The aim of this work was to design an in silico and in vitro approach to prioritize compounds and perform a quantitative safety assessment. To this end, we pursue a tiered approach taking into account bioactivity and bioavailability of chemicals and their metabolites using a human uterine epithelial cell (Ishikawa)-based assay. This biologically relevant fit-for-purpose assay was designed to quantitatively recapitulate in vivo human response and establish a margin of safety.

1. Scoring and ranking of metabolic trees to computationally prioritize chemicals
for testing using fit-for-purpose in vitro estrogen receptor assay
Kamel Mansouri1, Jeremy A. Leonard2,Yu-MeiTan3, MiyoungYoon1, Patrick McMullen1 and Rebecca Clewell1
1 Scitovation LLC, ResearchTriangle Park, NC
2 Oak Ridge Institute for Science and Education, Oak Ridge,TN
3 National Exposure Research Laboratory, U.S. EPA, Durham, NC
Introduction
Increasing awareness about endocrine disrupting
chemicals (EDCs) in the environment has driven concern about
their potential impact on human health and wildlife. Tens of
thousands of natural and synthetic xenobiotics are presently in
commerce with little to no toxicity data and therefore uncertainty
about their impact on estrogen receptor (ER) signaling pathways
and other toxicity endpoints. As such, there is a need for
strategies that make use of available data to prioritize chemicals
for testing.
One of the major achievements within the EPA’s Endocrine
Disruptor Screening Program (EDSP), was the network model
combining 18 ER in vitro assays from ToxCast to predict in vivo
estrogenic activity. This model overcomes the limitations of single
in vitro assays at different steps of the ER pathway. However, it
lacks many relevant features required to estimate safe exposure
levels and the composite assays do not consider the complex
metabolic processes that might produce bioactive entities in a
living system. This problem is typically addressed using in vivo
assays.
The aim of this work was to design an in silico and in vitro
approach to prioritize compounds and perform a quantitative
safety assessment. To this end, we pursue a tiered approach
taking into account bioactivity and bioavailability of chemicals and
their metabolites using a human uterine epithelial cell (Ishikawa)-
based assay. This biologically relevant fit-for-purpose assay was
designed to quantitatively recapitulate in vivo human response
and establish a margin of safety.
In order to overcome the overwhelming number of
metabolites to test, a prioritization workflow was developed based
on ToxCast chemicals (1677) and their predicted metabolites
(15,406). A scoring function was used to rank the metabolic trees
of the considered chemicals combining in vitro data from ToxCast
and the literature in addition to in silico data from the
Collaborative Estrogen Receptor Activity Prediction Project
(CERAPP) consensus model and five of its single QSAR models.
The bioavailability of the parent chemicals as well as the
metabolites and their structures were predicted using ChemAxon
metabolizer software. The designed workflow categorized the
metabolic trees into true positives, true negatives, false positives
and false negatives. The final output was a top priority list of 345
ranked chemicals and related metabolites from the ToxCast library
as well as an additional list of 593 purchasable chemicals with
known CASRNs. We are currently moving forward to test the
highest-priority metabolic trees in the Ishikawa assay and are
using a liver bioreactor to confirm important metabolites.
METHODS AND RESULTS
AC K N O WLEDG EMEN TS
Prioritization and testing
REFERENCES
Funding for this research was provided by the American Chemistry Council
Long-range Research Initiative (ACC-LRI).
Analysis of available ER data
Disclaimer: The views expressed in this presentation are those of the authors and do not
necessarily represent the views or policies of the U.S. EPA.
Tier 0: Prioritizing compounds for T1 testing
• Data-mining and cheminformatics tools to analyze the data:
KNIME
• QSAR and read-across to predict chemical bioactivity: EPA
CERAPP consensus (used to screen EDSP) and single model
predictions [1].
• Metabolite structure and availability prediction: ChemAxon
metabolizer [2].
Tier1: HT-screening for estrogens
• ToxCast high-throughput screening (HTS) data: 18 estrogen
assays representing Erα and ERβ binding, dimerization,
transactivation, proliferation and cytotoxicity.
