Author Archives: Vera

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. 

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. 

The identification of metabolites is almost exclusively done with liquid chromatography/tandem mass spectrometry (LC/MSMS) and despite the enormous progress in the development of these techniques and software for handling of data this is a time-consuming task. In this study the use of quadrupole time-of-flight (QTOF)-generated MS(E) and MS/MS data were compared with respect to rationalization of metabolites. In addition Mass-MetaSite, a semi-automated software for metabolite identification, was evaluated. The program combines the information from MS raw data, in the form of collision-induced dissociation spectra, with a prediction of the site of metabolism in order to assign the structure of a metabolite. The aim of the software is to mimic the rationalization of fragment ions performed by a biotransformation scientist in the process of structural elucidation. For this evaluation, metabolite identification in human liver microsomes was accomplished for 19 commercially available compounds and 15 in-house compounds. The results were very encouraging and for 96% of the metabolites the same structures were assigned using MS(E) compared with MSMS acquired data. The possibility of using MS(E) could considerably reduce the analysis time. Moreover, Mass-MetaSite performed well and the correct assigned structure, compared to manual inspection of the data, was picked in the first rank in ∼80% of the cases. In conclusion MS(E) could be successfully used for metabolite identification in order to reduce time of analysis and Mass-MetaSite could alleviate the work of a biotransformation scientist and decrease the workload by assigning the structure for a majority of the metabolites.