Cyanide Trapping of Iminium Ion Reactive Metabolites: Implications for Clinical Hepatotoxicity.

Reactive metabolite formation is a major mechanism of hepatotoxicity. Although reactive electrophiles can be soft or hard in nature, screening strategies have generally focused on the use of glutathione trapping assays to screen for soft electrophiles, with many data sets available to support their use. The use of a similar assay for hard electrophiles using cyanide as the trapping agent is far less common, and there is a lack of studies with sufficient supporting data. Using a set of 260 compounds with a defined hepatotoxicity status by the FDA, a comprehensive literature search yielded cyanide trapping data on an unbalanced set of 20 compounds that were all clinically hepatotoxic. Thus, a further set of 19 compounds was selected to generate cyanide trapping data, resulting in a more balanced data set of 39 compounds. Analysis of the data demonstrated that the cyanide trapping assay had high specificity (92%) and a positive predictive value (83%) such that hepatotoxic compounds would be confidently flagged. Structural analysis of the adducts formed revealed artifactual methylated cyanide adducts to also occur, highlighting the importance of full structural identification to confirm the nature of the adduct formed. The assay was demonstrated to add the most value for compounds containing typical structural alerts for hard electrophile formation: half of the severe hepatotoxins with these structural alerts formed cyanide adducts, while none of the severe hepatotoxins with no relevant structural alerts formed adducts. The assay conditions used included cytosolic enzymes (e.g., aldehyde oxidase) and an optimized cyanide concentration to minimize the inhibition of cytochrome P450 enzymes by cyanide. Based on the demonstrated added value of this assay, it is to be initiated for use at GSK as part of the integrated hepatotoxicity strategy, with its performance being reviewed periodically as more data is generated.

A discovery biotransformation strategy: combining in silico tools with high-resolution mass spectrometry and software-assisted data analysis for high-throughput metabolism.

Understanding compound metabolism in early drug discovery aids medicinal chemistry in designing molecules with improved safety and ADME properties. While advancements in metabolite prediction brings increased confidence, structural decisions require experimental data. In vitro metabolism studies using liquid chromatography and high-resolution mass spectrometry (LC-MS) are generally resource intensive and performed on very few compounds, limiting the chemical space that can be examined.Here, we describe a novel metabolism strategy increasing compound throughput using residual in vitro clearance samples conducted at drug concentrations of 0.5 µM. Analysis by robust ultra high-performance liquid chromatography separation and accurate-mass MS detection ensures major metabolites are identified from a single injection. In silico prediction (parent cLogD) tailors chromatographic conditions, with data-dependent tandem mass spectroscopy targeting predicted metabolites. Software-assisted data mining, structure elucidation and automatic reporting are used.Confidence in the globally aligned workflow is demonstrated with 16 marketed drugs. The approach is now implemented routinely across our laboratories. To date, the success rate for identification of at least one major metabolite is 85%. The utility of these data has been demonstrated across multiple projects, allowing earlier medicinal chemistry decisions to increase efficiency and impact of the design-make-test cycle thus improving the translatability of early in vitro metabolism data.

Definition of Haptens Derived from Sulfamethoxazole: In Vitro and in Vivo.

Hypersensitivity reactions occur frequently in patients upon treatment with sulfamethoxazole (SMX). These adverse effects have been attributed to nitroso sulfamethoxazole (SMX-NO), the reactive product formed from auto-oxidation of the metabolite SMX hydroxylamine. The ability of SMX-NO to prime naïve T-cells in vitro and also activate T-cells derived from hypersensitive patients has illustrated that T-cell activation may occur through the binding of SMX-NO to proteins or through the direct modification of MHC-bound peptides. SMX-NO has been shown to modify cysteine residues in glutathione, designer peptides, and proteins in vitro; however, the presence of these adducts have not yet been characterized in vivo. In this study a parallel in vitro and in vivo analysis of SMX-NO adducts was conducted using mass spectrometry. In addition to the known cysteine adducts, multiple SMX-NO-derived haptenic structures were found on lysine and tyrosine residues of human serum albumin (HSA) in vitro. On lysine residues two haptenic structures were identified including an arylazoalkane adduct and a Schiff base adduct. Interestingly, these adducts are labile to heat and susceptible to hydrolysis as shown by the presence of allysine. Furthermore, SMX-modified HSA adducts were detected in patients on long-term SMX therapy illustrated by the presence of an arylazoalkane adduct derived from a proposed carboxylic acid metabolite of SMX-NO. The presence of these adducts could provide an explanation for the immunogenicity of SMX and the strong responses to SMX-NO observed in T-cell culture assays. Also, the degradation of these adducts to allysine could lead to a stress-related innate immune response required for T-cell activation.

