This presentation was given at the XPAND2022 Annual Meeting for the Household & Commercial Products Association and focused non-targeted analysis mass spectrometry and applications of the CompTox Chemicals Dashboard.

1. Integrating Mass Spectrometry
Non-Targeted Analysis and Computational
Chemistry to Characterize Chemicals
December 2022: Household Consumer Products Association
http://www.orcid.org/0000-0002-2668-4821
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
Antony Williams
Center for Computational Toxicology and Exposure, US-EPA, RTP, NC

2. Overview
• Why does EPA need measurement data?
• How much coverage do our databases have?
• Targeted versus non-targeted mass spec
• Applications of NTA to consumer products
• The CompTox Chemicals Dashboard
• Proof-of-concept tools in development
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3. Why Does EPA Need Measurement Data?
2
• Measurement data needed to ensure chemical safety
• Characterize risk
• Regulate use & disposal
• Manage human & ecological exposures
• Ensure compliance under federal statutes
Chemical Monitoring Needs
Exposure
Assessment
Dose-
Response
Assessment
Risk
Characterization
Hazard
Identification

4. Data Disparity: Have vs. Need
3

5. Challenges
• High-quality monitoring data are unavailable for most chemicals
• Measurement data normally generated using “targeted” methods
• Targeted analytical methods:
- Require a priori knowledge of chemicals of interest
- Produce data for few selected analytes (10s-100s)
- Standards for method development & compound quantitation
- Are blind to emerging contaminants
- Can’t keep pace with needs of 21st century risk characterizations
• Data gaps being filled with exposure models and “NTA” methods
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6. Rudimentary NTA Workflow
5

7. Relevant Questions of NTA Studies?
• Which chemicals are where?
• Do we see any “new” chemicals?
• Do observed co-occurrences highlight:
– Important exposure sources?
– Stressor-response relationships?
– What is the concentration of each chemical?
– Do estimated concentrations suggest unacceptable risk?
• Some examples…
6

8. Example 1: Consumer Product Analysis
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9. Example 1: Consumer Product Analysis
8
Many chemicals
observed in
consumer product
extracts
More observed
chemicals not
known to be in
consumer products
Why might the
‘other’ chemicals be
in the products?
Many observed
chemicals known to
be in consumer
products

10. Example 1: Consumer Product Analysis
9
Few chemicals
confirmed due to
limited availability
of standards
Even more
chemicals only
identified at the
“class” level
(e.g., isomers)
How do we provide
further evidence for
correct structures?
Many chemicals
only tentatively
identified

11. Example 1: Consumer Product Analysis
10
Known functional uses
support presence in
specific products
Predicted functional
uses can support
tentative chemical
identifications
Certain functional
uses are represented
across many
products
Other functional uses
are more product-
specific

12. Example 2: Recycled Product Analysis
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13. Example 2: Recycled Product Analysis
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Significant differences
between chemicals in
recycled vs. virgin products
for certain product & use
categories
Most differences observed in
paper products and
construction materials
Some uses (e.g., fragrances)
highly represented across all
product/use categories

14. Example 2: Recycled Product Analysis
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Some feature clusters (e.g.,
#2) show broad presence
across product types &
categories
Some feature clusters (e.g.,
#5) show specificity to a
particular recycled product
Some feature clusters (e.g.,
#9) show specificity to a
product type across both
categories

15. Example 2: Recycled Product Analysis
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Chemical use information is often consistent
with desired product characteristics

16. Example 3: Placental Tissue Analysis
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17. Example 3: Placental Tissue Analysis
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Elevated in
Preeclampsia
Patients
Reduced in
Preeclampsia
Patients
NTA on placenta samples:
 Normotensive (n = 17) and
preeclamptic (n = 18)
 183 molecular features found
significantly different (~6000
potential candidates)
 Feature chemicals prioritized for targeted
confirmatory work via:
 Reference MS2 spectrum match
 In silico MS2 spectrum match
 Data source counts
 CPCat database presence
 46 chemicals prioritized / acquired
 25 chemicals confirmed via targeted analyses

