xvEPA
United States
Environmental Protection
Agency
Threshold of Toxicological Concern (TTC) a useful tool
the Computational Toxicology armory
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Grace Patlewicz
Center for Computational Toxicology and Exposure (CCTE), US EPA
University of Louisville : Human Health Risk Assessment
10th April 2020
The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA

-------
k	Conflict of Interest Statement
•	No conflict of interest to declare.
Disclaimer:
•	The views expressed herein are those of the presenter and do not
necessarily reflect the views or policies of the U.S. EPA

-------
vvEPA
United States
Environmental Protection
Agency
•	Regulatory and Non Regulatory drivers
•	Computational Toxicology approaches
•	Integrated Approaches to Testing and Assessment (IATA)
•	Decision contexts
•	Threshold for Toxicological Concern (TTC)
•	Summary remarks
•	Acknowledgements
Outline

-------
oE
United States
Environmental Protection
Agency
Regulatory and Non-Regulatory drivers
Societal demands for safer and sustainable chemical products are
stimulating changes in toxicity testing and assessment frameworks
Chemical safety assessments are expected to be conducted faster and
with fewer animals, yet the number of chemicals that require
assessment is also rising with the number of different regulatory
programmes worldwide.
In the EU, the use of alternatives to animal testing is promoted.
Animal testing is prohibited in some sectors e.g. EU Cosmetics
regulation
The European Registration, Evaluation, Authorisation and Restriction
of Chemicals (REACH) legislation lays out specific information
requirements, based on tonnage level triggers.
REACH-like schemes also have been established in China, South Korea,
and Turkey.

-------
oE
United States
Environmental Protection
Agency
Regulatory and Non-Regulatory drivers
In the US, the new Frank Lautenberg Chemical Safety for the 21st
Century Act (LCSA) requires that a risk based prioritisation is
conducted for all substances in commerce, ~40,000, many of which
are lacking sufficient publicly available toxicity information.
EPA Administrator signed memo 10/9/19 to "direct the agency to
aggressively reduce animal testing, including reducing mammal study
requests and funding 30% by 2025 and completely eliminating them by
2035"
Risk based prioritisation is also an important aspect of regulatory
frameworks in Canada (the Domestics Substance List), Australia and
the EU.
New Approach Methods (NAMs) offer a means of facilitating the
regulatory challenges in chemical safety assessment

-------
Integrated Approaches to Testing and
Assessment (IATA)
•	"Integrated Testing Strategies (ITS) are .... approaches
that integrate different types of data and information
into the decision-making process. ..."
•	"A tiered approach to data gathering, testing, and
assessment that integrates different types of data
(including physicochemical and other chemical properties
as well as in vitro and in vivo toxicity data). When
combined with estimates of exposure in an appropriate
manner, the IATA provides predictions of risk."

-------
General framework of an I ATA
Agency
Available information
provides sound
conclusive evidence for
the specific regulatory
need
Make a weight of evidence assessment or apply predefined decision
criteria (e.g. ITS, STS).
Gather and evaluate existing information (in vivo, in vitro, in silico
(e.g. (Q)SAR), read across and chemical category data).
Problem formulation. Definition of the regulatory need (e.g. hazard
identification, hazard characterisation, safety assessment etc.) and
the information/parameters that are relevant to satisfy the need,
including consideration of existing constraints and, if applicable,
consideration of the level of certainty required.
If available information does not provide sufficient evidence
consider what additional information from non-testing, non-animal
testing methods and, as a last resort, from animal methods would be
needed to generate sufficient evidence.
Make a weight of evidence assessment or apply predefined decision
criteria (i.e. ITS, STS).
Available information
provides sound
conclusive evidence for
the specific regulatory
need
From OECD 7

-------
Historical information on the
chemical of interest
Non-standard	tests
Information from "similar"
chemicals
Predictions from other 'non-
testing' approaches such as
(Q)SAR, TT	C
In chemicotests
In vitro tests
Molecular biology, -omics
Exposure, (bio-)kinetics

-------
oE
United States
Environmental Protection
Agency
Computational toxicology tools add
value to most regulatory decisions
Screening level hazard assessment
Risk-based Prioritisation
Risk Assessment
Exposure Assessment

