@article{KnocheLisecSchwerdtleetal.2022, author = {Knoche, Lisa and Lisec, Jan and Schwerdtle, Tanja and Koch, Matthias}, title = {LC-HRMS-Based identification of transformation products of the drug salinomycin generated by electrochemistry and liver microsome}, series = {Antibiotics}, volume = {11}, journal = {Antibiotics}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2079-6382}, doi = {10.3390/antibiotics11020155}, pages = {12}, year = {2022}, abstract = {The drug salinomycin (SAL) is a polyether antibiotic and used in veterinary medicine as coccidiostat and growth promoter. Recently, SAL was suggested as a potential anticancer drug. However, transformation products (TPs) resulting from metabolic and environmental degradation of SAL are incompletely known and structural information is missing. In this study, we therefore systematically investigated the formation and identification of SAL derived TPs using electrochemistry (EC) in an electrochemical reactor and rat and human liver microsome incubation (RLM and HLM) as TP generating methods. Liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS) was applied to determine accurate masses in a suspected target analysis to identify TPs and to deduce occurring modification reactions of derived TPs. A total of 14 new, structurally different TPs were found (two EC-TPs, five RLM-TPs, and 11 HLM-TPs). The main modification reactions are decarbonylation for EC-TPs and oxidation (hydroxylation) for RLM/HLM-TPs. Of particular interest are potassium-based TPs identified after liver microsome incubation because these might have been overlooked or declared as oxidated sodium adducts in previous, non-HRMS-based studies due to the small mass difference between K and O + Na of 21 mDa. The MS fragmentation pattern of TPs was used to predict the position of identified modifications in the SAL molecule. The obtained knowledge regarding transformation reactions and novel TPs of SAL will contribute to elucidate SAL-metabolites with regards to structural prediction.}, language = {en} }