A Macrophage Infection Model to Predict Drug Efficacy Against Mycobacterium Tuberculosis

Kaitlyn Schaaf, Virginia Hayley, Alexander Speer, Frank Wolschendorf, Michael Niederweis, Olaf Kutsch, Jim Sun

Research output: Contribution to journalArticleAcademic

15 Citations (Scopus)


In the last 40 years, only a single new antituberculosis drug was FDA approved. New tools that improve the drug development process will be essential to accelerate the development of next-generation antituberculosis drugs. The drug development process seems to be hampered by the inefficient transition of initially promising hits to candidate compounds that are effective in vivo. In this study, we introduce an inexpensive, rapid, and BSL-2 compatible infection model using macrophage-passaged Mycobacterium tuberculosis (Mtb) that forms densely packed Mtb/macrophage aggregate structures suitable for drug efficacy testing. Susceptibility to antituberculosis drugs determined with this Mtb/macrophage aggregate model differed from commonly used in vitro broth-grown single-cell Mtb cultures. Importantly, altered drug susceptibility correlated well with the reported ability of the respective drugs to generate high tissue and cerebrospinal fluid concentrations relative to their serum concentrations, which seems to be the best predictors of in vivo efficacy. Production of these Mtb/macrophage aggregates could be easily scaled up to support throughput efforts. Overall, its simplicity and scalability should make this Mtb/macrophage aggregate model a valuable addition to the currently available Mtb drug discovery tools.

Original languageEnglish
Pages (from-to)345-54
Number of pages10
JournalAssay and drug development technologies
Issue number6
Publication statusPublished - Aug 2016


  • Journal Article

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