6 Mode-of-Action-Guided, Molecular …
103
6.1.2 Mechanism-Based Toxicity Prediction: MIE
and “Critical Target” Concept
Allen et al. recently defined an AOP as a conceptual framework, presented as a logical sequence of events or processes within biological systems, which can be used
to understand adverse effects and refine current risk assessment practices in ecotoxicology, and an MIE as the initial interaction between a chemical and a biomolecule
that can be causally linked to an outcome via a pathway [27]. According to the AOP
concept, any phenotypic endpoint of toxicity, i.e., an adverse outcome resulting from
a series of biological processes can be eventually linked to a unique MIE [18, 28].
It is now widely accepted that QSAR-based predictive toxicology has more success
when the mechanism of toxicity end point is well understood or can be linked to
a well-defined molecular target [29]. In a recent perspective review on the future
research of predictive toxicology, Daston et al. [30] recommended to focus on the
identification of “critical biological targets” relevant for toxicity and to test their
suitability for being used as anchors for predicting toxicity. Chemicals can interfere with normal biological processes or pathways at the molecular level through
a multitude of different mechanisms that vary from non-selective binding (to intracellular proteins) to selective agonism/antagonism of a particular nuclear or another
receptor [30]. Decades of conventional animal toxicity testing have accumulated a
myriad of information on toxicological mechanisms that may be further explored
for use in in vitro and computational modeling-based predictive toxicology. For
instance, Tox21 researchers have developed high throughput, cell-based or cell-free
in vitro assays that evaluate critical cellular targets or processes involved in toxicity
response [19]. They have also published a prioritized set of 2750 targeted sentinel
genes (referred to as Human S1500+ Gene Set Ver2) whose transcriptional changes
are responsive to exposures to a wide variety of toxic agents [31].
6.1.3 Limitations of Current In Vitro and In Silico
Approaches
While significant progress has been made in developing mechanism-based in vitro
assays and testing platforms (including high-content toxicogenomics platforms), it is
recognized that quantitative in vitro to in vivo extrapolation still faces many technical
barriers, such as the choice of appropriate cell lines and the lack of metabolism [8, 19].
Although in silico approaches are rapid and inexpensive compared to experimental
approaches, the growth of in silico-based predictive toxicology tools is unsatisfactory.
Conventional QSAR models often suffer from low prediction accuracy [32] because
they do not consider the structure and flexibility of target biomacromolecules, especially when applied toward the more elusive goal of predicting potential toxicity
outcomes for in vitro cell cultures or in vivo animal test systems [29]. In these systems, the toxicity end point (e.g., cytotoxicity, mutagenicity, developmental toxicity
103
6.1.2 Mechanism-Based Toxicity Prediction: MIE
and “Critical Target” Concept
Allen et al. recently defined an AOP as a conceptual framework, presented as a logical sequence of events or processes within biological systems, which can be used
to understand adverse effects and refine current risk assessment practices in ecotoxicology, and an MIE as the initial interaction between a chemical and a biomolecule
that can be causally linked to an outcome via a pathway [27]. According to the AOP
concept, any phenotypic endpoint of toxicity, i.e., an adverse outcome resulting from
a series of biological processes can be eventually linked to a unique MIE [18, 28].
It is now widely accepted that QSAR-based predictive toxicology has more success
when the mechanism of toxicity end point is well understood or can be linked to
a well-defined molecular target [29]. In a recent perspective review on the future
research of predictive toxicology, Daston et al. [30] recommended to focus on the
identification of “critical biological targets” relevant for toxicity and to test their
suitability for being used as anchors for predicting toxicity. Chemicals can interfere with normal biological processes or pathways at the molecular level through
a multitude of different mechanisms that vary from non-selective binding (to intracellular proteins) to selective agonism/antagonism of a particular nuclear or another
receptor [30]. Decades of conventional animal toxicity testing have accumulated a
myriad of information on toxicological mechanisms that may be further explored
for use in in vitro and computational modeling-based predictive toxicology. For
instance, Tox21 researchers have developed high throughput, cell-based or cell-free
in vitro assays that evaluate critical cellular targets or processes involved in toxicity
response [19]. They have also published a prioritized set of 2750 targeted sentinel
genes (referred to as Human S1500+ Gene Set Ver2) whose transcriptional changes
are responsive to exposures to a wide variety of toxic agents [31].
6.1.3 Limitations of Current In Vitro and In Silico
Approaches
While significant progress has been made in developing mechanism-based in vitro
assays and testing platforms (including high-content toxicogenomics platforms), it is
recognized that quantitative in vitro to in vivo extrapolation still faces many technical
barriers, such as the choice of appropriate cell lines and the lack of metabolism [8, 19].
Although in silico approaches are rapid and inexpensive compared to experimental
approaches, the growth of in silico-based predictive toxicology tools is unsatisfactory.
Conventional QSAR models often suffer from low prediction accuracy [32] because
they do not consider the structure and flexibility of target biomacromolecules, especially when applied toward the more elusive goal of predicting potential toxicity
outcomes for in vitro cell cultures or in vivo animal test systems [29]. In these systems, the toxicity end point (e.g., cytotoxicity, mutagenicity, developmental toxicity