Received for publication December 26, 2007.
Revised June 9, 2008.
Accepted for publication June 9, 2008.

IDENTIFICATION OF THE HUMAN ENZYMES INVOLVED IN THE OXIDATIVE METABOLISM OF DASATINIB. AN EFFECTIVE APPROACH FOR DETERMINING METABOLITE FORMATION KINETICS

Abstract

Dasatinib (SPRYCEL^®, BMS-354825) is a potent protein kinases inhibitor to treat chronic myeloid leukemia. In vivo studies have shown that the primary oxidative metabolites of dasatinib are M4 (N dealkylation), M5 (N-oxidation), M6 (carboxylic acid formation), M20 and M24 (hydroxylation). To identify the enzymes responsible for the formation of these metabolites, [¹⁴C]dasatinib and non-radiolabeled dasatinib were incubated with human cDNA-expressed enzymes (CYPs and FMO3) or HLM in the presence of selective CYP inhibitors (antibodies and chemical inhibitors). The results of these experiments showed that metabolites M4, M20, and M24 were mainly generated by CYP3A4; M5 was primarily formed by FMO3; M6 was formed by a cytosolic oxidoreductase. The enzyme kinetic analysis showed that the formation of M4 and M5 in HLM followed the Michaelis-Menten kinetics and the formation data of M20 and M24 fitted well to a partial substrate inhibition kinetic model. The K_m values were determined by the kinetic analysis of the substrate-dependent metabolite formation plots from a large number of incubations with the non-labeled dasatinib; the V_max values were calculated with the pre-determined K_m values and the metabolite formation rates from a limited number of incubations with [¹⁴C]dasatinib. The intrinsic formation clearance values (V_max/K_m)of 52, 14, 274, and 20 µL/mg protein/min for the formation of M4, M5, M20, and M24, respectively, suggested that the formation of M20 was more efficient than other metabolites. Collectively, multiple in vitro experiments demonstrated that dasatinib was predominately metabolized by CYP3A4.

Key words: CYP inhibition, CYP3A, cytochrome P450 catalyzed oxidations, flavin-containing monooxygenase, human CYP enzymes, in vitro-in vivo prediction, liver microsomes

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