HLA-A*33:03-Restricted Activation of Ticlopidine-Specific T-Cells from Human Donors.

The HLA class I allele HLA-A*33:03 is a risk factor for ticlopidine-induced liver injury. Herein, we show HLA class I-restricted ticlopidine-specific CD8+ T-cell activation in healthy donors expressing HLA-A*33:03. Cloned CD8+ T-cells proliferated and secreted IFN-γ in the presence of ticlopidine and autologous antigen presenting cells. A reduction of the T-cell response after blocking with HLA-class I and HLA-A*33 antibodies indicates that the interaction between drugs and the HLA allele detected in genetic association studies may be important for T-cell activation.

Elucidation of Drug Metabolite Structural Isomers Using Molecular Modeling Coupled with Ion Mobility Mass Spectrometry.

Ion mobility-mass spectrometry (IM-MS) in combination with molecular modeling offers the potential for small molecule structural isomer identification by measurement of their gas phase collision cross sections (CCSs). Successful application of this approach to drug metabolite identification would facilitate resource reduction, including animal usage, and may benefit other areas of pharmaceutical structural characterization including impurity profiling and degradation chemistry. However, the conformational behavior of drug molecules and their metabolites in the gas phase is poorly understood. Here the gas phase conformational space of drug and drug-like molecules has been investigated as well as the influence of protonation and adduct formation on the conformations of drug metabolite structural isomers. The use of CCSs, measured from IM-MS and molecular modeling information, for the structural identification of drug metabolites has also been critically assessed. Detection of structural isomers of drug metabolites using IM-MS is demonstrated and, in addition, a molecular modeling approach has been developed offering rapid conformational searching and energy assessment of candidate structures which agree with experimental CCSs. Here it is illustrated that isomers must possess markedly dissimilar CCS values for structural differentiation, the existence and extent of CCS differences being ionization state and molecule dependent. The results present that IM-MS and molecular modeling can inform on the identity of drug metabolites and highlight the limitations of this approach in differentiating structural isomers.

From definition to implementation: a cross-industry perspective of past, current and future MIST strategies.

The article describes and discusses the evolution of strategies to characterize metabolites in support of safety studies over the last 40 years, as well as future trends. Approaches to derive qualitative and quantitative information on metabolites are described, with a particular focus on the comparison of options to quantify metabolites in the absence of authentic standards. Current strategies to assess metabolite profiles are summarized into four general approaches and compared against a number of key criteria. Potential future strategies are discussed, including the use of clinical samples as the starting point for metabolite investigations, minimizing the need for animal radiolabelled studies and establishing metabolite safety without radiolabelled studies in animals or human.

Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using In Silico and Experimental Collision Cross-Section Values.

Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation (cf. MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different in silico models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set.

Sites of metabolic substitution: investigating metabolite structures utilising ion mobility and molecular modelling.

Drug metabolism is an integral part of the drug development and drug discovery process. It is required to validate the toxicity of metabolites in support of safety testing and in particular provide information on the potential to form pharmacologically active or toxic metabolites. The current methodologies of choice for metabolite structural elucidation are liquid chromatography/tandem mass spectrometry (LC/MS/MS) and nuclear magnetic resonance (NMR) spectroscopy. There are, in certain cases, examples of metabolites whose sites of metabolism cannot be unequivocally identified by MS/MS alone. Utilising commercially available molecular dynamics packages and known quantum chemistry basis sets, an ensemble of lowest energy structures were generated for a group of aromatic hydroxylated metabolites of the model compound ondansetron. Theoretical collision cross-sections were calculated for each structure. Travelling-wave ion mobility (IMS) measurements were also performed on the compounds, thus enabling experimentally derived collision cross-sections to be calculated. A comparison of the theoretical and experimentally derived collision cross-sections were utilised for the accurate assignment of isomeric drug metabolites. The UPLC/IMS-MS method, described herein, demonstrates the ability to measure reproducibly by ion mobility, metabolite structural isomers, which differ in collision cross-section, both theoretical and experimentally derived, by less than 1 Å(2). This application has the potential to supplement and/or complement current methods of metabolite structural characterisation.

