Article

Metabolomics in Toxicology and Preclinical Research

BASF SE, Experimental Toxicology and Ecology, Ludwigshafen, Germany.
ALTEX 05/2013; 30(2):209-25.
Source: PubMed

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.

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ALTEX 30, 2/13
209
t
4
Report*
Metabolomics in Toxicology and
Preclinical Research
Tzutzuy Ramirez
, Mardas Daneshian
, Hennicke Kamp
1
, Frederic Y. Bois
3
, Malcolm R. Clench
4
,
Muireann Coen
5
, Beth Donley
6
, Steven M. Fischer
7
, Drew R. Ekman
8
, Eric Fabian
1
,
Claude Guillou
9
, Joachim Heuer
10
, Helena T. Hogberg
11
, Harald Jungnickel
12
, Hector C. Keun
5
,
Gerhard Krennrich
13
, Eckart Krupp
14
, Andreas Luch
12
, Fozia Noor
15
, Erik Peter
16
,
Bjoern Riefke
17
, Mark Seymour
18
, Nigel Skinner
19
, Lena Smirnova
11,12
, Elwin Verheij
20
,
Silvia Wagner
16
, Thomas Hartung
2,1
, Bennard van Ravenzwaay
, and Marcel Leist
2, 21§
1
BASF SE, Experimental Toxicology and Ecology, Ludwigshafen, Germany;
2
Center for Alternatives to Animal Testing – Europe
(CAAT-Europe), University of Konstanz, Konstanz, Germany;
3
Royallieu Research Center, Technological University of Compiegne,
Compiegne, France;
4
Biomedical Research Centre, Shefeld Hallam University, Shefeld, UK;
5
Imperial College London, London,
UK;
6
Stemina Biomarker Discovery Inc., Madison, USA;
7
Agilent Life Sciences Group, Agilent Technologies, Santa Clara, USA;
8
U.S. EPA, National Exposure Research Laboratory, Ecosystems Research Division, Athens, GA, USA;
9
Joint Research Centre of
the European Commission, Institute for Health & Consumer Protection, Ispra, Italy;
10
Federal Institute for Risk Assessment (BfR),
Department of Scientic Services, Berlin, Germany;
11
Johns Hopkins University, Bloomberg School of Public Health, Center for
Alternatives to Animal Testing (CAAT), Baltimore, USA;
12
Federal Institute for Risk Assessment (BfR), Department of Product
Safety, Berlin, Germany;
13
BASF SE, GVC/S Scientic Computing, Ludwigshafen, Germany;
14
Sano-Aventis Deutschland GmbH,
Genetic & Investigative Toxicology, Frankfurt, Germany;
15
Biochemical Engineering, Saarland University, Saarbruecken, Germany;
16
metanomics GmbH, Berlin, Germany;
17
Bayer Pharma AG, Investigational Toxicology, Berlin, Germany;
18
Syngenta, Jealott’s Hill
International Research Centre, Berkshire, UK;
19
Agilent Technologies, London, UK;
20
TNO Quality of Life, Zeist, The Netherlands ;
21
Doerenkamp-Zbinden chair for in vitro toxicology and biomedicine, Konstanz, Germany
* a report of t
4
– the transatlantic think tank for toxicology, a collaboration of the toxicologically oriented chairs in Baltimore,
Konstanz and Utrecht sponsored by the Doerenkamp-Zbinden Foundation. The opinions expressed in this report are those
of the participants as individuals and do not necessarily reect the opinions of the organizations they are afliated with;
participants do not necessarily endorse all recommendations made.
§
These authors contributed equally
Summary
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 elds, ranging from the analysis of the physiological status of genetically
modied 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 identication 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 efcacy
of drug therapies. The rst 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 21
st
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.
Keywords: metabolomics, toxicology, preclinical research, regulatory toxicology
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of major importance. This is the basis for non-invasive or mini-
mally-invasive sampling of body uids (blood, urine, etc.), and
metabolomics analysis on such samples to gain information on
target organ toxicities that would otherwise only be identiable
by highly invasive (histopathological) methods (Ebbels et al.,
2007; Lindon et al., 2003). Also, time course studies within one
study subject or animal are greatly facilitated by this particular
advantage of the metabolomics approach (Ebbels et al., 2007;
van Ravenzwaay et al., 2007, 2012).
In addition to providing information for a large number
of metabolites in one measurement, either from body uids,
tissues, or whole organisms (i.e., fungi, aquatic organisms,
etc.), metabolomics has been applied to in vitro cell systems
for understanding drug effects (Balcke et al., 2011; Strigun et
al., 2011a,b). First pilot studies show that future applications
of the metabolomics approaches are high throughput chemi-
cal screening applications (http://www.stemina.com). Finally,
new imaging techniques are not only capable of locating en-
vironmental toxicants within biological systems but can be
used in combination with metabolomics approaches to describe
specic toxicological effects within cells (Haase et al., 2011;
Tentschert et al., in press).
Due to the increasing use of metabolomics in toxicology and
safety sciences, a workshop was organized in Berlin on Febru-
ary 14-15, 2012. Scientists from academia, industry, and regu-
latory bodies discussed the current status of this approach and
its present/future applicability. One day prior to the workshop,
an international symposium was organized by BASF/CAAT-
Europe to present the state of the art regarding the use of me-
tabolomics for addressing a variety of pertinent toxicological
questions. Participants identied several hurdles in the wider
application of metabolomics in safety assessments and for in
vitro compound screening. This paved the way for in-depth dis-
cussions on these issues in the workshop that followed. Here,
we summarize the result of these discussions and offer solu-
tions for successfully moving forward with this important area
of research.
2 Metabolomics in vitro
The application of metabolomics in vitro is an emerging theme
that has been driven mostly by two major factors: (1) a better
understanding of the biochemical changes provoked by a toxic
insult in a dened and controllable experimental system and (2)
the increasing need to move towards the use of human-relevant
non-animal alternatives in toxicology in accordance with poli-
cies endorsing the 3Rs concept (Reduction, Renement, and
Replacement of animal testing). Special challenges for the ap-
plication of metabolomics in vitro can be summarized as 1) dif-
ferent requirements of models, 2) quality criteria and quality
control, 3) application areas, 4) investigation strategies, 5) tech-
nical challenges of the analysis, and 6) extrapolation to the in
vivo situation.
Apart from the evident benets of reducing animal testing
and getting better insights into the molecular targets of xe-
nobiotics and their mode of action (MoA), the application of
1 Introduction
Metabolomics is the comprehensive analysis of hundreds of
metabolites in a biological sample; it provides detailed infor-
mation on the physiological status of a living organism, a cell,
or a subcellular compartment at any given moment. The ana-
lytes of interest are the small endogenous molecules, such as
carbohydrates, amino acids, nucleotides, phospholipids, ster-
oids, or fatty acids and their derivatives, which are produced
and/or transformed by cells as a result of cellular metabolism
(Lindon et al., 2004; Patti et al., 2012). Since these metabolites
directly reect the biochemical processes of the system under
investigation, their analysis offers the opportunity not only to
gain insight into the activity of biochemical pathways giving
a particular metabolite prole, but also into the alteration of
such pathways. As such, metabolomics can be used to study
human physiology not only under normal conditions but also
in pathological situations.