-------
^epa
EPA CompTox Chemicals Dashboard
United States
Environmental Protection
Agency
A publicly accessible website delivering access:
*	~875,000 chemicals with related property data
*	Experimental and predicted physicochemical property data
*	Integration to "biological assay data" for 1000s of chemicals
*	Information regarding consumer products containing chemicals
*	Links to other agency websites and public data resources
Literature" searches for chemicals using public resources
Batch searching" for thousands of chemicals
DOWNLOADABLE Open Data for reuse and repurposing
ui
Ul
https://comptox.epa.gov/

-------
EPA
United Slates
Environmental Protection
Agency
Environmental Topics Laws & Regulations Alx>ut EPA
(B®[iD[pTfen
08
evaluating chemicals
potential endocrine"
[feln CcfeKOV
Available
Till *v
Search EPA.gov

V
I ^
jToxGast
Dashboard
[High-throughput
screening data^ H
11

-------
oE
United States
Environmental Protection
Agency
CompTox Chemicals Dashboard:
Landing Page
Different entry points depending on domain of interest
v>EPA
United States
Environmental Protection Home Advanced Search Batch Search Lists
Agency
v Predictions Downloads
875 Thousand Chemicals
Chemicals
Product/Use Categories Assay/Gene
Q, Bisphenol A

Bisphenol A
D TXSID7020782
Bisphenol A bis(2-hydroxyethyl ether) diacrylate
DTXSID6066991
Bisphenol A bis(2-hydroxyethyl ether) dimethacrylate
DTXSID1066992
Bisphenol A bis(2-hydroxypropyl) ether
D TXSID8051592
Bisphenol A carbonate polymer
DTXSID6027840
Bisphenol A diglycidyl ether
DTXSID6024624
Bisphenol A glycidyl methacrylate
D TXSID704484 7
Bisphenol A propoxylate diglycidyl ether
D TXSID 10399098

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EPA
United States
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
Agency
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATE/TRANSPORT
HAZARD
~	ADME
~	EXPOSURE
~	BIOACTIVITY
SIMILAR COMPOUNDS
GENRA (BETA)
RELATED SUBSTANCES
SYNONYMS
k LITERATURE
LINKS
COMMENTS
Bisphenol A
80-05-7 I DTXSID7020182
Searched by DSSTox Substance Id.
H3C
.ch3



//
\ )

II
HO

OH
Intrinsic Properties
Quality Control Notes
Linked Substances
Structural Identifiers
Record Information
Presence in Lists
Bisphenol A ;BPA) is an organic synthetic compound with the chemical formula (CH3)2C(C3H40H)2 belonging to the group of diphenylmethane
derivatives and bisphenois. with two hydroxyphenyl groups. It is a colorless solid that is soluble in organic solvents, but poorly soluble in water. It has
been in commercial use since 1957.
BPA is a starting material for the synthesis of plastics, primarily
Read more
Wikipedia
13

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EPA
United States
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
Agency
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATE/TRANSPORT
HAZARD
~ ADME
~ EXPOSURE
BIOACTIVITY
TOXCAST: SUMMARY
EDSP21
TOXCAST/TOX21
PUBCHEM
Bisphenol A
80-05-7 | DTXSID7020182
Searched by Expert Validated Synonym.
Executive Summary
Quantitative Risk Assessment Values
© IRIS values available C?
Q No PPRTV values
© EPA RSL values available G?
O Minimum RfD: 0.050 mg/kg-day (chronic, IRIS, oral, 8) Gf
Q Mo RfC calculated
Q IVIVE POD not calculated
Quantitative Hazard Values
Q Minimum oral POD: 3.8 mg/kg-day (reproductive, HPVIS, oral, 6) C?
© No inhalation POD values
Q Lowest Observed Bioactivity Equivalent Level: CYP1A1, CYP1A2, Tpo, ESR2, ESR1,
ESR1, NR1I3, PPARA, NR1I2, Cyp2c11, MMP3, Esr1
Cancer Information
© No cancer slope factor
© No inhalation unit risk value
(j) Carcinogenicity data available: University of Maryland carcinogenicity warning; G?
No genotoxicity findings reported
Reproductive Toxicology
Q 200 Reproductive toxicity PODs available Gf
Class
risk-based SSL (mg/kg)
GIABS (unspecified)
GIABS (unspecified)
ABS (unspecified)
RFDo (mg/kg-day)
screening level (residential Soil) (mg/kg)
screening level (industrial soil) (mg/kg)
REGIONAL SCREENING
THQ
THQ = 0.1
THQ = 1
THQ = 0.1
THQ = 0.1
THQ = 0.1
THQ = 0.1
THQ = 0.1
Value
5.8
1
1
0.1
0.05
320
4100