Using hyperpolarised NMR and DFT to rationalise the unexpected hydrogenation of quinazoline to 3,4-dihydroquinazoline.

PHIP and SABRE hyperpolarized NMR methods are used to follow the unexpected metal-catalysed hydrogenation of quinazoline (Qu) to 3,4-dihydroquinazoline as the sole product. A solution of [IrCl(IMes)(COD)] in dichloromethane reacts with H2 and Qu to form [IrCl(H)2(IMes)(Qu)2] (2). The addition of methanol then results in its conversion to [Ir(H)2(IMes)(Qu)3]Cl (3) which catalyses the hydrogenation reaction. Density functional theory calculations are used to rationalise a proposed outer sphere mechanism in which (3) converts to [IrCl(H)2(H2)(IMes)(Qu)2]Cl (4) and neutral [Ir(H)3(IMes)(Qu)2] (6), both of which are involved in the formation of 3,4-dihydroquinazoline via the stepwise transfer of H+ and H-, with H2 identified as the reductant. Successive ligand exchange in 3 results in the production of thermodynamically stable [Ir(H)2(IMes)(3,4-dihydroquinazoline)3]Cl (5).

Approaches for the rapid identification of drug metabolites in early clinical studies.

Understanding the metabolism of a novel drug candidate in drug discovery and drug development is as important today as it was 30 years ago. What has changed in this period is the technology available for proficient metabolite characterization from complex biological sources. High-efficiency chromatography, sensitive MS and information-rich NMR spectroscopy are approaches that are now commonplace in the modern laboratory. These advancements in analytical technology have led to unequivocal metabolite identification often being performed at the earliest opportunity, following the first dose to man. For this reason an alternative approach is to shift from predicting and extrapolating possible human metabolism from in silico and nonclinical sources, to actual characterization at steady state within early clinical trials. This review provides an overview of modern approaches for characterizing drug metabolites in these early clinical studies. Since much of this progress has come from technology development over the years, the review is concluded with a forward-looking perspective on how this progression may continue into the next decade.

A gender-specific discriminator in Sprague-Dawley rat urine: the deployment of a metabolic profiling strategy for biomarker discovery and identification.

The use of nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) as complementary analytical techniques for open metabolic profiling is illustrated in the context of defining urinary biochemical discriminators between male and female Sprague-Dawley rats. Subsequent to the discovery of a female-specific urinary discriminator by LC-MS, further LC, MS, and NMR methods have been applied in a coordinated effort to identify this urinary component. Thereafter, the biological relevance and context of the identified component, in this case a steroid metabolite, has been achieved. This approach will be deployed in future studies of disease, drug efficacy, and toxicity to discover and identify biologically relevant markers.

Use of liquid chromatography/time-of-flight mass spectrometry and multivariate statistical analysis shows promise for the detection of drug metabolites in biological fluids.

The process of metabolite identification is essential to the drug discovery and development process; this is usually achieved by liquid chromatography/tandem mass spectrometry (LC/MS/MS) or a combination of liquid chromatography/mass spectrometry (LC/MS) and nuclear magnetic resonance (NMR) spectroscopy. Metabolite identification is, however, a time-consuming process requiring an experienced skilled scientist. Multivariate statistical analysis has been used in the field of metabonomics to elucidate differences in endogenous biological profiling due to a toxic effect or a disease state. In this paper we show how a combination of liquid chromatography/time-of-flight mass spectrometry (LC/TOFMS) and multivariate statistical analysis can be used to detect drug metabolites in a biological fluid with no prior knowledge of the compound administered.

Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development.

The application of liquid chromatography/mass spectrometry (LC/MS) followed by principal components analysis (PCA) has been successfully applied to the screening of rat urine following the administration of three candidate pharmaceuticals. With this methodology it was possible to differentiate the control samples from the dosed samples and to identify the components of the mass spectrum responsible for the separation. These data clearly show that LC/MS is a viable alternative, or complementary, technique to proton NMR for metabonomics applications in drug discovery and development.