This opens up the possibility for its application in clinical
settings; as such an approach allows the monitoring of treat-
ment success at a very early stage. This is essential given the
rise of combinatory multi-drug treatment scenarios. In this
context, metabolomics has been expanding in scope from
a basic research approach to an applied science, not only in
medicine but also in the elds of biotechnology and toxicol-
ogy (Bouhifd et al., 2013; Jungnickel and Luch, 2012; Jung-
nickel et al., 2012; Llorach et al., 2012; Nguyen et al., 2012;
van Ravenzwaay et al., 2012; Zhang et al., 2012b). For in-
stance, in biotechnology, metabolomics offers the possibility
of assessing the relationship of a genetic modication(s) to a
specic desired phenotype in an effort to determine the critical
biochemical pathways involved. For example, it allows identi-
cation of increased activation of certain metabolic pathways
for improved yield or production (Kim et al., 2012). In clinical
medicine and pharmacology, metabolomics is becoming an es-
tablished tool for the identication of pathologies through the
use of more relevant biomarkers (Patti et al., 2012; Rhee and
Gerszten, 2012).
Metabolomics is ideally positioned to address the challenges
of toxicology in the 21
st
century (Tox-21c). It represents a pow-
erful tool for collecting rich mechanistic information indicat-
ing not only the extent of a toxic insult but also its underlying
mechanisms.
From the currently available data it seems that metabolomics
information can be more easily related to classical toxicologi-
cal endpoints used in animal studies than, e.g., transcriptomics
data. One reason for this may be that the changes in the meta-
bolic prole are often “downstream” of those initial changes
that occur at the level of the genome, transcriptome, and pro-
teome (van Ravenzwaay et al., 2007). In addition, the relatively
small number of metabolites (i.e., hundreds/thousands) present
in a tissue or bio-uid, compared to the tens of thousands of
transcripts, or hundreds of thousands of proteins can be advan-
tageous when working to determine meaningful changes asso-
ciated with a toxic effect (Strauss et al., 2012; van Ravenzwaay
et al., 2007). For toxicological studies, the fact that extracel-
lular metabolites somehow reect the intracellular situation is
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with reference standards for metabolomics are essential for re-
search purposes and for the credibility of this approach (Hol-
mes et al., 2010). Therefore, the most important issues include
the denition and the availability of negative and positive test
controls. Notably, standards will allow a clear communication
of results and integration of metabolomics data with other -om-
ics approaches, e.g., proteomics and transcriptomics (Holmes
et al., 2010).
Moreover, the denition of the test and its respective accept-
ance criteria, sterility issues, assurance of the identity and the
condition of the cells (e.g., cell aging or spontaneous mutations),
measures for the ratio of cell types and differentiation stages,
adequate measures for viability assessment and availability of
in vitro bio-kinetics data, determination of free concentrations
of test components, information on the cellular concentrations
of compounds, and the contribution of metabolism in case of
metabolically active systems.
Apart from the biological variability, analytics and sample
processing also are potential sources of variability. Therefore,
the group agreed that there is an urgent need for harmoniza-
tion of metabolomics protocols, allowing integration of quality
criteria not only at the biological level, but also at the analytical
level. Moreover, it is also relevant to have a quality control for
measurement and data processing bias.
Metabolomics-specic quality criteria
All information about technical details, such as the extraction
methods and the storage methods should be compiled in stand-
ard operating procedures. Ideally, other responses of the model
will be evaluated in parallel with metabolomics data (e.g., cell
morphology and/or viability parameters). This allows the an-
choring of the metabolomics data set to physiologic or function-
al responses. In this respect, verication of the presence of es-
sential targets, signaling pathways, and response features needs
to be veried. In this way it can be ensured that the changes in
the metabolic prole are related to specic physiological con-
ditions. Another important but frequently neglected criterion is
whether the chosen in vitro model can be positioned reasonably
within a decision tree, i.e., how the information obtained from
the model can contribute to an overall evaluation or strategy.
Quantication and inter-experiment comparability
This is still an area of active development, and generally ap-
plicable solutions are not available. Differences exist between
the analysis of the metabolome in medium (cell supernatant)
and in cells/tissues. In the rst case, leakage from cells needs to
be controlled, but sample spiking with isotope-labeled reference
standards is easier than for the analysis of intracellular metabo-
lites. Sets of standards covering different pathways may be used
(Roede et al., 2012). Different normalization approaches have
been tried for measurements within cells.
These include the use of “house-keeping” metabolites or
combinations thereof, or the introduction of isotope labeled
standards. The availability of a “housekeeping” metabolite or
any other form of normalization standard is of utmost impor-
tance to correct for errors and variation in the cell number, in
cell harvesting, during the extraction procedure, during sample
metabolomics to in vitro systems allows the application of this
approach at a high throughput level. Due to the increasing inter-
est in in vitro systems combined with metabolomics, a working
group specically discussed the current uses, overall strengths,
and pitfalls of in vitro metabolomics. The major topics that were
discussed are:
Adaptation to the diversity of in vitro models;
– Guidance for experimental design, with special consideration
of quality criteria;
– Technical challenges in terms of sample processing;
– Guidance for data analysis and interpretation.
The subchapters below give a summary of the discussion.
2.1 Use of diverse in vitro models
As for other methods, standardization is essential for compara-
bility and reproducibility of results. The in vitro metabolomics
approach faces difculties similar to other in vitro approaches
with respect to the heterogeneity and special requirements of
the experimental models (Hartung, 2007). These concerns can
be classied according to their level of complexity and the han-
dling requirements as:
– Cell lines
– Primary cells and stem cell derivatives
– Co-cultures in 2D or 3D format
– Tissues in vitro or ex vivo
In addition, model organisms, such as zebrash (D. rerio) and
nematodes (C. elegans) often are used in an in vitro manner, but
with the advantage of a complete living organism also endowed
with metabolic capacity. The impact of handling and special re-
quirements of the in vitro systems should be subjected to routine
evaluation. For instance, changes in central metabolism also can
be due to changes in the cell culture conditions rather than to the
chemical insult. Therefore, profound knowledge of the system
used and performance of controls for all potential inuence fac-
tors of the in vitro model are crucial (see also Good Cell Culture
Practice guidance, Coecke et al., 2005).
2.2 Quality criteria for in vitro systems
for metabolomics
The relevance and reproducibility of in vitro data depends
strongly upon the quality of the test system and its analytical
endpoint (Hartung, 2009a; Kadereit et al., 2012; Leist et al.,
2010, 2012a). Therefore, establishment of a complete set of
quality criteria and guidelines is crucial for the acceptance and
further use of in vitro metabolomics approaches. Such criteria
can be classied into three categories, (1) general requirements
(as also dened for Good Cell Culture Practice) (Coecke et al.,
2005; Hartung et al., 2002), (2) criteria specic for the use of
model systems for the purpose of metabolomics analysis, and
(3) criteria referring to the quantication of in vitro metabo-
lomics data.
General requirements
For metabolomics, i.e., an approach that aims at a simultaneous
determination of hundreds of metabolites, quality criteria are
crucial in order 1) to avoid artifacts and 2) to facilitate the com-
parison of generated data. Establishment of reference systems
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Prediction models
A major goal of the eld is the use of metabolomics informa-
tion in comparison to known standards to predict actions of un-
known chemicals in biological systems (Rusyn et al., 2012). To
develop such models, metabolite patterns related to well known
training compounds would be used to develop classication
schemes. These would then be applied to the metabolome pat-
terns triggered by unknown compounds in order to predict their
toxicological hazard (Fig. 1).
Lead prioritization in screening: A slightly less ambitious ap-
plication would be relative ranking, i.e., metabolomics would
provide relative information within a group of compounds to
rank them, e.g., according to toxicity and to facilitate decisions
on further development.
