Fourteen metabolites of methylprednisolone have been analysed by gradient-elution high-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS-MS). The compounds were separated on a Cp Spherisorb 5 microm ODS column connected to a guard column packed with pellicular reversed phase. The mobile phase was an acetonitrile- 1.0% aqueous acetic acid gradient at a flow rate of 1.5 mL min(-1) The analysis gave a complete picture of parent drug, prodrugs and metabolites, and the alpha/beta stereochemistry was resolved. The short (1-2 h) elimination half-life of methylprednisolone is explained by extensive metabolism. The overall picture of the metabolic pathways of methylprednisolone is apparently simple-reduction of the C20 carbonyl group and further oxidation of the C20,C21 side chain (into C21COOH and C20COOH), in competition with or in addition to oxidation at the C6 position.
Kinetic models are among the tools that can be used for optimization of biocatalytic reactions as well as for facilitating process design and upscaling in order to improve productivity and economy of these processes. Mechanism pathways for multi-substrate multi-product enzyme-catalyzed reactions can become very complex and lead to kinetic models comprising several tens of terms. Hence the models comprise too many parameters, which are in general highly correlated and their estimations are often prone to huge errors. In this study, Novozym(®) 435 catalyzed esterification reaction between oleic acid (OA) and trimethylolpropane (TMP) with continuous removal of side-product (water) was carried out as an example for reactions that follow multi-substrate multi-product ping-pong mechanisms. A kinetic model was developed based on a simplified ping-pong mechanism proposed for the reaction. The model considered both enzymatic and spontaneous reactions involved and also the effect of product removal during the reaction. The kinetic model parameters were estimated using nonlinear curve fitting through unconstrained optimization methodology and the model was verified by using empirical data from different experiments and showed good predictability of the reaction under different conditions. This approach can be applied to similar biocatalytic processes to facilitate their optimization and design.
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