over time. The models developed will allow us to make predictions about whether treatment would work under new conditions using computer simulations (for example if a higher dose or new combination is used) and identify the combination and doses of drugs that have the highest chance of effectively treatment the infection.     Potential Applications and Benefits  The findings of this work have the potential to directly improve the lives of people who suffer from TB brain infection by identifying treatments which are more effective. The findings from the project will provide evidence to plan clinical trials of new and existing treatments, and assist national and international organisations that publish guidelines and recommendations on which drugs and doses doctors should use to treat TB. The work will also develop new methods to study how well drugs reach their site of action using of state-of-the-art scans and mathematical models. As well as giving information about the best treatments for TB these methods may be used to provide information on how to best treat many other diseases affecting the brain and infections elsewhere in the body, such as epilepsy, stroke or cancer. The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world.