Mode of Action: This approach can help in understanding
the effect of chemicals on the complexity of the metabolic net-
works (Roede et al., 2012). Furthermore, this is expected to
lead to the discovery of the metabolic pathways that are per-
processing, in detection sensitivity, and in many other steps re-
lated to the overall analysis of the metabolome. Further features
required for a robust and reproducible quantication are a stable
baseline metabolite pattern and a reproducible response to posi-
tive controls.
2.3 Potential applications of metabolomics
approaches in vitro
Metabolomics is a versatile approach with multiple potential
applications in drug discovery and safety proling. The in vi-
vo metabolomics approach could be proven advantageous al-
ready, not only in clinical applications but also in toxicology.
Examples are: for identication of toxic modes of action (van
Ravenzwaay et al., 2012) or toxicological screenings provid-
ing insights into the potential toxic effects of chemicals under
development, which lead to accelerating the decision-making
processes (Kolle et al., 2012). Potential uses of the in vitro me-
tabolomics approach include the following:
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 prole will yield “toxicity patterns,” i.e.,
metabolite patterns that are correlated to dened 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.
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Idiosyncratic toxicity in vitro: Metabolomics approaches also
may open the door to human-relevant research on idiosyncratic
toxicity; such toxicities occur when a convergence of risk fac-
tors (disease, age, gender, co-medications, nutritional status,
physiological status, microbiome, and genetic predispositions)
disturbs the otherwise stable homeostasis and allows adverse
chemical effects at otherwise innocuous concentrations (Clay-
ton et al., 2006; Coen et al., 2009). Metabolomics studies allow
insight into the cellular homeostasis under different experimen-
tal conditions, and the data may explain the conditions under
which unexpected toxicities would occur.
Flux-analysis (Fluxomics): Standard metabolomics methods
measure concentrations of metabolites “frozen” at a certain
time point but not the speed of their turnover. Knowledge
of the complete set of metabolites is not enough to predict the
phenotype, especially for higher cells in which the distinct met-
abolic processes involved in their production and degradation
are nely regulated and interconnected. In these cases, quanti-
tative knowledge of intracellular uxes is required for a com-
prehensive characterization of metabolic networks and their
functional operation (Cascante and Marin, 2008). Under given
homeostatic conditions, for instance, a glycolysis metabolite
may show similar concentrations but, e.g., low turnover when
mitochondria are functioning and high turnover when mito-
chondria are unable to meet energy requirements. For instance,
depletion of ATP in livers due to high fructose exposure does
not yield information on whether glycolysis or mitochondria
are affected, while uxomics would deliver clear results (Latta
et al., 2000). Metabolic ux analysis has been used, e.g., to
study drug effects on the metabolome of HepG2 cells (Niklas
et al., 2009). By using isotope-labeled substrates of metabolism
in combination with time series experiments, information on
the turnover uxes in different pathways can be obtained.
The use of isotope labeling in metabolomics and uxomics has
been recently reviewed (Klein and Heinzle, 2012). This type
of metabolomics data covers an important aspect relevant to
chemical hazards, which is necessary for a systems biology
type of modeling but does not yet represent a routine approach
in toxicology (Hartung et al., 2012).
Sensitivity and speed: Compared to the sequential measure-
ment of individual endpoints, large increases in the speed of
data acquisition are to be expected. The simultaneous availabil-
ity of data on a large number of metabolites also is likely to
increase the sensitivity.
Biomarker discovery: The qualitative and quantitative
analysis of the metabolome in vitro opens the opportunity for
discovering biomarkers, which could be used for diagnostic
purposes; other metabolites may be useful as biomarkers for
the efcacy of drugs, and/or they may help to quantify the
progression of human relevant diseases or the extent of organ
damage.
Contribution of cell-cell interactions: a still unsolved large
challenge of in vitro toxicology is the understanding of commu-
nication between cells that contributes to adverse effects. This is
particularly important for interactions involving inammatory
and non-parenchymal cells. For instance, interaction between
neurons and glial cells (Falsig et al., 2004; Henn et al., 2009,
turbed by the chemical. Such information would help to pin-
point potential targets of the chemical and drugs and to predict
their mode-of-action, as demonstrated by recent studies (Stri-
gun et al., 2011a, 2012). Recently, simple metabolome analy-
sis was shown to be useful to classify drugs into MoA classes
(Strigun et al., 2011b).
Pathways of Toxicity (PoT): In an extension of the mode-of-
action approach, metabolomics can be used for the mapping
of toxicity-related pathways (Hartung and McBride, 2011;
Hartung et al., 2012; Shintu et al., 2012). The essential chal-
lenge is the identication of the critical pathways that lead to
toxicity, as opposed to other chemical-induced changes that
are only adaptations, counter-regulations, or epiphenomena
(Andersen et al., 2011; Bhattacharya et al., 2011; Boekelheide
and Andersen, 2010; Hartung et al., 2012). The pathways of
toxicity (PoT) may be specic for the cell types and model
systems, but some may allow general predictions from in vitro
to in vivo.
Organ specicity of toxicity: One of the great expectations is
that information from different in vitro models will allow predic-
tions of potential target organs of toxicity in vivo (Zidek et al.,
2007). This would require the identication of responses and of
the activation of PoT in a concentration-dependent manner and
in different systems predictive of processes in various organs.
Moreover, background information on the relevant metabolite
changes or activation of PoT in vivo would be required for vari-
ous target organs, as well as for various modes of toxicity that
may affect them (e.g., classifying hepatotoxicants as producing
cholestasis, hyperlipidosis, or necrosis in the liver).
Points of departure (PoD): The PoD is a concentration of a
test chemical that results in a signicant change in the in vitro
system, which is considered predictive for the in vivo situation.
The PoD is used for in vitro-in vivo-extrapolation calculation
(IVIVE) to determine the relevant in vivo dose or exposure.
Upon exposure of a model system to a chemical, multiple
changes take place. The shift in metabolite patterns will de-
pend on the test concentration and exposure time. It will be
important to identify for each model the type of change (i.e., the
combination of metabolites and the extent of their change) that
predicts toxicity. The conditions leading to these changes (i.e.,
the free concentration of the chemical or its key metabolite)
will be used as points of departure (PoD) to extrapolate ndings
mathematically to in vivo doses.
Xenobiotics-metabolism: Not only may endogenous metabo-
lites be detected, but other questions may be addressed as well:
What are the concentrations of different xenobiotics’ metabo-
lites, and how do they change over time?
Intraspecies variability: Human cells with different genetic
backgrounds may vary in their responses to toxicants (Ingel-
man-Sundberg, 2001). Metabolomics may be useful to identify
such differences. Information gained therefrom will be useful to
model subpopulations with different susceptibilities.
Species differences: In the case of distinct reactions of cells
from different species, metabolomics may help identify the rea-
sons and consequences of inter-species variability. Such knowl-
edge would improve species extrapolations, e.g., from rodents
to man.
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endogenous metabolome) or extracellularly (the cell secre-
tome). For instance, leakage of metabolites from cells can be
a problem. Large differences exist between the analysis of the
intracellular and extracellular metabolites. For intracellular me-
tabolites, leakage from cells or cell debris can be controlled by
gentle centrifugation of the samples to avoid contamination of
the supernatants. In the second case, which is actually the most
challenging, the processing must be carried out with high speed
in order to avoid changes of the intracellular metabolite con-
centrations but also contamination with exogenous metabolites
from cell culture media. Therefore, washing steps have to be
included, which can delay the processing of the samples. Dif-
ferent washing procedures could already have different effects
on cellular metabolites. The choice of sampling time points
poses particular challenges, and the cellular reaction to toxi-
cants changes over time by the activation of counter-regulatory
pathways.
