The assessment of the potential hepatotoxicity of new drugs by in vitro metabolomics

Drug hepatotoxicity assessment is a relevant issue both in the course of drug development as well as in the post marketing phase. The use of human relevant in vitro models in combination with powerful analytical methods (metabolomic analysis) is a promising approach to anticipate, as well as to understand and investigate the effects and mechanisms of drug hepatotoxicity in man. The metabolic profile analysis of biological liver models treated with hepatotoxins, as compared to that of those treated with non-hepatotoxic compounds, provides useful information for identifying disturbed cellular metabolic reactions, pathways, and networks. This can later be used to anticipate, as well to assess, the potential hepatotoxicity of new compounds. However, the applicability of the metabolomic analysis to assess the hepatotoxicity of drugs is complex and requires careful and systematic work, precise controls, wise data preprocessing and appropriate biological interpretation to make meaningful interpretations and/or predictions of drug hepatotoxicity. This review provides an updated look at recent in vitro studies which used principally mass spectrometry-based metabolomics to evaluate the hepatotoxicity of drugs. It also analyzes the principal drawbacks that still limit its general applicability in safety assessment screenings. We discuss the analytical workflow, essential factors that need to be considered and suggestions to overcome these drawbacks, as well as recent advancements made in this rapidly growing field of research

Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey

REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) is a global strategy and regulation policy of the EU that aims to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances. It entered into force on 1st June 2007 (EC 1907/2006). REACH and EU policies plead for the use of robust high-throughput 'omic' techniques for the in vitro investigation of the toxicity of chemicals that can provide an estimation of their hazards as well as information regarding the underlying mechanisms of toxicity. In agreement with the 3R's principles, cultured cells are nowadays widely used for this purpose, where metabolomics can provide a real-time picture of the metabolic effects caused by exposure of cells to xenobiotics, enabling the estimations about their toxicological hazards. High quality and robust metabolomics data sets are essential for precise and accurate hazard predictions. Currently, the acquisition of consistent and representative metabolomic data is hampered by experimental drawbacks that hinder reproducibility and difficult robust hazard interpretation. Using the differentiated human liver HepG2 cells as model system, and incubating with hepatotoxic (acetaminophen and valproic acid) and non-hepatotoxic compounds (citric acid), we evaluated in-depth the impact of several key experimental factors (namely, cell passage, processing day and storage time, and compound treatment) and instrumental factors (batch effect) on the outcome of an UPLC-MS metabolomic analysis data set. Results showed that processing day and storage time had a significant impact on the retrieved cell's metabolome, while the effect of cell passage was minor. Meta-analysis of results from pathway analysis showed that batch effect corrections and quality control (QC) measures are critical to enable consistent and meaningful estimations of the effects caused by compounds on cells. The quantitative analysis of the changes in metabolic pathways upon bioactive compound treatment remained consistent despite the concurrent causes of metabolomic data variation. Thus, upon appropriate data retrieval and correction and by an innovative metabolic pathway analysis, the metabolic alteration predictions remained conclusive despite the acknowledged sources of variability

Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis

Extraction of meaningful biological information from longitudinal metabolomic studies is a major challenge and typically involves multivariate analysis and dimensional reduction methods for data visualization such as Principal Component Analysis or Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Besides, a variety of computational tools have been developed to identify changes in metabolic pathways including functional analysis and pathway analysis. In this work, the joint analysis of results from MCR-ALS and metabolic pathway analysis is proposed to facilitate the interpretation of dynamic changes in longitudinal metabolomic data. The strategy is based on the use of MCR-ALS to remove unstructured random variation in the raw data, thus facilitating the interpretation of dynamic changes observed by metabolic pathway analysis over time. A simulated data set representing dynamic longitudinal changes in the intensities of a subset of metabolites from three metabolic pathways was initially used to test the applicability of MCR-ALS to support pathway analysis for detecting pathway perturbations. Then, the strategy is applied to real data acquired for the analysis of changes during CD8+ T cell activation. Results obtained show that MCR-ALS facilitates the interpretation of longitudinal metabolomic profiles in multivariate data sets by identifying metabolic pathways associated with each detected dynamic component

The effect of Holder pasteurization on the lipid and metabolite composition of human milk

Human milk (HM) is the gold standard for newborn nutrition. When own mother's milk is not sufficiently available, pasteurized donor human milk becomes a valuable alternative. In this study we analyzed the impact of Holder pasteurization (HoP) on the metabolic and lipidomic composition of HM. Metabolomic and lipidomic profiles of twelve paired HM samples were analysed before and after HoP by liquid chromatography-mass spectrometry (MS) and gas chromatography-MS. Lipidomic analysis enabled the annotation of 786 features in HM out of which 289 were significantly altered upon pasteurization. Fatty acid analysis showed a significant decrease of 22 out of 29 detectable fatty acids. The observed changes were associated to five metabolic pathways. Lipid ontology enrichment analysis provided insight into the effect of pasteurization on physical and chemical properties, cellular components, and functions. Future research should focus on nutritional and/or developmental consequences of these changes

Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC-MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC-MS features selected as the precursor ion for MS2, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required

Metabolomic Diversity of Human Milk Cells over the Course of Lactation - A Preliminary Study

Human milk (HM) is a complex biofluid containing a wide cell variety including epithelial cells and leukocytes. However, the cellular compositions and their phenotypic properties over the course of lactation are poorly understood. The aim of this preliminary study was to characterize the cellular metabolome of HM over the course of lactation. Cells were isolated via centrifugation and the cellular fraction was characterized via cytomorphology and immunocytochemical staining. Cell metabolites were extracted and analyzed using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-QqTOF-MS) in the positive and negative electrospray ionization modes. Immunocytochemical analysis revealed a high variability of the number of detected cells with relative median abundances of 98% of glandular epithelial cells, 1% of leukocytes, and 1% of keratinocytes. Significant correlations between the milk postnatal age with percentage of epithelial cells and leukocytes, and with total cell count were observed. Results from the Hierarchical Cluster Analysis of immunocytochemical profiles were very similar to those observed in the analysis of the metabolomic profiles. In addition, metabolic pathway analysis showed alterations in seven metabolic pathways correlating with postnatal age. This work paves the way for future investigations on changes in the metabolomic fraction of the cellular compartment of HM

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a "toxicity index" for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration