Integration of Knowledge-Based Metabolic Predictions with Liquid Chromatography Data-Dependent Tandem Mass Spectrometry for Drug Metabolism Studies:  Application to Studies on the Biotransformation of Indinavir

Abstract

Despite recent advances in the application of data-dependent liquid chromatography/tandem mass spectrometry (LC/MS/MS) to the identification of drug metabolites in complex biological matrixes, a prior knowledge of the likely routes of biotransformation of the therapeutic agent of interest greatly facilitates the detection and structural characterization of its metabolites. Thus, prediction of the [M + H]+ m/z values of expected metabolites allows for the construction of user-defined MS(n) protocols that frequently reveal the presence of minor drug metabolites, even in the presence of a vast excess of coeluting endogenous constituents. However, this approach suffers from inherent user bias, as a result of which additional "survey scans" (e.g., precursor ion and constant neutral loss scans) are required to ensure detection of as many drug-related components in the sample as possible. In the present study, a novel approach to this problem has been evaluated, in which knowledge-based predictions of metabolic pathways are first derived from a commercial database, the output from which is used to formulate a list-dependent LC/MS(n) data acquisition protocol. Using indinavir as a model drug, a substructure similarity search on the MDL metabolism database with a similarity index of 60% yielded 188 "hits", pointing to the possible operation of two hydrolytic, two N-dealkylation, three N-glucuronidation, one N-methylation, and several aromatic and aliphatic oxidation pathways. Integration of this information with data-dependent LC/MS(n) analysis using an ion trap mass spectrometer led to the identification of 18 metabolites of indinavir following incubation of the drug with human hepatic postmitochondrial preparations. This result was accomplished with only a single LC/MS(n) run, representing significant savings in instrument use and operator time, and afforded an accurate view of the complex in vitro metabolic profile of this drug.

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“…Two desalkylated, eleven monoxydated, three deoxygenated and two desalkylated/ monoxydated metabolites were identified, and the major derivatives resulted from the oxidation of pyridine, indanyl and fenylmethyl groups, and from N-desalkylation of piperazinil (Anari et al, 2004). Similar metabolites were found previously in another experiment employing LC-MS/MS analysis of the metabolites obtained from in vitro study (Yu, Cui, Davis, 1999).…”

“…Anari and co-workers (2004) described the metabolic profile of indinavir, combining data obtained after analysis of human liver post-mitochondrial preparations in liquid chromatography coupled to mass spectroscopy (LC-MS/MS), and metabolic predictions generated by research based on structural similarity using the MDL Metabolite Database software. Two desalkylated, eleven monoxydated, three deoxygenated and two desalkylated/ monoxydated metabolites were identified, and the major derivatives resulted from the oxidation of pyridine, indanyl and fenylmethyl groups, and from N-desalkylation of piperazinil (Anari et al, 2004). Similar metabolites were found previously in another experiment employing LC-MS/MS analysis of the metabolites obtained from in vitro study (Yu, Cui, Davis, 1999).…”

Detection and Characterization of Metabolites in Biological Matrices Using Mass Defect Filtering of Liquid Chromatography/High Resolution Mass Spectrometry Data

“…Both the detection of expected metabolites and the acquisition of their product ion spectra can be accomplished in a single LC/MS analysis (Gangl et al, 2002;Xia et al, 2003;Mortishire-Smith et al, 2005). With the availability of comprehensive metabolite databases developed from a knowledge of biotransformations, the list-dependent product ion scan has been very successful in screening for predicted metabolites, especially in vitro metabolites (Anari et al, 2004). The disadvantage of these approaches is that only predictable metabolites can be detected, and therefore, the robustness of the metabolite prediction algorithms being used becomes a critical factor.…”

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“…47) A combination of di erent analytical technologies and data mining tools is needed to e ciently and thoroughly identify metabolites for discovering the biomarkers of exposure, toxic e ects, or diseases. To achieve that, IPF is a stable isotope-based metabolomics approach using compound labeled with 2 H, 13 C, 15 N, or 18 O that can be used to facilitate identi cation of the metabolites.…”