Speed of sample processing prior to quenching also is a
crucial step, since metabolite concentrations can drastical-
ly change in a very short period of time. Therefore, the fast
“freezing” of the biochemical processes by sample quenching
is of high relevance for obtaining reliable data. In addition, de-
pending on the in vitro system, more technical steps could be
included, affecting the quality and consistency of the analyzed
metabolites.
Sample throughput: The strength of in vitro systems is the
control and easy variation of parameters. To fully prot from
these features, a large number of samples need to be measured.
Increase in sample throughput (with respect to sample prepara-
tion, measurement, and analysis) is a major factor determining
future widespread use of in vitro metabolomics.
Sample size: As for other -omics approaches, undersampling
(too low sample number) leads to overtting. Simply put, statis-
tical tables contain too many columns (endpoints) compared to
rows (sample replicates). The choice of the right sample size is
essential for a conclusion regarding whether a marker behaves
differently from the controls or not. The sample size should
be determined from preliminary experiments in which differ-
ent sample replicates are set and the internal variability among
samples is used to estimate the number of replicates to achieve
statistically signicant results. Statistical rules suggest that the
sample size needs to be matched to the number of metabolites
and to the required statistical power. For the estimation of the
right sample size for metabolomics approaches, some in silico
tools can be recruited, e.g., the programs nemaed samr, ssize,
and ssize.fdr. Notably, the variables for these estimation tools
have to be well chosen, e.g., number of measured metabolites
and the relative abundance of the metabolite concentrations. In
practice, it often will not be possible to adhere completely to
the stringent rules of statistics. Compromises need to be found
that still allow technical feasibility.
Reproducibility: In order to identify the robustness and re-
producibility of the system it is essential to understand the
baseline metabolite pattern of the cell system under the stand-
ard conditions of culturing and prior to a toxic insult. Only
by doing so can reliable parameters be dened for comparison
2011; Kremer et al., 2010; Schildknecht et al., 2012; Hirt et al.,
2000) involve multiple metabolite exchanges. Communication
in co-cultures may be bidirectional, and the overall response
would not be understood from the reaction of single cells alone
(Gantner et al., 1996). Metabolomics approaches may help elu-
cidate chemical communication between cells in the context of
adverse reactions.
3Rs aspects (Reduction, Renement and Replacement of ani-
mal testing): The combination of metabolomics with good in
vitro models has great potential for the eld of 3Rs (Hartung
and Leist, 2008; Leist et al., 2008a,b). In vivo metabolomics al-
ready substantially contributes to the 3Rs principle by reducing
animal testing through renement (Kolle et al., 2012). In vitro
predictions may lead directly to the replacement of animals, as
well as to the improvement of the chemical risk assessment of
pharmaceuticals and environmental toxicants. The rich infor-
mation also would help to optimize in vivo testing. For instance,
relevant endpoints could be chosen and the study design opti-
mized. This would lead to a reduction in the use of animals. The
use of early biomarkers also would shorten studies and thus lead
to a renement.
2.4 Investigation strategy
Two different strategies may be followed for the use of metabo-
lomics in safety evaluations. The more long-term perspective
is that a large set of rich data, comprising metabolomics and
transcriptomics information, would be sufcient on its own to
predict potential hazard. One requirement would be broad back-
ground knowledge of systems toxicology and the human tox-
ome. This would be realistic in the more distant future.
In the immediate future the strategy will not be based on vast
biological background data but rather on pattern comparison.
Reference compounds will be tested in an in vitro model battery.
The metabolome analysis of unknown compounds then will be
aligned with the known pattern of the reference material. Here
also, data from other -omics approaches can be implemented
into the alignment pattern (Fig. 1).
2.5 Technical challenges
To promote the widespread application of in vitro metabolomics,
several technical challenges need to be solved.
Quality control: Metabolomics is particularly challenging
with respect to quality control, as the data set obtained is the
result of a multi-step process. Each of these steps can create
potential artifacts. It also should be noted that the in silico
handling of large amounts of data requires a dened quality
assured workow. Also, sample preparation steps are critical,
as the desired metabolites are usually embedded in biological
matrices. Thus, metabolites have to be extracted without com-
promising their structure and concentration. Some metabolic
processes are so fast that the metabolite pattern may change
during sample preparation. Every in vitro model comes with its
own particular issues concerning quality control. Thus, guide-
lines and SOP both require continuous adaptation.
Sampling: Different challenges apply depending on whether
samples are collected intracellularly (cell lysates reecting the
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ly constant, but the connections originating from them may
change (altered metabolic ux).
2.6 In vitro-in vivo extrapolation (IVIVE)
The ultimate challenge of the in vitro metabolomics approach is
the extrapolation of the in vitro data to obtain relevant informa-
tion for the in vivo situation. This will require further advances
in the eld of physiology-based pharmacokinetic modeling
(Blaauboer et al., 2012; Leist et al., 2012b; Louisse et al., 2012;
Prot and Leclerc, 2012).
More immediate goals will be to provide qualitative informa-
tion on, for instance, what a potential target organ may be or
whether developmental toxicity is to be expected (Kleinstreuer
et al., 2011). The overall vision is that in vitro metabolomics
facilitates qualitative in vivo predictions. For instance, key me-
tabolites (or groups thereof) may be selected that predict in vivo
toxicity (Yoon et al., 2012). Their concentrations would be used
to dene benchmark concentrations to be used as the point-of-
departure for IVIVE. With the denition of the points-of-de-
parture and the employment of PBPK-modeling (ADME), the
NOAEL for the in vivo situation can be calculated.
3 Metabolomics and systems toxicology
approaches
3.1 Identification of PoT by using metabolomics
The challenge of information-rich technologies (high-through-
put and high-content, for overview see (van Vliet, 2011)) is
to make sense of extremely large datasets. This requires the
integration of data, likely from different technologies and
test systems (Leist et al., 2012b). Systems biology proposes
to make use of our increasing understanding of the biological
systems, i.e., how the different endpoints are physiologically
interconnected. In the end, it attempts the modeling of the dy-
namics of the biological system (especially on a biochemical
and molecular biology level) and of its response to perturba-
tions such as disease. For toxicology, an analogous approach,
i.e., a “systems toxicology” could be envisaged (Hartung et al.,
2012) where the impact of an agent on the biological system is
modeled. This concept represents an extension of moving from
black-box models of effects (from apical endpoints), where ef-
fects are recorded without understanding the underlying mech-
anisms, to an approach based on knowledge of the MoA. The
2007 report of the US National Research Council has called for
exactly this (NRC, 2007). The buzzword “Toxicology for the
21
st
Century” (Tox-21c) or similarly “Toxicity Testing for the
21
st
Century,” has been taken up to describe the variety of ac-
tivities implementing the report. Among them an NIH funded
initiative to map the human toxome by systems toxicology is
attempting to create a process for pathway-of-toxicity annota-
tion and sharing (http://www.humantoxome.com). To enable a
systems toxicology approach and to allow quantitative mode-
ling, we have to move beyond a rather qualitative MoA knowl-
edge, and rather describe molecularly dened pathways. The
abbreviation PoT for a pathway-of-toxicity has been coined
when applying a toxic compound and changing the metabo-
lomics prole.
Sensitivity: Cellular metabolites can be present at concentra-
tions spanning at least 6 orders of magnitude, but they cannot
be amplied like DNA, and they are chemically much more
diverse than proteins. This poses a particular analytical chal-
lenge for metabolomics, and the sensitivity of the method to
cover low abundance metabolites needs to be increased con-
siderably.
Normalization: Normalization allows reduction of the poten-
tial variability among replicates or experimental samples, e.g.,
due to viability changes of cells. Normalization will correct for
slight changes in cell harvesting, during the extraction proce-
dure, during sample processing, or in detection sensitivity. Nor-
malization parameters are essential for analysis and comparison
of in vitro metabolomics data. Different options in this regard
include the use of external standards, such as protein concen-
trations, quantication of cell number, as well as the use of
internal references, such as “housekeeping” metabolites (Ruiz-
Aracama et al., 2011). Instead of external standards or internal
“housekeeping” metabolites, intra-sample normalization based
on overall metabolite quantity could be performed. For this
procedure, exclusion of contaminants and artifacts is crucial.
For instance, plasticizers may be present in varying amounts
in the “metabolite” spectrum. Such contaminants would spoil
normalization to a sum of total metabolites.
Metabolome coverage: Metabolites are a chemically ex-
tremely diverse group of compounds. They range from highly
charged phosphoesters or organic cations to extremely hydro-
phobic lipid constituents. Moreover, many metabolites exist as
isomers or epimers that need to be separated. At present, a com-
bination of analytical approaches is used to target metabolites
with different physiochemical properties. This requires differ-
ent analytical technologies, e.g., NMR, GC-MS, UPLC-ESI-
MS/MS, TLC/GC-FID, DFI MS/MS. A big technical challenge
is to optimize the technology in a way to allow analysis of all
metabolites with only few methods.
Database for metabolites: A metabolomics-specic database
is still lacking, but some technology providers, such as Agilent
and Bruker, have started providing rst solutions. These need to
meet several challenges, i.e. (a) identication of metabolites: It
is still common that the analysis yields a large number of me-
tabolite peaks that cannot be unequivocally assigned to a chemi-
cal structure; (b) assignment of identied metabolites to known
metabolic pathways or PoT; (c) combination of metabolite in-
formation with other data, e.g., transcriptomics.
Pathway analysis: The traditional perception of a metabolic
pathway is a sequence of steps leading from an educt to a prod-
uct. The analysis of metabolic pathways aims to determine the
concentration and the fate of the relevant molecules at every
stage of the procedure. The challenge arises from the fact that
pathways are not linear, one-way roads but rather should be
seen as parts of an intricate metabolic network. In this sense,
each analyzed molecule is a node of such a network. In cells
exposed to toxicants such nodes may change (altered metabo-
lite concentration). Alternatively, nodes may remain relative-
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on pathways. The complete measurement of many endpoints
represents, rather, a challenge to this preconception (not to say
prejudice) opening up opportunities for new PoT identication
or for balancing the relative importance of different PoT.
Workshop participants felt that current metabolomics tech-
nologies are largely t for the purpose of Tox-21c, while there
is a tremendous need to (1) dene standard procedures for qual-
ity control and data reporting, (2) annotation of metabolites and
pathways, and (3) quantication of metabolites required for bio-
logical modeling.
3.2 Technological challenges
A number of technological challenges were identied:
Statistical pathway integration: Available methods appear
straightforward but current pathway databases may not reect
reality.
Need for ux assessment: Routine metabolomics does not di-
rectly report on metabolic uxes, which are necessary for mod-
eling as discussed above.
Mathematical modeling of cell (patho)physiology: Obviously,
this is the holy grail of systems biology, which is only emerg-
ing as a discipline. It is still difcult to obtain the required data
(forerunners are, for example, the Metabolights repository (EBI
at http://www.ebi.ac.uk/metabolights) or DIXA (at http://www.
dixa-fp7.eu) for toxicogenomics and metabolomics data). It rep-
resents a major challenge at a computational level, for which
bioinformatics resources need to evolve. The good news is that
toxicology is not alone: The entire eld of biomedicine is em-
bracing systems approaches, and each discipline benets from
cross-fertilization.
Validation: We have to distinguish here between (a) com-
pound screening (typically based on signatures), which should
allow an early-on regulatory use of metabolomics, as discussed
in the previous chapters and (b) validating causal pathways for
the purpose of Tox-21c. The validation of the former screening
approach would be based on gathering data on lots of compounds
that are historically well understood and looking for similarity
of signatures/predictivity/anecdotal evidence of mechanistic
relevance. However, we will not necessarily understand how
changes in these biochemical pathways actually cause disease/
pathology. The mechanistic approach of Tox-21c, however, re-
quires interfering with critical points in pathways identied in
order to prove causality. This is difcult and laborious, but more
convincing than deduction from phenotypic changes. Modeling
strategies might bridge the gap, as they would allow virtual
experiments to check the plausibility of suggested PoT before
validation of a causal role is undertaken.
Translation of PoT ndings: Current work on PoT identica-
tion is focused on in vitro systems. Therefore, the relevance for
in vivo situations and correspondence of PoT will have to be
established. Currently, multiple species often are a prerequisite
for regulatory acceptance; the translation of PoT between spe-
cies needs to be established. A similarity of signatures argues for
predictivity and mechanistic relevance, but stability of signa-
tures under various experimental conditions and their relevance
to humans need to be established.
to differentiate PoT from MoA/toxicity pathways, which are
typically dened in a narrative way (Blaauboer et al., 2012;
Hartung and McBride, 2011). The opportunities lying in such a
systems toxicology approach were discussed intensively in the
consensus process to a roadmap for replacing systemic toxico-
logical animal testing (Basketter et al., 2012).
The established networks within an organism, which form the
basis for modeling in systems toxicology, are based on molecular
biology and biochemistry. Transcriptomics in all of its variants,
including the increasing use of deep sequencing technologies, is
the key approach for the molecular biology part, with a minor
additional contribution by proteomics studies. Metabolomics is
the core approach for the biochemistry part of this modeling. In
this sense the advent of metabolomics in toxicology represents
a “kick-start” into systems toxicology.
This can be initially viewed as a mostly academic exercise
aimed at the generation of new knowledge that is not aimed at a
specic regulatory purpose. However, society has large expec-
tations of toxicology: this science has the potential to identify
potential hazards of chemicals, and to provide improved safety
to the consumer (Hartung, 2009b; Leist et al., 2008a). This situ-
ation calls for the exploitation of new powerful technologies
such as metabolomics, and the goal of making regulatory use of
this approach should be kept in mind. Early-stage uses, before
denitive regulatory decisions are made on the basis of systems
toxicology information, could be the screening for high risk
compounds. This means that the right questions must be asked
early in the process, i.e., to focus testing on substances with a
higher likelihood of being identied as a problem.
The concept of PoT is key to the Tox-21c and systems toxi-
cology concept. Ironically, even after some years of discussion,
no denition of PoT has been agreed upon, though various such
initiatives are on the way. Two very different views prevail at
present, as discussed elsewhere (Hartung et al., 2012): (a) PoT
represent the cascade of events leading to the perturbation of a
system; (b) PoT represent the downstream signaling triggered
by perturbed physiology (Fig. 2). Intuitively, PoT is understood
as the initiating event (Fig. 3). However, neither metabolomics
nor transcriptomics is currently used to assess these early events,
instead we typically seek to assess the established new homeos-
tasis under stress (Hartung et al., 2012).
