DESCRIPTION (provided by applicant): Multidrug-resistant (MDR-) TB threatens to undermine the progress made in treating this morbid disease. Current treatment regimens are toxic, and require ¿ 20 months of administration and cure only about 50% of patients. For the first time in 50 years, 7 new drugs from 4 different classes are in clinical development for TB and may provide for transformational new regimens containing 2 or more novel classes. Indeed, bedaquiline, an ATP synthase inhibitor, was recently approved to treat MDR-TB and two nitroimidazole derivatives (i.e., PA-824 and delamanid) are in phase II/III trials. Although assuring adherence to combination therapy remains an important aspect of resistance prevention, emerging evidence suggests that optimal strategies for preventing MDR-TB and/or protect new agents against emergence of resistance should include optimized drug dosing, dose scheduling, and regimen composition. Yet current drug development efforts largely ignore the "durability" of regimens, that is, the robustness of regimens to emergence of resistance. We propose a novel translational platform for evaluating and optimizing the durability of TB regimens using a multiscale modeling, or integrative pharmacology, approach that takes into account the interaction of host, pathogen, and drug-related factors which determine the emergence of drug resistance. Using a combination of in vitro combination time-kill studies, experiments in an in vitro pharmacodynamic system (hollow fiber model), and multidrug treatment trials in 3 murine models (conventional BALB/c mice, immunodeficient nude mice, and a novel cavitary TB model in C3H3B/FeJ mice) with multiple Mycobacterium tuberculosis (MTB) strains to define the determinants of resistance emergence in the context of the current 1st-line regimen and novel regimens based on the combination of PA-824, moxifloxacin and pyrazinamide (¿ bedaquiline). We will (1) develop a model framework for determining the pharmacological and bacteriological determinants of selection of resistance to isoniazid and rifampin during combination chemotherapy using in vitro time-kill studies, an in vitro pharmacodynamic system, and BALB/c mice, (2) explore host-related determinants of resistance selection using a mouse model of cavitary pulmonary TB (effects of pathology) and nude mice (effects of immune status), with measurement of tissue concentrations of TB drugs in infected tissues to assess drug concentrations at the effect site; and (3) evaluate interventions to reduce the risk of emergence of resistance to novel treatment regimens involving PA-824 and bedaquiline using in vitro and mouse experiments. An integrative modeling approach will be used to synthesize and interpret the multiscale data (host, pathogen, drug) that emerge from our experiments. Results will be used to inform pharmacological interventions to reduce the risk of MDR-TB and resistance to precious new anti-TB drugs. An effort will be made to validate the model using data from an ongoing clinical trial of a novel TB regimen. The integrative pharmacology platform we develop will be a novel tool that can be updated with emerging information and used to systematically assess the durability of future TB regimens.

PUBLIC HEALTH RELEVANCE: Drug-resistant tuberculosis (TB), including multidrug-resistant (MDR) TB, is very difficult to treat, has high mortality, and is transmitted by the airborne route - it is a major global health threat, and it is manmade. We know little about how to prevent resistance to first-line TB drugs or to new drugs that are set to enter clinical use because we do not understand the complex interplay among patient, bacterial, drug-related factors that produce resistance. In this project, we will take an innovative integrative pharmacology approach (one that takes into account bug, drug, and host effects) using in vitro studies together with experiments in two novel mouse models - one with lung pathology similar to that of human TB and another with reduced immunity -- to determine the drug doses and drug combinations that will most effectively prevent resistance to existing and future TB drugs.