• Assays combined in in an AUC scores representing potency [3].
Tier 2: Testing with fit-for-purpose assays
• Fit-for-purpose in vitro assays designed to recapitulate in vivo
human dose-response for pathway level responses provide point
of departure (PoD) [4].
• Human relevant metabolism and its role of bioactivation in
chemical toxicity: prioritize compounds based on the combined
effect of parent and predicted metabolites (metabolic trees).
• Estimate margin of safety (MOS) at which no increased risk is
expected in humans.
Tier 3: Only if required
• Confirm region of safety predictions minimizing animals testing
through early identification of MOA and dose-response.
Tiered Testing Strategy
In vitro:
Combined ToxCast AUC scores from 18 assays [0-1] range: all
chemicals
Curated literature data from 1 to ~400 sources (CERAPP test set):
1497 chemicals
In silico:
CERAPP binding consensus from ~40 models: all chemicals
In vitro:
Combined ToxCast AUC scores from 18 assays [0-1] range: 1243
metabolites
Curated literature data from 1 to ~400 sources: 3478 metabolites
In silico:
CERAPP binding consensus from ~40 models: 4884 metabolites
(1060 unique)
Consensus of 5 binding models from CERAPP: all metabolites (392
inconclusive)
Parents:
ToxCast AUC Binding score: quantitative [0-1] range
Literature combined sources: qualitative [active – nonactive]
CERAPP binding consensus: quantitative/qualitative [5 potency
classes 0:0.25:1]
Metabolites:
ToxCast AUC Binding score: quantitative [0-1] range
Literature combined sources: qualitative [active – nonactive]
CERAPP binding consensus: quantitative/qualitative [5 potency
classes 0:0.25:1]
Consensus of 5 binding models from CERAPP: qualitative [active –
nonactive]
Production (amount of metabolite produced): quantitative [0-1]
range
Accumulation (probability to remain the body): quantitative [0-1]
range
Parents:
𝑆𝑐𝑜𝑟𝑒_𝑃𝑖 = 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑃. 𝑆𝑖𝑣,𝑖 + 𝑆𝑖𝑠,𝑖 ;
𝑆𝑖𝑣,𝑖 = 𝑗=1
𝑝
𝑤𝑖𝑣,𝑗. 𝑃𝑖𝑣,𝑖,𝑗 ; 𝑃𝑖𝑣,𝑖,1 = 𝐴𝑈𝐶𝑖 , 𝑤𝑖𝑣,1 = 0.6 ;
𝑃𝑖𝑣,𝑖,2 = 𝐿𝑖𝑡𝐴𝑖 , 𝑤𝑖𝑣,2 = 0.4 ; 𝑆𝑖𝑠,𝑖 = 𝐶𝐸𝑅𝐴𝑃𝑃𝐵,𝑖 ;
Metabolites:
𝑆𝑐𝑜𝑟𝑒_𝑀𝑖 = max
𝑚
𝑆𝑐𝑜𝑟𝑒_𝑚 𝑚 ;
𝑆𝑐𝑜𝑟𝑒_𝑚𝑖 = 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑚. 𝑗=1
𝑝
𝑤𝑗,𝑖. 𝑃𝑗,𝑖;
𝑃1,𝑖 = 𝐴𝑈𝐶𝑖 , 𝑤1,𝑖 = 0.3 ; 𝑃2,𝑖 = 𝐿𝑖𝑡𝐴𝑖 , 𝑤2,𝑖 = 0.25
𝑃3,𝑖 = 𝐶𝐸𝑅𝐴𝑃𝑃𝑖 , 𝑤3,𝑖 = 0.25 ; 𝑃4,𝑖 = 𝐶5𝑀𝑖 , 𝑤4,𝑖 = 0.2
Metabolic trees: Parents + Metabolites
𝑆𝑐𝑜𝑟𝑒_𝐹𝑖 = 𝑆𝑐𝑜𝑟𝑒_𝑃𝑖 + 𝑆𝑐𝑜𝑟𝑒_𝑀𝑖 ;
True positive:
Parent (active, available)
False positive:
Parent (active, non-available)/Metabolite (inactive,
available)
False negative:
Parent (inactive)/Metabolite (active, available)
True negative:
Parent (inactive)/Metabolite (inactive)
Parent (inactive)/Metabolite (active, non-available)
Accidental true positive:
Parent (active, non-available)/Metabolite (active,
available)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 200 400 600 800 1000 1200 1400 1600 1800
Rankingscore
Parent chemicals: metabolic trees
FN TP TN FP AccTP
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 200 400 600 800 1000 1200 1400 1600 1800
Rankingscore
Parent chemicals
Brown et al.…
Production (Prod) Parents (P) =100%
Prod Metabolites generation 1 (M-G1) + Accumulation (Acc) P +
Elimination (Elim) P = Prod P= 100%
Prod M-G2 + Acc M-G1 + Elim M-G1 = Prod M-G1
