Enhancing throughput of glutathione adduct formation studies and structural identification using a software-assisted workflow based on high resolution mass spectrometry (HRMS) data 

Enhancing throughput of glutathione adduct formation studies and structural identification using a software-assisted workflow based on high resolution mass spectrometry (HRMS) data 

October 2016.

E.N. Cece-Esencan, F. Fontaine, G. Plasencia, M. Teppner, A. Brink, A. PahlerI. Zamora.

Abstract

The bioactivation of drugs to Reactive Metabolites (RM) has been related to drug-induced liver injury and hypersensitivity reactions in patients. Therefore, many pharmaceutical companies are investigating the potential to form reactive metabolites in vitro as an integral part of the optimization of drug candidates. A computerassisted workflow to efficiently analyze larger numbers of compounds for the formation of glutathione trappable RM is presented here. A set of 95 compounds with known bioactivation potential was selected for this study.

Incubations with human liver microsomes were prepared with GSH. The acquisition of MS/MS spectra was triggered by ion intensity. MS with singly and doubly charged ions were used for peak detection and MS/MS spectra were used for structural elucidation. A confidence classification system for the GSH peak detection (high, medium, low) was developed based on the detection of characteristic fragment ions or neutral losses and applied to remove potential false positive results. A comparative analysis of the HRMS results with literature data was carried out.

The most frequently observed Neutral Loss (NL) found in singly charged GSH adducts (drug-glutathione conjugates) were, the Neutral Loss (NL, 129 Da) and Fragment Ion (FI, m/z 308) and in the doubly charged ones the Fragment Ion (FI, m/z 130). These NL and FI were used to identify GSH-related drug metabolites. MS/MS spectra were inspected to aid structural elucidations: 17% of drug substrates and 29 % of GSH adduct metabolites were identified with only doubly charged ions, stressing the importance of considering this charge state in the identification workflow.

A total of 41 compounds that form GSH adducts were retrieved from literature (HRMS, identified 28 compounds (68%) in high confidence, and the same result was obtained using precursor ion scan). By the confidence analysis of GSH peaks, the quality of the each GSH adduct was determined. 

Post-acquisition analysis of untargeted accurate mass quadrupole time-of-flight MS(E) data for multiple collision-induced neutral losses and fragment ions of glutathione conjugates

Post-acquisition analysis of untargeted accurate mass quadrupole time-of-flight MS(E) data for multiple collision-induced neutral losses and fragment ions of glutathione conjugates

December 2014.

Brink A; Fontaine F; Marschmann M; Steinhuber B; Cece EN; Zamora I; Pähler A

Abstract

Rationale: Analytical methods to assess glutathione (GSH) conjugate formation based on mass spectrometry usually take advantage of the specific fragmentation behavior of the glutathione moiety. However, most methods used for GSH adduct screening monitor only one specific neutral loss or one fragment ion, even though the peptide moiety of GSH adducts shows a number of other specific neutral fragments and fragment ions which can be used for identification.

Methods: Nine reference drugs well known to form GSH adducts were incubated with human liver microsomes. Mass spectrometric analysis was performed with a quadrupole time-of-flight mass spectrometer in untargeted accurate mass MS(E) mode. The data analysis and evaluation was achieved in an automated approach with software to extract and identify GSH conjugates based on the presence of multiple collision-induced neutral losses and fragment ions specific for glutathione conjugates in the high-energy MS spectra.

Results: In total 42 GSH adducts were identified. Eight (18%) adducts did not show the neutral loss of 129 but were identified based on the appearance of other GSH-specific neutral losses or fragment ions. In high-energy MS(E) spectra the GSH-specific fragment ions of m/z 308 and 179 as well as the neutral loss of 275 Da were complementary to the commonly used neutral loss of 129 Da. Further, one abundant (yet unpublished) GSH conjugate of troglitazone formed in human liver microsomes was found.

Conclusions: A software-aided approach was developed to reliably retrieve GSH adduct formation data out of untargeted complex full scan QTOFMS(E) data in a fast and efficient way. The present approach to detect and analyze multiple collision-induced neutral losses and fragment ions of glutathione conjugates in untargeted MS(E) data might be applicable to higher throughput to assess reactive metabolite formation in drug discovery.

Software-aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery

Software-aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery

January 2016.

Cece-Esencan EN; Fontaine F; Plasencia G; Teppner M; Brink A; Pähler A; Zamora I

Abstract

Rationale: Cytochrome P450 (CYP450) reaction phenotyping (CRP) and kinetic studies are essential in early drug discovery to determine which metabolic enzymes react with new drug entities. A new semi-automated computer-assisted workflow for CRP is introduced in this work. This workflow provides not only information regarding parent disappearance, but also metabolite identification and relative metabolite formation rates for kinetic analysis.

Methods: Time-course experiments based on incubating six probe substrates (dextromethorphan, imipramine, buspirone, midazolam, ethoxyresorufin and diclofenac) with recombinant human enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) and human liver microsomes (HLM) were performed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) analysis was conducted with an internal standard to obtain high-resolution full-scan and MS/MS data. Data were analyzed using Mass-MetaSite software. A server application (WebMetabase) was used for data visualization and review.

Results: CRP experiments were performed, and the data were analyzed using a software-aided approach. This automated-evaluation approach led to (1) the detection of the CYP450 enzymes responsible for both substrate depletion and metabolite formation, (2) the identification of specific biotransformations, (3) the elucidation of metabolite structures based on MS/MS fragment analysis, and (4) the determination of the initial relative formation rates of major metabolites by CYP450 enzymes.

Conclusions: This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery.

