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Clostridium difficile (C. difficile) is a Gram positive, anaerobic bacterium that infects the lumen of the large intestine and produces toxins. This results in a range of syndromes from mild diarrhea to severe toxic megacolon and death. Alarmingly, the prevalence and severity of C. difficile infection are increasing; thus, associated morbidity and mortality rates are rising. 4-Aminothiazolyl analogues of the antibiotic natural product GE2270 A (1) were designed, synthesized, and optimized for the treatment of C. difficile infection. The medicinal chemistry effort focused on enhancing aqueous solubility relative to that of the natural product and previous development candidates (2, 3) and improving antibacterial activity. Structure-activity relationships, cocrystallographic interactions, pharmacokinetics, and efficacy in animal models of infection were characterized. These studies identified a series of dicarboxylic acid derivatives, which enhanced solubility/efficacy profile by several orders of magnitude compared to previously studied compounds and led to the selection of LFF571 (4) as an investigational new drug for treating C. difficile infection.

Argyrins, produced by myxobacteria and actinomycetes, are cyclic octapeptides with antibacterial and antitumor activity. Here, we identify elongation factor G (EF-G) as the cellular target of argyrin B in bacteria, via resistant mutant selection and whole genome sequencing, biophysical binding studies and crystallography. Argyrin B binds a novel allosteric pocket in EF-G, distinct from the known EF-G inhibitor antibiotic fusidic acid, revealing a new mode of protein synthesis inhibition. In eukaryotic cells, argyrin B was found to target mitochondrial elongation factor G1 (EF-G1), the closest homologue of bacterial EF-G. By blocking mitochondrial translation, argyrin B depletes electron transport components and inhibits the growth of yeast and tumor cells. Further supporting direct inhibition of EF-G1, expression of an argyrin B-binding deficient EF-G1 L693Q variant partially rescued argyrin B-sensitivity in tumor cells. In summary, we show that argyrin B is an antibacterial and cytotoxic agent that inhibits the evolutionarily conserved target EF-G, blocking protein synthesis in bacteria and mitochondrial translation in yeast and mammalian cells.

Background: The calcium-activated chloride channel ANO1 is highly expressed in cancer.Results: Inhibition of ANO1 activity alone is not sufficient to inhibit cancer cell proliferation, suggesting a novel function of ANO1 protein in cancer.Conclusion: The ANO1 inhibitor CaCCinh-A01 inhibits cancer cell proliferation by facilitating degradation of ANO1.Significance: Our results may provide a new targeting approach for antitumor therapy in ANO1-amplified cancers.

BackgroundIn drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.ResultsIn trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.ConclusionsDespite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.