Fast identification of off-target liabilities in early antibiotic discovery with Fourier-transform infrared spectroscopy.

Structural modifications of known antibiotic scaffolds have kept the upper hand on resistance, but we are on the verge of not having antibiotics for many common infections. Mechanism-based discovery assays reveal novelty, exclude off-target liabilities, and guide lead optimization. For that, we developed a fast and automatable protocol using high-throughput Fourier-transform infrared spectroscopy (FTIRS). Metabolic fingerprints of Staphylococcus aureus and Escherichia coli exposed to 35 compounds, dissolved in dimethyl sulfoxide (DMSO) or water, were acquired. Our data analysis pipeline identified biomarkers of off-target effects, optimized spectral preprocessing, and identified the top-performing machine learning algorithms for off-target liabilities and mechanism of action (MOA) identification. Spectral bands with known biochemical associations more often yielded more significant biomarkers of off-target liabilities when bacteria were exposed to compounds dissolved in water than DMSO. Highly discriminative models distinguished compounds with predominant off-target effects from antibiotics with well-defined MOA (AUROC > 0.87, AUPR > 0.79, F1 > 0.81), and from the latter predicted their MOA (AUROC > 0.88, AUPR > 0.70, F1 > 0.70). The compound solvent did not affect predictive models. FTIRS is fast, simple, inexpensive, automatable, and highly effective at predicting MOA and off-target liabilities. As such, FTIRS mechanism-based screening assays can be applied for hit discovery and to guide lead optimization during the early stages of antibiotic discovery.

Characterization of gastric cells infection by diverse Helicobacter pylori strains through Fourier-transform infrared spectroscopy.

The infection of Helicobacter pylori, covering 50% of the world-population, leads to diverse gastric diseases as ulcers and cancer along the life-time of the human host. To promote the discovery of biomarkers of bacterial infection, in the present work, Fourier-transform infrared spectra were acquired from adenocarcinoma gastric cells, incubated with H. pylori strains presenting different genotypes concerning the virulent factors cytotoxin associated gene A and vacuolating cytotoxin A. Defined absorbance ratios were evaluated by diverse methods of statistical inference, according to the fulfillment of the tests assumptions. It was possible to define from the gastric cells, diverse absorbance ratios enabling to discriminate: i) The infection; ii) the bacteria genotype; and iii) the gastric disease of the patients from which the bacteria were isolated. These biomarkers could fasten the knowledge of the complex infection process while promoting a platform for a new diagnostic method, rapid but also specific and sensitive towards the diagnosis of both infection and bacterial virulence.

An extension of Gompertzian growth dynamics: Weibull and Frechet models.

In this work a new probabilistic and dynamical approach to an extension of the Gompertz law is proposed. A generalized family of probability density functions, designated by Beta • (p,q), which is proportional to the right hand side of the Tsoularis-Wallace model, is studied. In particular, for p=2, the investigation is extended to the extreme value models of Weibull and Frechet type. These models, described by differential equations, are proportional to the hyper-Gompertz growth model. It is proved that the Beta• (2,q) densities are a power of betas mixture, and that its dynamics are determined by a non-linear coupling of probabilities. The dynamical analysis is performed using techniques of symbolic dynamics and the system complexity is measured using topological entropy. Generally, the natural history of a malignant tumour is reflected through bifurcation diagrams, in which are identified regions of regression, stability, bifurcation, chaos and terminus.