Development of higher-thoughput metabolic soft spot assay with integrated assessment of Glutathione adduct formation

Development of higher-thoughput metabolic soft spot assay with integrated assessment of Glutathione adduct formation

64th ASMS Conference on Mass Spectrometry and Allied Topics, San Antonio, TX (United States of America) 05 June 2016 

Abstract

Introduction High clearance due to extensive metabolism by cytochrome P450 is a common ADME liability of discovery-stage compounds. To provide structural information to facilitate ADME optimization, we have established a higher-throughput in vitro soft spot identification assay using HRMS/MS, sample pooling and software-assisted structure elucidation, as reported previously1. In this work, we present an additional improvement to the assay by integrating the assessment of glutathione (GSH) adduct formation, which could indicate bioactivation to reactive metabolites, in an enhanced analysis and data-review workflow. Methods A dual-concentration (30 and 0.5 µM) incubation approach was used, with liver microsomes that were supplemented by both NADPH and GSH. A Shimadzu Nexera HPLC system and a Thermo Q Exactive tandem mass spectrometer were used for LC-MS analysis. A 10-min gradient was used with an Agilent Eclipse+ C18 column and a mobile phase of 0.2% formic acid in water and acetonitrile. During analysis of the 30 µM samples, the mass spectrometer was operated with alternating full scans and data dependent scans with an inclusion list generated automatically by Mass-MetaSite. For the 0.5 µM samples, only full scan data was collected. Software-assisted soft spot ID was performed using Mass-MetaSite, and semi-quantitation of metabolites was performed using GMSU/QC software. Preliminary Data The use of an inclusion list of both singly and doubly charged masses generated by Mass-MetaSite for the data dependent acquisition ensured the collection of important MS/MS spectra for potential metabolites and GSH adducts. In addition, all-ion fragmentation data was acquired following a separate injection in case an unexpected metabolite failed to trigger MS/MS spectrum acquisition. All data from the 30 µM samples was processed by Mass-MetaSite software, which compared chromatograms to identify metabolites and then assigned structures by comparing their theoretical and experimental MS/MS spectra in an unattended fashion. After major metabolites were assigned by Mass-MetaSite, their accurate masses were imported into GMSU/QC software for automatic peak extraction and integration, and %-remaining vs. time plots of metabolites detected in the 0.5 µM samples were generated automatically. Using this workflow, we established the feasibility of a higher-throughput assay for soft-spot identification with integrated assessment of GSH adduct formation, at a per batch capacity of 8 individual compounds in liver microsomes from 2 species. The results obtained from this workflow were consistent with those previously reported. The detailed assay incubation, data acquisition and data processing workflow, as well as results from literature compounds will be presented. 1 Paiva A, Klakouski C, Zvyaga T, Johnson B, Josephs J, Humphreys WG, Weller H and Shou WZ, “Optimization of a High-throughput Metabolic Soft Spot Assay with Pooled Sample Analysis and Software-assisted Structure Elucidation,” presented at the 61st Annual Conference of American Society for Mass Spectrometry (ASMS) on Mass Spectrometry and Allied Topics, Minneapolis, Minnesota, 2013. Novel Aspect The integrated assessment of glutathione adduct formation in a high-throughput metabolic soft spot assay. 

Kinetic analysis during the metabolite identification process

Fitting Kinetic equations derived from metabolic pathways to LC/MS-MS integrated peak areas

64th ASMS Conference on Mass Spectrometry and Allied Topics, San Antonio, TX (United States of America) 05 June 2016 

Abstract

Introduction WebMetabase allows drawing metabolic pathways to describe how a parent compound is transformed into metabolites in in vitro or in vivo experiments. Assuming that reactions occur in a single compartment (like is the case for an in vitro incubation) and also that each arrow corresponds to a single-step irreversible Chemical reaction (even though it’s not true, i.e., they are often catalyzed by enzymes) we can build a system of differential equations whose equations, when solved, can be fitted to the MS area integrated signals. This theoretical curve and the integrated signal MS area often fit well, and when they do not, information useful to improve the metabolic pathway can be extracted from it. Methods LC/MS of several compounds was processed by MassMetaSite and loaded into WebMetabase. A metabolic pathway was built for each parent compound, and the system of differential equations, assuming single step irreversible chemical reactions, was built, solved, and fitted to the experimental integrated MS area using Maxima. Visual inspection of the plot of the theoretical MS area curve and experimental MS area points vs. time sometimes allows, when the fitting quality is bad, to derive information that can be used to change the position of the compound in the metabolic pathway and improve the fitting quality Preliminary Data Chemical kinetics theory is a good approximation of enzyme kinetics in in vitro assays because typically cytochromes do not have a high affinity for the compounds they metabolize, and also because the concentration of metabolites is much lower than the enzyme affinity, thus simplifying the Michaelis-Menten enzyme kinetics equation to a first order chemical kinetics equation. We used chemical kinetics analysis of metabolic pathway, as implemented in WebMetabase, as guidance to build metabolic pathways for several compounds. In several cases, the observed MS area vs. time of compounds included in the metabolic pathways fitted the theoretical equation that would correspond to them if the metabolic pathway scheme would consist of single-step irreversible chemical equations, thus confirming that the assumptions made were correct in these cases. There were some cases where experimental MS area vs. time points does not fit the theoretical chemical kinetics curve and could not be improved by rearranging them in the metabolic pathway. In these cases where fitting was bad or failed, probably one of the previous assumptions is false, for instance they are not formed or metabolized in single-step reactions, or chemical kinetics theory is not a good approximation of the real enzyme kinetics. Novel Aspect To the best of our knowledge, chemical kinetics theory has not been previously used to verify a metabolic pathway scheme.