Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time

• Published in: Nature communications Vol. 8; no. 1; p. 14183
• Main Authors: Lee, Bai-Yu, Clemens, Daniel L., Silva, Aleidy, Dillon, Barbara Jane, Masleša-Galić, Saša, Nava, Susana, Ding, Xianting, Ho, Chih-Ming, Horwitz, Marcus A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.01.2017; Nature Publishing Group; Nature Portfolio
• Subjects: 631/154/436; 631/326/22/1290; 692/699/255/1856; Antimicrobial agents; Drug dosages; Drug resistance; Humanities and Social Sciences; Metabolism; multidisciplinary; Science; Science (multidisciplinary); Tuberculosis; Los Angeles California; United States > US; California
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms14183

The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis. Current antibiotic therapies for tuberculosis are lengthy and onerous. Here, the authors use an output-driven approach to optimize drug doses for two experimental drug regimens in a mouse model of tuberculosis, leading to improved regimens that reduce treatment time by 75%.