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Previously, using an output-driven optimization platform and an <jats:italic>in vitro<\/jats:italic> macrophage model of <jats:italic>Mycobacterium tuberculosis<\/jats:italic> 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 <jats:italic>in vivo<\/jats:italic> drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill <jats:italic>M. tuberculosis<\/jats:italic> 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. 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