# Identifying Optimal Treatment Strategies for Tuberculosis Treatment

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $749,427

## Abstract

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
(<=four months) and high-risk patients infected with high-risk bacteria who will need treatment for longer than
six months. Through our analyses, we will be able to select for each patient the regimen that results in the highest
likelihood of cure. Our findings will completely change the future of TB clinical trials and care worldwide.
 This study will address fundamental questions, such as what the exposure-response/safety relationships
and favorable AUC/MIC targets are for all first-line TB drugs using a major clinical outcome (relapse) and how
early response to treatment relates to clinical outcome in a large and diverse patient population. The project has
unprecedented support from the TBTC/ACTG leadership and our industry partner (Sanofi Aventis). The funds in
this R01 requests the budget needed to complete drug measures and MIC not included in Study ...

## Key facts

- **NIH application ID:** 10075216
- **Project number:** 5R01AI135124-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** David Alland
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $749,427
- **Award type:** 5
- **Project period:** 2019-01-16 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10075216

## Citation

> US National Institutes of Health, RePORTER application 10075216, Identifying Optimal Treatment Strategies for Tuberculosis Treatment (5R01AI135124-03). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10075216. Licensed CC0.

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