The Novel DNA Binding Mechanism of Ridinilazole, a Precision Clostridiodes difficile Antibiotic.

Clostridioides difficile infection (CDI) causes substantial morbidity and mortality worldwide with limited antibiotic treatment options. Ridinilazole is a precision bisbenzimidazole antibiotic being developed to treat CDI and reduce unacceptably high rates of infection recurrence in patients. Although in late clinical development, the precise mechanism of action by which ridinilazole elicits its bactericidal activity has remained elusive. Here, we present conclusive biochemical and structural data to demonstrate that ridinilazole has a primary DNA binding mechanism, with a co-complex structure confirming binding to the DNA minor groove. Additional RNA-seq data indicated early pleiotropic changes to transcription, with broad effects on multiple C. difficile compartments and significant effects on energy generation pathways particularly. DNA binding and genomic localization was confirmed through confocal microscopy utilizing the intrinsic fluorescence of ridinilazole upon DNA binding. As such, ridinilazole has the potential to be the first antibiotic approved with a DNA minor groove binding mechanism of action.

Targeted microbiome-sparing antibiotics.

A factor in our inability to meet the challenge of clinical antibiotic resistance has been the low productivity of research and development (R&D) efforts, with only incremental improvements on existing broad-spectrum classes coming into clinical use recently. The disappointing returns from this approach have focussed attention on narrower-spectrum antibiotics; such new agents are directed against the pathogen of relevance with the additional benefit of preserving the human microbiome(s). Our knowledge of the gut microbiome and its contribution to health homeostasis increases yearly and suggests that broad-spectrum treatments incur health costs beyond the initial infection. Improved diagnostics, antibiotic stewardship, and the crucial role of the gut microbiome in health indicate targeted agents as a more viable approach for future antibiotic R&D.

Machine learning-powered antibiotics phenotypic drug discovery.

Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery.

Drug-resistant aurora A mutants for cellular target validation of the small molecule kinase inhibitors MLN8054 and MLN8237.

The Aurora kinases regulate multiple aspects of mitotic progression, and their overexpression in diverse tumor types makes them appealing oncology targets. An intensive research effort over the past decade has led to the discovery of chemically distinct families of small molecule Aurora kinase inhibitors, many of which have demonstrated therapeutic potential in model systems. These agents are also important tools to help dissect signaling pathways that are orchestrated by Aurora kinases, and the antiproliferative target of pan-Aurora inhibitors such as VX-680 has been validated using chemical genetic techniques. In many cases the nonspecific nature of Aurora inhibitors toward unrelated kinases is well established, potentially broadening the spectrum of cancers to which these compounds might be applied. However, unambiguously demonstrating the molecular target(s) for clinical kinase inhibitors is an important challenge, one that is absolutely critical for deciphering the molecular basis of compound specificity, resistance, and efficacy. In this paper, we have investigated amino acid requirements for Aurora A sensitivity to the benzazepine-based Aurora inhibitor MLN8054 and the close analogue MLN8237, a second-generation compound that is in phase II clinical trials. A crystallographic analysis facilitated the design and biochemical investigation of a panel of resistant Aurora A mutants, a subset of which were then selected as candidate drug-resistance targets for further evaluation. Using inducible human cell lines, we show that cells expressing near-physiological levels of a functional but partially drug-resistant Aurora A T217D mutant survive in the presence of MLN8054 or MLN8237, authenticating Aurora A as a critical antiproliferative target of these compounds.