Abstract
Tuberculosis (TB) remains one of the leading causes of death by a communicable agent, infecting up to one-quarter of the world's population, predominantly in disadvantaged communities. Pharmacometrics employ quantitative mathematical models to describe the relationships between pharmacokinetics and pharmacodynamics, and to predict drug doses, exposures and responses. Pharmacometric approaches have provided a scientific basis for improved dosing of anti-TB drugs and concomitantly administered antiretrovirals at the population level. The development of modelling frameworks including physiologically based pharmacokinetics, quantitative systems pharmacology and machine learning provides an opportunity to extend the role of pharmacometrics to in-silico quantification of drug-drug interactions, prediction of doses for special populations, dose optimization and individualization, and understanding the complex exposure-response relationships of multi-drug regimens in terms of both efficacy and safety, informing regimen design for future study. This short, clinically focused review explores what has been done, and what opportunities exist for pharmacometrics to impact TB pharmacotherapy.