-------
AEPA
United States
Environmental Protection
Agency
QSAR Predictions
OPERA Models: LogP: Octanol-Water
Bisphenol A
80-05-7 I DTXSID7020182
Predicted value: 3.B5
Global applicability domain: | Inside|
Local[ applicability domain index: 0.877
Confidence level: 0.813
Weighted KNN model
5-fold CV (75%)
Training (75%)
Test (25%)
Q2
RMSE
R2
RMSE
R2
RMSE
0.850
0.690
0.860
0.670
0.860
0.780
t Neighbors from the Training Set
Bisphenol A
Measured:3.32
Predicted: 3.35076
BUTANOIC ACID.2-f4-BIPHENYLYL)-:
HYDROXY-3-METHY
Measured:3.25
Predicted: 3.39062
Flurbiprofen
Measured:4.16
Predicted: 3.94445
<
2.2-Diphenvlpropionic acid
Measured:2.69
Predicted: 2.84603
H3C	o.

-------
IP Generalised Read - Across (GenRA)

OETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATE-TRANSPORT
HAZARD
» ADME
~ EXPOSURE
¦w bjoactivity
TOXCAST SUMMARY
PUBCHEM
TOXCAST DATA
TOXCAST. MODELS
SIMILAR COMPOU NDS
RELATED SUBSTANCES
SYNONYMS
» IJTERATURE
LINKS
COMMENTS
Bisphenol A
80-05-7 | DTXSID7020182
Searched by Expert Validated Synonym.







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-------
S!P Screening level hazard assessment
•	Another approach to consider is TTC - Threshold of
Toxicological Concern
•	TTC is a principle that refers to the establishment of a
human exposure threshold value for (groups of) chemicals
below which there would be no appreciable risk to human
health
•	Relies on past accumulated knowledge regarding the
distribution of potencies of relevant classes or chemicals
for which good toxicity data do exist
17

-------
vvEPA
%^-TTC - Threshold of Toxicological Concern
* Based on this knowledge, an estimate of the probability
of no adverse effects occurring for a substance of
unknown toxicity at a specified daily intake is made
• Useful substitute for substance-specific hazard
information in situations where there is exposure
information which indicates that human exposure is very
low and there is limited or no information on the toxiciTy
of the chemical
18

-------
Environmental Protection	«¦!	|	II	f	•	1	•	1
TTC - Threshold of Toxicological Concern
• Helpful for prioritising substances for risk assessment
e.g. food flavouring substances, food contact materials,
pesticide metabolites in groundwater, impurities in
pharmaceutical manufacturing operations.
The TTC concept is not intended to be applied to
chemicals which are regulated and for wnich specific
requirements exist regarding their hazard assessment

-------
vvEPA
TC- Threshold of Toxicological Concern
•	Two types of TTCs:
•	'General'TT Cis based on a predicted tumour risk of 1 in
a million, derived through an analysis of cancer data
•	Structural based TTCs are based on frequency
distributions (5th percentile) of NO(A)ELs of non-cancer
endpoints
20

-------
oE PA
A bit of history..
United States
Environmental Protection
Agency
•	Efforts to derive structural based TTCs on endpoints other
than carcinogenicity have typically made use of the structural
decision rules defined by Cramer et al. (1978)
•	AAunro et al. (1996) explored the relationship between
structure and toxicity by compiling a large database of ~600
substances that had been tesied for a variety of non-cancer
endpoints (chronic effects from repeated dose, repro,
developmental etc studies)
•	The resulting dataset contained 2941 NOELs for a total of
613 organic substances
•	The substances were then assigned to one of three structural
classes as defined by Cramer et al (1978)
21

