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Many pharmaceutical companies are avoiding the development of novel antibacterials due to a range of rational reasons and the high risk of failure. However, there is an urgent need for novel antibiotics especially against resistant bacterial strains. Available models suffer from many drawbacks and, therefore, are not applicable for scoring novel molecules with high structural diversity by their antibacterial potency. Considering this, the overall aim of this study was to develop an efficient model able to find compounds that have plenty of chances to exhibit antibacterial activity. Based on a proprietary screening campaign, we have accumulated a representative dataset of more than 140,000 molecules with antibacterial activity against assessed in the same assay and under the same conditions. This intriguing set has no analogue in the scientific literature. We applied six techniques to mine these data. For external validation, we used 5,000 compounds with low similarity towards training samples. The antibacterial activity of the selected molecules against was assessed using a comprehensive biological study. Kohonen-based nonlinear mapping was used for the first time and provided the best predictive power (av. 75.5%). Several compounds showed an outstanding antibacterial potency and were identified as translation machinery inhibitors and . For the best compounds, MIC and CC values were determined to allow us to estimate a selectivity index (SI). Many active compounds have a robust IP position.

The present report describes our efforts to identify new structural classes of compounds having promising antibacterial activity using previously published double-reporter system pDualrep2. This semi-automated high-throughput screening (HTS) platform has been applied to perform a large-scale screen of a diverse small-molecule compound library. We have selected a set of more than 125,000 molecules and evaluated them for their antibacterial activity. On the basis of HTS results, eight compounds containing 2-pyrazol-1-yl-thiazole scaffold exhibited moderate-to-high activity against ΔTolC Escherichia coli. Minimum inhibitory concentration (MIC) values for these molecules were in the range of 0.037–8 μg ml−1. The most active compound 8 demonstrated high antibacterial potency (MIC = 0.037 μg ml−1), that significantly exceed that measured for erythromycin (MIC = 2.5 μg ml−1) and was comparable with the activity of levofloxacin (MIC = 0.016 μg ml−1). Unfortunately, this compound showed only moderate selectivity toward HEK293 eukaryotic cell line. On the contrary, compound 7 was less potent (MIC = 0.8 μg ml−1) but displayed only slight cytotoxicity. Thus, 2-pyrazol-1-yl-thiazoles can be considered as a valuable starting point for subsequent optimization and morphing.

A series of 5-oxo-4H-pyrrolo[3,2-b]pyridine derivatives was identified as novel class of highly potent antibacterial agents during an extensive large-scale high-throughput screening (HTS) program utilizing a unique double-reporter system—pDualrep2. The construction of the reporter system allows us to perform visual inspection of the underlying mechanism of action due to two genes—Katushka2S and RFP—which encode the proteins with different imaging signatures. Antibacterial activity of the compounds was evaluated during the initial HTS round and subsequent rescreen procedure. The most active molecule demonstrated a MIC value of 3.35 µg/mL against E. coli with some signs of translation blockage (low Katushka2S signal) and no SOS response. The compound did not demonstrate cytotoxicity in standard cell viability assay. Subsequent structural morphing and follow-up synthesis may result in novel compounds with a meaningful antibacterial potency which can be reasonably regarded as an attractive starting point for further in vivo investigation and optimization.

Clinical trials investigate treatment endpoints that usually include measurements of pharmacodynamic and efficacy biomarkers in early‐phase studies and patient‐reported outcomes as well as event risks or rates in late‐phase studies. In recent years, a systematic trend in clinical trial data analytics and modeling has been observed, where retrospective data are integrated into a quantitative framework to prospectively support analyses of interim data and design of ongoing and future studies of novel therapeutics. Joint modeling is an advanced statistical methodology that allows for the investigation of clinical trial outcomes by quantifying the association between baseline and/or longitudinal biomarkers and event risk. Using an exemplar data set from non‐small cell lung cancer studies, we propose and test a workflow for joint modeling. It allows a modeling scientist to comprehensively explore the data, build survival models, investigate goodness‐of‐fit, and subsequently perform outcome predictions using interim biomarker data from an ongoing study. The workflow illustrates a full process, from data exploration to predictive simulations, for selected multivariate linear and nonlinear mixed‐effects models and software tools in an integrative and exhaustive manner.

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants.