Targeted Data-Independent Acquisition and Mining Strategy for Trace Drug Metabolite Identification Using Liquid Chromatography Coupled with Tandem Mass Spectrometry.

Detection and identification of unknown or low-level drug-related metabolites in complex biological materials is an ongoing challenge. A highly selective and sensitive method could be a possible solution. Here, we proposed a targeted data-independent acquisition and mining (TDIAM) strategy for the rapid identification of trace drug metabolites using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS). In this strategy, raw data is acquired by a novel tm-MS scan, which contains an interleaved full MS scan with a targeted mass range and a product ion scan by selecting all ions in the targeted mass range as precursor ions. For efficient discovery of metabolites, raw data are analyzed by a new postacquisition processing method, Molecule- and Fragmentation-driven Mass Defect Filters (MF-MDFs), which was developed based on the fragmentation of parent drug to pick out molecular ions and fragment ions of potential metabolites from the complex matrix. When applying the proposed strategy to paclitaxel metabolism research, we successfully identified 10 metabolites, among which six were not previously reported. The results demonstrated that TDIAM greatly improved throughput, detective sensitivity, and selectivity and, more importantly, yielded almost the same spectrum as traditional HRMS/MS. Therefore, TDIAM provides structure-enriched evidence to confirm the existence and elucidate the structures of metabolites. This strategy is suitable for identification of metabolites present at low concentrations in a complex matrix, and it has the potential to provide an efficient, sensitive, and labor-saving solution for drug metabolite research.

Global and targeted metabolomics of esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers.

Diagnostic and therapeutic biomarkers useful for esophageal squamous cell carcinoma (ESCC) have the ability to increase the long term survival of cancer patients. A metabolomics study, using plasma from four groups including ESCC patients before, during, and after chemoradiotherapy (CRT) and healthy controls, was originally carried out by LC-MS to determine global alterations in the metabolic profiles and find biomarkers potentially applicable to diagnosis and monitoring treatment effects. It is worth pointing out that a clear clustering and separation of metabolic data from the four groups was observed, which indicated that disease status and treatment intervention resulted in specific metabolic perturbations in the patients. A series of metabolites were found to be significantly altered in ESCC patients versus healthy controls and in pre- versus post-treatment patients based on multivariate statistical data analysis (MVDA). To further validate the reliability of these potential biomarkers, an independent validation was performed by using the selected reaction monitoring (SRM) based targeted approach. Finally, 18 most significantly altered plasma metabolites in ESCC patients, relative to healthy controls, were tentatively identified as lysophosphatidylcholines (lysoPCs), fatty acids, l-carnitine, acylcarnitines, organic acids, and a sterol metabolite. The classification performance of these metabolites were analyzed by receiver operating characteristic (ROC)(1) analysis and a biomarker panel was generated. Together, biological significance of these metabolites was discussed. Comparison between pre- and post-treatment patients generated 11 metabolites as potential therapeutic biomarkers that were tentatively identified as amino acids, acylcarnitines, and lysoPCs. Levels of three of these (octanoylcarnitine, lysoPC(16:1), and decanoylcarnitine) were closely correlated with treatment effect. Moreover, variation of these three potential biomarkers was investigated over the treatment course. The results suggest that these biomarkers may be useful in diagnosis, as well as in monitoring therapeutic responses and predicting outcomes of the ESCC.

Plasma metabolome analysis by integrated ionization rapid-resolution liquid chromatography/tandem mass spectrometry.

RATIONALE: Acquiring global information on plasma-endogenous metabolites challenges metabolomics. This study has been designed to investigate the suitability of integrated ionization rapid-resolution liquid chromatography/tandem mass spectrometry (RRLC/MS/MS) for different kinds of metabolites in complex plasma, and provides an approach for plasma metabolomics in acquiring more comprehensive data of metabolites. METHODS: Integrated ionization of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combined with RRLC/MS/MS has been carried out to perform analysis on the global plasma metabolome of healthy volunteers. The contributions to the total numbers of ion features by RRLC/MS with ESI, APCI, and APPI in positive and negative ion modes were calculated. Representative unique and identical ions were identified. The intensities of identical ions were compared. RESULTS: Each of ESI, APCI, and APPI coupled with RRLC/MS has its own advantage over the other two techniques for certain types of metabolites in plasma. LC/ESI-MS is very sensitive for detecting glycerophosphocholines, glycerophosphoethanolamines, acyl carnitines, bile acids, sulfate, etc. LC/APCI-MS is suitable for analyzing cyclic alcohols, fatty acids, and linoleic acids. LC/APPI-MS proves to be appropriate in detecting steroids, sphingolipids, some amino acids, nucleosides, and purines in plasma. CONCLUSIONS: It is suggested that the integrated ionization LC/MS approach should be applied for global plasma metabolomics. Moreover, the results obtained demonstrate that it is preferable to choose certain techniques from LC/ESI-MS, LC/APCI-MS, and LC/APPI-MS for metabolite target analysis.

[Rapid screening and quality evaluation for the harmful substance 5-hydroxymethyl furfural in commercially available traditional Chinese medicine injection using LC-MS/MS method].

To screen the harmful substance 5-hydroxymethyl furfural content in commercially available traditional Chinese medicine injection which are commonly used, and to preliminarily evaluate the quality of these injections, 5-hydroxymethyl furfural was taken as an index. The contents of 5-hydroxymethyl furfural in 56 samples which consist of 23 kinds of traditional Chinese medicine injections and glucose injection were determined using LC-MS/MS, and 5-hydroxymethyl furfural was detected in 52 of these samples. The minimal content was 0.0038 microg x L(-1) and the maximum content was 1420 microg x mL(-1). The contents of 5-hydroxymethyl furfural were significantly different in traditional Chinese medicine injection which came from different kinds, manufacturers or batches. The results showed the quality difference of commercially available traditional Chinese medicine injection is significant taking 5-hydroxymethyl furfural content as assessment index. More attention should be paid to the safety of 5-hydroxymethyl furfural in traditional Chinese medicine injection, and unified limitation standard should be set to improve medication safety of traditional Chinese medicine injection.

Integrated ionization approach for RRLC-MS/MS-based metabonomics: finding potential biomarkers for lung cancer.

An integrated ionization approach of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combining with rapid resolution liquid chromatography mass spectrometry (RRLC-MS) has been developed for performing global metabonomic analysis on complex biological samples. This approach was designed to overcome the low ionization efficiencies of endogenous metabolites due to diverse physicochemical properties as well as ion suppression, and obtain comprehensive metabolite profiles in LC-MS analysis. Ionization capability and applicability were manifested by improved ionization efficiency and enlarged metabolite coverage in analysis on typical urinary metabolite standards and urine samples from healthy volunteers. The method was validated by the limit of detection and precision. When applied to the global metabonomic studies of lung cancer, more comprehensive biomarker candidates were obtained to reflect metabolic traits between healthy volunteers and lung cancer patients, including 74 potential biomarkers in positive ion mode and 59 in negative ion mode. Taking identical potential biomarkers of any two or three ionization methods into account, analysis using ESI-MS in positive (+) and negative (-) ion mode contributed to 70 and 64% of the total potential biomarkers, respectively. The biomarker discovery capability of (+/-) APCI-MS accounted for 45 and 42% of the overall; meanwhile (+/-) APPI-MS amounted for 39 and 54%. These results indicated that potential biomarkers with vital biological information could be missed if only a single ionization method was used. Furthermore, 11 potential biomarkers were identified including amino acids, nucleosides, and a metabolite of indole. They revealed elevated amino acid and nucleoside metabolism as well as protein degradation in lung cancer patients. This proposed approach provided a more comprehensive picture of the metabolic changes and further verified identical biomarkers that were obtained simultaneously using different ionization methods.

RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer.

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

[Interaction between anticancer drugs and DNA studied by using electrospray ionization mass spectrometry].

To elucidate further sequence selectivity and nature of the binding of anticancer drugs to DNA, the interaction between anticancer drugs, which are minor groove ligands (distamycin A, DM and netropsin, NP) and intercalator (mitoxantrone, MT), and DNA were studied by electrospray ionization mass spectrometry. The 2 : 1 specific complex of DM and AT-rich DNA were observed principally, while only 1 : 1 specific complex of NP and AT-rich DNA were observed. MT specifically binds to GC-rich DNA. In addition, DM binds to DNA containing 5 A/T bases minor groove almost in a 2 : 1 mode and does not bind to DNA containing 3 A/T bases minor groove. NP binds most strongly to DNA containing 4 A/T bases minor groove. The 1 : 1 specific complex of MT and 6-mer DNA was also observed. The result of competitive binding experiment shows that DM binds more strongly to AT-rich DNA than NP does. These results provide bases for investigating the mechanism of interaction between the drugs and DNA and for improving the structure of target drug.