18. The need for supporting tools
• NTA methods can detect many analytes in virtually any
sample matrix
• Tentative IDs in NTA studies far outweigh confirmed IDs
• Methods and tools are needed to prioritize tentative IDs
for confirmation
• Prioritization should be based on:
• Likelihood of presence (informed by source and use
information)
• Likelihood of importance (informed by provisional risk
metrics)
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19. Identifying “Known Unknowns”
Bigger is NOT Better
19

20. CompTox Chemicals Dashboard
>1.2M chemicals

21. The Charge for the Dashboard
• Develop a “first-stop-shop” for environmental chemical data
to support EPA and partner decision making:
– Centralized location for relevant chemical data
– Chemistry, exposure, hazard and dosimetry
– Combination of existing data and predictive models
– Publicly accessible, periodically updated, curated
• Easy access to data improves efficiency and ultimately
accelerates chemical risk assessment
• Dashboard data can support structure ID and NTA

22. 22
SEARCH
TOX DATA
BIOACTIVITY
SIMILARITY
READ-ACROSS
PUBMED
BATCH SEARCH
CompTox Chemicals Dashboard
https://comptox.epa.gov/dashboard

23. Detailed Chemical Pages
• Chemical page: Wikipedia snippet when available, intrinsic
properties, structural identifiers, linked substances

24. “Executive Summary”
Benzo(a)pyrene
• Overview of toxicity-
related info
• Quantitative values
• Physchem. and
Fate & Transport
• Adverse Outcome
Pathway links
• In vitro bioactivity
summary plot

25. Chemical Hazard Data
ToxVal Database
• >50k chemicals
• >770k tox. values
• >30 sources of
data
• ~5k journals cited
• ~70k citations

26. Safety Data

27. Sources of Exposure to Chemicals

28. Chemical Lists of
Interest…
28

29. Lists of Extractables and Leachables
https://comptox.epa.gov/dashboard/chemical-
lists?filtered=&search=extractables
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• Chemical lists with extractables & leachables
• Expands with literature extraction

30. The ELSIE Database
https://comptox.epa.gov/dashboard/chemical-lists/ELSIE
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31. Extractables
31

32. Consumer Products Database
32

33. Download data, send to batch
search for data harvesting
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34. Batch Searching

35. Batch Searching
• Singleton searches are useful but we work
with thousands of masses and formulae!
• Typical questions
– What is the list of chemicals for the formula CxHyOz
– What is the list of chemicals for a mass +/- error
– Can I get chemical lists in Excel files? In SDF files?
– Can I include properties in the download file?
35

36. Batch Search

37. Batch Search – Excel, CSV, SDF file

38. Batch Search

39. Work in
Progress
39

40. We have proven the value…
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41. CASMI
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42. CASMI
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43. Hazard Module to PROFILE chemicals
https://www.epa.gov/chemical-
research/cheminformatics

44. Structure/Substructure/Similarity

45. Work in Progress
https://acs.digitellinc.com/acs/sessions/445216/view
• Presently working with scientists from NIST
• Dataset to be released as a list
• Spectra will be added to our experimental spectral database
for searching
45

46. Collaboration with NIST
>10,000 plastic related chemicals
46

47. Conclusion
• Dashboard access to data for ~1.2M chemicals and
related data
• The chemical lists continue to expand and chemicals
in consumer products is of course a focus
47
• Data continues to grow with
ongoing curation activities
• Proof-of-concept developments
released publicly
• Do you want to learn more? I
am here for two days and can
do demos

48. Contact
Antony Williams
CCTE, US EPA Office of Research and Development,
Williams.Antony@epa.gov
ORCID: https://orcid.org/0000-0002-2668-4821
48
https://doi.org/10.1186/s13321-017-0247-6

49. Acknowledgements
49
EPA ORD
Alex Chao
Chris Grulke
Kristin Isaacs
Charlie Lowe
Andrew McEachran*
Jeff Minucci
Ashley Pfirrman*
Katherine Phillips
Tom Purucker
Ann Richard
Caroline Ring
Rusty Thomas
Elin Ulrich
John Wambaugh
Jon Sobus
* = ORISE/ORAU
SWRI
Kristin Favela
Alice Yau
UNC Chapel Hill
Kim Boggess
Celeste Carberry
Rebecca Fry
Yunjia Lai
Kun Lu
Julia Rager
John Szilagyi
Agilent
Jarod Grossman