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

Bai-Yu Lee, Daniel L. Clemens, Aleidy Silva, Barbara Jane Dillon, Saša Masleša-Galić, Susana Nava, Xianting Ding, Chih-Ming Ho and Marcus A. Horwitz ()
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Bai-Yu Lee: University of California
Daniel L. Clemens: University of California
Aleidy Silva: University of California
Barbara Jane Dillon: University of California
Saša Masleša-Galić: University of California
Susana Nava: University of California
Xianting Ding: Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University
Chih-Ming Ho: University of California
Marcus A. Horwitz: University of California

Nature Communications, 2017, vol. 8, issue 1, 1-11

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1038/ncomms14183

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