Oral administration of drug products is a strict requirement in many medical indications. Therefore, bioavailability prediction models are of high importance for prioritization of compound candidates in the drug discovery process. However, oral exposure and bioavailability are difficult to predict, as they are the result of various highly complex factors and/or processes influenced by the physicochemical properties of a compound, such as solubility, lipophilicity, or charge state, as well as by interactions with the organism, for instance, metabolism or membrane permeation. In this study, we assess whether it is possible to predict intravenous (iv) or oral drug exposure and oral bioavailability in rats. As input parameters, we use (i) six experimentally determined in vitro and physicochemical endpoints, namely, membrane permeation, free fraction, metabolic stability, solubility, pK a value, and lipophilicity; (ii) the outputs of six in silico absorption, distribution, metabolism, and excretion models trained on the same endpoints, or (iii) the chemical structure encoded as fingerprints or simplified molecular input line entry system strings. The underlying data set for the models is an unprecedented collection of almost 1900 data points with high-quality in vivo experiments performed in rats. We find that drug exposure after iv administration can be predicted similarly well using hybrid models with in vitro-or in silico-predicted endpoints as inputs, with fold change errors (FCE) of 2.28 and 2.08, respectively. The FCEs for exposure after oral administration are higher, and here, the prediction from in vitro inputs performs significantly better in comparison to in silico-based models with FCEs of 3.49 and 2.40, respectively, most probably reflecting the higher complexity of oral bioavailability. Simplifying the prediction task to a binary alert for low oral bioavailability, based only on chemical structure, we achieve accuracy and precision close to 70%.

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We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-g and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A*01:01-restricted CD8+ ORF3a epitope FTSDYYQLY 207-215 ; due to P13L, P13S, and P13T in the B*27:05-restricted CD8+ nucleocapsid epitope QRNAP-RITF 9-17 ; and due to T362I and P365S in the A*03:01/A*11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK 361-369 . CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.

Vancomycin is an important glycopeptide antibiotic which is used to treat serious infections caused by Gram-positive bacteria. However, during the last years, a tremendous rise in vancomycin resistances, especially among Enterococci, was reported, making fast diagnostic methods inevitable. In this contribution, we apply Raman spectroscopy to systematically characterize vancomycin-enterococci interactions over a time span of 90 min using a sensitive Enterococcus faecalis strain and two different vancomycin concentrations above the minimal inhibitory concentration (MIC). Successful action of the drug on the pathogen could be observed already after 30 min of interaction time. Characteristic spectral changes are visualized with the help of multivariate statistical analysis (linear discriminant analysis and partial least squares regressions). Those changes were employed to train a statistical model to predict vancomycin treatment based on the Raman spectra. The robustness of the model was tested using data recorded by an independent operator. Classification accuracies of >90 % were obtained for vancomycin concentrations in the lower range of a typical trough serum concentration recommended for most patients during appropriate vancomycin therapy. Characterization of drug-pathogen interactions by means of label-free spectroscopic methods, such as Raman spectroscopy, can provide the knowledge base for innovative and fast susceptibility tests which could speed up microbiological analysis as well as finding applications in novel antibiotic screenings assays. Graphical Abstract E. faecalis is incubated with vancomycin and characterized by means of Raman spectroscopy after different time points. Characteristic spectral changes reveal efficient vancomycin-enterococci-interaction.

Microbial contamination of recreation waters is a major concern globally, with pollutants originating from many sources, including human and other animal wastes often introduced during storm events. Fecal contamination is traditionally monitored by employing culture methods targeting fecal indicator bacteria (FIB), namely E. coli and enterococci, which provides only limited information of a few microbial taxa and no information on their sources. Host-associated qPCR and metagenomic DNA sequencing are complementary methods for FIB monitoring that can provide enhanced understanding of microbial communities and sources of fecal pollution. Whole metagenome sequencing (WMS), quantitative real-time PCR (qPCR), and culture-based FIB tests were performed in an urban watershed before and after a rainfall event to determine the feasibility and application of employing a multi-assay approach for examining microbial content of ambient source waters. Cultivated E. coli and enterococci enumeration confirmed presence of fecal contamination in all samples exceeding local single sample recreational water quality thresholds (E. coli, 410 MPN/100 mL; enterococci, 107 MPN/100 mL) following a rainfall. Test results obtained with qPCR showed concentrations of E. coli, enterococci, and human-associated genetic markers increased after rainfall by 1.52-, 1.26-, and 1.11-fold log10 copies per 100 mL, respectively. Taxonomic analysis of the surface water microbiome and detection of antibiotic resistance genes, general FIB, and human-associated microorganisms were also employed. Results showed that fecal contamination from multiple sources (human, avian, dog, and ruminant), as well as FIB, enteric microorganisms, and antibiotic resistance genes increased demonstrably after a storm event. In summary, the addition of qPCR and WMS to traditional surrogate techniques may provide enhanced characterization and improved understanding of microbial pollution sources in ambient waters.