For Tox-21c and systems toxicology we need high resolu-
tion sampling to capture time-dependent changes (dynamics)
and the dose-response behavior of systems challenged with
toxicants. We need to monitor a wide range of phenotypes (haz-
ards). Metabolomics is especially well suited for this purpose as
it (1) is most closely related to phenotypic changes representing
functional endpoints, (2) assesses many such processes simul-
taneously, (3) has some protocols that are already broadly t-
for-purpose in terms of throughput, cost, sensitivity, coverage
of the metabolome, and reproducibility, (4) achieves the sam-
ple throughput required for detailed dynamic / dose response
studies, (5) can sometimes be non-invasive (especially NMR
and secretome technologies), and (6) is future-proof since an
untargeted approach can be employed. The latter means that we
do not necessarily remain inuenced by established knowledge
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grate metabolomics information with classical endpoints from
clinics, pathology, histology, etc. This poses some difculties
with regard to the time point of sampling. Classic endpoints
represent “late” events (Fig. 3). Sampling at time points when
these become evident may not be optimal for metabolomics
endpoints and the identication of activated PoT (these are
rather early events). Actually, we might need to control for the
occurrence of late, generally-degenerative events as confound-
ers for PoT identication, “taking them out of the equation”
by measuring, for example, at subtoxic concentrations or early,
before functional manifestations.
Taken together, metabolomics is core to the implementation
of the Tox-21c concept. It will be a workhorse for PoT iden-
tication and possibly later for the testing of PoT activation/
perturbation as it is multiplexing information on various PoT.
3.3 Identification versus application of PoT
We should keep in mind that the future use of PoT may be much
simpler than the methods used to nd them: Ultimately, iden-
tied PoT should allow the design of rapid and targeted as-
says, e.g., for high throughput platforms. Metabolomics will
not be the stand-alone approach to identify PoT. The combina-
tion with transcriptomics can help resolve relevant pathways,
as metabolites typically play a role in several pathways. Me-
tabolomics could be used to screen for candidate PoT, which
are targeted in subsequent assays. The question arises whether
metabolomics should be prioritized over other -omics for PoT
identication. Many of the aspects discussed above argue in
favor of this, including the low costs and high throughput once
the method is established and the relative ease of interpreting
metabolite changes. However, there is a strong need to inte-
Fig. 2: The role of in vitro metabolomics in identication,
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.
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Regardless of input from regulators, scientists using metabo-
lomics should strive towards: 1) a high quality study design, 2)
the development of appropriate standard operating procedures
(SOP), and 3) a high level of standardization. Once the method
used is well described, it is important to follow the developed
SOP strictly in order to minimize changes over time and to en-
sure comparability of results. Thus, overall attention to quality
management will be one of the essential features for laboratories
using metabolomics, and it will lead to increased condence in
the approach from risk assessors.
The participants of the working group felt that it would be
useful to obtain validation for metabolomics, but given the lack
of standardization, for the time being it may not be useful to try
to achieve complete validation of all elements of MoA identi-
cation with the metabolomics approach. The evidence-based
toxicology (EBT) initiative may provide alternative ways to
evaluate test performance (Stephens et al., 2013). For instance,
procedures have been suggested for high throughput screens
that may be used as models for the evaluation of the useful-
ness and robustness of metabolomics approaches (Judson et
al., 2013). Participants were of the opinion that, as regulators
The major step to convert metabolomics information into high
throughput test systems is the transitioning from a largely un-
targeted PoT identication to the targeted measurement of
the predictive metabolite changes that are characteristic for
known PoT.
4 The road to regulatory acceptance
of metabolomics approaches and data
The transition of “omics” technologies from basic to applied re-
search may yield approaches that drastically improve our ability
to conduct both predictive and diagnostic assessments of chemi-
cal toxicity and increase the efciency for development of new
drugs. In addition, information from omics technologies can
improve the regulatory assessment of the safety prole of new
compounds. However, regulators need to be convinced about
the validity of such data. Here, policy makers play an essential
role in speeding up the acceptance of these approaches for regu-
latory purposes. In order to achieve this, a major effort should
be undertaken to design validation strategies tailored for omics
technologies.
Today, a key challenge for the regulatory framework is to
adapt more exibly to rapidly-emerging technologies while at
the same time ensuring safety for humans and the environment.
However, the onus for the integration of these new data also
rests with the researchers, who have a responsibility to objec-
tively convey the strengths and weaknesses of the underlying
techniques and to work in conjunction with regulators for the
validation of these new methods. The main issues discussed at
the workshop are summarized below:
4.1 Is the current state of the art sufficient to
identify modes of action?
In all case studies presented at the workshop symposium, me-
tabolomics analysis was able to reliably identify toxicologi-
cal MoA. This was independent of the technological platform
(e.g., mass spectrometric or nuclear magnetic resonance spec-
troscopic identication of the metabolites). Therefore, the
question of whether metabolomics is suitable for MoA identi-
cation was afrmed.
There is a need to discuss with regulators on a case-by-case
basis as to whether the evidence obtained with metabolomics is
sufcient for identication of mode of action. One issue may be
that, currently, neither standardization of metabolomics meth-
ods nor guidance on how this could be achieved is available. As
the identication of MoA is not a mandatory regulatory (“stand-
ard”) requirement and also not a toxicological endpoint, it is,
at present, included in studies only on a voluntary basis. How-
ever, MoA identication is becoming more important in regula-
tory frameworks. For instance, the identication of endocrine
disruptors is one of the targets of both EU and US legislation.
Therefore, it is clear that knowing the MoA of a chemical will
result in a better interpretation of the toxicological data, and it is
likely to contribute positively to the entire risk assessment proc-
ess (van Ravenzwaay et al., 2012), for example, by addressing
species-specicity (Forgacs et al., 2012).
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., inammation). 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 unspecic late
changes determined by events in phase 2 and 3.
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of parameters being changed randomly. Bayesian statistics and
considerations of biological relevance based on existing back-
ground knowledge are required to dene meaningful endpoints.
Experience already has shown that some MoA can be detected
with a relatively low number of consistently altered parameters.
To better evaluate the consistency of an effect it would be desir-
able to investigate multiple time points, but this is not always
possible. If a series of parameters is found to be changed con-
sistently, and if these parameters are known to be associated
with a known pathway, then this set of changes would consti-
tute a metabolomics effect useful for consideration as a toxico-
logical endpoint. However, not all such metabolomics patterns
need to result in pathological conditions or adverse effects in
general. For instance, liver enzyme induction correlates with a
specic metabolome pattern but does not necessarily result in
functional or structural damage. With increased knowledge of
the signicance of metabolomics pathways, compensatory reac-
tions may also become visible. These may represent good (in
vitro) biomarkers of toxicity BoT (Blaauboer et al., 2012),
and they need to be taken into account for systems biology mod-
els of overall adverse outcomes (Hartung and McBride, 2011;
Hartung et al., 2012). In addition, metabolomics could add to
our understanding of the difference between compensatory re-
actions (adaptation) and those changes that are linked to cell
fate decisions. Indeed, metabolomics patterns might constitute
useful BoT (Blaauboer et al., 2012) that can help dening ap-
propriate NOAEL.
In summary, the identication of altered metabolic pathways
by metabolomics approaches does not necessarily mean that
they lead to an adverse outcome. Consequently, for the time be-
ing, metabolomics is not a stand-alone approach in toxicology;
it needs and can be matched with other toxicological data. An
interesting and fruitful approach is to correlate metabolomics ef-
fects (patterns of change) with adverse toxicological outcomes,
and to develop prediction models. Moreover, the relevance of
reversibility is not yet clear for metabolomics parameters, and
requires further studies.
In contrast to metabolomics data, the relevance of transcrip-
tomics ndings is often less clear, as these changes rarely
can be linked directly to phenotypic changes. From a statis-
tical point of view, transcriptomics is also more problematic
than metabolomics because there are many more parameters
relative to the sample size. But the combination of both tran-
scriptomics and metabolomics may signicantly enhance data
interpretation, especially when results from time series experi-
ments are available.
