# Translational modeling of individual- and population-level outcomes of novel tuberculosis drug regimens

> **NIH NIH K08** · JOHNS HOPKINS UNIVERSITY · 2020 · $147,550

## Abstract

PROJECT SUMMARY/ABSTRACT
Tuberculosis (TB) persists as a major public health problem, and TB infections are increasingly resistant to the
drugs typically used to treat them. Data exist – including results from in vitro and animal experiments,
pharmacological evaluations of existing and novel tuberculosis drugs, past successful and unsuccessful clinical
trials of tuberculosis treatment regimens, epidemiologic data, and clinical experience – that might be useful in
optimizing tuberculosis treatment. But TB care lacks frameworks to integrate that wealth of data and apply it in
clinical decision-making. This mentored clinical scientist research career development award will allow the
recipient to develop a research career pursuing more effective tuberculosis treatment regimens, through the
development and testing of mechanistic models based on data from a variety of sources.
The recipient of this award is a translational and computational investigator and an infectious diseases
physician at Johns Hopkins University, with a background in laboratory and clinical research and a growing
proficiency in the mathematical modeling of drug-resistant tuberculosis epidemics. During this award period,
she will be mentored by a team whose expertise spans preclinical TB drug development, clinical trials of TB
treatment, computational pharmacology, and population-level TB modeling. As a long-term career goal, the
recipient aims to guide successful tailoring of tuberculosis therapy to individual patients, based on their
personal characteristics, on the TB strains with which they are infected, and on their particular epidemiologic
setting. In the short term, the proposed research focuses on first identifying TB patients in whom drug
resistance is likely to emerge during treatment and then developing strategies to mitigate that risk.
In this project, a model of TB treatment will be developed which incorporates detailed dynamics of drug
resistance and the impact of resistance on overall regimen efficacy. Simulated treatment courses will be used
to identify risk factors for treatment failure or new drug resistance and potential strategies for preventing those
unfavorable outcomes. Then, data and specimens from ongoing TB clinical trials of novel regimens will be
analyzed to understand how drug-resistant subpopulations within patient's TB infections impact those patients'
responses to treatment. Finally, a population-level model will be developed that evaluates the interplay
between TB treatment regimens' efficacy, their barriers to resistance, accompanying drug susceptibility tests,
and treatment algorithms. This model will be used to improve implementation of novel TB treatment regimens
in a way that maximizes benefit to patients while preventing spread of resistance to TB drugs.
This mentored research will be accompanied by relevant skills training in TB pharmacology, advanced
statistical methods, and pharmacometric, within-host, and population-level modeling. Collect...

## Key facts

- **NIH application ID:** 9828057
- **Project number:** 5K08AI127908-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Emily A Kendall
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $147,550
- **Award type:** 5
- **Project period:** 2016-12-22 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9828057

## Citation

> US National Institutes of Health, RePORTER application 9828057, Translational modeling of individual- and population-level outcomes of novel tuberculosis drug regimens (5K08AI127908-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9828057. Licensed CC0.

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