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The importance of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis is expected to grow substantially due to recent failures in detecting severe toxicity issues of new chemical entities during preclinical/clinical development. 

Traditionally, safety risk assessment studies for humans have been conducted in animals during advanced preclinical or clinical phase of drug development. However, potential drug toxicity in humans now needs to be detected in the drug discovery process as soon as possible without reliance on animal studies. The “omics”, such as genomics, proteomics, and metabolomics, have recently entered pharmaceutical research in both drug discovery and drug development, but to the best of our knowledge, no applications in high-throughput safety risk assessment have been attempted so far. This paper reports an innovative method to anticipate adverse drug effects in an early discovery phase based on lipid fingerprints using human three-dimensional microtissues. The risk of clinical hepatotoxicity potential was evaluated for a data set of 22 drugs belonging to five different therapeutic chemical classes and with various drug-induced liver injury effect. The treatment of microtissues with repeated doses of each drug allowed collecting lipid fingerprints for five time points (2, 4, 7, 9, and 11 days), and multivariate statistical analysis was applied to search for correlations with the hepatotoxic effect. The method allowed clustering of the drugs based on their hepatotoxic effect, and the observed lipid impairments for a number of drugs was confirmed by literature sources. Compared to traditional screening methods, here multiple interconnected variables (lipids) are measured simultaneously, providing a snapshot of the cellular status from the lipid perspective at a molecular level. Applied here to hepatotoxicity, the proposed workflow can be applied to several tissues, being tridimensional microtissues from various origins. 

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. 

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.