-------
vvEPA
United States	.
Environmental Protection	_	I	• •	%
Cramer	decision tree
Decision tree of 33 questions
m i
igoxuo;,(23i m
30J,(I9>
22 H
33
nr i I87U9)
I n 32	i8,(i9) m x
22 H I H
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33 n
22, (iS.)
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nr i 33 n
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in

-------
vvEPA
United States
Environmental Protection	^1	I	• •	*
Cramer decision tree
•	Decision tree of33 questions
•	CLASS I = simple structures efficiently metabolised to
innocuous products; anticipated low order of oral toxicity
•	CLASS II = intermediate structures (less innocuous than
substances in Class I, but no positive indication of toxic
potential)
•	CLASS III = complex structures; metabolism to reactive
products suggestive of potential toxicity
•	The distributions of NOELs were found to differ for the
three classes of chemicals revealing how structural class
has an important bearing on toxicity

-------
oE
United States
Environmental Protection
Agency
Cumulative Distributions of Structural Class
NOELs
fc)
O
£_
fc)
Q_
100
90
80
70
60
50
40
30
20
10
a
Fitted
hktrihiitior
Class I
Class II
v
Class IIP
		
		
U.01 0.1
1.0
10
	I
100 1000 10000
	I
NOEL (mq/kq/dayl)

-------
oE
United States
Environmental Protection
Agency
TTC values based on Cramer structural
classes


5th Percentile
Structural
No. of
NOEL
Class"
Chemicals
(//g/kg/day)
I
137
2,993
II
28
906
III
447
147
Threshold
(pg/day)b
1,800 (30 vg/kg
bw/d)
540 (9 fjg/kg bw/d)
90 (1.5 fjg/kg bw/d)
a Cramer et al. (1978) structural classes
b The human exposure threshold was calculated by multiplying the 5th percentile NOEL
by 60 (assuming an individual weighs 60 kg) and dividing by a safety factor of 100.
25

-------
<8-EPA
United States
Environmental Protection
Agency
Replicating Munro's TTC values in practice
•	EFSA has published the Munro dataset in electronic
format
•	See supporting information
https: //efsa. onl inel ibrary. wiley. com/doi/10.2903/sp. ef sa
.2011.EN-159
•	Download the Munro original dataset as a csv file.

A | B C
D
E
F G
H
1
P
1
Structu -t IDMunro NAME_Munro_1996
CASoriginal
Species tested
Exposure 'Study type Exposure
Exposure
NOEL_calculated_Munro_mg/kg/day Reference
67
3 1(1 -naphthyl)ethylene-diamine dihydro chloride, N-
1465-25-4
rat
728 chr
fod
Oral - diet
39 NCI, 1979k
68
3 2 (2-chloroethyl)trimethyl-ammonium chloride
999-81-5
rat
756 chr
fod
Oral - diet
138 NCI 1979c
69
3 3 (chloroacetyl)-acetanilide, 4'-
140-49-8
rat
609 chr
fod
Oral - diet
790 NCI, 1979c
70
3 4 l,r~(2,2,2-trichloroethylidene) bis(4-chloro)-benzene
50-29-3
rat
546 chr
fod
Oral - diet
16 NCI, 1978c
71
3 5 ll-oxo-HH-pyrido(2,l-b) quinazoline-2-carboxylic acid

rat
11 terat
gav
Oral - gav;
90 Nishimura
72
3 6 2(2,4,5-trichlorophenoxy) propionic acid
93-72-1
rat
730 chr
fod
Oral - diet
2.6 Mullison,
73
3 7 2-(2-methyl-4-chlorophenoxy) propionic acid
93-65-2
rat
90 sub
rod
Oral-diet
2.5 Verschuur
74
3 8 4-(2-methyl-4-chlorophenoxy) butyric acid
94-81-5
rat
91 sub
rod
Oral - diet
12 Rhodia Inc
75
3 9 C.I. Disperse Blue 1
2475-45-8
rat
91 sub
rod
Oral - diet
62 NTP, 1986
76
3 10 C.I. Orange 3
6373-74-6
mus
91 sub
gav
Oral - gav;
500 NTP, 1988
77
3 11 C.I. Acid Red 14
3567-69-9
mus
91 sub
fod
Oral - diet
1171 NTP, 1982
78
3 12 C.I. Disperse Yellow
2832-40-8
rat
91 sub
fod
Oral - diet
250 NTP, 1982

