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Clozapine is an antipsychotic drug whose accumulation in white cells can sometimes prove toxic; understanding the transporters and alleles responsible is thus highly desirable. We used a strategy in which a yeast (Saccharomyces cerevisiae) CRISPR-Cas9 knock-out library was exposed to cytotoxic concentrations of clozapine to determine those transporters whose absence made it more resistant; we also recognised the structural similarity of the fluorescent dye safranin O (also known as safranin T) to clozapine, allowing it to be used as a surrogate marker. Strains lacking the mitochondrial ABC transporter MDL1 (encoded by YLR188W) showed substantial resistance to clozapine. MDL1 overexpression also conferred extra sensitivity to clozapine and admitted a massive increase in the cellular and mitochondrial uptake of safranin O, as determined using flow cytometry and microscopically. Yeast lacking mitochondria showed no such unusual accumulation. Mitochondrial MDL1 is thus the main means of accumulation of clozapine in S. cerevisiae. The closest human homologue of S. cerevisiae MDL1 is ABCB10. [ABSTRACT FROM AUTHOR]

Up until recently, it was believed that pharmaceutical drugs and their metabolites enter into the cell to gain access to their targets via simple diffusion across the hydrophobic lipid cellular membrane, at a rate which is based on their lipophilicity. An increasing amount of evidence indicates that the phospholipid bilayer-mediated drug diffusion is in fact negligible, and that drugs pass through cell membranes via proteinaceous membrane transporters or carriers which are normally used for the transportation of nutrients and intermediate metabolites. Drugs can be targeted to specific cells and tissues which express the relevant transporters, leading to the design of safe and efficacious treatments. Furthermore, transporter expression levels can be manipulated, systematically and in a high-throughput manner, allowing for considerable progress in determining which transporters are used by specific drugs. The ever-expanding field of miRNA therapeutics is not without its challenges, with the most notable one being the safe and effective delivery of the miRNA mimic/antagonist safely to the target cell cytoplasm for attaining the desired clinical outcome, particularly in miRNA-based cancer therapeutics, due to the poor efficiency of neo-vascular systems revolting around the tumour site, brought about by tumour-induced angiogenesis. This acquisition of resistance to several types of anticancer drugs can be as a result of an upregulation of efflux transporters expression, which eject drugs from cells, hence lowering drug efficacy, resulting in multidrug resistance. In this article, the latest available data on human microRNAs has been reviewed, together with the most recently described mechanisms for miRNA uptake in cells, for future therapeutic enhancements against cancer chemoresistance. [ABSTRACT FROM AUTHOR]

Introduction: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model., Objectives: Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased)., Methods: High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created., Results: The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86)., Conclusion: Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment., (© 2021. The Author(s).)