Antitubercular Triazines: Optimization and Intrabacterial Metabolism

Abstract

The triazine antitubercular JSF-2019 was of interest due to its in vitro efficacy and the nitro group shared with the clinically relevant delamanid and pretomanid. JSF-2019 undergoes activation requiring F₄₂₀H₂ and one or more nitroreductases in addition to Ddn. An intrabacterial drug metabolism (IBDM) platform was leveraged to demonstrate the system kinetics, evidencing formation of NO^⋅ and a des-nitro metabolite. Structure-activity relationship studies focused on improving the solubility and mouse pharmacokinetic profile of JSF-2019 and culminated in JSF-2513, relying on the key introduction of a morpholine. Mechanistic studies with JSF-2019, JSF-2513, and other triazines stressed the significance of achieving potent in vitro efficacy via release of intrabacterial NO^⋅ along with inhibition of InhA and, more generally, the FAS-II pathway. This study highlights the importance of probing IBDM and its potential to clarify mechanism of action, which in this case is a combination of NO^⋅ release and InhA inhibition.

Keywords: Bayesian models; Mycobacterium tuberculosis; intrabacterial drug metabolism; nitrofuran; triazine.

Figure 1.. Chemical structures of A) pretomanid and B) delamanid, and C) the evolution from JSF-2019 to JSF-2513 enabled by D) a facile synthetic route.

Chemical structures of pretomanid and delamanid were depicted in (A) and (B), respectively. (C) The evolution of the triazine series from the initial Bayesian model hit JSF-2019 to JSF-2513. (D) The general route involved sequential amine additions to cyanuric chloride. The hydrazine was then added and capped with either an aldehyde to afford the hydrazones or with an acid chloride to prepare the acyl hydrazides.

Figure 2.. JSF-2019 produces NO• and JSF-2026 in a process dependent on F₄₂₀H₂.

(A) Proposed scheme for JSF-2019 activation. (B) and (C) M. tuberculosis strains H37Rv and fgd1∷tn, respectively, were treated with 20 μM compound followed by nitrite quantification via the Griess reagent. Numbers above each data point indicate the molarity ratio of nitrite versus triazine dose. Grey dashed line indicates nitrite lower limit of detection (LLOD) as 0.80 μM. Data points were shown as mean ± standard error with three independent experiments. Statistical significance was analyzed by paired Student’s t-test. * p < 0.05; ** p < 0.01; *** p < 0.001; n.s. = not statistically significant. (D), (E) and (F) H37Rv and fgd1∷tn were treated with 20 μM JSF-2019 followed by study of the kinetics of intrabacterial metabolism and NO• release. Data points were shown as mean ± standard error with three independent experiments. Statistical significance was analyzed by paired multiple t tests corrected for multiple comparison using the Holm-Sidak method. * p < 0.05; ** p < 0.01; *** p < 0.001. (G) and (H) Mid-log phase H37Rv were treated with 10x MIC of JSF-2019 and JSF-2026 followed by hierarchical analysis of transcriptional response comparison with INH, RIF (rifampicin), pretomanid and DETA/NO (diethylenetriamine/nitric oxide adduct). (G) demonstrates responses in the dosR regulon and (H) shows responses in 212 NO• sensitive non-dosR genes.

Figure 3.. JSF-2513 intrabacterial metabolism releases NO• with JSF-4328 formation and FAS-II inhibition.

(A) Proposed scheme for JSF-2513 activation. (B) and (C) M. tuberculosis strains H37Rv and fgd1∷tn were treated with 20 μM compound followed by nitrite quantification by Griess reagent. Numbers above each data point indicate the molarity ratio of nitrite versus triazine dose. Grey dashed line indicates nitrite lower limit of detection (LLOD) as 0.80 μM. Data points were shown as mean ± standard error with three independent experiments. Statistical significance was analyzed by paired Student’s t-test. * p < 0.05; ** p < 0.01; *** p < 0.001; n.s. = not statistically significant. (D) and (E) H37Rv and fgd1∷tn were treated with 20 μM JSF-2513 and then the time courses for intrabacterial metabolism (JSF-4328 formation) and NO• release were followed. Data points were shown as mean ± standard error with three independent experiments. Statistical significance was analyzed by paired multiple t tests corrected for multiple comparison using the Holm-Sidak method. * p < 0.05; ** p < 0.01; *** p < 0.001. (F) and (G) M. tuberculosis strains H37Rv, mc²4914, and fgd1:tn were treated with 10x MIC of JSF-2513 or INH, as a control, for 8 h and assessed for ¹⁴C-acetate labeling after 16 h. FAMEs and MAMEs were extracted and then analyzed via TLC. Dose of JSF-2513 in H37Rv: 1x (0.63 μM), 5x, 10x and 40x MIC. Dose of JSF-2513 in mc²4914: 1x (2.5 μM), 5x, 10x and 40x. Dose of JSF-2513 in fgd1∷tn: 1x (10 μM), 5x and 10x. INH was at 10x MIC. (H) and (I) Grey scale densitometric analysis of MAMEs and FAMEs from panel (F) and (G) respectively using ImageJ.

Figure 4.. JSF-2019 is an InhA inhibitor.

(A) Mid-log phase M. tuberculosis cultures were treated with 10x MIC of JSF-2019 or JSF-2026 for 6 h, followed by mRNA extraction and RNA-seq analysis. Hierarchical clustering analysis with Euclidean distance was applied to the gene expression profiles to compare transcriptional responses in FAS-II genes induced by JSF-2019 or JSF-2026 to responses generated by other stress conditions. RIF, rifampicin; INH, isoniazid; ETA, ethionamide; DNP, 2,4-dinitrophenol; EMB, ethambutol; GSNO, S-nitrosoglutathione; TRC, triclosan. (B) The InhA in vitro enzyme assay scheme is depicted. JSF-2019 IC₅₀ for InhA inhibition was quantified with and without 1% CMC Tween-20. Each data point is shown as mean ± standard error, and IC₅₀ was determined via non-linear regression model. (C) FAS TLC for M. tuberculosis H37Rv treated with JSF-2019 at 1x, 5x, 10x and 40x MIC for 4 h, with INH at 10x MIC and pretomanid at 10x MIC as controls. (D) Grey scale densitometric analysis of MAMEs and FAMEs from panel (C) using ImageJ.