-------
vvEPA
United States
Agency"1611131 Pro,ectio']^	I •	, •	A A	«	¦ ¦ /«	I	•	, •
Replicating Munros TTC values in practice
•	Munro et al (1996) found that the data fitted a log normal
distribution well. They derived the 5th percentile of the cumulative
distribution function
•	This 5th percentile was multiplied by 60kg and divided by a safety
factor of 100 to derive the associated value
•	In R, the easiest way to do this is as follows:
•	Library(dplyr)
•	Library(fitdistrplus)
•	Munro <- read.csv('munro_original_dataset.csv') [moke sure to adjust the NOELs
reported depending on whether they ore chronic or subchronic]
•	Fin = fitdist(Munro$NOEL, 'Inorm')
•	Quantile(Fln, probs = 0.05)
•	Estimate = 0.153
•	Reported Munro 5th percentile is 0.15 mg/kg bw/day

-------
vvEPA
United States
Environmental Protection
Agency
Replicating Munro's TTC values in practice
•	In python
•	Import numpy, pandas and scipy libraries
•	Calculate mean, std of the AAunro dataset for a specific
structural class but having converted the Munro NOELs to
their LoglO equivalents
•	mean = np.mean(munro['LogNOEL'])
•	std = np.std(munro['LogNOEL'])
•	Use the mean, std to create a sample normal distribution
•	samples = np. random, nor ma I (mean, std, size = 1000)
•	Take the 5th percentile of the theoretical distribution
•	10**(np.percentile(samples, 5)

-------
vvEPA
United States
Environmental Protection
Agency
Applying TTC in practice
•	Assign substance based on the Cramer structural rules
into one of the 3 class using of the software tools
(Toxtree, OECD Toolbox)
•	Requires a structure format such as SMILES
representation or a mol file
•	The structural class designation will permit the selection
of the most appropriate TTC value to use..
• BUT	it is not quite that simple!

-------
SEPA	_ ^
T oxtree
United States
Environmental Protection
Agency
Toxtree - select Cramer
rules
Introduce chemical
structure
Click Estimate to
produce the Cramer class
assignment
^ Toxtree (Estimation of Toxic Hazard - A Decision Tree Approach) v3.1.0-1851 -1525... — ~ X
File Edit Chemical Compounds Toxic Hazard Method Help
« » Chemical identifier | v Go!
Available structure attributes
Toxic Hazard
Names Created from SMILES
1 Estimate 1
SMILES CCCCCC
Low (Class I)
Intermediate (Class II)
High (Class III)



Structure diagram
0 Verbose explanation
/C\.
c c c

First Prev Next Last


30

-------
 | 0 Structure info
0 Parameters
0 Physical Chemical Properties
1_Selerted J [+] Environmental Fate and Transport
0 Ecotoxicological Information
0 Human Health Hazards
Profiling
3 General Mechanistic
- Toxic hazard classification by Cramer
| About | Option)
1
2

Na

















High (Class III)
High (Class III)
Introduce chemical
structure(s)
Metabolism/Transformations
Options a	0 Selected
Select All I Unselect All j| Invert
Select Toxic hazard
classification under
the Profiling methods
to produce the Cramer
class assignment
1'iicroDiai mecaDoiism simulator
Rat liver S9 metabolism simulator
Skin metabolism simulator
Tautomerism
31

