Metabolomics in toxicology and preclinical research

Abstract

Metabolomics, the comprehensive analysis of metabolites in a biological system, provides detailed information about the biochemical/physiological status of a biological system, and about the changes caused by chemicals. Metabolomics analysis is used in many fields, ranging from the analysis of the physiological status of genetically modified organisms in safety science to the evaluation of human health conditions. In toxicology, metabolomics is the -omics discipline that is most closely related to classical knowledge of disturbed biochemical pathways. It allows rapid identification of the potential targets of a hazardous compound. It can give information on target organs and often can help to improve our understanding regarding the mode-of-action of a given compound. Such insights aid the discovery of biomarkers that either indicate pathophysiological conditions or help the monitoring of the efficacy of drug therapies. The first toxicological applications of metabolomics were for mechanistic research, but different ways to use the technology in a regulatory context are being explored. Ideally, further progress in that direction will position the metabolomics approach to address the challenges of toxicology of the 21st century. To address these issues, scientists from academia, industry, and regulatory bodies came together in a workshop to discuss the current status of applied metabolomics and its potential in the safety assessment of compounds. We report here on the conclusions of three working groups addressing questions regarding 1) metabolomics for in vitro studies 2) the appropriate use of metabolomics in systems toxicology, and 3) use of metabolomics in a regulatory context.

Fig. 1. Investigation strategy for immediate uses of metabolomics and related information-rich technologies to predict potential hazard of unknown compounds

The comparison of metabolite patterns of known reference compounds with their in vivo toxicological profile will yield “toxicity patterns,” i.e., metabolite patterns that are correlated to defined toxicological endpoints. Alignment of metabolite patterns of unknown compounds with these toxicity patterns will allow calculation of the degree of overlap. This information is then used for toxicological predictions.

Fig. 2. The role of in vitro metabolomics in identification, mapping, and use of pathways-of-toxicity (PoT) and hazard prediction

Xenobiotics (X) can be metabolized/metabolically activated (X*), transported into different cell compartments, and interact as parent compounds or as metabolites with endogenous targets (T). The interaction with some targets forms the molecular initiating events (MIE) that trigger immediate cellular changes related to metabolism, signaling, and/or transcription. These very initial steps are sometimes circumscribed as upstream PoT. In an attempt to re-establish homeostasis, and as consequence of the initial disturbance, several well-conserved cellular reactions are triggered that decide on the overall cell fate. For instance, several stress response pathways (SRP) are activated. In addition, cell death programs are activated and/or functional loss is observed (e.g., uncoupling of mitochondria, loss of ATP, inability to buffer intracellular calcium). The latter two events favor/augment toxicity (TOX). TOX and SRP can interact in many ways, e.g., the p53 pathway is initially a typical SRP but can also trigger apoptosis when over-activated. The different inputs from SRP and TOX pathways decide whether the cell can buffer the damage (re-establish a new homeostasis) or whether it loses function/viability irreversibly. Biomarkers-of-toxicity (BoT) ideally correlate with the cell fate switch. In many cases, they are not single molecular events, but reactions of a network that requires modeling. The concentration of a xenobiotic that leads to a breaking of cell homeostasis can be used as point-of-departure (PoD) for quantitative risk assessment. The network of events that entails the cellular reaction to insult and leads to the cell fate decision can be termed “downstream PoT.” A metabolomics approach, if particularly well suited to identify and measure the whole metabolite network related to downstream PoT.

Fig. 3. Activation of pathways-of-toxicity (PoT) as part of the cell injury response

The response of cells/tissues to toxic insult may be divided into different phases. First (1), the network of disturbances and stress response pathways (collectively termed PoT) is triggered and decides on the cell fate. After a cell has reached the point-of-no-return towards death, still many biochemical reactions are activated (2). These are responsible for degradative events and the response to injury that leads to the classical (apical) endpoints in toxicology (e.g., inflammation). Thus, classic endpoints represent “late” events. A third phase entails mostly passive processes, such as disintegration driven by many types of proteases and lipases. Metabolomics approaches would measure the activation of PoT and allow prediction of toxicity independent of the unspecific late changes determined by events in phase 2 and 3.

Fig. 4. Use of metabolomics in a regulatory context

Two different uses can be envisaged for metabolomics approaches. The first is the identification of the mode-of-action (MoA) of compounds. This is a potential stand-alone approach, not necessarily requiring additional technologies. A major technical challenge is to keep the variability of the technology and of the sample preparation low. For use in a regulatory context, validation of specific standard operation procedures would be required. At present, information on the MoA usually is not required in the regulatory process and would be regarded as supplementary information. The MoA is a mandatory requirement only for few types of compounds (e.g., endocrine disrupters). The second use of metabolomics would entail the definition of the “no adverse effects level” (NOAEL). This would require additional information from other technical approaches. At present, this is not a realistic use of metabolomics approaches in the regulatory context, and generation of more data and gaining of experience will be required to judge the validity of the approach. The future acceptance of the method for either application will depend on the introduction and routine use of stringent quality assurance procedures.