Biotransformation of metoprolol by the fungus Cunninghamella blakesleeana
Abstract
Aim: To investigate the biotransformation of metoprolol, a β1-cardioselective adrenoceptor antagonist, by filamentous fungus, and to compare the parallels between microbial transformation and mammalian metabolism.
Methods: Five strains of Cunninghamella (C elegans AS 3.156, C elegans AS 3.2028, C echinulata AS 3.2004, C blakesleeana AS 3.153 and AS 3.910) were screened for the ability to transform metoprolol. The metabolites of metoprolol produced by C blakesleeana AS 3.153 were separated and assayed by liquid chromatography-tandem mass spectrometry (LC/MSn). The major metabolites were isolated by semipreparative HPLC and the structures were identified by a combination of LC/MSn and nuclear magnetic resonance analysis.
Results: Metoprolol was transformed to 7 metabolites; 2 were identified as new metabolites and 5 were known metabolites in mammals.
Conclusion: The microbial transformation of metoprolol was similar to the metabolism in mammals. The fungi belonging to Cunninghamella species could be used as complementary models for predicting in vivo metabolism and producing quantities of metabolite references for drugs like metoprolol.
Keywords:
Methods: Five strains of Cunninghamella (C elegans AS 3.156, C elegans AS 3.2028, C echinulata AS 3.2004, C blakesleeana AS 3.153 and AS 3.910) were screened for the ability to transform metoprolol. The metabolites of metoprolol produced by C blakesleeana AS 3.153 were separated and assayed by liquid chromatography-tandem mass spectrometry (LC/MSn). The major metabolites were isolated by semipreparative HPLC and the structures were identified by a combination of LC/MSn and nuclear magnetic resonance analysis.
Results: Metoprolol was transformed to 7 metabolites; 2 were identified as new metabolites and 5 were known metabolites in mammals.
Conclusion: The microbial transformation of metoprolol was similar to the metabolism in mammals. The fungi belonging to Cunninghamella species could be used as complementary models for predicting in vivo metabolism and producing quantities of metabolite references for drugs like metoprolol.