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functions, establish relationships between
in vitro perturbation (toxicity pathways)
and in vivo outcomes (adverse outcome
pathways), and provide broader coverage
of chemicals and biological activities with
less dependence on animals (3Rs).
Predictive models built from ToxCast
Phase-I (309 chemicals) include apical
endpoints in ToxRefDB [2-4] and
pathways from biomedical literature [5-
7]. The general idea has been to mine
signatures of toxicity from in vitro highthroughput
screening (HTS) data and
prioritise chemicals by specific endpoints
and pathway targets. Early results for
developmental toxicity [4] suggest that
Figure 1. Chemical-target perturbation network
for developmental toxicity. Predictive model
built from AC50s for 309 Phase-I chemicals
tested in 662 in vitro assays and mined against
the ToxRefDB for 17 developmental endpoints
in pregnant rats (251 chemicals) and rabbits
(234 chemicals) [4]. Nodes annotated by GO
biological process and edges represent linkages;
black nodes: significant univariate features;
blue nodes: angiogenesis; red nodes: multivariate
features selected by a simple linear model.
susceptible processes form a complex
web of interactions (Figure 1). This may
account for species differences, target
tissue and stage susceptibility. As such,
having scientifically accepted predictive
tools will enable the more efficient and
rational use of animals as testing is
directed to the highest priority chemicals
and pathways.
Computational models are needed to analyse
complex systems and gauge the biological
plausibility of predictive signatures
Cellular and molecular information
captured by in vitro profiling may be more
closely linked to critical pathways than
could be inferred from in vivo exposure
in pregnant rats or rabbits; however,
reconstructing toxicity pathways from
these data remains an important challenge
for the HTS paradigm. The number of
parts that compose an embryonic system
is massive; each part in itself has its own
developmental trajectory, is subjected
to weak physical relations depending on
network topology, and may be influenced
by overall network dynamics during
perturbation. Resiliency of a complex
adaptive system to perturbation increases
as parts are assembled into higher levels
of organisation. As such, the ways in which
parts come together must be looked at
very closely during normal and abnormal
development.
Modelling and simulation tools are
necessary to understand and analyse
the complex relationships observed
between signaling networks and
multicellular behaviours. The impact of
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AXLR8-2 WORKSHOP REPORT
Progress Report 2011 & AXLR8-2 Workshop Report