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Analytical chemistry offers important tools that provide insight and step-by-step understanding. However, natural samples containing many thousands of different compounds are subject to natural variance which makes it complex for artificial intelligence and human understanding. Instead of the prevailing comprehensive separation/detection approach, a prioritization approach is discussed for dereplication that focuses on the most necessary to discover. It is extremely helpful to combine on the same surface two disciplines, i.e. chemistry for separation of mixtures and biology for detection of biological effects, to filter out and prioritize the important compounds that then need to be identified. Complex mixtures are separated in parallel with imaging high-performance thin-layer chromatography, detected non-targeted with planar multiplex bioassays to prioritize compounds and elute the important ones to orthogonal column chromatography−diode array detection−high-resolution mass spectrometry for further characterization. Super-hyphenations reduce thousands of compounds to a manageable number of important active compounds. Its potential lies not only in the prioritization of compounds, but also detection of unknown compounds that were not previously the focus of analysts. An image is worth a thousand words and an effect image even more so. On-surface multiplex bioassays enable the differentiation of opposing signals/effects of compounds in a complex natural sample, which only makes understanding possible. On-surface metabolization enables the study of any changes in the effects through digestion or de-/toxification (S9 liver system). By these new tools applied on the same surface, the mechanisms of action of complex mixtures can be better understood. The latest open-source 2LabsToGo system combines the chemistry and biology laboratory in a miniaturized system developed for wider use of this innovative technique. [ABSTRACT FROM AUTHOR]

The total synthesis of two new marine natural products, (±)-marinoaziridine B 7 and (±)-N-methyl marinoaziridine A 8, was accomplished. The (±)-marinoaziridine 7 was prepared in a six-step linear sequence with a 2% overall yield. The key steps in our strategy were the preparation of the chiral epoxide (±)-5 using the Johnson Corey Chaykovsky reaction, followed by the ring-opening reaction and the Staudinger reaction. The N,N-dimethylation of compound (±)-7 gives (±)-N-methyl marinoaziridine A 8. The NMR spectra of synthetized (±)-marinoaziridine B 7 and isolated natural product did not match. The compounds are biologically characterized using relevant in silico, in vitro and in vivo methods. In silico ADMET and bioactivity profiling predicted toxic and neuromodulatory effects. In vitro screening by MTT assay on three cell lines (MCF-7, H-460, HEK293T) showed that both compounds exhibited moderate to strong antiproliferative and cytotoxic effects. Antimicrobial tests on bacterial cultures of Escherichia coli and Staphylococcus aureus demonstrated the dose-dependent inhibition of the growth of both bacteria. In vivo toxicological tests were performed on zebrafish Danio rerio and showed a significant reduction of zebrafish mortality due to N-methylation in (±)-8. [ABSTRACT FROM AUTHOR]

PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022). [ABSTRACT FROM AUTHOR]

The complexity of human biology has made prediction of health effects as a consequence of exposure to environmental chemicals especially challenging. Complex cell systems, such as the Biologically Multiplexed Activity Profiling (BioMAP) primary, human, cell-based disease models, leverage cellular regulatory networks to detect and distinguish chemicals with a broad range of target mechanisms and biological processes relevant to human toxicity. Here the authors use the BioMAP human cell systems to characterize effects relevant to human tissue and inflammatory disease biology following exposure to the 320 environmental chemicals in the Environmental Protection Agency's (EPA's) ToxCast phase I library. The ToxCast chemicals were assayed at 4 concentrations in 8 BioMAP cell systems, with a total of 87 assay endpoints resulting in more than 100,000 data points. Within the context of the BioMAP database, ToxCast compounds could be classified based on their ability to cause overt cytotoxicity in primary human cell types or according to toxicity mechanism class derived from comparisons to activity profiles of BioMAP reference compounds. ToxCast chemicals with similarity to inducers of mitochondrial dysfunction, cAMP elevators, inhibitors of tubulin function, inducers of endoplasmic reticulum stress, or NFκB pathway inhibitors were identified based on this BioMAP analysis. This data set is being combined with additional ToxCast data sets for development of predictive toxicity models at the EPA. [ABSTRACT FROM AUTHOR]

In order to assess bioactivity profiles for small organic molecules we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual molecules. In this proof of principle study, carried out with the new structure-based pharmacophore modelling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their known biological targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approximately 90% of all input molecules. Additionally, we discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biological activities of a compound or a compound library by screening it against a series of pharmacophore queries. [ABSTRACT FROM AUTHOR]