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-------
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wBW	TTC values
United States
Environmental Protection
Agency
Type of substance
Hg/person/day Q/g/kg-day for 60 kg
adult)
Alerts for potential genotoxic
carcinogenicity
Kroes: 0.15 (0.0025 pg/kg-day)
ICH: 1.5 (0.025 yg/kg-day)
Acetylcholinesterase inhibitors
(AChEl)
Organophosphate/carbamate
18 (0.3 pg/kg-day )
Cramer Class III
90 (1.5 pg/kg-day)
Cramer Class II
540 (9.0 yg/kg-day)
Cramer Class I
1800 (30 yg/kg-day)
-------
A EPA
United States
Environmental Protection
Agency
T oxtree
Introduce chemical of
interest
Introduce exposure level
Process substance through
the Kroes workflow to
determine TTC value that is
most applicable or whether a
substance specific risk
assessment is required
Kroes
^Toxtree (Estimation of Toxic Hazard - A Decision Tree Approach) v3.1.0-1851 -1525... — ~ X
File Edit Chemical Compounds Toxic Hazard Method Help
! « » Chemical identifier v Go!
Available structure attributes
Toxic Hazard bv Kroes TTC decision tree
Names Created from SMILES
I » Estimate I
SMILES CCCCCC
Substance would not be expected to be a safety
concern
Negligible risk (low probability of a life-time
cancer risk greater than 1 in 1QA6
Risk assessment requires compound-specific
toxicity data



Structure diagram
Q Verbose explanation
C\ .x
C C C
Kroes I 11 decision Iree
Decision method has not been applied yet!
First Prev Next Last


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vvEPA
United States	^	- 0
Assumptions
•	TTC assumes a lifetime exposure (every day for ~70
years)
•	TTC values that are established are for the ORAL route
of entry
•	Are there situations when higher TTC values could be
proposed when exposure duration is likely to be more
shorter term <1 year
•	Proposals have been made in the pharma sector to
evaluate genotoxic impurities (can a higher TTC value be
set to accommodate the risk/benefit of a particular
pharmaceutical, proposals for higher TTC values when
accounting for occupational vs consumer exposures - can a
1 in 105 risk be tolerated instead of a 1 in 106
-------
SERA
Staged TTC values
United States
Environmental Protection
Agency
Acceptable Daily Intakes* for an Individual Impurity, ng/day
Clinical trials or marketed product

Single
Dose
< 14
days
< 1
mo.
< 3
mo.
< 6
mo.
< 12
mo.
>1 - 10
years
>10 years
to
lifetime
M7
**
**
120
20
20
20
10
1.5
EMA
120
60
60
30
10
5
1.5
(marketed)
1.5
*Compound-specific risk assessments to derive acceptable intakes should be applied
instead of the TTC-based acceptable intakes where sufficient carcinogenicity data exist.
**Clinical trials of up to 14 days - class 3 impurities can be treated as normal impurities
-------
"tP— Risk-Based prioritisation
•	Rank ordering large numbers of chemicals at the same time that are
data poor and for which no exposure information might be known
apriori
•	One approach considered involved coupling Threshold of Toxicological
Concern (TTC) with High Throughput Exposure (HTE) modelling
(predicted exposure values) to rank order substances for further
evaluation
•	Wambaugh and colleagues (2014) developed a rapid heuristic high
throughput exposure (HTE) model that enables prediction of
potential human exposure to thousands of substances for which little
or no empirical exposure data are available.
•	The HTE model was calibrated by comparison to NHANES urinary
data that reflects total exposure (all routes/sources)
38
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oE
United States
Environmental Protection
Agency
Integrating TT Cwith predicted HT
exposures
Compared the conservative Cramer Class III TTC value of 1.5 pg/kg-
day to the previously calculated median and upper 95% credible interval
(UCI) of total daily median exposure rates for 7968 chemicals
TTC = 0.0015 mg/kg/d
only 273 (fewer than 5%) were found to have UCI
daily exposures estimates that exceeded the
Cramer Class III TTC value of 1.5 pg/kg-day
0.2	0.4	0.6	0.8
Cumulative Frequency
Initial evaluation showed the approach of using the ratio of
exposure to TTC (HTE: TTC) appeared promising for risk-based
prioritisation
		
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»Y
I80G pVday
Cramer Class I
3D LjiV-vdiv

