Unser 10. Leibniz Plant Biochemistry Symposium am 7. und 8. Mai war ein großer Erfolg. Thematisch ging es in diesem Jahr um neue Methoden und Forschungsansätze der Naturstoffchemie. Die exzellenten Vorträge über Wirkstoffe…
Omanische Heilpflanze im Fokus der Phytochemie IPB-Wissenschaftler und Partner aus Dhofar haben jüngst die omanische Heilpflanze Terminalia dhofarica unter die phytochemische Lupe genommen. Die Pflanze ist reich an…
Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Tessema, E. N.; Gebre-Mariam, T.; Frolov, A.; Wohlrab, J.; Neubert, R. H. H.;Development and validation of LC/APCI-MS method for the quantification of oat ceramides in skin permeation studiesAnal. Bioanal. Chem.4104775-4785(2018)DOI: 10.1007/s00216-018-1162-z
Ceramides (CERs) are the backbone of the intercellular lipid lamellae of the stratum corneum (SC), the outer layer of the skin. Skin diseases such as atopic dermatitis, psoriasis, and aged skin are characterized by dysfunctional skin barrier and dryness which are associated with reduced levels of CERs. Replenishing the depleted epidermal CERs with exogenous CERs has been shown to have beneficial effects in improving the skin barrier and hydration. The exogenous CERs such as phyto-derived CERs (PhytoCERs) can be delivered deep into the SC using novel topical formulations. This, however, requires investigating the rate and extent of skin permeation of CERs. In this study, an LC/APCI-MS method to detect and quantify PhytoCERs in different layers of the skin has been developed and validated. The method was used to investigate the skin permeation of PhytoCERs using Franz diffusion cells after applying an amphiphilic cream containing PhytoCERs to the surface of ex vivo human skin. As plant-specific CERs are not commercially available, well-characterized CERs isolated from oat (Avena abyssinica) were used as reference standards for the development and validation of the method. The method was linear over the range of 30–1050 ng/mL and sensitive with limit of detection and quantification of 10 and 30 ng/mL, respectively. The method was also selective, accurate, and precise with minimal matrix effect (with mean matrix factor around 100%). Even if more than 85% of oat CERs in the cream remained in the cream after the incubation periods of 30, 100, and 300 min, it was possible to quantify the small quantities of oat CERs distributed across the SC, epidermis, and dermis of the skin indicating the method’s sensitivity. Therefore, the method can be used to investigate the skin permeation of oat CERs from the various pharmaceutical and cosmeceutical products without any interference from the skin constituents such as the epidermal lipids.
Publikation
Hu, M.; Müller, E.; Schymanski, E. L.; Ruttkies, C.; Schulze, T.; Brack, W.; Krauss, M.;Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMSAnal. Bioanal. Chem.4101931-1941(2018)DOI: 10.1007/s00216-018-0857-5
In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI− mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI−, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods.