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Tuberculosis (TB) is the leading cause of global morbidity and mortality resulting from infectious disease, with over 10.6 million new cases and 1.4 million deaths in 2021. This global emergency is exacerbated by the emergence of multidrug-resistant MDR-TB and extensively drug-resistant XDR-TB; therefore, new drugs and new drug targets are urgently required. From a whole cell phenotypic screen, a series of azetidines derivatives termed BGAz, which elicit potent bactericidal activity with MIC 99 values <10 μM against drug-sensitive Mycobacterium tuberculosis and MDR-TB, were identified. These compounds demonstrate no detectable drug resistance. The mode of action and target deconvolution studies suggest that these compounds inhibit mycobacterial growth by interfering with cell envelope biogenesis, specifically late-stage mycolic acid biosynthesis. Transcriptomic analysis demonstrates that the BGAz compounds tested display a mode of action distinct from the existing mycobacterial cell wall inhibitors. In addition, the compounds tested exhibit toxicological and PK/PD profiles that pave the way for their development as antitubercular chemotherapies.

Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, and competitor compound intelligence to understand the potential safety risks associated with modulating a drug target. However, there is a vast amount of information, updated daily that must be considered for each TSA. We have developed a data science-based approach that allows acquisition of relevant evidence for an optimal TSA. This is built on expert-led conventional and artificial intelligence-based mining of literature and other bioinformatics databases. Potential safety risks are identified according to an evidence framework, adjusted to the degree of target novelty. Expert knowledge is necessary to interpret the evidence and to take account of the nuances of drug safety, the modality, and the intended patient population for each TSA within each project. Overall, TSAs take full advantage of the most recent developments in data science and can be used within drug projects to identify and mitigate risks, helping with informed decision-making and resource management. These approaches should be used in the earliest stages of a drug project to guide decisions such as target selection, discovery chemistry options, in vitro assay choice, and end points for investigative in vivo studies., Competing Interests: Declaration Of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RR is co-founder and co-director of ApconiX, an integrated toxicology and ion channel research company that provides expert advice on non-clinical aspects of drug discovery and drug development to academia, industry, government, and not-for-profit organizations. JES and NJC are the employees of ApconiX.

Three-dimensional (3D) cellular aggregates, termed spheroids, have become the forefront of in vitro cell culture in recent years. In contrast to culturing cells as two-dimensional, single-cell monolayers (2D culture), spheroid cell culture promotes, regulates, and supports physiological cellular architecture and characteristics that exist in vivo, including the expression of extracellular matrix proteins, cell signaling, gene expression, protein production, differentiation, and proliferation. The importance of 3D culture has been recognized in many research fields, including oncology, diabetes, stem cell biology, and tissue engineering. Over the last decade, improved methods have been developed to produce spheroids and assess their metabolic function and fate. Extracellular flux (XF) analyzers have been used to explore mitochondrial function in 3D microtissues such as spheroids using either an XF24 islet capture plate or an XFe96 spheroid microplate. However, distinct protocols and the optimization of probing mitochondrial energy metabolism in spheroids using XF technology have not been described in detail. This paper provides detailed protocols for probing mitochondrial energy metabolism in single 3D spheroids using spheroid microplates with the XFe96 XF analyzer. Using different cancer cell lines, XF technology is demonstrated to be capable of distinguishing between cellular respiration in 3D spheroids of not only different sizes but also different volumes, cell numbers, DNA content and type. The optimal mitochondrial effector compound concentrations of oligomycin, BAM15, rotenone, and antimycin A are used to probe specific parameters of mitochondrial energy metabolism in 3D spheroids. This paper also discusses methods to normalize data obtained from spheroids and addresses many considerations that should be considered when exploring spheroid metabolism using XF technology. This protocol will help drive research in advanced in vitro spheroid models.