Intracellular pharmacodynamic modelling (PDi) is predictive of the clinical activity of fluoroquinolones against Tuberculosis

Clinical studies of new anti-tubercular drugs are costly and time consuming. Owing to the extensive TB treatment periods, the ability to identify drug candidates based on their predicted clinical efficacy is vital to accelerate the pipeline of new therapies. Recent failures of pre-clinical models in predicting the activity of fluoroquinolones underlines the importance of developing new and more robust predictive tools that will optimise the design of future trials. Here, we have used high-content imaging screening and pharmacodynamic intracellular modelling (PDi) to identify and prioritise fluoroquinolones for TB treatment. In a set of studies designed to validate this approach, we show moxifloxacin to be the most effective fluoroquinolone, and PDi modelling-based Monte Carlo simulations accurately predict negative culture conversion (sputum sterilisation) rates when compared against 8-independent clinical trials. Additionally, PDi-based simulations were used to predict the risk of relapse. Our analyses show that the duration of treatment following culture conversion can be used to predict relapse rate. These data further support that PDi-based modelling offers a much-needed decision making tool for the TB drug development pipeline