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. Collectively, this research and career development training provide a pathway to an independent career as a clinical investigator focused on optimizing and individualizing the treatment of tuberculosis.
Eliminating the current global epidemic of drug-resistant tuberculosis will require better drugs and treatment strategies. The research conducted through this award will use information from multiple laboratory experiments and human studies of tuberculosis treatment to develop computer-based aids for choosing the best drug combinations and doses to treat tuberculosis patients. Through this mentored research training award, the recipient will gain expertise in improving and individualizing the treatment of tuberculosis, in order to increase the number of patients successfully treated while limiting the spread of antibiotic resistance.
|Kendall, Emily A; Azman, Andrew S; Maartens, Gary et al. (2018) Projected population-wide impact of antiretroviral therapy-linked isoniazid preventive therapy in a high-burden setting. AIDS :|
|Kendall, Emily A; Brigden, Grania; Lienhardt, Christian et al. (2018) Would pan-tuberculosis treatment regimens be cost-effective? Lancet Respir Med 6:486-488|
|Fojo, Anthony T; Kendall, Emily A; Kasaie, Parastu et al. (2017) Mathematical Modeling of ""Chronic"" Infectious Diseases: Unpacking the Black Box. Open Forum Infect Dis 4:ofx172|
|Salvatore, Phillip P; Proaño, Alvaro; Kendall, Emily A et al. (2017) Linking Individual Natural History to Population Outcomes in Tuberculosis. J Infect Dis 217:112-121|
|Dowdy, David W; Theron, Grant; Tornheim, Jeffrey A et al. (2017) Drug-resistant tuberculosis in 2017: at a crossroads. Lancet Respir Med :|
|Kendall, Emily A; Schumacher, Samuel G; Denkinger, Claudia M et al. (2017) Estimated clinical impact of the Xpert MTB/RIF Ultra cartridge for diagnosis of pulmonary tuberculosis: A modeling study. PLoS Med 14:e1002472|
|Kendall, Emily A (2017) Tuberculosis in children: under-counted and under-treated. Lancet Glob Health 5:e845-e846|
|Kendall, Emily A; Cohen, Ted; Mitnick, Carole D et al. (2017) Second line drug susceptibility testing to inform the treatment of rifampin-resistant tuberculosis: a quantitative perspective. Int J Infect Dis 56:185-189|
|Fojo, Anthony T; Stennis, Natalie L; Azman, Andrew S et al. (2017) Current and future trends in tuberculosis incidence in New York City: a dynamic modelling analysis. Lancet Public Health 2:e323-e330|