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

Abstract

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.

Keywords: Mycobacterium tuberculosis; drug combination optimization; feedback system control; tuberculosis.

Conflict of interest statement

Conflict of interest statement: The authors have filed patent applications covering the findings described in this paper.

Figures

Fig. 1.

FSC.II schematic. The diagram depicts the FSC.II technique loop used for drug optimization with the fluorescence-based assay. The FSC.II methodology has four phases: (i) dose–response curve established for each drug (1), (ii) screening test with two-level orthogonal array design (results used to construct a first-order linear model; 2), (iii) iterations testing drug combinations based on orthogonal or orthogonal array central composite design (OACD) design (results used to construct a second-order quadratic model and surface model; 3); and (iv) optimization of the drug combinations and drug ratios from the final surface model constructed in phase 3 (4).

Fig. 2.

3D surface modeling of drug–drug interactions. Surface representations of drug–drug interactions obtained during phase 4 of the FSC.II methodology. Although the drug–drug interactions between both (Left) TMC and PRO and (Right) PZA and PRO are antagonistic, increasing the drug concentrations yields higher projected inhibitions because of the individual effect of each drug.

Fig. 3.

Efficacy of the top three- and four-drug combinations in killing Mtb in macrophages. The three-drug regimens A1–A3 and B–D and the four-drug regimens E–J are plotted against the 1980s standard regimen (SR) at 16× concentrations. Regimens A2 and A3 are also plotted against the SR at 16× concentrations; however, with regimen A2, the dose of EMB was halved, and with regimen A3, the doses of CLZ and PRO were halved (L indicates one-half of the regular dose, and H is the regular dose). The drug dose for the 1× concentration is shown in SI Appendix, Table S1.