The participants of the working group recommended building
a data base using metabolomics data from regulatory studies in
order to validate its use for predicting adverse effects and/or
identifying MoA. Using samples from regulatory studies would
provide the necessary standards to correlate changes of metabo-
lomics data with classical toxicological parameters.
ECETOC’s guidance to derive a meaningful NOAEL rec-
ommended that (1) only specic patterns of change (in any
type of -omics study) should be used to conclude that a poten-
tially relevant biological effect is taking place, (2) as changes
in -omics pathways do not necessarily implicate that changes at
become more familiar with metabolomics, they are likely to
recognize the value and advantages of this approach. They
might then request its more frequent use (as has happened for
markers of kidney toxicity). One of the additional advantages
of metabolomics would be that it could put species compari-
sons (e.g., rat, mouse, human) on a more solid data basis. Me-
tabolomics also can contribute to the assessment of additive or
synergistic effects in co-exposure scenarios for both pharma-
ceuticals and environmental toxicants, which are more the rule
than the exception in daily life.
A future perspective might be deduced from knowledge about
other related (-omics) technologies. First examples for the use
of transcriptomics can be found in the development of new
pharmaceuticals, as well as in the safety evaluation of geneti-
cally modied crops (EC, 2011). The value (credibility) of MoA
determined by ngerprints or biomarkers can be conrmed if
the changes observed can be causally linked to toxicological
pathways. It should also be noted that -omics data could be ob-
tained routinely from regulatory studies, thus reducing the need
for additional experiments and providing a highly standardized
experimental setup of the biological study. To further enhance
the acceptance of metabolomics, a careful design of biological
experiments and high quality data are essential (e.g., appropri-
ate biological model, treatment regime, and sampling method).
In addition, proper controls, reference compounds, phenotypic
anchoring as well as appropriate validation procedures should
be used to ensure the quality of the generated data. Overall, me-
tabolomics appears to be ready to be incorporated into regulato-
ry testing as an additional robust source of relevant information,
in a toxicological weight of evidence approach.
4.2 Definition of metabolomics no adverse
effect level (NOAEL)
One of the critical elements of any regulatory study is the deter-
mination of a NOAEL. Sufcient guidance is available for expe-
rienced toxicologists to consistently determine a NOAEL based
on the classical parameters observed in standard toxicological
studies. However, for new technologies, such as metabolomics,
there is very little guidance available. The absence of guidance
criteria on how to determine a NOAEL in metabolomics is a
hurdle for introducing such studies within a regulatory context.
Therefore, dening criteria or providing guidance on metabo-
lomics NOAEL setting is of utmost importance. It would reduce
planning insecurity, especially for management decisions driv-
en by nancial factors and considerations of the time-to-market.
Currently, there is only general guidance on how to determine a
NOAEL in -omics studies from two ECETOC workshop reports
(ECETOC, 2008) – see note on “ECETOC guidance” at the end
of this section.
For scientists involved in metabolomics, or any other -omics
approach, it is clear that with hundreds or even thousands of
parameters measured, a single parameter cannot be used to de-
termine a NOAEL. Classical stochastic-based statistical meth-
ods would result in an overly high false discovery rate, and a
large degree of unreliability. Rened statistics can resolve this
problem to some extent. For example, the use of false discovery
rate corrections can be introduced to estimate the probability
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higher than the biological variability encountered in controlled
animal experiments. Therefore, much larger sample sizes and
enhanced sub-grouping of the population are needed. Human
variability also will be an important factor to be taken into ac-
count when translating metabolomics ndings from animal
studies to humans. Again, standardization will be very impor-
tant, but factors such as lifestyle, diet, disease state, etc. will
inevitably introduce elements of variability.
For in vitro studies, the situation often is more complex than
might be expected. As cell culture procedures can involve many
steps, variability introduced by the experimental setup may be
quite high. Initial experiences of several participants suggest
that variability associated with in vivo systems may be less than
that associated with in vitro systems.
The participants concluded that, due to lack of standardiza-
tion, currently no general guidance can be provided for evalua-
tion of the variability and that each individual researcher needs
to assess the variability of their system/procedure. Guidance for
adequate study design (e.g., how to determine adequate group
sizes) based on statistical considerations (e.g., strength of the
effect, prevalence, etc.) would be helpful. Such adaptations of
the study design are not easily possible for regulatory studies
that follow a strict protocol and build on historical background
data. With a more mid-term or long-term perspective, regula-
tory study designs from already existing protocols may need to
be changed to allow incorporation of modern endpoints such as
those from the metabolomics approach.
4.4 Validation of metabolomics for
toxicological regulatory purposes
In view of the many opportunities that metabolomics has to offer
for toxicology, particularly in terms of identifying MoA, it would
be desirable to make the metabolomics approach acceptable for
regulatory purposes (Fig. 4). This would require some sort of
validation process, as it is common for any other new method.
Assuming that the regulatory use of metabolomics for the time
being would concentrate on MoA identication it was recom-
mended by the workshop that each individual metabolome pat-
tern, indicating a particular MoA, should be validated. In order
to ensure that adverse outcome pathways would be addressed in
such an exercise, it would be necessary to clearly demonstrate
a good correlation with toxicological effects such as pathology.
Plausibility of the metabolomics changes and observed toxico-
logical effects should be one of the key elements for validation.
Before the start of any type of regulatory validation, it seemed
advisable rst to consult with regulators to explain the useful-
ness of metabolomics in a regulatory context and to ensure that,
following validation, such data would also be acceptable for
regulatory purposes (Fig. 4). This requires rst of all more com-
munication with regulators and the publication/communication
of success stories. It also will become more important to reach
out to those more involved in regulatory and risk assessment
aspects of toxicology. One example for regulatory acceptance
is the altered metabolomics biomarker pattern for the detection
of certain types of kidney damage (Dieterle et al., 2010; Fuchs
and Hewitt, 2011).
cellular, individual, or population levels will necessarily occur,
these pathways need to be correlated to observable histological
changes at the microscopic or macroscopic level, and (3) to use
changes in an -omics pattern for NOAEL purposes, it must be
assured that the pathway identied is related to an adverse effect
(ECETOC, 2010).
4.3 Dealing with inherent variability during the
use of metabolomics for toxicological purposes
There are two major sources, namely, technical and biological,
that contribute to the overall variability, and they need to be
handled separately. Technical variability results from the ana-
lytical process, starting with sample preparation and ranging
to the separation and detection of metabolites. Optimization of
procedures, the use of quality control samples, as well as com-
pliance with SOP and the exact monitoring and documentation
of observed deviations from SOP protocols, can help to reduce
this variability. Randomization of samples, and quality control
also are important measures to reduce variability. The second
source of variability is the one inherent to the biological sys-
tem used. Here also, standardization and the development of
SOP will help to reduce variability. Moreover, it has been not-
ed that each additional step in the experimental protocol will
introduce more variability. Therefore, reducing complexity is
essential. The risk of high variability is that it can mask sub-
tle but important effects and thus reduce the sensitivity of the
technology in obtaining biologically relevant data. As indicat-
ed above, variability is associated with the protocols and SOP
used, therefore variability needs to be determined and dened
for each individual “test system,” and only then is it possible
to decide how the test system can be used, i.e., which questions
can be addressed and which cannot in terms of signal to noise
ratios. At high noise levels (= high variability), only large
signals can be studied. For example, in a study using differ-
ent rat strains, the metabolome patterns and MoA induced by
2-methyl-4-chlorophenoxyacetic acid were still clearly visible,
despite the additional noise introduced by using different rat
strains. Weak changes, as those associated with anemia, were
less clear when using different rat strains (Strauss et al., 2009).
