ClpC1 is an emerging new target for the treatment of Mycobacterium tuberculosis infections, and several cyclic peptides (ecumicin, cyclomarin A, and lassomycin) are known to act on this target. This study identified another group of peptides, the rufomycins (RUFs), as bactericidal to M. tuberculosis through the inhibition of ClpC1 and subsequent modulation of protein degradation of intracellular proteins. Rufomycin I (RUFI) was found to be a potent and selective lead compound for both M. tuberculosis (MIC, 0.02 μM) and Mycobacterium abscessus (MIC, 0.4 μM). Spontaneously generated mutants resistant to RUFI involved seven unique single nucleotide polymorphism (SNP) mutations at three distinct codons within the N-terminal domain of clpC1 (V13, H77, and F80). RUFI also significantly decreased the proteolytic capabilities of the ClpC1/P1/P2 complex to degrade casein, while having no significant effect on the ATPase activity of ClpC1. This represents a marked difference from ecumicin, which inhibits ClpC1 proteolysis but stimulates the ATPase activity, thereby providing evidence that although these peptides share ClpC1 as a macromolecular target, their downstream effects are distinct, likely due to differences in binding.

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The rise of multi- and extensively drug-resistant Mycobacterium tuberculosis (M. tb) strains and co-infection with human immunodeficiency virus has escalated the need for new anti-M. tb drugs. Numerous challenges associated with the M. tb, in particular slow growth and pathogenicity level 3, discouraged use of this organism in past primary screening efforts. From current knowledge of the physiology and drug susceptibility of mycobacteria in general and M. tb specifically, it can be assumed that many potentially useful drug leads were missed by failing to screen directly against this pathogen. This review discusses recent high-throughput phenotypic screening strategies for anti-M. tb drug discovery. Emphasis is placed on prioritization of hits, including their extensive biological and chemical profiling, as well as the development status of promising drug candidates discovered with phenotypic screening.

New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6–12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new chalcone-like compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 chalcones-like compounds (4, 8, 9, 11, 13, 17–20, and 23) were found to exhibit nanomolar activity against replicating micobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 µM and <10 µM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11–545). Our results suggest that our designed chalcone-like compounds, due to their high potency and selectivity, are promising anti-TB agents.

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TLC-based strategies were proposed in 1979 (Hostettmann et al.) and 2005 (Friesen & Pauli; GUESS method) to minimize the number of partitioning experiments required for countercurrent separation (CCS) solvent system selection. As semi-empirical approaches, both proposed that the K values defining the sweet spot of optimal CCS corresponded to a matching Rf value range from the silica gel TLC plate developed in the organic phase of a biphasic or a corresponding monophasic solvent system. Despite their simplicity, there has been an absence of theoretical support and a deficiency of reported experimental evidence. The present study explores the theory required to develop correlations between Rf and K. All theoretical models surmise that the optimal Rf value range should be centered at 0.5. In order to validate the feasibility of the concept of matching Rf and K values, 43 natural products and six solvent system families were investigated. Out of 62 correlations, 45 resulted in matched Rf and K values. Based on this study, practical guidelines for the TLC-based prediction strategy are provided. These approaches will equip CCS users with an updated understanding of how to apply the TLC-based solvent system selection strategy to accelerate a targeted selection of CCS conditions.

While natural products constitute an established source of lead compounds, the classical iterative bioassay-guided isolation process is both time- and labor-intensive and prone to failing to identify active minor constituents. (HP)TLC-bioautography-MS/NMR, which combines cutting-edge microbiological, chromatographic, and spectrometric technologies, was developed to accelerate anti-tuberculosis (TB) drug discovery from natural sources by acquiring structural information at a very early stage of the isolation process. Using the avirulent, bioluminescent Mtb strain mc27000 luxABCDE, three variations of bioautography were evaluated and optimized for sensitivity in detecting anti-TB agents, including established clinical agents and new leads with novel mechanisms of action. Several exemplary applications of this approach to microbial extracts demonstrate its potential as a routine method in anti-TB drug discovery from natural sources.