Systemic sclerosis (SSc) is a rare systemic autoimmune disorder marked by high morbidity and increased risk of mortality. Our study aimed to analyze metabolomic profiles of plasma from SSc patients by using targeted and untargeted metabolomics approaches. Furthermore, we aimed to detect biochemical mechanisms relevant to the pathophysiology of SSc. Experiments were performed using high-performance liquid chromatography coupled to mass spectrometry technology. The investigation of plasma samples from SSc patients (n = 52) compared to a control group (n = 48) allowed us to identify four different dysfunctional metabolic mechanisms, which can be assigned to the kynurenine pathway, the urea cycle, lipid metabolism, and the gut microbiome. These significantly altered metabolic pathways are associated with inflammation, vascular damage, fibrosis, and gut dysbiosis and might be relevant for the pathophysiology of SSc. Further studies are needed to explore the role of these metabolomic networks as possible therapeutic targets of SSc.

Investigations into the interaction of xenobiotics with plants (and in particular edible plants) have gained substantial interest, as water scarcity due to climate-change-related droughts requires the more frequent use of reclaimed wastewaters for irrigation in agriculture. Non-steroidal anti-inflammatory drugs are common contaminants found in wastewater treatment plant effluents. For this reason, the interaction of nine edible plants with diclofenac (DCF), a widely used representative of this group of drugs, was investigated. For this purpose, plants were hydroponically grown in a medium containing DCF. For the detection of unknown DCF-related metabolites formed in the plant upon uptake of the parent drug‚ a new workflow based on the use of HPLC coupled to drift-tube ion-mobility quadrupole time-of-flight/mass spectrometry (DTIM QTOF-MS) was developed. Thereby‚ for chromatographic peaks eluting from the HPLC, drift times were recorded, and analytes were subsequently fragmented in the DTIM QTOF-MS to provide significant fragments. All information available (retention times, drift times, fragment spectra, accurate mass) was finally combined‚ allowing the suggestion of molecular formulas for 30 DCF-related metabolites formed in the plant, whereby 23 of them were not yet known from the literature.

The (tentative) identification of unknown drug‐related phase II metabolites in plants upon drug uptake remains a challenging task despite improved analytical instrument performance. To broaden the knowledge of possible drug metabolization, a fast‐screening approach for the tentative identification of drug‐related phase II metabolites is presented in this work. Therefore, an in silico database for the three non‐steroidal anti‐inflammatory drugs (ketoprofen, mefenamic acid, and naproxen) and a sub‐group of their theoretical phase II metabolites (based on combinations with glucose, glucuronic acid, and malonic acid) was created. Next, the theoretical exact masses (protonated species and ammonia adducts) were calculated and used as precursor ions in an autoMS/MS measurement method. The applicability of this workflow was tested on the example of eleven edible plants, which were hydroponically grown in solutions containing the respective drug at a concentration level of 20 mg/L. For the three drugs investigated this led to the tentative identification of 41 metabolites (some of them so far not described in this context), such as combinations of hydroxylated mefenamic acid with up to four glucose units or hydroxylated mefenamic acid with two glucose and three malonic acid units.

Successful drug administration to the central nervous system requires accurate adjustment of the drugs’ molecular properties. Therefore, structure-derived descriptors of potential brain therapeutic agents are essential for an early evaluation of pharmacokinetics during drug development. The collision cross section (CCS) of molecules was recently introduced as a novel measurable parameter to describe blood-brain barrier (BBB) permeation. This descriptor combines molecular information about mass, structure, volume, branching and flexibility. As these chemical properties are known to influence cerebral pharmacokinetics, CCS determination of new drug candidates may provide important additional spatial information to support existing models of BBB penetration of drugs. Besides measuring CCS, calculation is also possible; but however, the reliability of computed CCS values for an evaluation of BBB permeation has not yet been fully investigated. In this work, prediction tools based on machine learning were used to compute CCS values of a large number of compounds listed in drug libraries as negative or positive with respect to brain penetration (BBB⁺ and BBB⁻ compounds). Statistical evaluation of computed CCS and several other descriptors could prove the high value of CCS. Further, CCS-deduced maximum molecular size of BBB⁺ drugs matched the dimensions of BBB pores. A threshold for transcellular penetration and possible permeation through pore-like openings of cellular tight-junctions is suggested. In sum, CCS evaluation with modern in silico tools shows high potential for its use in the drug development process.

The aim of this study was to investigate the uptake of four beta-blockers by the model plant Lepidium sativum (garden cress) and their possible metabolization over a time period of 8 days. Therefore, cress was grown hydroponically in tap water for a week until they were matured, following irrigation with drug-containing water over the course of another 8 days. Samples were taken at days 1, 2, 4, and 8 after irrigation started. All four beta-blockers were taken up by the plants and the different octanol-water coefficients (log P) of the drugs have an influence on the uptake speed in the roots of the plants. The log P seems to have no influence on the translocation of the drugs from the root to the shoots. Furthermore, neither phase I nor phase II metabolization occurred inside the plants.