With increasing knowledge of how metabolites respond to dif-
ferent confounding factors such as reduced food consumption,
dietary changes, age, etc. such effects can be recognized and
compensated for. New statistical methods also allow the iden-
tication of outliers in -omics studies and thus help to reduce
variability in experimental groups in which, e.g., one animal
behaved quite differently from the rest. Therefore, statistical
models need to be developed that have the capability of “learn-
ing.” This means that recursive cycles of new data generation
and improved analysis will improve the already existing model
and make it more and more accurate.
The use of metabolomics in human samples is highly attrac-
tive because relevant body uids such as blood or urine can be
easily obtained. One example is collection of specimens in na-
tional bio-monitoring banks, such as the German environmental
specimen bank, where sample specimens are stored and can be
re-analyzed in the future. However, human variability is much
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outcome of the studies by regulatory agencies. Certainly the
latter would require that regulatory agencies be more famil-
iar with the metabolomics approach. Ideally, they would use it
themselves to better understand the strengths and weaknesses
of this approach and to build condence in the data obtained.
The working group believed that regulators would hardly ac-
cept metabolomics data unless they have gained their own ex-
perience with this technology.
The question was asked whether the regulatory framework
for metabolomics should be different for pharmaceutical ac-
tive ingredients, pesticides, or industrial chemicals. There was
agreement that the regulatory framework should be identical
for all sectors, as far as identication of the MoA in toxicology
is addressed. For some special modes of action, e.g., endocrine
disruption, there is a regulatory demand for identifying them
(Hecker and Hollert, 2011). Consequently, MoA identication
by means of metabolomics should be attractive, as this could be
done without additional/animal studies (Fig. 4) by using vari-
ous biomatrices (e.g., blood and urine) from regulatory studies
(van Ravenzwaay et al., 2010; Zhang et al., 2012a). For the
time being, the integration of metabolomics data into a regula-
tory decision-making framework may be limited to MoA iden-
tication for the three sectors. It was noted by some participants
that, in the absence of any toxicological ndings, which is not
uncommon for certain classes of industrial chemicals (evalu-
ated under REACH), there is no merit in MoA identication
by metabolomics (or any other approach). For pharmaceutical
compounds, there could be more (regulatory) options for the
use of metabolomics, particularly with respect to human rel-
evance and the comparison of metabolite responses in differ-
ent species. A further practical application of metabolomics in
a regulatory context is its use in diagnostics and food quality
evaluation (Shepherd et al., 2011).
An aspect of metabolomics that has not received much atten-
tion, but could be very attractive for both research and regula-
tory purposes, is the fact that metabolomics data include in-
formation on both normal constituents of the organisms tested
and on the test substance and its metabolites. Additionally,
by integrating imaging techniques in metabolomics studies,
the obtained results give insights into the actual distribution
of metabolite patterns and pharmaceuticals or environmental
toxicants and their metabolites within tissue or single cells.
Thus, metabolomics could simultaneously provide information
on the chemical exposure in the organisms/cells tested and on
the perturbation triggered thereby. Although this may require
adaptation of the technical equipment, tracking exposure and
analyzing internal dose-response relationships is highly attrac-
tive. Overall, this will add to the weight of evidence concerning
toxicological effects following chemical exposure.
For metabolomics information obtained from in vitro data, an
important aspect is the translation to the in vivo situation. This
has to be demonstrated before such data can be used in a regula-
tory framework. Concerning the combination of metabolomics
data with information obtained from other -omics technologies
(often referred to as systems biology), integration of transcrip-
tomics and metabolomics data has already been shown to be
The participants concluded that some guidance needs to be
provided on how validation of metabolomics methods could
be achieved. This guidance should be developed jointly by
multiple stakeholders together with regulators and risk assess-
ment institutions.
4.5 How can metabolomics data be integrated
into a regulatory framework?
Currently, metabolomics is used mainly for academic research
purposes, and only a few companies have started to use this
approach for the early identication of toxicological effects.
The use and application of metabolomics in toxicology would
advance more rapidly if it was also used for regulatory pur-
poses. This would require at least some type of validation pro-
tocols (see aforementioned questions) and acceptance of the
Fig. 4: Use of metabolomics in a regulatory context
Two different uses can be envisaged for metabolomics
approaches. The rst is the identication 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 specic 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 denition 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.
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The rapidly emerging use of metabolomics analysis as end-
point for in vitro test systems requires special attention. Often
such test systems allow a high throughput and a large degree of
control of the experimental conditions. However, the extrapola-
tion of in vitro data to the in vivo situation is still a substantial
scientic challenge. In many cases, information from multiple
systems may need to be combined to account for tissue effects
such as cell-cell interactions, compensatory regulations, and
communication between different organs. The interpretation of
data from individual systems often requires ample experience.
A second major issue is the susceptibility of in vitro systems to
experimental artifacts due to poor study design or small varia-
tions of the experimental conditions. Therefore, now more than
ever, the quality control of study design as well as all the condi-
tions crucial for the good performance of the system must be
taken very seriously.
Implementation of metabolomics in the regulatory context
will require an intense collaboration among the different stake-
holders, whether they belong to academia, industry, or regula-
tory bodies. It will be crucial to jointly investigate and dene
the relevance of the changes observed. If this is achieved, then
the innovative methodology of metabolomics can be rapidly in-
tegrated into the regulatory process to provide more complete
information on chemical effects on the physiological/cellular
levels, information about the spatial distribution not only of the
toxicants but also of specic marker metabolites within whole
tissues and single cells, as well as on the safety of humans and
the environment.
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Correspondence to
Mardas Daneshian, PhD
Center for Alternatives to Animal Testing – Europe
University of Konstanz
Universitätsstr. 10
POB 600
78457 Konstanz, Germany
e-mail: mardas.daneshian@uni-konstanz.de
Thomas Hartung, MD PhD
Center for Alternatives to Animal Testing
Johns Hopkins Bloomberg School of Public Health
615 North Wolfe Street
W7032, Baltimore, MD 21205, USA
e-mail: thartung@jhsph.edu
Marcel Leist, PhD
Center for Alternatives to Animal Testing – Europe
University of Konstanz
Universitätsstr. 10
POB 657
78457 Konstanz, Germany
e-mail: marcel.leist@uni-konstanz.de
Tzutzuy Ramirez, PhD
BASF – The Chemical Company
BASF SE, GV/TB – Z570
67056 Ludwigshafen, Germany
e-mail: tzutzuy.ramirez-hernandez@basf.com
Bennard van Ravenzwaay, PhD
BASF – The Chemical Company
BASF SE, GV/T – Z470
67056 Ludwigshafen, Germany
e-mail: bennard.ravenzwaay@basf.com
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Acknowledgements
Work by Thomas Hartung and Helena Hogberg on metabolomics
is supported by the NIH transformative research grant “Mapping
the Human Toxome by Systems Toxicology” (RO1ES020750)
and the FDA grant “DNTox-21c identication of pathways of
developmental neurotoxicity for high throughput testing by me-
tabolomics” (U01FD004230). Work by Mardas Daneshian and
Marcel Leist was supported by the State of Baden-Württemberg
and the Doerenkamp-Zbinden Foundation.
The views expressed in this article are those of the author(s)
and do not necessarily represent the views or policies of the U.S.
Environmental Protection Agency.
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