Identifying Optimal Treatment Strategies for Tuberculosis Treatment

Project Summary/Abstract The current standard of care for drug-sensitive TB is a “one-size-fits-all” approach, putting hard-to-treat patients at higher risk of relapse and mycobacteria at higher risk of acquiring drug resistance. The Phase 3 treatment-shortening study TBTC/ACTG (Study 31/A5349) is evaluating the efficacy and safety of two new short-course regimens containing high-dose rifapentine. The primary aim of our proposal is to embed full pharmacology and microbiology analyses (PK/PD) in this clinical trial to provide detailed drug pharmacokinetic, MIC response and safety data - including novel data (markers of persisters) for more than 2,000 patients. Our goal is to understand and quantify the interactions among individual drug PK/PD, MICs, new markers of genome load, new markers for persisters, active disease severity and early treatment response in a diverse patient population and recognize how they relate to clinical outcome and safety events. By doing so, we will be able to understand and quantify the contributions of pharmacological (multidrug pharmacokinetic) and non- pharmacological (host, disease severity) components of treatment response and to understand the phenotypes of patients who are hard to treat, allowing us to derive optimal treatment strategies for all patients with drug- sensitive TB, including choice of regimen, treatment duration, and dose. We propose the innovative hypothesis that both the infecting bacteria and the host can be seen as “low” and “high” risk and that it is the combination of these two risks that together determine treatment outcome and the required duration of treatment, regardless of the drugs used. Our approach will stratify bacterial risk by burden, MIC - even among drug-susceptible Mtb - and the presence of drug-tolerant subpopulations. The host risk will be stratified by disease severity, HIV status and ability to absorb and metabolize drugs (PK). We will then use advanced analytic and modeling strategies to develop tools and algorithms to identify low-risk patients infected with low-risk bacteria who can be treated with ultra-short treatment (