Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using a macrophage cell culture model

Improved regimens for treatment of tuberculosis are needed to shorten the duration of treatment and combat the emergence of drug resistance. Selection of optimized regimens requires assessment of numerous combinations of existing drugs at multiple dose levels. This requirement presents a challenge because of the exponentially large number of combinations—N^M for N doses of M drugs. We show here using a high-throughput macrophage model of Mycobacterium tuberculosis infection that a feedback system control technique can determine optimal drug treatment regimens by testing a relatively small number of drug–dose combinations. In an independent assay measuring intramacrophage killing of M. tuberculosis, the optimized regimens are superior to the current standard regimen.

Tuberculosis (TB) remains a major global public health problem, and improved treatments are needed to shorten duration of therapy, decrease disease burden, improve compliance, and combat emergence of drug resistance. Ideally, the most effective regimen would be identified by a systematic and comprehensive combinatorial search of large numbers of TB drugs. However, optimization of regimens by standard methods is challenging, especially as the number of drugs increases, because of the extremely large number of drug–dose combinations requiring testing. Herein, we used an optimization platform, feedback system control (FSC) methodology, to identify improved drug–dose combinations for TB treatment using a fluorescence-based human macrophage cell culture model of TB, in which macrophages are infected with isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible green fluorescent protein (GFP)-expressing Mycobacterium tuberculosis (Mtb). On the basis of only a single screening test and three iterations, we identified highly efficacious three- and four-drug combinations. To verify the efficacy of these combinations, we further evaluated them using a methodologically independent assay for intramacrophage killing of Mtb; the optimized combinations showed greater efficacy than the current standard TB drug regimen. Surprisingly, all top three- and four-drug optimized regimens included the third-line drug clofazimine, and none included the first-line drugs isoniazid and rifampin, which had insignificant or antagonistic impacts on efficacy. Because top regimens also did not include a fluoroquinolone or aminoglycoside, they are potentially of use for treating many cases of multidrug- and extensively drug-resistant TB. Our study shows the power of an FSC platform to identify promising previously unidentified drug–dose combinations for treatment of TB.