Compare and
Rank HT
Exposures to
TTC Values

Candidates for
further evaluation of
hazard and exposure

Lower priority for
further evaluation at
this time
None of the substances categorised as Cramer Class I or Cramer Class II exceeded their respective TTC
values.
No more than 2% of substances categorised as Cramer Class III or acetylcholinesterase inhibitors exceeded
their respective TTC values.
Majority of chemicals with genotoxicity structural alerts did exceed the relevant TTC - recommendations were
roposed for next steps
Patlewicz et al, 2018
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United States
Environmental Protection
Agency
¦SBft
Risk-Based prior itisation
•	Processed substances through the Kroes module within
Toxtree but some adaptations needed to made since the
batch process required exposure information upfront
•	Deconstructed the Kroes workflow into different steps to
mirror the published workflow
•	Created ad hoc modules to identify steroids,
organophosphates, carbamates ana scripts were written
to parse out relevant outputs from an initial batch
profiling of the substances through the Kroes workflow
•	R scripts are provided as supplementary information in
Patlewicz et al (2018)
41
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!—¦»— Risk-Based prioritisation
•	Investigate relevance of existing TTC values for substances of
interest to EPA
•	Extracted data from EPA's ToxValDB, which aggregates
testing data from over 40 sources including US federal and
state agencies, as well as international agencies such as the
European Chemicals Agency and the World Health Organisation
•	Objectives were:
•	Reproduce the TT Cvalues developed by AAunro et al (1996)
•	Follow the Kroes et ol (2004) workflow to assign substances present in
ToxVal to their respective Cramer classes and use the associated
repeat dose toxicity data to derive new TT Cvalues
•	Evaluate whether the TTC values from ToxVal and Munro are
statistically equivalent
•	Derive confidence intervals for the new TTC values
•	Compare and contrast the chemistry of the two data sets to
rationalise any (dis)similarities in TTC values
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oE
United States
Environmental Protection
Agency
Risk-Based prioritisation
Bootstrap sampling used to quantify the uncertainty around the 5th
percentiles values for both ToxVal and Munro data sets
Differences were observed for substances assigned as Cramer Class
III
CO
5
0.0
w
<
O
Class II
Cramer Class
Munro ToxVal
• Presence of OP/carbamates in the Munro Cramer class III set largely
explained the difference in 5th percentile values
Derived new modules for OPs
Nelms et al, 20194
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®I8L_ Risk-Based prioritisation:
Agency	I
inhalation route of entry
•	Whilst TT Cvalues for oral route of exposure are well
established, there are no established TTC valued for
inhalation
•	Current focus is investigating the feasibility of deriving new
TTC values using the ToxValDB
•	Processing the substances with NO(A)EL/NO(A)EC values
through the Kroes workflow -replicates other similar efforts
published by Carthew et al (2009) and Escher et al (2010)
45
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siL_ Risk-Based prioritisation:
Agency	I
inhalation route of entry
•	For substances assigned into the Cramer structural classes,
have found that the Cramer classes are not effective at
discriminating the potency - other approaches to subcategorise
the substances are being explored
•	Furthermore the distribution of toxicity values do not fit a log
normal distribution - bootstrapping the percentile of the
empirical data to derive a value for TTC purposes is an
alternative approach
46
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vvEPA
United States
Environmental Protection	•	I	•	I	III	0
Disclaimers - only scratched the surface
•	TTCs for other endpoints - e.g. skin sensitisation
•	eco TTCs
•	Other routes of exposure...beyond oral routes of entry
•	Other chemical/substances of interest e.g. cosmetics,
medical devices
•	Augmenting Cramer structural class II with more chemicals
e.g. work t>y RIFM
•	Internal TTCs vs external TTCs e.g. work led by P&G
•	Cancer endpoints - work is ongoing to augment and curate the
original Carcinogenicity Potency Database that was used to
derive the cancer TTC threshold originally used by the FDA
and the conservative threshold used in Kroes et al (2004)
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oE
United States
Environmental Protection
Agency
Take home messages
•	Computational toxicology approaches impact many aspects of
regulatory contexts
•	Outlined how computational approaches fit within an I ATA
•	Described the TTC approach and how is it evolved and how it
is used in practice in screening level hazard assessment
decision contexts
•	Illustrated how coupling HTE and TTC can be used as part of
risk-based prioritisation application
•	Discussed ongoing research efforts in this field
•	TTC - Threshold of Toxicological Concern is a pragmatic
means of waiving testing when exposures are v low and when
little or no toxicity data exists.
y&UT it does not overrule traditional risk assessment practices
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