=> Availability M-G2= Acc M-G2
=> Availability M-G1= Acc M-G1
=> Availability P= Prod P – Elim P = Accu P
Elim P ~=0 => Accu P = Prod P – Prod M-G1
Active parents according to ToxCast (in vitro)
Inactive parents according to ToxCast (in vitro)
Metabolic trees scoring and ranking
 A list of 345 top ranked ToxCast chemicals representing
metabolic trees from the different classes (TP, TN, FP, FN,
AccTP) were prioritized for testing.
 Most of the active chemicals identified by Browne at al.
(2015) are included in the prioritized list since they
belong to the TP class.
 Additional 269 non ToxCast chemicals (metabolites) with
identified CASRNs, thus purchasable, can be queued for
further testing.
 The fit for purpose ER assay will be used to test the
prioritized metabolic trees (parents and where possible
toxicologically important metabolites) to determine a
point of departure (PoD) for estrogenic activity.
 High-throughput in vitro to in vivo extrapolation (HT-
IVIVE) will be performed for chemicals with available
intrinsic clearance data
 For high priority compounds with uncertain metabolic
parameters, we will use the liver bioreactor to generate
metabolites for metabolite identification and
quantification.
 This prioritization did not account for exposure
information related to parent chemicals and
metabolites.
 Would that information be useful to define regions
of safety for chemicals that have potential to
activate the estrogen receptor signaling pathway?
 How would such data be incorporate with the
predicted data used to make the prioritization?
1)Mansouri, K. et al. CERAPP: Collaborative estrogen receptor
activity prediction project. Environ. Health. Perspect. 2016; 124:
1023-1033
2)ChemAxon Metabolizer
https://www.chemaxon.com/library/?search=Metabolizer
3) Judson, R. et al. Integrated model of chemical perturbations of
a biological pathway using 18 in vitro high-throughput screening
assays for the estrogen receptor. Toxicological Sciences. 2015;
148: 137-154.
4)Miller M. et al. Development of an in vitro assay measuring
uterine-specific estrogenic responses for use in chemical safety
assessment. Toxicological Sciences. 2017
5)Browne P. et al. Screening chemicals for estrogen receptor
bioactivity using a computational model. Environ Sci. Technol .
2015; 49, 8804–8814.
Distribution of bioactivity of parents and metabolites
Distribution of the number of generated metabolites per parent chemical
Generated metabolites (I&II): 15406 (8708 unique)
ToxCast parent chemicals: 1677 Ranking parameters:
Observations from ChemAxon predictions
Proposed formulas for scoring
Ranked metabolic trees using the scoring functions implemented in KNIME workflow
Relationship between metabolite predictions (production, accumulation) and
experimental Human CLint
Parent/ metabolites classification
Ranking of the different ToxCast metabolic trees
classes based on the developed scoring functions.
Scores of bioactivity classes (active/inactive) of ToxCast
parent chemical according to Browne et al. (2015) [5].
Future work
where P: parameter, p: parent, m: metabolite, w: weight, iv: in vitro, is: in
silico, C5M: consensus of 5 models, LitA: literature activity.
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