The thought behind this principle is that a chemical, when comes into contact with
the organism at a sufficiently high dose, triggers a molecular initiating event (MIE),
i.e., receptor binding, which then develops via several key events into a pathology
considered as an adverse outcome. This framework is explored on the basis of
investigating the initial interaction between a chemical and a biomolecule or
biosystem that can be causally linked to an outcome via a pathway. AOPs were
first outlined for environmental risk assessment by Ankley in 2010 (Fig. 8) and can
be defined as a sequence of events from the exposure of an individual to a chemical
through to an understanding of the adverse effect at the population level. AOPs span
multiple levels of biological organization but always contain an initial molecular
interaction between a compound and the organism that triggers subsequent effects at
higher levels of biological organization.
The predictive principle of this method is based on the fact that the chemistry of the
molecule allows it to have specific MIEs. Therefore, once the chemical has reacted
with the biomolecule of the exposed organism in the MIE in a compound-specific
way, the development of the pathology throughout the different levels of organization
is independent of the chemical, sharing different MIE common disease outcomes. A
single MIE could be the cause of multiple toxicological endpoints, or a single
endpoint may be the result of several MIEs. Thus ideally, knowing how the chemical
interacts with the organism at the first time allows to predict the pathology that the
organism is likely to develop. Because of the chemical-specific MIE(s), links between
chemical structure or chemical property and MIE will undoubtedly be stronger than
links to toxicological endpoints, due to a smaller “jump” between chemical exposure
and MIE. With the help of structure-activity relationship (SAR) and quantitative
structure-activity relationship (QSAR) models, the prediction of effects of a certain
chemical shall be vastly simplified, allowing the grouping of compounds on the basis
of an understanding of their MIEs, predicting their expected disease outcome, and
thus reducing experimental effect evaluation. Thus, by understanding the individual
key events, one can better understand what the health outcome will be. The identification of the MIE of a chemical has greatly been aided by the development of
sensitive, high-throughput molecular techniques such as transcriptomics, proteomics,
and metabolomics in environmental toxicology which has helped to understand
biological processes in exposed organisms, shedding a light on the mechanistic
mode of action and adverse outcome pathways. By combining knowledge about a
certain MIE of a compound and dose-response data and an understanding of adverse
outcomes downstream in the AOP, quantitative predictions for new compounds
could be made. Thus, mechanistic insights feed into a combination of approaches
that can help to reduce reliance on animal methods [99].
From a practical point of view, AOPwiki (https://aopwiki.org/) is an AOP
knowledge database that aims to serve as the central repository for all AOPs
developed as part of the OECD AOP Development Effort by the extended Advisory
Group on Molecular Screening and Toxicogenomics. Exploring available information for DF and IB reveals the existence of a common AOP, which is renal failure
Ibuprofen and Diclofenac: Effects on Freshwater and Marine Aquatic Organisms –. . .
181
the organism at a sufficiently high dose, triggers a molecular initiating event (MIE),
i.e., receptor binding, which then develops via several key events into a pathology
considered as an adverse outcome. This framework is explored on the basis of
investigating the initial interaction between a chemical and a biomolecule or
biosystem that can be causally linked to an outcome via a pathway. AOPs were
first outlined for environmental risk assessment by Ankley in 2010 (Fig. 8) and can
be defined as a sequence of events from the exposure of an individual to a chemical
through to an understanding of the adverse effect at the population level. AOPs span
multiple levels of biological organization but always contain an initial molecular
interaction between a compound and the organism that triggers subsequent effects at
higher levels of biological organization.
The predictive principle of this method is based on the fact that the chemistry of the
molecule allows it to have specific MIEs. Therefore, once the chemical has reacted
with the biomolecule of the exposed organism in the MIE in a compound-specific
way, the development of the pathology throughout the different levels of organization
is independent of the chemical, sharing different MIE common disease outcomes. A
single MIE could be the cause of multiple toxicological endpoints, or a single
endpoint may be the result of several MIEs. Thus ideally, knowing how the chemical
interacts with the organism at the first time allows to predict the pathology that the
organism is likely to develop. Because of the chemical-specific MIE(s), links between
chemical structure or chemical property and MIE will undoubtedly be stronger than
links to toxicological endpoints, due to a smaller “jump” between chemical exposure
and MIE. With the help of structure-activity relationship (SAR) and quantitative
structure-activity relationship (QSAR) models, the prediction of effects of a certain
chemical shall be vastly simplified, allowing the grouping of compounds on the basis
of an understanding of their MIEs, predicting their expected disease outcome, and
thus reducing experimental effect evaluation. Thus, by understanding the individual
key events, one can better understand what the health outcome will be. The identification of the MIE of a chemical has greatly been aided by the development of
sensitive, high-throughput molecular techniques such as transcriptomics, proteomics,
and metabolomics in environmental toxicology which has helped to understand
biological processes in exposed organisms, shedding a light on the mechanistic
mode of action and adverse outcome pathways. By combining knowledge about a
certain MIE of a compound and dose-response data and an understanding of adverse
outcomes downstream in the AOP, quantitative predictions for new compounds
could be made. Thus, mechanistic insights feed into a combination of approaches
that can help to reduce reliance on animal methods [99].
From a practical point of view, AOPwiki (https://aopwiki.org/) is an AOP
knowledge database that aims to serve as the central repository for all AOPs
developed as part of the OECD AOP Development Effort by the extended Advisory
Group on Molecular Screening and Toxicogenomics. Exploring available information for DF and IB reveals the existence of a common AOP, which is renal failure
Ibuprofen and Diclofenac: Effects on Freshwater and Marine Aquatic Organisms –. . .
181