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• W4225323193 startingPage "5072" @default.
• W4225323193 abstract "The Michaelis–Menten model of enzyme kinetic assumes the free ligand approximation, the steady-state approximation and the rapid equilibrium approximation. Analytical methods to model slow-binding inhibitors by the analysis of initial velocities have been developed but, due to their inherent complexity, they are seldom employed. In order to circumvent the complications that arise from the violation of the rapid equilibrium assumption, inhibition is commonly evaluated by pre-incubating the enzyme and the inhibitors so that, even for slow inhibitors, the binding equilibrium is established before the reaction is started. Here, we show that for long drug-target residence time inhibitors, the conventional analysis of initial velocities by the linear regression of double-reciprocal plots fails to provide a correct description of the inhibition mechanism. As a case study, the inhibition of acetylcholinesterase by galantamine, a drug approved for the symptomatic treatment of Alzheimer’s disease, is reported. For over 50 years, analysis based on the conventional steady-state model has overlooked the time-dependent nature of galantamine inhibition, leading to an erroneous assessment of the drug potency and, hence, to discrepancies between biochemical data and the pharmacological evidence. Re-examination of acetylcholinesterase inhibition by pre-steady state analysis of the reaction progress curves showed that the potency of galantamine has indeed been underestimated by a factor of ~100." @default.
• W4225323193 created "2022-05-05" @default.
• W4225323193 creator A5009559253 @default.
• W4225323193 creator A5034494140 @default.
• W4225323193 date "2022-05-03" @default.
• W4225323193 modified "2023-09-27" @default.
• W4225323193 title "Kinetic Modeling of Time-Dependent Enzyme Inhibition by Pre-Steady-State Analysis of Progress Curves: The Case Study of the Anti-Alzheimer’s Drug Galantamine" @default.
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• W4225323193 doi "https://doi.org/10.3390/ijms23095072" @default.
• W4225323193 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35563466" @default.
• W4225323193 hasPublicationYear "2022" @default.
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