Tuberculosis (TB) is caused by the inhalation of Mycobacterium tuberculosis (Mtb), leading to ~1.5 million deaths every year. Mtb mainly infects lungs and triggers the formation of dense cellular structures composed of immune cells, bacteria, and dead tissue, called granulomas. The complex structure of granulomas prevents the effective penetration of antibiotics used to treat TB. Moreover, the heterogeneity of granulomas gives rise to various microenvironments for Mtb, where bacteria acquire different metabolic states that determine the potency of antibiotics either singly or in combination. Due to these reasons, TB treatment requires treatment with multiple antibiotics over long periods (6-9 months), causing prolonged side effects and compliance issues. Optimizing multidrug therapies and regimens for TB is essential to treat TB more effectively. In this study, we aim to combine in vitro drug interaction predictions within GranSim, our computational model of granuloma formation and drug activity that simulates spatio-temporal granuloma drug dynamics. By systematically testing drug candidate regimens and considering drug interactions, we aim to predict optimal drug regimens to be tested in vivo. This study will potentially lead to the discovery of more effective drug regimens that shorten the treatment window and have fewer side effects. Speaker(s): Maral Budak (University of Michigan)